Water and nutrient balance in deep soils under shifting cultiva
cultivation
with and without burning in the Eastern Amazon
Doctoral Dissertation
Submitted for the degree of Doctor of Agricultural Science
of the Faculty of Agricultural Science
Georg-August-University of Göttingen (Germany)
by
Rolf Sommer
born in Marburg
Göttingen, November 2000
D7
1st examiner: Prof. P.L.G. Vlek
2nd examiner: Prof. H. Fölster
Day of oral examination: 23rd of November 2000
Contents
1 Introduction...........................................................................................................1
2 Literature review...................................................................................................4
3 Study region ....................................................................................................... 13
3.1
3.2
3.3
3.4
3.5
Location ...............................................................................................................13
Climate.................................................................................................................14
Soil........................................................................................................................15
Vegetation............................................................................................................16
Land use ..............................................................................................................17
4 Material and Methods ....................................................................................... 19
4.1 Site selection and site preparation....................................................................19
4.1.1 Description of experimental sites .................................................................... 19
4.1.2 Installations ....................................................................................................... 21
4.1.3 Cultivation.......................................................................................................... 22
4.2 Water balance .....................................................................................................24
4.2.1 Precipitation ...................................................................................................... 24
4.2.2 Evapotranspiration............................................................................................ 26
4.2.3 Modeling soil water movement ........................................................................ 34
4.3 Nutrient balance..................................................................................................44
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
Soil-nutrient dynamics ...................................................................................... 44
Aboveground biomass stock ............................................................................ 46
Volatilization losses........................................................................................... 46
Fertilizer input and harvest exports ................................................................. 47
Nutrients in the soil solution - leaching losses................................................ 47
4.4 Ground water – well water .................................................................................48
4.5 Statistical analyses .............................................................................................48
I
5 Results and Discussion..................................................................................... 49
5.1 Water balance .....................................................................................................49
5.1.1 Precipitation ...................................................................................................... 49
5.1.2 Potential and actual evapotranspiration ......................................................... 61
5.1.3 Soil water movement ........................................................................................ 78
5.2 Nutrient balance............................................................................................... 106
5.2.1
5.2.2
5.2.3
5.2.4
Soil fertility .......................................................................................................106
Aboveground fluxes.........................................................................................113
Soil-water-solute nutrient fluxes.....................................................................123
Net-balance .....................................................................................................143
5.3 Ground water – well water .............................................................................. 148
6 General Discussion.......................................................................................... 152
6.1 Methodology and concepts ............................................................................. 152
6.2 Deep soil water uptake.................................................................................... 156
6.3 Nutrient uptake ................................................................................................ 157
6.4 Sustainability of slash-and-mulch agriculture................................................ 158
7 Conclusions...................................................................................................... 160
8 Summary/Zusammenfassung/Resumo ........................................................ 162
9 References ....................................................................................................... 177
10 Appendix........................................................................................................... 197
II
Index of Tables
Page
Table 1: Mean chemical properties, soil density and textural distribution for the soils of the study region (Fölster, unpublished; n=7; SE in parenthesis)
16
Table 2: Land holdings in Igarapé-Açu in 1995 according to the size of the property, and the magnitude of these areas (IBGE, 1997)
18
Table 3: Location of installation of tensiometers and suction cup lysimeters on the experimental sites
and reading/collecting intervals of soil-water pressure head/soil-water solution
22
Table 4: Sequence of cropping operations on site 1 and site 2
23
Table 5: Micro-meteorological Instrumentation, its measuring height and resolution
28
Table 6: Time discretization criteria used in the soil water model
35
Table 7: Linear regression for the estimate of %-throughfall under the fallow site (independent variable is 'days of year 1997', i.e. beginning with 'day 1' at the 1st of January 1997 ending with
'day 730' at the 31st of December 1998)
51
Table 8: Canopy storage capacity (S) of different vegetation
55
Table 9: Interception during the two years of cropping on the fallow site and on the cultivation sites
57
Table 10: Interception (I) and its division into throughfall (PT) and stemflow (PS) of different vegetation in
relation to gross precipitation (P)
58
Table 11: Monthly mean net radiation (Rn) and temperature as well as mean daytime humidity and median daytime wind speed measured over the fallow site (Min. and Max.-values on hourly data
basis; Min.-Wind-speed in all cases = 0)
61
Table 12: Monthly mean potential and actual evapotranspiration and the mean kc-value of the fallow
site (based on daily data), as well as the median Bowen ratio (n = considered hours per
month for Bowen ratio; q. = quartile)
62
Table 13: Regression equation to estimate daily potential evapotranspiration [mm d-1], to estimate the
aerodynamic term of the Penman-FAO equation (bold letters within dotted lines) and regression of Penman-FAO ET and Penman-Piche ET (italic letters); n=313 in all cases
64
Table 14: Monthly mean Penman-Piche potential evapotranspiration for the fallow vegetation
65
Table 15: Monthly median daytime aerodynamic resistance (ra) and canopy resistance (rc) of the fallow
vegetation on basis of hourly data and calculated rc based on regression analysis
67
Table 16: Regression analysis to predict the log-transformed canopy resistance with the saturation vapor deficit and the net radiation (n=2446; R2 = 0.654)
68
Table 17: Mean, minimum and maximum stomata resistances (related to both sides of the leaf area) of
the most abundant species at the fallow site and of species at fallow sites of different age in
the study region; n.g. = not given
71
Table 18: Initially set and adjusted Van Genuchten parameter for soil hydraulic property (initial = initially
set; adj. = final adjustment)
79
Table 19: Initially set and adjusted scaling factors (αθ and αK) for the soil profiles within 105 and
1000 cm, at soil depths, where comparable (measured vs. modeled) data were available
79
Table 20: Pore connectivity value l and its range cited in literature
82
Table 21: Root growth parameter for the root-growth scenario on the two cultivation sites according to
the Verhulst-Pearl logistic growth function
86
Table 22: Water balance of the fallow site in 1997 and 1998 according to results of the soil water
model and comparable annual micro-meteorological evapotranspiration (P = gross precipitation, I = Interception; Pn = net precipitation; Esoil = soil evaporation; Tmodel = modeled transpiration; D10m = Drainage at 10 m depth; ET = micro-meteorological evapotranspiration: 1997 ≅
92
III
actual, 1998 ≅ potential)
Table 23: Soil water extraction within the period of 22nd of August 1997 and 8th of January 1998 and
for the year 1997 and 1998 under the fallow vegetation considering different soil layers; percentage values are related to extraction of 0-6 m
94
Table 24: Water balance of site 1 for the cultivated crops and for 1997 and 1998 according to results 100
of the soil water model and comparable annual micro-meteorological evapotranspiration (P =
gross precipitation, I = Interception; Pn = net precipitation; Esoil = soil evaporation; Tmodel =
modeled transpiration; D10m = Drainage at 10 m depth; ETcrop = potential crop evapotranspiration)
Table 25: Transpiration (Tmodel) and evapotranspiration (I + Tmodel) as well as drainage at 10 m soil depth 100
(D10 m) of site 2; remaining parameter of the water balance did not differ from site 1 (see Table 24)
Table 26: Accumulated drainage distinguished according to the cropping sequence at different soil 102
depths under site 1; 0 m soil depth corresponds to the net precipitation minus evaporation
Table 27: Change in soil water store of site 1 of marked soil layers comparing the beginning and the 103
end of 1997 and 1998, respectively, and the root water uptake of 1997 and 1998 out of
these layers (≅ transpiration); negative values denote a store depletion
Table 28: Mean plant-available P (Mehlich I extraction) of the soils of the study sites at five different 107
depths and three dates and the LSD between sites (=least significant difference, p≤0.05, after one way repeated measure GLM; dependent variable: sites; n=4); shaded cells denote
significantly higher concentration in comparison to the fallow
Table 29: Mean exchangeable cations, ECEC [cmolc kg-1] and pH of the soils of the study sites at five 110
different depths and three dates and the LSD between sites (see description of upper table);
lightly shaded cells denote significantly higher and dark-shaded cells lower concentrations
compared to the fallow
Table 30: Exchangeable amounts of cations of the soils to 3 m depth under the study sites and under 111
different other site of the study region (range of n=8; Fölster unpublished data); 0-30 cm
based on Embrapa-Belém measurements, 30-300 cm based on IBW-measurements; soil
density in the field assumed to be 1.5 g cm-3 (0-10 cm: 1.0 g cm-3); ECEC not including Na,
except for "study region"; italic values based on Embrapa determinations
Table 31: Mean aboveground biomass of the initial (before cropping) fallow vegetation on the cultiva- 113
tion sites distinguished into leaves, wood and litter compartment (litter also comprising dead
branches; n=10, including results of Schmitt, 1997)
Table 32: Mean nutrient concentration of the compartments of the fallow vegetation on site 1 and 2 be- 114
fore cropping (n=3)
Table 33: Mean nutrient stocks bound in the biomass of the initial fallow vegetation on the cultivation 114
sites and its percentage distribution in wood (wo.), leaves (le.) and litter (lit.)
Table 34: Mean postburn residues distinguished into charcoal (+ incompletely burned remains, >2 mm) 115
and ash of the fallow vegetation on site 1 and 2; n=24 minus those rejected, when exceeding
the 4-sigma-range; for median n=24
Table 35: Mean nutrient concentration of postburn residues distinguished into charcoal (+ incompletely 116
burned remains >2 mm) and ash (n=3)
Table 36: Mean nutrient stocks remaining in the postburn residues on site 1 and 2
117
Table 37: Mean percentages of aboveground dry matter and nutrient loss due to volatilization
117
Table 38: Nutrient stocks withdrawn by harvest goods and its mean percentage extraction by each crop
119
Table 39: Dry matter and nutrient stocks as well as nutrient concentrations of green matter (leaves), 120
woody compartments and litter (including dead branches) of fallow vegetation of different age
in the study region; sulfur was not determined (n.d.) in the cited studies
Table 40: Nutrient losses through leaching considering different soil depths under the burned and 133
mulched plots of site 1 and 2; negative values denote losses (1997: site 1 n=2, site 2 n=6;
1998: n=1; values of single sample of 1998 also shown separately in 1997)
Table 41: Reduction of elements during percolation from 0.9 m to 3 m soil depth under the burned and 134
IV
mulched plots of site 1 and 2, respectively of the year 1997 and 1998; negative values indicate an increase (=release of this elements out of the considered profile)
Table 42: Exchangeable amounts of cations of the soil profile of 0.9 m to 3 m soil depths under the 136
study sites and under different other sites (range of n=8) and their percentages saturation
(compare Table 30), as well as anion exchange capacity (AEC); n.d. = not determined; ECECcalculation based on results of NH4Cl-extraction and a soil density of 1.5 g cm-3; AEC calculated based on determination (2 mM CaCl2-percolation) of Anurugsa (1998) on soil samples
out of 30-50 cm depth and different pH ranging from 6.5 to 3.1
Table 43: Nutrient balance of site 1 and 2, burned and mulched land preparation, considering the com- 143
plete cropping cycle (3.5 and 7 years of fallow, respectively and 2 years of cultivation); deposition according to Hölscher (1995), BNF according to Thielen-Klinge (1997); leaching based
on measurements at 3 m depths, amounts in the second year of site 2 were assumed to describe also those of site 1
Table 44: Amounts of potassium present on site 1 and 2 in different compartments, their percentages 147
of the total and withdrawal through slash-and-burn agriculture; root-K: assuming a root biomass of 25 t ha-1 (Sommer et al., 2000) with a K concentration of 3.5 mg g-1 (≅ concentration
of wooden above-ground biomass)
Table 45: Median, minimum and maximum nutrient concentrations, pH and EC of the well water of 26th 150
of November 1997 and 23rd of April 1998 and the percentage increase within these dates as
well as the nutrient concentration in the soil solution of 6 m depth under the fallow at the 4th
of March 1998; n=8, the well water of Sr. Fransisco was not considered due to extremely biasing concentrations (contamination)
V
Index of Figures
Page
Figure 1: a) Street map of Northeast Pará State; b) Topographic map of the study region; left and bottom border: UTM-coordinates; top and right border: degree of longitude and latitude
13
Figure 2: Mean monthly precipitation and temperature (diagram according to Walter) of Castanhal and
Igarapé-Açu; observation period: Castanhal: 1973-1987 (Embrapa-Cpatu, 1987), IgarapéAçu: 1994-1998 ("Estação Marcelino", Embrapa, unpublished)
14
Figure 3: Land cover of the municipality of Igarapé-Açu in 1995 (IBGE, 1997a, considering a sub-area
of totally 46655 ha)
17
Figure 4: Aerial-photo of the fallow site and site 1 (subdivided into slash-and-burn-plot [left] and slashand-mulch plot [right]); photo taken in September 1998 by K. Vielhauer
20
Figure 5: Daily gross precipitation of the study area in 1997 and 1998
50
Figure 6: Relative mean throughfall under the fallow vegetation during the study period of two years
(Sá, unpublished data; dotted black line: linear regression; dotted gray lines: linear regression of 95%-confidence intervals of throughfall, see Table 7)
51
Figure 7: Mean %-throughfall (in relation to gross precipitation) under maize at four different distances
towards plants, mean values of all distances, fitted progress and %-stemflow; bars denote
the standard error, SE (to avoid clutter in the figure only SE of mean throughfall is shown)
52
Figure 8: Plant height of maize and related %-stemflow (bold point = determined stemflow)
53
Figure 9: Mean %-throughfall (in relation to gross precipitation) under cowpea; bars denote the SE
53
Figure 10: Mean %-throughfall (in relation to gross precipitation) under cassava at two different distances towards plants, mean values of both distances (all) and fitted progress; bars denote
the SE
54
Figure 11: Calculated percentage net precipitation of the fallow site on basis of the linear regression of
throughfall measurements and a canopy storage capacity of 1 mm (hourly data from 15th of
April 1997 to 30th of March 1998, others: daily data)
56
Figure 12: Calculated percentage net precipitation of the cultivation sites on basis of the sum of
throughfall and stemflow and a canopy storage capacity of 1 mm for all storm events
57
Figure 13: Median diurnal dynamic (hourly data) of the canopy resistance during the distinguished seasons
67
Figure 14: Comparison of actual evapotranspiration (Bowen ratio) and potential ET according to Penman-FAO as well as stand evapotranspiration according to Penman-Monteith (hourly data;
n=2963; SEy = standard error of estimate; SEa = standard error of slope)
70
Figure 15: Crop coefficients (kc) for maize, cowpea, cassava and the regrowing fallow vegetation
75
Figure 16: Cumulative evapotranspiration of the fallow vegetation over the observation period of two
year according to the Bowen ratio energy balance (actual ET), the Penman-FAO method (potential ET, kc=1) and, then considering the cultivation sites, including crop coefficients for
maize, beans, cassava and regrowing fallow vegetation (potential crop ET, assuming well watered crops)
76
Figure 17: Estimated initial pressure head distribution for the modeling procedure of 1996 and resulting distributions of pressure head within the soil profile at the end of year 1996 for the fallow
and for the cultivation sites
78
Figure 18: Soil-water retention curves of the three experimental sites
83
Figure 19: Hydraulic conductivity in relation to the pressure head of the three experimental sites; for
105-1000 cm (right side) additionally the 'scaled range' of K(h) is given (through lowest and
highest values of αK from Table 19)
84
Figure 20: Root water uptake function of the four different vegetation types (marked are the h50-point of
each curve)
85
Figure 21: Root mass density under the fallow vegetation according to earlier studies (='Traditional land
use', i.e. weighted mean of n=60, bares denote the SE; Sommer, 1996) and after adjustment
VI
87
in the modeling procedure, as well as the cumulative percentage distribution of root biomass
(secondary x-axis at the bottom).
Figure 22: Measured and modeled pressure head dynamic at 30 cm depth on site 1 over the two-year
observation period
88
Figure 23: Measured pressure head dynamics and the modeled pressure head dynamics at 120 cm
and 300 cm soil depth on site 1 over two years of cropping
89
Figure 24: Measured and modeled water content of the soil profile of the fallow site at the 6th of November and at the 10th of December 1997 (bars denote SE; n=2)
91
Figure 25: Modeled soil water content and soil water fluxes over the 10 m profile under the fallow vegetation at three different times reflecting maximum soil water storage (9/4/1997), beginning
dry season (22/8/1997) and minimum soil water storage (8/1/1998); negative values designate downward oriented fluxes
94
Figure 26: Soil water storage dynamics under the fallow vegetation in 1997 and 1998 separated into
0-6 m depth and 6-10 m depth
95
Figure 27: Cumulative water fluxes under the fallow vegetation at different soil depths over the observation period of two years; marked data points designate the 22nd of August 1997 and the 8th
of January 1998
96
Figure 28: Drainage rates at different soil depths under site 1 during 1997 and 1998
101
Figure 29: Soil water storage dynamic under the two cultivation sites in comparison to that of the fallow 103
vegetation in 1997 and 1998.
Figure 30: Comparing determinations of the organic carbon content and the plant-available phosphate 106
carried out in the Embrapa-Belém soil laboratory and in the IAT-Göttingen laboratory
Figure 31: (Mean) exchangeable K and Ca of the soil to 3 m depth of the study sites in 1998 according 108
to determinations of Embrapa-Belém (0-100 cm, n=4) and IBW (30-300 cm, n=1)
Figure 32: (Mean) ECEC of the soil to 3 m depth of the study sites in 1998
109
Figure 33: Maize, cowpea and cassava yields on site 1 and 2 on the slash-and-bun and slash-and- 118
mulch plots (13 % moisture for grains, but oven-dry for cassava)
Figure 34: Annual or two-year dynamics of the pH of the soil water samples taken at different soil 123
depths under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under
the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998:
n=1)
Figure 35: Annual or two-year dynamics of potassium concentrations in the soil water samples taken at 124
different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at
6 m under the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2
n=6; 1998: n=1)
Figure 36: Annual or two-year dynamics of calcium concentrations in the soil water samples taken at 125
different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at
6 m under the fallow vegetation, bars denote the standard error (1997: site 1 n=2, site 2
n=6; 1998: n=1)
Figure 37: Annual or two-year dynamics of magnesium concentrations in the soil water samples taken 126
at different soil depths under the burned and mulched plots of site 1 and 2, respectively, and
at 6 m under the fallow vegetation, bars denote the standard error (1997: site 1 n=2, site 2
n=6; 1998: n=1)
Figure 38: Annual or two-year dynamics of nitrate concentrations in the soil water samples taken at dif- 127
ferent soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6
m under the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2 n=6;
1998: n=1)
Figure 39: Annual or two-year dynamics of chloride concentrations in the soil water samples taken at 128
different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at
6 m under the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2
n=6; 1998: n=1)
VII
Figure 40: Annual or two-year dynamics of sulfate concentrations in the soil water samples taken at dif- 129
ferent soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6
m under the fallow vegetation, bars denote the standard error (1997: site 1 n=2, site 2 n=6;
1998: n=1)
Figure 41: Annual or two-year dynamics of phosphate concentrations in the soil water samples taken at 130
different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at
6 m under the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2
n=6; 1998: n=1)
Figure 42: pH dependence of Al in the soil solution; data with concentration > 0 are shown, dotted line 131
indicates AES-measuring limit (0.04 mg l-1)
Figure 43: pH dependence of the electrical balance
131
Figure 44: Magnitude of annual retention (0.9 – 3 m soil depth) of cations and anions in relation to 136
their mean annual concentration at 0.9 m soil depth; relation comprises the pooled data of
site 1 and 2, both treatments and both years; filled in circles: Na data of 1998
Figure 45: Mean annual nutrient balance on both sites and both land preparations
144
Figure 46: Changes of well water levels from September 1997 to August 1998 and corresponding soil 148
water store change in 6-10 m depth (right axis) as modeled for the fallow site (compare Figure 26 chapter 5.1.3); shown are the relative changes in relation to mean annual well water
levels/soil water store (= depths in parentheses)
VIII
Index of Tables in the Appendix
Page
Table A-1: Textural distribution of the three study sites (n=1)
202
Table A-2: Percentage cover of species on the fallow site and steadiness (all plants included with height 210
> 50 cm; plot size: 2 m2)
Table A-3: Times and amounts of upward orientated fluxes under the three experimental sites
214
Table A-4: Mean ratios of Ca to each of the considered elements of the distinguished pre- and postburn 215
compartments
Table A-5: Nutrient concentration of harvested crops of site 1 and 2 of both treatments
216
Table A-6: Mean, minimum and maximum concentration of iron, manganese, aluminum, ammonium 219
and organic N and the conductivity and electrical balance in the soil water solution distinguished according to site, treatment and depth
Table A-7: Correlation coefficients (Pearson) of concentration of solute elements in the soil water under 220
the two cultivation sites distinguished according to burned (upper right triangle) and mulched
(lower left triangle) land preparation; also given: significance (second line) and n (third line);
bold number ≅ most pronounced
Table A-8: Amounts of nutrients present on site 1 and 2 in different compartments, their percentages of 224
the total and amounts withdrawn under slash-and-burn; assuming a root biomass of 25 t ha-1
with nutrient concentrations ≅ those of wooden above-ground biomass
IX
Index of Figures in the Appendix
Page
Figure A-1: Histogram of precipitation intensity divided up into 0.5mm-classes, considering 15minutely 203
and hourly data and their total share (considering only P>0)
Figure A-2: Daily probability of precipitation (including also rainless times, n=54261, 15minutely rec- 203
ords)
Figure A-3: Histogram of throughfall as percentage share on gross precipitation of the fallow site
204
Figure A-4: Daily median wind speed (based on hourly data) over the fallow vegetation of the three dis- 205
tinguished seasons (dotted gray lines are lower and upper quartile)
Figure A-5: Dynamic of daily actual (Bowen ratio) and potential (Penman-FAO) evapotranspiration of 206
the fallow vegetation and their relation kc within the measuring period
Figure A-6: Energy balance (based on mean hourly data) of the fallow vegetation of the three distin- 207
guished seasons
Figure A-7: Diurnal Bowen ratio dynamic (based on median hourly data) of the fallow vegetation of the 208
three distinguished seasons (dotted gray lines are the lower and upper quartile)
Figure A-8: Median diurnal canopy resistance dynamic (hourly data; bold line) of the fallow vegetation 209
of the three distinguished seasons; dotted gray lines are the lower and upper quartiles;
thin lines are calculated rc based on the regression equation (extrapolation is dotted)
Figure A-9: Measured and modeled pressure head dynamics at 30 cm and 120 cm depth in the ob- 211
servation period of 1997 and 1998 on the fallow site
Figure A-10: Measured and modeled pressure head dynamics at 300 cm, 600 cm and 735 cm depth
212
Figure A-11: Gravimetrically determined soil water content in relation to corresponding pressure head 213
at different soil depths; values with pressure heads > 200 cm are reflecting the desiccated
profile under the fallow vegetation (6th Nov. and 10th of Dec. '97), remaining values are
those of the cultivation sites (11th of May and 17th of June 1998)
Figure A-12: Annual or two-year dynamics of sodium concentrations in the soil water samples taken at 217
different soil depths under the burned and mulched plots of cultivation site 1 and 2, respectively, and at 3 m under the fallow vegetation; bars denote the standard error (1997:
site 1 n=2, site 2 n=6; 1998: n=1)
Figure A-13: Annual or two-year dynamics of aluminum concentrations in the soil water samples taken 218
at different soil depths under the burned and mulched plots of cultivation site 1 and 2, respectively, and at 3 m under the fallow vegetation; bars denote the standard error (1997:
site 1 n=2, site 2 n=6; 1998: n=1)
Figure A-14: Accumulative nitrate and phosphate fluxes on site 1 and site 2 on the burned and mulched 221
plots at 0.9, 1.8 and 3 m depth
Figure A-15: Accumulative potassium and calcium fluxes on site 1 and site 2 on the burned and 222
mulched plots at 0.9, 1.8 and 3 m depth
Figure A-16: Accumulative magnesium and sulfate fluxes on site 1 and site 2 on the burned and 223
mulched plots at 0.9, 1.8 and 3 m depth
X
1 Introduction
1 Introduction
The fate of tropical rainforest-ecosystems is a crucial issue in environmental policies.
Since the 1992-UNCED Conference held in Rio de Janeiro, their protection has been accepted worldwide as an international task. Moreover, the Rio-Conference strengthened
the consciousness of a sustainable use of the earth's resources linking environmental
protection with the demands of economic development.
Nevertheless, tropical rainforests disappearance is undiminished. Main reasons for their
destruction are the conversion into agricultural land due to expansion of pastures and
permanent cropland, as well as increasing shifting cultivation. But, also logging for timber
production and clearance for mining or exploration of oil are responsible.
The importance of shifting cultivation is obvious: about 500 million people worldwide, out
of a total agricultural population of 1.2 billion, rely on shifting cultivation (Lanly, 1985).
About 60 to 80 million of these shifting cultivators are located in Latin America occupying
an area of about 1.8 million km2, which is yearly increasing by about 18 000 km2 (Houghton et al., 1991).
In the Amazon region most agriculture is based on shifting cultivation as well, which is a
primary source of subsistence in smallholder agriculture. Conversion of primary forest is
on one hand caused by a growing population at the forest margins, on the other hand by
migration of landless people into the heart of the Amazon region.
In more densely populated regions with limited land resources, such as the eastern part
of the Amazon region, the feasibility of shifting cultivation is threatened by intensified
land use and shortened fallow periods, which cause a declining productivity of the lowfertile soils. The Bragantina region is located in this region and, therefore, was considered
the appropriate location to study aspects of sustainability of intensified shifting cultivation.
Based on a nutrient balance, Hölscher et al. (1996) assumed that in this region crop production under shifting cultivation is actually achieved by soil mining. Burning the fallow
vegetation for land preparation has been proved to be responsible for the major nutrient
losses in the cropping cycle, which were not balanced by supplementary inputs. Substituting burning by mulching, therefore, was seen as a promising alternative to overcome
deterioration of soil fertility.
1
1 Introduction
The main objective of the SHIFT1-Capoeira project, in which the present study was embedded, is the optimization of the traditional slash-and-burn agriculture. Promotion of
mulching instead of burning is a major target. Consequently, the influence of mulching on
crop production, performance of fallow regrowth and soil chemical and physical properties is studied.
The effect of mulching on crop production focusing on the phosphate and nitrogen dynamics was explored by Kato et al. (1999). A complete nutrient balance of the favored
slash-and-mulch system is, however, still missing. In comparison to slash-and-burn, the
main pathways of nutrient fluxes are modified. Instead of burning the biomass and thus
volatilizing most of the bound C, N, P, K, Mg, and S, the biomass is chopped and spread
over the soil surface. Subsequent decomposition might promote a release of these elements into the soil solution. It was unknown whether a large amount of mulched biomass
would lead to comparably higher loss of nutrients by leaching and counterbalance the
benefit of avoiding volatilization of nutrients by burning, threatening the sustainability of
modified shifting cultivation.
Therefore, a water balance was established for fields under slash-and-mulch and slashand-burn cultivation. In combination with measurements of solute element concentrations in the soil solution, the nutrient losses due to leaching were quantified. The water
balance was calculated using a soil water model, which was calibrated and validated with
in situ measurements.
The studies were extended to the deeper soil profile, since deep roots were found to
reach at least 6 m soil depth (Sommer et. al., 2000). Furthermore, the contribution of
deep-soil water to the water balance of a primary forest close to the study region was
shown by Klinge (1997) applying a soil water model. Also, Hölscher (1995), based on a
micro-meteorological study on fallow vegetation in the Bragantina region, suggested that
a deep-soil water contribution was made to transpiration during the dry season. To verify
Hölscher's water balance, the depletion of deep-soil water under fallow vegetation was
measured directly in this study.
If the deep-rooted fallow vegetation is able to deplete deep-soil water, a capacity for nutrient uptake out of these soil layers is also likely. This would substantially alter the nutri-
1
SHIFT (Studies on Human Impact on Forest and Floodplains in the Tropics) is a bilateral German-Brazilian
Cooperation comprising different projects realized in Brazil. The research activities of the Shift-Capoeira
project are carried out in the Bragantina region (Capoeira is the local name of the fallow vegetation).
2
1 Introduction
ent balance of this agro-ecosystem.
Thus, the objectives of this study are to:
ƒ
Establish a nutrient balance for the slash-and-mulch system in comparison to traditional slash-and-burn with special consideration of the influence of deep soil layers
on the transport processes of solute nutrients
ƒ
Establish a water balance for the fallow vegetation focusing on soil water depletion in the dry season
3
2 Literature review
2 Literature review
The Brazilian Amazonian region contains the world’s largest untouched rainforest nowadays covering about 4 million km2. But also its actual deforestation rate is the highest in
the world. From 1978 to 1988, forest was cleared at a rate of 15 000 to 22 000 km2 per
year, slightly declining to about 11 000 km2 a-1 due to economic recession in the beginning 1990th (Skole & Tucker, 1993; Fearnside, 1993), then increasing again to 18 000 to
20 000 km2 a-1 during 1993 to 1996. In late 1990th annual Brazilian Amazonian deforestation was estimated to lie between 10 000 and 16 000 km2 a-1 (INPE, 1997). However,
Nepstad et al. (1999) assumed that these assertions based on satellite images account
only for half of the actually impoverished area. In their opinion, satellite images do only
track areas with "fresh" disturbance, but not those, where logging took place 1 to 5 years
ago. Yet, these disturbed areas might be vulnerable to further destruction e.g. by fireevents in the dry season.
Reasons for deforestation are: a) conversion into agricultural land (pastures and permanent cropland) b) increasing shifting cultivation c) logging for timber production and d)
clearance for mining or exploration of oil (Repetto, 1990; Alexandratos, 1995). The ecological impact of these activities varies greatly, as is the case for the underlying causes
(Contreras-Hermosilla, 2000). Which of the agricultural activities has the greatest share
on actual deforestation, has often been subject of emotional discussions. Fearnside
(1993) estimated that about 70 % could be attributed to pastures established by medium
to large ranchers, and the remaining 30 % to shifting cultivation.
It is mostly smallholders (properties <100 ha) who practice shifting cultivation. The traditional shifting cultivation cycle starts with slashing and burning a forest or fallow vegetation. Subsequently, for a period of one to two years a sequence of maize (Zea mays), rice
(Oryza sativa), beans (Vigna unguiculata) and cassava (Manihot esculenta) are grown. After that time the cropping site is abandoned, which allows the regrowth of fallow vegetation. Reestablishment of the fallow vegetation occurs mostly vegetatively by root suckers
and aboveground sprouts (Uhl & Jordan, 1984; Clausing, 1994; Kammesheidt, 1999). After abandonment the smallholder shifts to another area of his property to start a new cultivation.
Considering the whole Amazon region, the fallow period varies between about 3 years up
to several decades, depending on a variety of factors such as performance of regrowth
(linked to the basic soil fertility status), available labor force, remoteness of the area and
pressure on land use. The latter is high in regions with growing rural population and lim4
2 Literature review
ited land-resources and/or lacking property rights or equitable land-distribution, though it
is eventually attenuated by migration from the country to the towns. Lack of employment
outside agriculture additionally contributes to the pressure (Ehui et al., 1990). Consequently, pressure on land use leads to a reduction of the fallow period to enable the cultivation of a higher percentage of land at the same time. Nowadays, three to eight years of
fallow are quite common in the affected regions2.
The feasibility of sustainable shifting cultivation including slash-and-burn landpreparation has been subject of debate since the last four decades. Beginning in the late
1950th, the dynamic of soil fertility under slash-and-burn land use was investigated by
Nye and Greenland (1960) in Ghana, followed by a number of further studies (Ewel et al.,
1981; Sanchez et al., 1983; Andriesse & Schelhaas, 1987; Jordan, 1989; van Reuler &
Janssen, 1993a; Kleinman et al., 1995). Their results can be generalized as follows:
Slashing and burning of a fallow vegetation temporarily enriches the soil with major
nutrients. But, burning releases also considerable amounts of nutrients into the atmosphere. Nutrient losses furthermore occur during the cropping phase by plant uptake and harvest, as well as by erosion and leaching. The proportions of nutrient exported by these different pathways vary among the studies. A release of nutrients out
of decomposing soil organic matter still contribute to soil fertility, when nutrients provided by the ash have already been depleted. When that source of plant-available nutrients is also exhausted, soil fertility drops below the level considered necessary by
the farmer for further profitable cropping. Additionally, a prolonged cropping period
might be little lucrative due to increasing weed invasion and risk of pests (Van Reuler
& Jansen, 1993b). Finally, the cropping site is abandoned, the regrowing fallow vegetation can accumulate nutrients and soil fertility is successively restored.
There is a growing public concern about the environmental damage caused by slash-andburn. However, several authors recently pointed out that slash-and-burn could also be
part of a highly sophisticated form of agroforestry, when integrated in traditional shifting
cultivation (Pye-Smith, 1997; Helmuth, 1999). In shifting cultivation the slashed and
burned (fallow) vegetation can provide nutrients for crop production on basically nutrientpoor soils. The necessary soil fertility can be achieved without external input of fertilizer –
essential in subsistence agriculture. With fallow periods long enough to balance nutrient
2
Beckerman (1987) gives a general overview of shifting cultivation in the Amazon region highlighting socio-
economic aspects.
5
2 Literature review
exports through inputs by atmospheric deposition, such systems are ecologically stable
(Fölster, 1994). Besides the capacity to restore soil fertility, the fallow period is also acting as weed-break, where the regrowing secondary trees successfully shade-out and suppress herbaceous weeds (Rouw, 1995). Long fallow periods also control crop-pests and
diseases.
Indeed, indigenous peoples did use slash-and-burn agriculture in a sustainable way for
centuries (Gross et al., 1979; Hecht, 1989), and this is theoretically possible even today
by maintaining an extensive fallow length (Kleinman et al., 1996). But, such a cropping
system is land-demanding and, thus, in recent years has become non-sustainable in locations with pressure on land use. In such situations, crop production is achieved by mining
soil nutrient resources, which was shown by Brand and Pfund (1998) for a rice-fallowsystem in Madagascar or by Lumbanraja et al. (1998) after intensification of land use in
Sumatra. Consequently, the short to medium-term stability of the slash-and-burn system
is more a function of the total nutrient stock of the entire ecosystem than of the net
losses of the soil after slash-and-burn (Juo & Manu, 1996). In the long run, not only crop
production is endangered, but also the capacity for regeneration of the secondary vegetation might be lost (Uhl et al., 1989), promoting the formation of unproductive grasslands.
This is a major concern in the Bragantina region, as its soils have a low-fertility and have
been in slash-and-burn agriculture for over 100 years. However, soil mining might not be
instantly detectable in declining crop yields or worsened performance of fallow regrowth.
Soil fertility analyses, carried out in the northeastern Pará 30 years ago (Falesi, 1972), do
not clearly differ from results of recent analyses (Denich, 1989; Diekmann, 1997; Kato,
1998b). Information about soil chemical and physical properties prevailing 100 years ago
is not available. Thus, assessing soil fertility changes by crop yields might be a more
promising option. Alburquerque (1961), for instance, characterizing cropping and processing of cassava in the Bragantina region, considered a fresh yield of cassava tubers of
10 to 15 t ha-1 as a common, and 15 to 25 t ha-1 as a good yield for the region. In 1991,
an average cassava yield of 10 t ha-1 is given by the Geographical and Statistical Institute
of Brazil (IBGE, 1997b), which is at the lower edge of what were regular yields formerly.
On the other hand, maize grain yields seem to have been stable, to date ranging from 0.7
to 0.8 t ha-1 (IBGE, 1994b). Around 30 years ago Pereira (1971) found yields to vary from
0.3 to 0.7 t ha-1. He also studied the lower Amazon region, along the Xingu River, where
he recorded regular grain yields of maize of up to 2 t ha-1. These comparatively high
yields, in his opinion, were related to the less exploited soils (Latossolos amarelos) in this
region. Maize grain yields in the Xingu region were recently (re-) evaluated by Silva6
2 Literature review
Forsberg and Fearnside (1997). They reported yields ranging from 1.67 to 3.24 t ha-1.
Additionally, they detected a positive relationship between yield and preceding fallow
length. Thus, maize grain yield in the Bragantina region in the late 1960th may have already dropped noticeably due to low soil fertility and do not seem to have increased in
the following years despite improvement of cultivars.
Deterioration of soil fertility occurs quite unnoticeably, as apparently weathering primary
minerals to a certain degree still replenish the plant-available nutrient stocks.
The calculation of net-exports by nutrient balancing showed that a fallow length of at
least a century is necessary to naturally restore original soil nutrient levels after burning
(Kauffman et al. 1993; Hölscher et al., 1996). This indicates that a traditional shifting
cultivation can only be sustainable in a rather "conservative" system, where burning
losses are small and harvest products are not irreversibly exported. However, such systems, especially those comprising a fallow length of several decades, are nowadays rarely
found. Therefore, the adoption of sustainable management technologies is highly advisable (Sanchez, 1993).
Kauffman et al. (1993) and Mackensen et al. (1996) as well as Araújo et al. (1999; focusing on C emission) also proved that burning is responsible for major losses occurring
in shifting cultivation. More than 95 % of C and N of the preburn vegetation-biomass was
lost by volatilization, but also between 30 % and 60 % of the less volatile elements K, Ca,
Mg and P where lost by the burning. The magnitudes generally increased with increasing
intensity and temperature of the fire (Feller, 1988). Thus, avoiding burning as a tool of
land-preparation by the establishment of a slash-and-mulch system is seen as a promising alternative to overcome deterioration of soil fertility (Greenland & Okigbo, 1983;
Schöningh, 1985; Denich, 1989; Thurston, 1997).
Due to the large amounts of woody biomass, manual mulch preparation would be impossible. Therefore, a mobile tractor-force driven bush chopper was developed, that cuts and
chips the woody vegetation, which is subsequently spread over the field (Denich & Lücke,
1998; Denich et al., 1998). At present, improvement of this chopper is pursued under
conditions of practical operation.
Mulching, however, requires moderate fertilization, to avoid unacceptable yield reduction
(Kato et al., 1999). This might be related to the lacking ash, but more important is the
immobilization of soil nutrients by microorganism. Smith and Sharpley (1990) carried out
a mulching experiment with different crop residues applied on the surface or incorporated using an Oklahoma surface soil (Ultisol). The concentration of mineralized soil-N
diminished considerably compared to a non-mulched control, the more so, the higher the
7
2 Literature review
C/N ratio of the residue biomass. Increased Nmin-concentration resulted, when legumes
(low C/N ratio) were mulched. Immobilization was enhanced when the residues were additionally incorporated, which improved the access of microorganisms and increased their
demand for soil-derived N. Only after 56 days, net N-mineralization was evident. Also Kato
et al. (1999) in studies carried out in the Bragantina region detected comparably lower
Nmin-concentrations in the soil solution of 40 cm depth due to mulching. Only after about
1.5 years, Nmin-concentration of mulched plots exceeded those measured on burned
plots.
Leaching losses due to slash-and-burn agriculture were rarely, if ever, determined using a
soil water model. Hölscher (1995) calculated leaching on basis of rainfall intensities assuming a direct relationship between both processes. Leaching losses of 13 kg N, 4 kg P,
15 kg K, 49 kg Ca and 11 kg Mg per hectare during a rotation-period of 9 years at a reference depth of 105 cm were negligible compared to the dominating losses by burning
and harvest. This concurs with results of a study of Williams and Melack (1997), who
measured leaching on a catchment scale in the central Amazon region of 9 kg N, 0.07 kg
P, 5 kg K, 4 kg Ca and 1 kg Mg per hectare and year. Though, already with these leaching-inputs into the natural environment they predicted alteration towards eutrophic conditions in the long run.
The present study compares the complete nutrient balance of slash-and-burn
with slash-and-mulch quantifying leaching losses with a soil water model.
Amazonian primary forests as well as the fallow vegetation have long been considered to
have a shallow root system. This was based on observations of a predominantly shortcircuit nutrient turnover, where nutrients released from decomposing organic matter are
immediately taken up by an intensive superficial root system. Nevertheless, deep roots
were found in some of these ecosystems already in the 1960th by Förster (1970) and
later by Longman and Jenik (1987) and Nepstad et al. (1991). Additionally, studies carried out by Poels (1987) in Suriname, by Nepstad et al. (1994) and Klinge (1997) in the
Eastern Amazon as well as by Hodnett et al. (1996a) and Ashby (1999) in the central
Amazon proved the importance of deep roots providing water for transpiration during the
dry season. Depending on the region and year, between about 100 mm and 400 mm water per year were taken up from the soil layer below 1 m. Nepstad et al. (1994) suggested
that an uptake of water took place from at least 8 m soil depth. Poels (1987) would not
exclude an uptake of water by roots connecting to groundwater, which seasonally
dropped below 10 m.
8
2 Literature review
Bruijnzeel (1990) conducted a survey of climatological and hydrological studies on
worldwide tropical (primary) lowland forests. Based on 22 different studies, he concluded
that, on average, 1430 mm water per year is evapotranspired by these forests. The
above-mentioned 100 mm to 400 mm of deep-soil water would thus equal 7 % to 28 % of
annual evapotranspiration. Deep soil water might make up between 10 % and 40 % (the
latter reported by Klinge, 1997) of the annual transpiration alone (obtained as the difference between ET and interception). However, such gross calculations can only give a
rough estimate of hydrological or climatological processes and have to be interpreted
with care. For instance, to obtain the average annual ET, Bruijnzeel (1990) had to exclude
11 of the 22 studies considered, as errors could not be ruled out e.g. caused by catchment leakage. Furthermore, the assumption of the existence of a deep root-system
should not simply be extrapolated to all tropical low-land forest, but has to be verified for
each individual case.
Only some attempts were made to assess the capacity of nutrient uptake by deep-rooting
fallow vegetation or trees:
Van Noordwijk (1989) studied the rooting depth of fallow trees and alley-trees with respect to the nutrient efficiency in different agroforestry systems in Nigeria. He showed
that Leucena leucocephala built a root system down to at least 4 m depth. Furthermore,
he found several other indigenous species with deep roots and, consequently, he assumed that natural fallow vegetation is also deep-rooted.
In agroforestry systems a deep-rooting tree component is always considered desirable, on
the one hand to diminish competition with the more superficial root system of most
crops, on the other hand to provide a safety net against nutrient leaching.
In his leaching model Van Noordwijk (1989) assumed that a fallow vegetation is capable
to take up all solute nutrients within its maximum effective rooting depth. Leaching, according to his model, is only a question of solute-transport rate, differentiated by the apparent adsorption coefficients of each considered element or compound, and is not dependent on the "mesh-width" of the safety net provided by the roots.
In contrast, Jordan (1989) considers nutrients in the soil solution at 12 cm depth as
leaching losses, thus not considering a deep-rooted vegetation with the ability to recycle
part of these nutrients. During three years of shifting cultivation on an Oxisol in the central Amazon more than 180 kg K ha-1 and 96 kg N ha-1 were leached below 12 cm depth.
Shepherd et al. (1996), in return, assumed that N uptake from below 0.5 m soil
depth contributes 50 % of total plant N uptake of deep-rooted shrubs and trees. The Nuptake was qualitatively evident, as nitrate stocks at 50 to 200 cm depth in comparison
9
2 Literature review
to those under maize and a weed fallow were reduced (shown in a corresponding study of
Hartemink et al., 1996). Both studies were carried out in Kenyan agroforestry systems
with hedgerows and/or with improved fallow of Sesbania sesban. Additionally, Shepherd
et al. (1996) cited a study dealing with the N-budget under coffee plantations using
15N-
labeled nitrate fertilizer, which showed that none of the applied N was leached out of the
upper 1.5 m soil, though 22 % of the fertilizer-N had percolated to 0.6 m depth. Fertilizer
disappearance in this case was assumed to be caused by retention through the positive
charge of the soil. Besides those rather contradictory results, direct quantification of nutrient uptake by deep-roots has not been achieved in recently published studies.
Quite promising seems the use of tracers to quantify nutrient uptake. This was done by
Dambrine et al. (1997) using the isotopic ratio of strontium-87 to strontium-86 comparing the ratio of Spanish forest soils with that of plant and root material. This allowed the
quantification of the Ca-uptake by eucalyptus and pine-trees out of deeper soil layers
(below 30 cm), as Ca and Sr are similarly taken up by plants and a natural isotope gradient was present in the studied soils. Comparable tracer studies were not found in the literature for tropical ecosystems. Radiocarbon (14C) has been used to study solely the
turnover of soil organic matter (Trumbore, 1993) and 15N to study the N2-fixation capacity
by leguminous trees (Thielen-Klinge, 1997; Paparcikova in preparation).
Besides water and nutrient supply out of deeper soil layers, the organic matter of the
deeply penetrating root system also provides a permanent carbon input. This has been
found important for potential deep-soil carbon sequestration (Nepstad et al., 1994; Lal &
Kimble, 1997; Rosell & Galantini, 1997, Sommer et al., 2000). Still, the contribution of
the fallow vegetation to the water and nutrient dynamics of shifting cultivation remains to
be fully evaluated.
The present study was confined to measure the deep-soil water uptake of the
fallow vegetation as a precondition for a potential capacity of nutrient recycling.
The deep-soil water uptake of a 2-year-old fallow vegetation was estimated by Hölscher
(1995). According to his calculation 322 mm per year of annual ETa were extracted out of
the soil layer below 1 m depth. But, this amount was only derived from the deficit of
measured transpiration and precipitation during the dry season and not directly measured. To definitively prove deep soil water uptake and to quantify the contribution of the
different soil layers, a soil water model is the only practicable assessment tool. Such a
model had never been applied to secondary vegetation in studies cited in literature.
10
2 Literature review
For the application of a water model simulating the soil water movement, the soil hydraulic properties have to be determined. These are the specific relationships between soil
moisture, soil water pressure head and soil hydraulic conductivity. To assess these relationships, besides some laboratory methods (Stolte et al., 1994), in situ methods are
quite common, though they are labor and equipment-intensive. Most widespread are the
so-called '(unsteady) drainage flux approach' (Richards et al., 1956), the 'instantaneous
profile method' (Rose et al., 1965) or the 'internal drainage method' (Hillel et al. 1972;
Hillel, 1998). In a wider sense the three methods differ only in details, but basically, the
above-named relationships are calculated on the basis of data regarding the redistribution-process of soil water after saturating the soil column. These data are time-series of
soil moisture and pressure head at distinct depths of the profile. This methodology is the
most disseminated tool for assessing soil hydraulic parameters and remained subject of
improvement or modification up to recent years (Flühler et al., 1976; Libardi et al., 1980;
Dane & Hruska, 1983; Green et al, 1986; Kool et al., 1987; Ahuja et al., 1990; Sisson &
Van Genuchten, 1991).
Furthermore, to describe the water movement in soils, the so-called 'boundary conditions'
have to be set properly. These are on the one hand the net-precipitation input into the soil
column and the water export via topsoil-evaporation (upper boundary). On the other hand,
the water is considered, which leaves the soil column at the lower boundary or lateral, as
is the case in a two or three-dimensional flow processes. Transpiration by the vegetation
affects different layers of the soil column and is considered in the flow equation by a sink
term. Thus, a soil water model approach under natural conditions always comprises
measurement of micrometeorological parameters, which raises the need of the appropriate determination of evapotranspiration.
To describe the potential evapotranspiration of a certain vegetation type, very often the
Penman equation (Penman, 1948) is used. The original Penman equation has undergone
considerable modification since it was first publicized in 1948. Several types and forms
appeared (e.g. Penman, 1963; Wright & Jensen, 1972; Wright, 1982). Doorenbros and
Pruitt (1977) formulated the FAO-version of the Penman equation and, including so-called
crop coefficients, applied this equation to calculate crop water requirements.
The actual evapotranspiration of a vegetation stand can be determined with the Bowen
ratio – energy balance method (Bowen, 1926). This method, however, is equipmentintensive and requires highly accurate measurements of flux gradients of air temperature
and vapor pressure. First measurements of that type for a fallow vegetation in the Eastern Amazon were done by Hölscher (1995). He estimated the annual ETa to 1364 mm.
11
2 Literature review
Thus, evapotranspiration of a young fallow vegetation was already comparable to those of
primary forests in the Amazon region (as given e.g. by Bruijnzeel, 1990; see above).
A different approach to determine the actual (stand) evapotranspiration is the PenmanMonteith method. Monteith (1965) introduced the aerodynamic and the canopy resistance into Penman's original equation to take into consideration their differentiated influence on stand/vegetation's evapotranspiration. Depending on the quality of data on
these resistances, the Penman-Monteith method might serve as a sophisticated tool to
determine actual (stand) evapotranspiration. Its application would be preferable to the
Bowen ratio energy balance, as measurements do not require flux gradient determination. Thus, high accuracy of temperature and vapor pressure determinations is not obligatory, though desirable.
Valuable information on the canopy resistance is indirectly obtainable by determining
stomata resistances. The canopy resistance – also called the bulk stomata resistance –
is theoretically an integrated value of stomata resistances. Sá et al. (1995 and 1999)
conducted a number of studies to assess the stomata resistances of the most-abundant
species of fallow vegetation in the Eastern Amazon region. The stomata resistances of
young fallow vegetation ranged annually between 70 and 140 m s-1. With age stomata
resistances approached those typically found for primary forest species (200 to
300 m s-1; Sá et al., 1996). Results indicate general alterations in the eco-physiological
strategy of growing fallow species. In the present study it was of methodological interest
to correlate results on the stomata resistance of fallow vegetation with own results on the
canopy resistance. In the future, therefore, available information on the stomata resistance might be used to apply the Penman-Monteith equation to determine the actual
evapotranspiration of a growing fallow vegetation.
Considering the required parameters, the application of a soil water model is a complex
and ambitious exercise. Consequently, parameter setting and model adjustment take up
considerable space in this Thesis. Nevertheless, the soil water model was considered the
most appropriate tool to meet the objectives of this study.
12
3.1 Location
3 Study region
3.1 Location
The study was conducted in the Bragantina region in the municipality of Igarapé-Açu
(along the Travessa Cumaru, Figure 1).
a)
47° 37’24’’ W
b)
47° 35’15’’ W
76
01° 07’15’’ S
72
01° 09’24’’ S
98
98
Fallow site and
cultivation site 1
01° 11’34’’ S
68
98
Cultivation site 2
2
08
4 km
2
12
Adapted from the topographical map MI-385 Castanhal;
Minis tério do Exército, Dir etoria de Serviço Geográfico
Figure 1: a) Street map of Northeast Pará State; b) Topographic map of the study region; left and bottom
border: UTM-coordinates; top and right border: degree of longitude and latitude
13
3.2 Climate
Igarapé-Açu comprises an area of 800 km2 (IBGE, 1994a). In 1996 the population was
about 31 000 inhabitants, i.e. 39 per square kilometer, with about half (49 %) living in
the rural area (IBGE, 1998). Igarapé-Açu, together with 13 municipalities forms the Bragantina (micro) region (11.609 km2). The Bragantina region comprises less then 1 % of
the total area of Pará, but about 5 to 6 % of its total population. Together with 14 further
micro-regions it builds the State of Pará (1 253 164 km2, i.e. 14.6 % of total Brazil). In
1991, 4.95 million inhabitants were living in Pará. At that time the population was increasing annually by 3.46 %, which is higher than the mean federal growing rate of
1.93 % (IBGE, 1991). Today, the population of Pará should have increased to more than
6.5 million inhabitants.
3.2 Climate
The climate in the Bragantina region is humid with a dry season from September to December, a mean annual precipitation between 1700–2700 mm, and a mean annual
temperature of 25 to 27 °C (Figure 2).
Figure 2: Mean monthly precipitation and temperature (diagram according to Walter) of Castanhal and Igarapé-Açu; observation period: Castanhal: 1973-1987 (Embrapa-Cpatu, 1987), Igarapé-Açu: 1994-1998
("Estação Marcelino", Embrapa, unpublished)
According to the classification of Köppen (Koch, 1930), the region is characterized as
Am-type, which is a tropical rainforest climate with a mean temperature of the coldest
month above 18 °C. The dry season becomes more intensive and prolonged in Bragantinian Southeast, which becomes visible in Figure 2 comparing the annual precipitation of
both locations. Thus, in the lower South, the climate changes to an Aw-type, which accounts for intensified arid months. The more humid region of the city of Belém and its direct vicinity is characterized as an Af-type not showing a distinct dry season.
Walter (1990) classified the climate to be in transition of the zono-biome I (equatorial14
3.3 Soil
humid daytime climate suitable for tropical evergreen forest) and the zono-biome II (humido-arid tropical summer-rain climate suitable for tropical deciduous forest). This climate according to his classification favors a semi-deciduous forest.
The main wind direction in the region is northeast prevailing annually on 76 % of all days.
On 16 % of the remaining days, wind comes exclusively from southeast (EMBRAPACPATU, 1977-1988).
The sunshine duration ranges from 2000 to 2400 hours per year (Diniz et al., 1986).
3.3 Soil
The Bragantina landscape has a flat to slightly undulating relief and is elevated 30 to
70 m above sea level.
The recent soils in the Bragantina region are predominantly 'terra firme' (=upland) soils.
The parent material was deposited during the Tertiary and Quaternary and originated
form weathered granite, gneiss and sandstone of the Guyana and Brazilian shield (Sioli,
1968). These formations of continental origin are also known as the 'Pará formation'
(Pleistocene) and the 'series of Barreiras' (Pliocene). Besides these, a third, older formation of maritime origin (Miocene) is the so-called Pirabas formation, which occurs in at
least one third of the Bragantina region and is often, but not always, overlain by the Pará
or Barreiras formation. A more detailed description of the geology of the study region is
given by Denich (1989).
Due to the chemical characteristics of the parent material and long-time leaching processes, the terra firme soils of the study region are highly and deeply weathered with generally low nutrient concentrations.
According to the Brazilian soil system the prevailing soils in the Bragantina region are
classified as (Viera et al., 1967):
-
Podzolicos Amarelos, corresponding to Ultisols
-
Latosolos Amarelos, corresponding to Ustoxs or Udoxs (Oxisols)
-
Areias Quartzosas, corresponding to Psamments (Entisols)
Due to lessiviation, the Podzolicos typically show a distinct clay gradient ("A/B abrúptico").
This feature is missing in the Latossolos. It is an open question, whether soils with a
moderate clay accumulation in the subsoil are Ultisols or Oxisols, especially as the lessiviation process is difficult to prove.
Latest studies of Rego et al. (1993) identified the predominating soils in the study region
to be Typic Kandiudults.
15
3.4 Vegetation
The soils are characterized by low C and N contents, as well as by low plant-available P
concentrations, a low cation exchange capacity (CEC) and high subsoil aluminum saturation. The texture is loamy sand in the topsoil and sandy clay loam in the deeper layers
(Table 1).
Table 1: Mean chemical properties, soil density and textural distribution for the soils of the study region
(Fölster, unpublished; n=7; SE in parenthesis)
Depth [cm]
2.5
pH (H2O)
-1
C
[g 100g ]
-1
N
[mg g ]
P (Mehlich-I) [mg kg-1]
-1
Ca
[cmol+ kg ]
-1
Mg
[cmol+ kg ]
K
Na
Al
ECEC
70
5.0 (0.04)
0.35 (0.011)
0.36 (0.018)
$
5.4 – 5.8
1.30 (0.111)
$
0.7 – 1.2
-1
0.03 –
0.01 –
0
–
1.6 –
-1
[cmol+ kg ]
-1
[cmol+ kg ]
-1
[cmol+ kg ]
$
0.08
$
0.05
$
0.6
$
4.1
400
600
4.8 (0.1)
0.15 (0.007)
0.18 (0.004)
4.8 (0.1)
0.07 (0.004)
~ 0.1
5.2 (0.3)
0.04 (0.003)
< 0.1
1.0 – 2.0
0.18 (0.036)
0.06 (0.012)
<1
0.20 (0.039)
0.05 (0.008)
<1
0.10 (0.010)
0.02 (0.003)
<1
0.08 (0.014)
0.03 (0.008)
0.02
0.06
0.81
1.14
0.01
0.06
0.41
0.73
(0.001)
0.01
0.06
0.26
0.44
(0.0003)
0.01
0.06
0.13
0.26
(0.001)
1.65
65
10
25
(0.022)
1.73
68
8
24
(0.015)
1.80
72
8
22
(0.010)
$
2.5 – 5.0
$
0.8 – 2.8
$
0.4 – 1.6
[cmol+ kg ]
200
$
(0.003)
(0.001)
(0.087)
(0.072)
-3
Soil density [mg cm ]
1.21 (0.051)
1.56 (0.016)
Sand [%]
80 (1)
65 (4)
Silt
[%]
10 (1)
8 (1)
Clay [%]
11 (1)
27 (3)
$ range, according to Thielen-Klinge (1997)
(0.002)
(0.078)
(0.036)
(3)
(2)
(3)
(0.003)
(0.018)
(0.020)
(3)
(2)
(3)
(0.001)
(0.028)
(0.024)
(2)
(2)
(2)
Topsoil chemical properties are presented in detail in chapter 5.2.1.
3.4 Vegetation
The natural vegetation of the Northeast of Pará state is an evergreen to semi-deciduous
tropical rainforest. Nowadays, however, this primary forest is only present along rivers
and in a few small areas, due to land clearance by human settlement. Based on Landsat
satellite images of 1991, Watrin (1994) estimated the primary forest to cover around 5 %
of the area of the municipality of Igarapé-Açu, while the estimate of Metzger, (1997), using satellite images of 1996, resulted in around 10 %. Differences are apparently related
to the difficulty of clearly separating old secondary and primary forest.
According to the agricultural census of IBGE (1997) carried out in 1995, 58 % of the area
of Igarapé-Açu is covered with fallow vegetation or primary forest (Figure 3). Accounting
for a primary forest share of 5 to 10 %, this is slightly less than 56 % estimated by Metzger (1997). However, the census included only 58 % of the total area of the municipality,
while satellite images evaluated by Metzger (1997) comprised the total area of Igarapé16
3.4 Vegetation
Açu.
Fallow vegetation,
4-10 years old
36%
Primary forest +
fallow vegetation,
>10 years old
15%
Forest plantation
0.2%
Fallow vegetation,
< 4 years old
7%
Natural pasture
4%
Annual crops
7%
Perennial crops
8%
Planted pasture
17%
Unused area
6%
Figure 3: Land cover of the municipality of Igarapé-Açu in 1995 (IBGE, 1997a, considering a sub-area of totally 46655 ha)
The IBGE survey (1997) distinguished 36 % (31 % according to Metzger, 1997) of the
land to be under temporal or permanent land use, divided into annual (maize, rice,
beans, cassava) and perennial crops (e.g. passion fruit, pepper, oil palm, citrus) as well
as pastures and forest plantations (Eucalyptus, Acacia, Pinus; Figure 3).
3.5 Land use
The Bragantina region was settled already at the mid to end of the 19th century mainly by
European emigrants, but also by settlers from the northeastern Brazilian region (e.g.
refugees of the disastrous drought period of 1877-79). At the beginning 20th century, additionally, Japanese farmers migrated into the Bragantina region. They were financially
and technically supported by the Japanese government and industry (Kohlhepp, 1994).
Agriculture thus has been practiced in some parts of the Bragantina region since more
than 100 years.
The Bragantina region is one of the most important agricultural zones in northeastern
Pará. Denich (1996) estimated the agricultural production of the former "Zona Bragantina", which besides the micro-region Bragantina included outskirts of the urban center of
Belém and parts of the Salgado micro-region (northwest of the Bragantina micro-region),
thus, covering approximately 20 000 km2. According to his investigations, agriculture of
17
3.5 Land use
the Zona Bragantina contributes 19 % of the value of the agricultural commodities comprising 44 % of the state's cassava production and 18 % of maize as well as cowpea production. Furthermore, about two third (68 %) of the passion fruit yield of Pará is produced
in this region. Mostly, small farming predominates. About 24 % of the smallholder land
(< 100 ha) of the state is concentrated in the Bragantina region. Smallholders also predominate in the municipality of Igarapé-Açu (Table 2).
Table 2: Land holdings in Igarapé-Açu in 1995 according to the size of the property, and the magnitude of
these areas (IBGE, 1997)
Size of property
<10 ha
10 to <20 ha
20 to <50 ha
50 to <100 ha
100 to <1000 ha
≥1000 ha
Total
Holdings
n
[%]
699
143
663
52
57
1
1615
43
9
41
3
4
0.06
Total area
[ha]
[%]
1755
2019
18667
3878
17837
2500
4
4
40
8
38
5
46655
In 1995 around 96 % of the properties in Igarapé-Açu were owned by smallholders comprising 56 % of the total area. In contrast to 1950, when the lower 50 % smallholders still
owned 46 % of the total land, in 1995 they possessed only 6.9 %. This is apparently related to the hereditary land-division in combination with population growth, but also to the
sale to larger landowners (Sousa Filho, personal communication). Thus, more people own
less land, inevitably leading to pressure on land use.
Smallholders predominantly practice shifting cultivation (as explained in the introduction).
But, also intensified land use, e.g. the establishment of semi-permanent plantations of
passion fruit or (less) pepper, is more and more carried out by smallholders.
Pepper and oil palm plantation are mostly managed by larger landowners. Those might
also be in the business of any other kind of (industrialized) production of cash crops (citrus, papaya) or of animal production (chicken farming or large-scale rancher).
Creating pastures and keeping cattle has become an additional diversification strategy
for smallholders. Consequently, cattle numbers owned by smallholders are increasing
since the eighties (Billot, 1995; Siegumund-Schultze et al., 2000).
18
4.1.1 Description of experimental sites
4 Material and Methods
4.1 Site selection and site preparation
4.1.1
Description of experimental sites
Three sites were selected for the experiment. The fields, in possession of small-farmers,
were located in the municipality of Igarapé-Açu close to the village of Cumaru (6 km
south-east of the town of Igarapé-Açu; see Figure 1b). According to the owners, they were
in traditional shifting cultivation for at least the last 40 years, including slash-and-burn
land preparation. At the beginning all three sites were fallow. On two fields a traditional
cultivation cycle including cropping of maize (Zea mays), cowpea (Vigna unguiculata) and
cassava (Manihot esculenta) was initiated. On the third site the fallow vegetation was left
intact. This site served as the 'natural' control for the two cultivation sites. Of special interest was the water balance of the fallow vegetation.
Cultivation sites
The two cultivation sites were initially covered with fallow vegetation of different age:
On site 1, the last cropping sequence including maize cowpea and cassava ended in May
1993, when cassava was harvested. Thus, at the end of November 1996 the regrowing
fallow vegetation was 3.5 years old. The field comprised an area of 792 m2 (18 m x
44 m).
On site 2, last cropping (rice, maize, cowpea, cassava) ended in November 1989 and
thus the subsequent fallow vegetation was 7 years old, i.e. twice as old as that of site 1,
when cultivation phase began3. The field comprised 1600 m2 (20 m x 80 m).
Slash-and-burn and slash-and-mulch treatments were imposed on one half (plot) of each
site. On the slash-and-burn plots, the fallow vegetation was slashed on the 25th and 26th
of November and burned the 10th and 11th of December 1996. On the slash-and-mulch
plots, the fallow vegetation was slashed and immediately chopped with a tractor-force
driven modified maize-chopper from the 4th to 6th of December. Slashing and spreading
the chopped vegetation equally over the plot was done by hand.
3
In the period of April 1992 to November 1993 this site was also subject of micro-meteorological studies of
Hölscher (1995; site 1 and 2 in his study).
19
4.1.1 Description of experimental sites
Fallow site
The fallow site and cultivation site 1 originally were one field. While on site 1 in 1993
cropped cassava was already harvested in May, on the remaining area, i.e. the fallow site,
cassava was grown until October of the same year. Thus, the duration of the fallow in November 1996 had just exceeded 3 years. On the SW-border the fallow site had a width of
150 m, on the NE-border the width was extending 110 m (Figure 4).
6 m pit
Climate tower
150 m
m
20
0
m
Fallow site
E
18
S
22 m
W
Cultivation
site 1
110 m
3 m pits
(No. 1 + 2)
N
Figure 4: Aerial-photo of the fallow site and site 1 (subdivided into slash-and-burn-plot [left] and slash-andmulch plot [right]); photo taken in September 1998 by K. Vielhauer
The site length was 200 m, so that the total area of the fallow site (including site 1) comprised about 2.6 ha. Initially, the fallow site was totally surrounded with secondary vegetation of different age: In the SW with a fallow of about 2 years age, in the SE with a fallow 8-10 years old, in the NE with a 1-year-old fallow and in the NW with a secondary forest at least 25 years old. At the end of 1997, however, the 2-year-old fallow in SE direction was slashed and burned by the owner and then cropped with cassava in 1998.
20
4.1.2 Installations
4.1.2
Installations
A 10m-mast for micro-meteorological measurements was installed on the fallow site at
the end of February 1997. Distances towards borders of the fallow sites were chosen to
meet (fetch-) requirements of micro-meteorological measurements. Thus, the minimum
distance towards boundary was about 50 m (SW direction), while the upwind-distance
towards the NE, that is the main wind direction (>75 % of all times), was about 150 m
(see Figure 4). Continuous micro-meteorological measurements (every 3 seconds) were
averaged and recorded automatically every 15 minutes with a solar energy supplied data
logger system (Imko, Germany).
To provide access to the soil profile of the experimental sites, soil pits (1.5 m x 2 m) were
established. On the fallow site a 6m-deep pit was dug. On site 1, one 3m-pit in the middle
of each plot (burned: No. 1; mulched: No. 2) and on site 2, three 3m-pits per plot
(mulched: No. 3 – No. 5; burned: No. 6 – No. 8) were dug. On site 2 the pits were arranged in a diagonal order, with a maximum distance between each pit and the borders
of the plots. To prevent soil evaporation the pit-walls were coated with a concretesolution, and the pits were roofed. Pit establishment on the cultivation sites was finished
at the beginning of 1997, while the 6m-pit was completed at the end of March 1997.
To assess the soil water balance under the experimental sites, the soil-water pressure
head was measured. Therefore, tensiometers (Silvaq, Germany), 1 m or 1.4 m long, were
installed horizontally, slightly inclined (10 %) in the front-wall of the pit with the ceramic
top reaching 0.6, 0.9, 1.2, 1.8, 2.4, 3 m soil depth, and in the 6m-pit also 4, 5 and 6 m
depth (Table 3). Additional tensiometers were installed vertically at 0.3 m soil depth, and,
at the end of October 1997, another tensiometer (inclined installation) was added to
reach 7.35 m soil depth under the fallow site. Automatic readings could be made with
one series of tensiometer of the fallow site (6 m-pit, 0.3 m to 7.35 m depth) and with one
series of tensiometer of site 1 under the burned plot (3m-pit No. 1, 0.3 m to 3 m depth)
from April 1997 until December 1998 in 15-minutes intervals. To this end, pressure head
transducers (Silvaq, Germany4) were used, which were connected with the data logger.
Also on site 2 automatic tensiometer measurements could be recorded occasionally under the mulched plot in pit No. 3 with an additional data logger system. Manual readings
of soil-water pressure head of the remaining tensiometers were carried out with a handheld pressure-head transducer device (Imko, Germany). Beginning in May 1997 and
4
For more detailed description of the equipment see also Klinge (1997)
21
4.1.2 Installations
ending in September 1998 those readings were done daily, except on Sundays.
Table 3: Location of installation of tensiometer and suction cup lysimeter on the experimental sites and
reading/collecting intervals of soil-water pressure head/soil-water solution
Site/Instrument
Depth
[m]
n
Location,
Pit No.
Automatic
Manual reading/
reading interval collecting interval
2
6m-pit
15 minutes
weekly
2x2
6m-pit
/
biweekly
4
3m-pit, 1+2
15 minutes
daily
3m-pit, 1+2
/
biweekly
occasionally
daily
Fallow site
Tensiometer
Suction cup
0.3, 0.6, 0.9, 1.2,
1.8, 2.4, 3, 4, 5, 6,
7.35
0.9, 1.8, 3, 4.5, 6
§
Site 1
Tensiometer
Suction cup
0.3, 0.6, 0.9, 1.2,
1.8, 2.4, 3
0.9, 1.8, 3
§
4x2
Site 2
Tensiometer
Suction cup
§
0.3, 0.6, 0.9, 1.2,
1.8, 2.4, 3
0.9, 1.8, 3
6
§
12 x 2
3m-pit, 3+4
and 6+7
3m-pit, 3 to 8
biweekly
each time two suction cups were discharging in one sampler
To obtain samples of percolating soil water, two suction cup lysimeters (ceramic cup P80,
Federal Porcelain Manufactory of Berlin, Germany) were installed horizontally at 0.9, 1.8
and 3 m depth on the cultivation sites and additionally at 4.5 and 6 m depth on the fallow
site (Table 3) at both sidewalls of the pits. Soil water samples, extracted with a suction of
–0.6 to -0.7 bars, were sampled biweekly from February 1997 to September 1998.
4.1.3
Cultivation
Cultivation on the two sites started after site preparation with sowing of maize at the 21st
and 22nd of January 1997 (Table 4). The locally common maize cultivar 'BR-106' was
used, manually sown 0.5 m by 1 m with a hand-held device ('tico-tico'). Germinated plants
were fertilized with 20 kg N ha-1 as urea, 26 kg P ha-1 as triple super-phosphate and
25 kg K ha-1 as potassium chloride, broadcasted by hand. Maize plants were thinned to
leave only 2 plants per plant-hole three weeks after germination. Four weeks after germination maize received a second N application of 40 kg ha-1.
22
4.1.3 Cultivation
Table 4: Sequence of cropping operations on site 1 and site 2
Date
25/11 – 26/11/96
4/12 – 6/12/96
10/12 – 11/12/96
21/1 – 22/1/97
30/1/97
18/2 – 19/2/97
26/2/97
5/3/97
8/5/97
22/5/97
28/5/97
11/6 – 12/6/97
25/6 – 26/6/97
26/06/97
5/8 – 6/8/97
26/8 – 28/8/97
16/12/97
16/3 – 18/3/98
25/6 – 26/6/98
Operations
Slashing
Slash-and-mulch
Burning
Sowing of maize (spacing: 0.5 m x 1 m)
-1
Fertilization (20/60/30 N/P2O5/K2O kg ha )
st
Thinning of maize plants and 1 weeding
-1
N-Fertilization (40 kg N ha )
Application of Insecticide (Decis 25 CE, AgroEvo)
nd
2 weeding and bending of maize-plants
Sowing of Cowpea (spacing: 0.3 m x 0.5 m)
Fertilization (10/50/50)
Harvest of maize
rd
3 weeding
Planting of cassava (spacing: 1 m x 1 m)
st
1 harvest of cowpea
nd
th
2 harvest of cowpea and 4 weeding
th
5 weeding
th
6 weeding
Harvest of cassava
The locally common cowpea cultivar 'BR 3–Tracuateua' was sown by hand 0.3 m by
0.5 m between the maize rows two weeks after maize was bent at half height to allow
better drying of its ripe cobs (Table 4). Cowpea was fertilized with 10 kg N ha-1,
22 kg P ha-1 and 41 kg k ha-1 (same fertilizer type as above). The cassava cultivar
'Pretinha' was planted 1 m by 1 m between the cowpea rows 6 weeks after sowing of
cowpea, using mature stem cuttings (15-20 cm long) planted horizontally at a depth of
approximately 5 cm.
Altogether 6 weedings per cultivation phase were carried out manually and additionally
an application of an insecticide was necessary to combat a caterpillar (presumably Spodaptera frugiperda J.E. Smith, Noctuidae, Lepidoptera, as identified by Bünemann, 1998)
harming the young maize leaves.
Harvest of maize (cobs), cowpea (peas with pods) and cassava (tubers) was done manually.
23
4.2.1 Precipitation
4.2 Water balance
4.2.1
Precipitation
As part of the water balance on the cultivation sites and on the fallow site, precipitation
and its vegetation-stand related components, canopy interception, throughfall and stemflow were determined.
Gross precipitation was measured above site 1 automatically every 15 minutes with a
rain gauge (collecting area: 210.0 cm2, Hellmann-type) connected to the data logger recording the pressure head of the rainwater column building up in a connected tube.
Measurement could be made from the 1st of April 1997 until beginning of December
1998. Temporarily, measurements of that type were also taken on site 2. Additionally,
data of daily gross precipitation were available from a small weather station (50 m away
from site 2) operated by the EMBRAPA-Belém meteorological department.
For the soil water balance the share of precipitation entering the soil, the so-called net
precipitation (Pn), had to be determined. According to the equation
(1)
Pn = P – I = PT + PS ,
I
P
PT
PS
=
=
=
=
canopy interception [mm]
gross precipitation [mm]
throughfall [mm]
stemflow [mm]
Pn can be directly measured as the sum of PT and PS. Rainfall interception is of interest as
reference parameter, but cannot easily be assessed and thus is generated out of the directly measurable water budget components (P, PT, PS).
The (accumulated amount of) throughfall of the fallow vegetation (fallow site) was measured biweekly during the study period of two years (Sá, unpublished data) based on the
methodology of Lloyd and Marques (1988). Fifty gauges, which consisted of a funnel with
a diameter of 10 cm connected to a two-liter bottle (total gauge height: 30 cm), were relocated randomly every month between 300 discrete positions beneath the fallow vegetation. The 300 positions were fixed within a rectangular 1m-grid along a 50 m long and
6 m wide transect. Throughfall data were expressed relative to the mean value of gross
precipitation from four gauges placed in an open area on the side of the fallow site, with
the funnel-height at 1 m above ground.
Accumulated throughfall was measured weekly on site 2, and it was assumed that these
data were also representative for site 1:
Under maize throughfall was determined systematically as a function of the location be24
4.2.1 Precipitation
tween the plants (spacing 0.5 m x 1 m). At four different places, ranging between minimum and maximum possible distance from a single plant, gauges (n=6) were randomly
positioned. Distances were: 0 m (right beside a maize plant), 0.25 m (within a row), 0.5 m
(between two rows) and 0.56 m (in the diagonal middle). The same methodology was applied for cassava with only two different distances (n=12 gauges), right beside a plant
(0 m) and mid-diagonal (0.70 m), due to different plant spacing (1 m x 1 m). Again,
throughfall data were set in relation to the mean value of gross precipitation of four
gauges. All gauges were of that type used on the fallow site.
Throughfall under cowpea was determined with nine gutters, 1.85 m long, 10%-inclined,
with a vertically exposed area of 1137 cm2 and equally distributed over the cultivation
site according to the methodology of DVWK (1986). Gutters instead of gauges were necessary as cowpea plants were too small to allow placement of 30cm-high gauges beneath
them. Throughfall in this case was expressed relative to gross-precipitation provided by
the EMBRAPA weather station.
Over two periods of one week, stemflow was measured for mature plants of maize and
cassava, respectively. Following the methodology of Von Hoyningen-Huene (1983), funnels (5 cm diameter; n=20) were cut out circularly on one side, wrapped around the
(pseudo-) stem of maize and cassava, taped and connected to 0.4-liter bottles. Stemflow
was expressed as the fraction of the gross-precipitation (EMBRAPA weather station).
Stemflow was not determined on the fallow site and for cowpea. In both cases stemflow
was assumed to be of minor importance in the water balance.
Throughfall and stemflow of single storm events could not be measured due to the remoteness of the area and lacking automatic measuring-devices. However, this knowledge
is necessary to feed a soil water model, which requires hourly (or daily) data of netprecipitation. These measurements would have been necessary to apply the Rutter model
(Rutter et al., 1971 and 1975). This model, once calibrated with data about rainfall partitioning of single storm events and related evaporation of intercepted precipitation, predicts net precipitation out of continuous gross precipitation measurements. Therefore, to
assess hourly (and daily) net precipitation, the following steps were made:
The systematic decline of biweekly accumulated throughfall (as percentage gross precipitation) of the fallow site over time could be described with a linear regression. The obtained regression equation was also applied for intermediate times (single storm events).
In the case of the cultivation sites the regression analysis failed to give reasonable estimates. Thus the average progress of throughfall (and stemflow, when available) was fitted (smoothed) individually for every crop excluding times with unreasonable high or low
25
4.2.1 Precipitation
percentage throughfall.
Additionally, the canopy storage capacity (S) was introduced. S is the maximum amount
of water [mm] that can be stored on the vegetation's surface during a single storm. Subsequently, the canopy capacity is reduced continuously by evaporation. A canopy storage
capacity derived from literature was combined with throughfall data (and stemflow, when
available) in the following way:
Net precipitation of single storms was set to equal the amount of percentage throughfall (+ stemflow; according to above mentioned fitted progress) as long as the difference between gross precipitation and throughfall did not exceed the canopy storage
capacity. In case this difference reached S, gross precipitation was reduced by this
amount only. Expressed in an equation:
(2)
Pn = PT
,
for PT > P – S
(3)
Pn = P – S ,
for PT ≤ P – S
where PT may include PS, when available
Gross precipitation data should not always be reduced by the (whole) canopy storage capacity. Moreover, measured throughfall amounts rather than a literature value of S
should determine net precipitation in such cases, when gross precipitation reduced by S
would lie below throughfall amounts. This was of special importance for developing crops.
On the other hand, it obviously would not be feasible to diminish heavy storms to a fixed
(bi-) weekly mean of percentage throughfall (+ stemflow), as in those cases a maximum
storage capacity is soon exceeded and further precipitation is not intercepted any longer
and fully enters as net-precipitation. It was assumed that intercepted water would need at
least one hour to fully evaporate (corresponding with field observation).
4.2.2
Evapotranspiration
Numerous methods exist to describe the evapotranspiration of agricultural crops or natural vegetation. Assessing evapotranspiration with regard to crop water requirements
Doorenbros and Pruitt (1977) stated that: "Primarily the choice of method must be based
on the type of climatic data available and on the accuracy required in determining water
needs." (Doorenbros & Pruitt, 1977, page 1). In the present study micro-meteorological
records and weather station data were available. Our measurements comprised all necessary data to calculate potential evapotranspiration according to Penman (1948) as well
as to use the Penman-Monteith extended combination equation (Monteith, 1965). Finally,
26
4.2.2 Evapotranspiration
flux-gradient measurements were done to apply the Bowen ratio – energy balance
method (Bowen, 1926).
Micro-meteorological data were collected exclusively on the fallow site and could be
made during approximately one year, beginning on the 9th of April 1997 ending the 29th
of March 1998 with some interruption due to malfunctioning of equipment (altogether 32
days). Evapotranspiration data, however, were needed over the total two years of cropping. Therefore, daily micro-meteorological data from the nearby EMBRAPA weather station were used to complete the data set. With these data it was possible to calculate the
so-called 'Penman-Piche' evapotranspiration. Finally, to include the growth related
changes of the cultivated crops in relation to evapotranspiration, the Penman evapotranspiration was used according to the FAO-24 Doorenbros and Pruitt modifications
(Doorenbros & Pruitt, 1977).
Penman-FAO method
The original Penman equation is:
(4)
λETp =
λ = latent heat of vaporization [MJ kg-1]
ETp = potential evapotranspiration [mm d-1]
λETp = vapor flux density or latent heat flux
[MJ m-2 d-1] (= [0.0864 W m-2])
Rn = net radiation [W m-2]
G = soil heat flux [W m-2]
∆ = slope of vapor pressure and temperature
relationship [kPa °C-1]
γ = psychrometric coefficient [kPa °C-1]
∆(R n − G)
γ
f (u) ⋅ f (e)
+
∆+γ
∆+γ
solar term
aerodynamic term
f(u) = 2.7(au + bu ∙ u), is the aerodynamic wind function, where au and bu are empirical
constants originally suggested to be 1 and 0.537, respectively for a short grass cover,
and u is the wind speed [m s-1] measured at 2 m above ground.
f(e) = es – ea, is the saturation vapor pressure deficit [kPa] measured as the difference
between saturated vapor pressure (es) and actual vapor pressure (ea)5 at ambient air
temperature.
The latent heat of vaporization (λ) was calculated according to the linear regression equation of Harrison (1963):
(5)
5
λ =– 2.36 ∙ 10-3 ∙ T + 2.501 , where T is the air temperature [°C].
One has to note that in some publications ea is used for the saturated vapor pressure. Furthermore, es
sometimes is also referred to as e0.
27
4.2.2 Evapotranspiration
The slope of vapor pressure and temperature relationship was obtained by using the differentiation of Tetens (1930) equation resulting in:
17.27 T
(6)
2503
∆=
e ( T + 237.3 )
2
(T + 237.3)
The psychrometric coefficient γ was set to 0.0672 kPa°C-1 in all calculations as is appropriate for standard atmospheric pressure 60 m above sea level at 25 °C.
Micro-meteorological parameters required for ETp-calculations were determined at selected heights on the fallow site above the fallow vegetation. Due to growth of the fallow
vegetation (from 2.3 m on the 1st of January 1997 to approximately 3.5 m on the 1st of
April 1998) the instruments' height was increased on the 21st of August (Table 5).
Table 5: Micro-meteorological Instrumentation, its measuring height and resolution
Parameter
Instrument
Measuring height
#
#
1.
2.
Resolution
Net-radiation
Net radiometer (Thies$),
0.3 to > 30 µm
3.70 m
4.85 m
Air temperature
Thermistor (Institute for Bioclimatology, Univ. Göttingen)
3.25 m
3.95 m
15 – 40 °C
sensitivity 0.01 °C
wind speed
Anemometer (Thies)
4.40 m
4.55 m
0.5 – 40 m s
0 – 1500 W m
-2
-1
st
Psychrometer (Thies), 1 level 3.25 m
3.95 m
§
sens. <0.01 kPa
nd
Psychrometer, 2 level *
4.75 m
6.75 m
$ for details about the instruments see http://www.thiesclima.com
# 1st level = time before 21/Aug./1997; 2nd level = time after 21/Aug./1997
§ based on the sensitivity value [°C] of the Thermistors according to manufacturer
* for sensible and latent heat flux gradient measurements (Bowen ratio), see below
Vapor pressure
The Penman-FAO equation to determine the potential evapotranspiration, first formulated
by Doorenbros and Puritt (1977), was used in the present study. Doorenbros and Puritt
(1977) slightly modified the original Penman equation through setting the empirical constant bu in the wind function to 0.864 and neglecting the soil heat flux G (assumed to be
of less importance). Additionally, they introduced a correction factor for wind speed data
not measured at 2 m height, which was the case in this study. The major improvement of
Doorenbros and Pruitt (1977) regarding the Penman evapotranspiration, however, was
the introduction of the so-called crop coefficient (kc), which relates ETp to the crop evapotranspiration (ETcrop) by the equation:
28
4.2.2 Evapotranspiration
(7)
ETcrop = kc ∙ ETp
Doorenbros and Pruitt (1977) considered ETp as the 'reference crop evaporation' (a term
first introduced by Jensen et al., 1970) and used the index 0 instead of p. Reference crop
evaporation refers to measurements of required micro-meteorological parameter above a
reference crop such as alfalfa or short grass, which was not the case in the present study.
Thus, in the following the term 'potential evapotranspiration' is used referring to ETp of the
fallow vegetation.
The crop coefficient is mainly affected by the main crop characteristics, the development
stage, length of growing season and general climatic conditions. Originally, crop development is divided into four stages: initial stage, crop development stage, mid-season
stage and late season stage. kc-values in relation to stages are obtained from tables and
intermediate stages are linearly interpolated (for details see Doorenbros and Pruitt,
1977). Following this approach, kc-values for the four crop development stages for maize,
cowpea and cassava were determined. The fallow regrowth at the end of 1998 also received "crop-coefficients". As no literature data were available, kc-values for the fallow regrowth were estimated according to field observations of abundance of the regrowing
vegetation.
Penman-Piche method
The following micro-meteorological parameter were measured daily at the EMBRAPA
weather station (English hut):
-
gross precipitation
-
minimum and maximum air temperature (mercury thermometer; 0.1°C-scale)
-
insolation, (=sunshine duration [h]; autograph according to Campbell-Stokes, for details see HMSO, 1982)
-
sheltered Piche evaporation (Piche-evaporimeter)
Applying the Penman-Piche method, the Penman-FAO equation (4) was distinguished into
its two summands:
1. the solar term, driven by the net radiation
2. the aerodynamic term, driven by the saturation vapor pressure deficit and wind speed
Including EMBRAPA weather station data, net radiation within the solar term could be
calculated with the following equations:
29
4.2.2 Evapotranspiration
(8) R n = R ns + R nl ,
assuming that
R ns = (1 − α) ⋅ R s ,
(9)
n

R s =  0.25 + 0.5  ⋅ R a
N


(10)
Rns =
Rnl =
α =
Rs =
n =
N =
Ra =
net short-wave radiation [W m-2]
net long-wave radiation [W m-2]
albedo [reflected/total]
solar Radiation [W m-2]
insolation [h]
maximum possible insolation [h]
extra terrestrial radiation [W m-2]
(according to Doorenbros & Pruitt, 1977)
and
(11)
(
)
4
4
 Tmax

+ Tmin
Rs
−7.77⋅10− 4 ⋅T2

Rnl = −
⋅ − 0.02 + 0.261⋅ e
⋅ σ
Ra
2


(adapted from Idso & Jackson [1969] and modified
according to Jensen et al. [1990] for humid areas)
T = average daily temperature [°C]= (Tmax+Tmin)/2
σ = Stefan-Boltzmann constant = 5.67 10-8 W m-2 K-4
Tmax = maximum daily
temperature [K]
Tmin = minimum daily
temperature [K]
Extra terrestrial radiation (Ra) was calculated by Maltez et al. (1986) for the Bragantina
region. Also their data on maximum possible insolation (N) and their albedo value
(=0.2, representing fallow vegetation) were used in the calculation.
As first suggested by Stanhill (1962) and Bouchet (1963) and later confirmed by several
authors (Thom et al. 1981; Papaioannou et al. 1996) the (second) aerodynamic term of
the Penman equation correlates with the sheltered Piche evaporation. It was assumed
that a linear relationship could be established according to:
(12)
γ
f (u) ⋅ f (e) = a ⋅ EPiche + b ,
∆+γ
where a and b are slope and intercept of the linear regression equation, respectively and
EPiche is the Piche evaporation. For periods of available data of the aerodynamic term
(317 daily data of Penman-FAO measurements), these were correlated with Piche evaporation data of the same days.
The resulting linear regression equation was subsequently used to calculate daily potential Penman-Piche evapotranspiration for those days, where own measurements were
lacking, i.e. for January until 9th of April 1997, for the year 1998 beginning on 30th of
March and for the year 1996 (pre-phase). For the cultivation sites Penman-Piche evapotranspiration was subsequently multiplied with appropriate kc values.
30
4.2.2 Evapotranspiration
Bowen ratio – energy balance method
The energy balance of a soil-vegetation-atmosphere-system is given by:
(13)
Rn + G + H + λET + P + C = 0
Rn
G
H
λET
P
=
=
=
=
=
net radiation [W m-2]
soil heat flux [W m-2]
sensible heat flux [W m-2]
latent heat flux [W m-2]
heat flux within vegetation's biomass
[W m-2]
C = photosynthetic and metabolic heat turnover [W m-2]
P and C are of minor importance within the energy balance and additionally, they are
rather difficult to determine. Therefore, they were neglected in the present study. The soil
heat flux (G) also was neglected (as in the Penman-FAO method), as it is near zero on
daily average for developed vegetation-stands (Oliver, 1982; Oliver et al., 1987; Brunel,
1989).
Rearranging the energy balance equation thus leads to:
(14)
λET =
− Rn
H
1+
λET
or
H=
− Rn
H
1 + 1/
λET
The quotient H/λET, first stated by Bowen (1926), is called the Bowen quotient or Bowen
ratio (β; β≠-1). To solve the above equations this ratio has to be determined. Bowen
(1926) assuming that the ratio of the eddy diffusivities of sensible and latent heat fluxes
(KH/KET; see Appendix) is unity, finally concluded that:
(15)
β=
∆T = vertical temperature difference [°C]
∆ea = vertical vapor pressure difference [kPa]
H
∆T
=γ
λET
∆e a
Thus, temperature and actual vapor pressure were measured at two different heights
above the fallow vegetation with psychrometers (see Table 5). These determinations were
demanding a high accuracy of wet and dry temperature measurements, thus thermistors
were used instead of platinum resistor thermometer (PT 100) as their accuracy might exceed PT 100 sensors by up to a factor of 250 (Ehrhardt, 1983). Measurement error was
additionally minimized by measuring the maximum possible vertical gradients, i.e. striving
for vertically widely spaced measurements. Regarding the latter, however, it had to be assured that temperature and vapor pressure at the upper level are still fully influenced by
the transpiring vegetation below. Therefore, the height of the atmospheric boundary layer,
which was sufficiently equilibrated through the transpiring vegetation, was determined
31
4.2.2 Evapotranspiration
according to the (fetch-to-height-ratio) equation given by Tiersch (1988):
(16)
δ = 0.1 ⋅ x0.8 ⋅ (100 z0)0.2
δ = boundary layer height [m]
x = horizontal distance to upwind discontinuity = 'fetch' [m]
z0 = roughness length [m], according to Monteith (1973) see below
Using this equation the maximum possible second psychrometer height (which should be
<δ) was obtained assuming that the lower psychrometer then would also be located in
the same layer of interest.
Penman-Monteith method
The Penman-Monteith combination equation for actual evapotranspiration of the vegetation under study was additionally applied (Monteith, 1965). The equation reads as follows:
(17)
λET =
where
ρ = density of the air [kg m-3]
Cp = coefficient of specific heat for moist air
at constant pressure [J kg-1 K-1]
ra = aerodynamic resistance to vapor and
heat diffusion [s m-1]
rc = canopy (bulk stomata) resistance [s m-1]
ρC p ⋅ f (e) / ra
∆R N
+
,
∆+γ*
∆+γ*
 r 
γ * = γ 1 + c 
 ra 
The density of the air (ρ) basically is depending on the vapor pressure, temperature and
atmospheric pressure, but was set to 1.145 kg m-3 in all calculation as is appropriate for
moist air at 30 °C (but also for dry air at 35 °C) at standard atmosphere pressure (following the ideal gas law). According to Fleagle and Businger (1980) an average value of
1010 J kg-1 K-1 was used for Cp.
Basically the aerodynamic resistance (ra) in the original Penman equation is substituted
by the aerodynamic wind function and, additionally, corresponds to the reciprocal value of
the coefficient of eddy diffusivity of latent heat flux and of sensible heat, respectively (see
Bowen ratio in the Appendix). It is commonly approximated using empirical equations (derived from the original equation based on the logarithmic wind function, see Monteith,
1965) including stand related parameters (roughness length and zero plane displacement). This was the case also in the present study using the equation recommended by
Thom and Oliver (1977), who improved Monteith's original equation by calibrating it additionally to unstable conditions:
32
4.2.2 Evapotranspiration
(18)
z = wind speed measuring height [m]
d = zero plane displacement height [m]
= 0.63 h (according to Monteith, 1973)
z0 = roughness length [m]
= 0.13 h (according to Monteith, 1973)
h = vegetation height [m]
 z − d
4.72

ra =
ln 2 
1 + 0.54u  z 0 
The canopy resistance (rc, related to the unit area of ground) accounts for the vegetation's surface influence on evapotranspiration and thus would have to be set to zero,
when reducing the Penman-Monteith equation to the original Penman equation, as this
method does not assume limitations on water availability. A canopy resistance equal
zero, however, is never met for any crop with vertical development (Perrier, 1975). The
canopy resistance was calculated using λET-results of the Bowen ratio – energy balance
method (previous chapter). With that, equation (17) was re-written and solved with rc as
the dependent variable. Canopy resistances, calculated in this way, were expressed as
monthly averages. The daily dynamic of rc within the dry season, within the rainy season
as well as within intermediate times were subject of interest.
Seasonal daily variations of the canopy resistance were compared with available information of stomata conductance measurements (Sá et al., 1995 and 1999), as the canopy
resistance – also called the bulk stomata resistance – is theoretically an integrated value
of stomata resistances (=stomata conductance-1). Monteith (1973) arguing on a strictly
physical basis, i.e. assuming that stomata are acting as parallel resistances, proposed for
amphi-stomatic plants that:
(19)
rc =
rst = stomata resistance [s m-1], related to the
leaf area (both sides)
LAI = leaf area index [-]
rst
,
2 ⋅ LAI
while Allen (1986) recommended the relationship
rst
rc =
(20)
,
0.5 ⋅ LAI
stating that only about one half of the canopy of a dense crop (hypo-stomatic) is active in
vapor and heat transport.
33
4.2.3 Modeling soil water movement
4.2.3
Modeling soil water movement
Determination of leaching losses requires detailed knowledge of water fluxes, which are
combined with the measured concentrations of solute nutrients in soil water. Assessing
soil water desiccation within the dry season through fallow vegetation per se requires a
monitoring of the processes of evapotranspiration and deep-soil water drainage. Therefore, detailed studies to assess soil water movement under the three sites were conducted. It was assumed that the soil water movement would obey the Richards equation,
which is based on a combination of Darcy's law and the law of conservation of matter (for
details see Appendix). The Richards equation is the most commonly used model that describes the variously saturated flow of water through the soil (or any other porous media).
It is a parabolic non-linear partial differential equation of secondary order, which can
analytically be solved only in very special cases (Klute, 1952; Gardener, 1958), but is
usually solved numerically by making certain specification (i.e. transforming the common
model into a specific model of a defined porous medium). Specifications are:
-
the spatial and temporal discretization of the porous medium and the flow-process,
respectively (using finite differences, finite elements or finite volumes)
-
the definition of initial conditions (water content or pressure head of the soil profile)
-
the setting of adequate boundary conditions
-
characterizing the porous medium (soil water retention, hydraulic conductivity)
-
the quantification of sources and/or sinks (e.g. root water uptake)
Numerical solution means a successive approximation to the mathematical solution and,
therefore is time and labor consuming. However, its high flexibility in relation to the above
named specification, together with the development of powerful micro-computers in recent years, let numerical solutions – above all the finite element method – become an
indispensable part of modeling.
Among the high number of well established Soil-Vegetation-Atmosphere-Transfer (SVAT)
modeling programs based on Richards equation the latest version of the soil water
model, Hydrus-1D (U.S. Salinity Laboratory, Riverside CA) was chosen, as it implements a
variety of useful tools to predict soil hydraulic conductivity, root water uptake and to account for root growth. This program solves the Richards equation numerically, written in a
mixed-form algorithm (Celia et al., 1990) with an improved convergence criterion (Huang
et al., 1996) using Galerkin-type linear finite element schemes (Vogel et al., 1996). It also
includes a heat and a solute movement modeling part. The model specification needed to
solve the Richards equation will be explained in the following chapters.
34
4.2.3 Modeling soil water movement
Space and time discretization
Water movement was simulated in an one-dimensional, vertical way. An one-dimensional
modeling approach is justified, as the soil of the study sites are not layered, deeply
weathered and without any inclination that would promote additional lateral (i.e. twodimensional) flow.
To apply the finite element method, first the soil profile has to be discretized into adjoining elements (in a one-dimensional case these are lines) connected through so-called
'nodes'. The nodal density defines how fine the discretization becomes. Boundaries or
transitions from one soil layer to another might require a relatively finer discretization. On
the other hand, a large number of finite elements demands a high computing effort, as
the Richards equation is solved for every finite element at every time step. Thus, discretization has to be balanced between resolution requirements and computing capacities.
In this study a 10m-deep soil profile was defined and divided into 209 elements (=210
nodes) beginning with a density of 0.5 cm at the soil surface successively reducing the
density to 2.75 cm at 1 m soil depth, 5.3 cm at 6 m to a density of 10.5 cm at 10 m soil
depth.
Time discretization of the model procedure generally is set by the user. The model starts
with an initial (user-defined) time increment, ∆t, and is then adjusted automatically between a prescribed minimum and maximum time step depending on the number of iterations necessary to reach convergence. The time step increased by a factor of 1.3, when
the number of iterations was smaller than or equal 3, and was reduced by a factor of 0.7,
when the number of iterations was larger than 7. The time discretization criteria were the
same in the modeling procedures for all sites (Table 6).
Table 6: Time discretization criteria used in the soil water model
Criteria
Initial time step
Minimum time step
Maximum time step
Maximum number of iterations
Lower optimal iteration range
Upper optimal iteration range
Lower time step multiplication factor
Upper time step multiplication factor
Value
0.1
[h]
0.000001 [h]
24
[h]
20
3
7
1.3
0.7
The time step was additionally controlled by the boundary conditions and the request for
printed outputs of simulation status defined a priori.
35
4.2.3 Modeling soil water movement
Initial conditions
The distribution of the pressure head within the soil profile at the end of year 1996 entered as initial condition in the subsequent modeling procedures for the three sites. To
obtain the initial pressure head distribution, the year before land use began (1996 = prephase) was modeled additionally in advance. Initial conditions for the pre-phase itself
were estimated. In 1996 all three sites were still in fallow. Consequently, the modeling
conditions for the fallow site also were used fore the modeling of 1996. But, as the fallow
vegetation was slashed at the 26th of November 1996 on the cultivation sites, modeling
conditions from that day on were split into 'ongoing fallow period' and 'slashed vegetation'. Potential transpiration (Penman-Piche) for the latter case was set to zero for the rest
of the year.
Boundary conditions
Two boundaries have to be defined in an one-dimensional model: the surface layer and
the bottom layer of the soil profile. The first represented a soil-air-interface and therefore
underlaid the system-dependent processes of evaporation and precipitation. The actual
surface flux (q) depends on the soil moisture conditions near the soil surface. Its absolute
value (|q|) according to Neuman et al. (1974) is limited through:
(21)
|q| ≤ E ,
where E is the maximum potential rate of infiltration or evaporation (user-defined current
conditions), and additionally through:
(22)
ha ≤ h ≤ hL ,
where h is the corresponding pressure head, ha is the minimum pressure head allowed at
the soil surface resulting from an equilibrium of soil water and atmospheric water vapor
(Feddes et al., 1974), and hL is the pressure head through a water layer on top of the soil
surface in case of intensive precipitation exceeding the maximum infiltration rate of the
soil surface.
Surface boundary conditions could therefore theoretically change from flux type to head
type conditions and vice-versa.
Net-precipitation, actual (Bowen ratio) and potential evapotranspiration — Penman-FAO
as well as Penman-Piche (see previous chapter) — entered the model to define the surface boundary conditions. Precipitation-water was allowed to build a surface reservoir
(hL>0), that would not be removed immediately, but enter in the next modeling step. This
36
4.2.3 Modeling soil water movement
represented field conditions, where the flat non-inclined relief of the landscape did not
promote noticeable surface run-off.
Boundary conditions for the bottom layer of the soil profile were defined to have a zerogradient, i.e. simulating a freely draining soil, were soil-water potential is a function of
gravity only and ground water level is without significant influence.
Soil hydraulic properties
Knowledge about soil water retention and soil hydraulic conductivity was required for
properly characterizing the soil. In detail this meant that the relation of soil moisture to
pressure head (θ(h)) and the relation of conductivity to soil moisture (K(θ)) or to pressure
head (K(h)) had to be clearly identified.
For this purpose it was planned to use the internal drainage method (Hillel, et al. 1972),
but unfortunately, this method never could be applied, as part of the necessary equipment (TDR-Probes) were in bond of Brazilian customs authorities over the whole fieldresearch period. Therefore, a different approach was chosen:
Soil water retention curves were determined in the EMBRAPA soil-laboratory with
100 cm3 undisturbed soil core samples using pressure plate procedure. Four repetitions
of core samples were taken at 15, 30, 60, 90, 120, 180, 240 and 300 cm soil depth under all sites and additionally at 400, 500 and 600 cm under the fallow site. Water content was measured in terms of de-sorption at pressure heads (high air pressure) of 60,
100, 300, 1000, 5000, 10000 and 15000 hPa (analogous to Maklouf et al., 1997). Additionally, laboratory-saturated water content (≅ total porosity) was determined. Van
Genuchten's soil water retention function (Van Genuchten, 1980) then was fitted to each
of these sets of soil water retention data points using least-square optimization technique
with the RETC program (Van Genuchten et al., 1991). Reciprocal values of the standard
deviation of the four repetitions entered as weighting coefficients of each data point. This
function of Van Genuchten is given by:
(23)
θ(h) = θr +
θ(h) = θs ,
θ s − θr
, for h<0
(1 + (α vG ⋅ h)n )m
for h≥0
θr = residual water content [cm cm-1]
θs = saturated water content [cm cm-1]
αvG, n, m = empirical constants [cm-1],[-],[-]
n>1
Assuming m=1-1/n, four independent parameters have to be fitted.
Using the statistical pore-size distribution model of Mualem (1976), Van Genuchten
37
4.2.3 Modeling soil water movement
(1980) introduced an equation to predict the unsaturated hydraulic conductivity function
in terms of these soil water retention parameters (Mualem-Van-Genuchten approach):
(24)
"
K(h) = K s ⋅ S e (1 − (1 − S e
Ks = saturated hydraulic conductivity [cm d-1]
1/ m m 2
) )
Se =
θ − θr
= effective water content
θ s − θr
The exponent, the pore-connectivity parameter l, was originally found to be about 0.5 as
the best estimate for many soils (Mualem, 1976).
Vogel and Císlerová (1988) extended the original Van Genuchten equations to give more
flexibility to the hydraulic properties near saturation (above all the hydraulic conductivity)
and to account for the so-called 'air-entry value' (see Appendix). In all model procedures
an air-entry pressure head of –2 cm was set.
Assessing the hydraulic conductivity behavior according to the Mualem-Van-Genuchten
approach the saturated soil hydraulic conductivity has to be determined. To this end, pedotransfer functions of Schaap and Bouten (1996) and Schaap et al. (1998 and 1999)
were used.
Pedotransfer functions (PTFs) in general use widely available basic soil data (texture,
bulk density, porosity, soil organic matter, etc.) as predictors (Rawls & Brakensiek,
1985; Haverkamp & Parlange, 1986; Wösten & Van Genuchten, 1988; Rajkai et al.,
1996). While some predictions are based on multivariate analyses, other, mostly newly
available techniques, use artificial neural networks (Pachepsky et al., 1999). Here a
multiple input (basic soil data) is weighted and activated and put in a transfer function
according to specific functions and then a single output value is produced.
Schaap et al. (1998 and 1989) established the 'Rosetta' program. On the basis of soil
textural classes (USDA classification) this application reads out Van Genuchten's soil hydraulic parameter from a table. Furthermore, on the basis of textural distribution an artificial neural network application is activated, that predicts Van Genuchten soil hydraulic
parameter. The prediction can be refined stepwise adding bulk density, water content at
a pressure head of -330 hPa and, finally, water content at pressure head of -15000 hPa.
Textural distribution and bulk density were determined on all sites at the above mentioned soil depths at EMBRAPA soil-laboratory (textural analysis in Appendix).
While the water content at -15000 hPa was directly available from the soil water retention curve, the value at a pressure head of -330 hPa was obtained by linearly interpolating the average values of -300 and -1000 hPa.
38
4.2.3 Modeling soil water movement
Finally, the six parameter, %-sand, %-silt, %-clay, bulk density,θ(-330) and θ(-15000) were
used for the artificial neural network prediction to obtain the Van Genuchten soil hydraulic parameter. But, only the saturated hydraulic conductivity of this prediction entered the
modeling procedure, while the other parameter were kept as fitted with the laboratory
water-retention data.
According to obvious differences in textural distribution and/or soil hydraulic parameters,
the soil profile was divided into segments ('materials' in the Hydrus model)6. Each segment was characterized by the soil hydraulic parameters of related soil depth. Five segments finally were distinguished: 0-22.5 cm; 22.5-45 cm; 45-75 cm; 75-105 cm; 1051000 cm. For the first four segments soil hydraulic parameters from analyses of samples
from 15, 30, 60 and 90 cm soil depth, respectively, were used. The last segment comprised almost 9 m soil profile including 4 soil depths (7 under the fallow site), for which
the above-mentioned analyses were conducted. But, differences in soil hydraulic properties were far less than within segments of the upper soil profile. Soil hydraulic parameters
of this segment therefore were calculated using the so-called 'scaling technique':
The scaling technique is based on the similar media concept of Miller and Miller (1956)
and was extended in various ways (Simmons et al., 1979; Tillotson and Nielsen, 1998).
According to Vogel et al. (1991) the variability of a soil profile can be approximated by
means of reference characteristics, θ*(h*) and K*(h*) and a set of linear scaling transformation factors for each profile depth. The relationships between reference characteristics, scaling factor and measured hydraulic characteristics at a certain depth of the soil
profile are as follows:
(25)
K(h) = αK K ∗ (h∗ )
(26)
θ(h) = θr + α θ (θ∗ (h∗ ) − θ∗r )
αK = scaling factor for the hydraulic conductivity
αθ = scaling factor for the water content
Using Van Genuchten's soil hydraulic parameter at each depth of each soil profile, sets of
equally distributed (from h=-1 cm to h=-15000 cm) single data points, K(h) and θ(h), were
computed (at 120, 180, 240 and 300 cm for all sites, as well as additionally at 400, 500,
600 cm soil depth on the fallow site). Reference data points of soil hydraulic conductivity,
K*(h*), and soil water content, θ*(h*), were calculated by averaging the single data
'layer' would probably be the apt expression, but is not used, as it refers to 'soil layer', which is not appropriate in this context.
6
39
4.2.3 Modeling soil water movement
points of all data sets. Then, once again, on the basis of the average data points, K*(h*)
and θ*(h*), Van Genuchten's soil hydraulic parameters were fitted. Scaling factors, αK,
for each depth of all profiles were obtained by means of linear regression according to
the upper equation. For this, log-values of K(h) were used to avoid bias towards saturated
values. The same method was applied for the scaling factor, αθ, after subtracting θr and
θ*r, respectively, from every single data point. Scaling factors entered in the model according to their related soil depths. Nodes within these soil depths obtained their linearly
interpolated values.
Root water uptake and root growth
Four different types of vegetation, and additionally a temporary overlapping of these
vegetation had to be considered: a secondary bush-vegetation on the fallow site, and a
sequence of maize, beans, cassava and regrowing secondary bush-vegetation, with additional intermediate re-sprouting of the latter on the cultivation sites. Root water uptake,
driven by transpiration, affects the soil water content over the whole rooting zone and enters as the so called 'sink-term', S, in the Richards equation. It is defined as the amount
of water removed from a unit volume of soil per unit time. According to Feddes et al.
(1978) the relationship between root water uptake and transpiration is:
(27)
S(h) = αr(h) Sp,
where Sp is the potential water uptake [cm d-1], that is given for instance by the potential
transpiration (Penman-FAO or Penman-Piche), and αr is a relative (0<αr<1), dimensionless factor dependent on the soil water pressure head averaged over the rooting zone
(the index stands for root and was given for better differentiation). Obviously, αr equals 1,
when soil water is not limiting transpiration, i.e. when the pressure head is small (not
considering anaerobic soil conditions). The behavior of αr, however, when soil water is
depleted, is highly dependent on the eco-physiology of the plants. This behavior, the function αr(h), often has been described with the so called 'roof function' of Feddes et al.
(1978). Here, linear relationships are set between certain values of h regarding the optimal range of αr and its decline due to lower values of h (the function of α(h) itself in the
original form had a 'roof-similar' shape). Van Genuchten (1987) reconsidered the original
Feddes-approach and brought it into a single mathematical equation:
40
4.2.3 Modeling soil water movement
(28)
αr (h) =
h50 = pressure head at 50%-reduced rootwater uptake
p = experimental constant
1
 h 

1 + 
 h50 
p
This function was used in the modeling procedure, as the S-shape character of this curve
seemed to describe a more realistic behavior of the reduction of transpiration due to soil
desiccation (Cardon & Letey, 1992). No salinity (osmotic) stress was assumed to influence soil water uptake.
Furthermore, the root distribution within the soil profile had to be introduced into the
model, as it affects the depth of direct soil water depletion due to root water uptake and
the relative distribution of the depletion process.
The Hydrus-1D model can use any arbitrary-shaped root distribution function, when this
function is assumed to remain the same during the modeling process. But, if root growth
is taking place, as was the case on the cultivation sites, only the following type of root distribution is possible (Van Genuchten, unpublished 7):
(29)
b(z) =
5 ,
3 ⋅ Lm
b(z) =
25
1 − z , for 0.2L ≤ z ≤ L
m
m
⋅
⋅
12 L m L m
b( z) = 0 ,
for z ≤ 0.2 Lm
b(z) = normalized water uptake distribution
[cm-1]
z = soil depth [cm]
Lm = maximum rooting depth [cm]
for z > Lm
Here, the normalized water uptake distribution, b(z), describes the spatial variation of the
above introduced potential water uptake, Sp as:
(30)
Sp = b(z) Tp
Tp = potential transpiration rate [cm d-1]
Applying the root growth scenario, a prescribed maximum rooting depth, Lm, is reached
starting with an initial rooting depth, L0, according to the Verhulst-Pearl logistic growth
function (Verhulst, 1996) given as:
The Hydrus-1D software explanation gives an 'exponential root distribution function' (according to Raats,
1974) that is, however, actually not used (Simunek, personal communication).
7
41
4.2.3 Modeling soil water movement
(31)
fr (t ) =
fr(t)= growth coefficient [-]
r = growth rate [d-1]
t = time [d]
L0
L 0 + (L m − L 0 ) ⋅ e −rt
The root growth coefficient, fr(t), determines the actual rooting depth according to:
(32)
L(t)= rooting depth [cm] at time t
L(t)=Lm ⋅ fr(t)
Thus, including root growth into the model required the following parameters to be prescribed:
-
initial rooting depth
-
maximum rooting depth
-
time of starting root growth
-
time of end of root growth
-
growth rate
To calculate the growth rate, r, one point of the Verhulst-Pearl growth function has to be
known, i.e. the rooting depth at a certain time between beginning and end of root growth.
(The logistic growth function is then rewritten and solved with r as the dependent variable.)
Root growth was applied for both cultivation sites. The required parameters were set according to the cultivation calendar and field observations. The fact that a sequence of
three different crops were planted with subsequent regrowth of fallow vegetation, made it
necessary to split the modeling procedure of each cultivation site into several parts according to the different root growth parameter to be set.
No root growth was used for the fallow site. Here, it was assumed, that root growth dynamics would already have reached steady state conditions. This situation gave more
flexibility regarding root distribution within the profile. The root mass density of former detailed studies on the rooting patterns of fallow vegetation (Sommer et al., 2000) entered
as the initial root distribution.
Inverse modeling - model validation
Above-mentioned soil hydraulic parameters, potential evapotranspiration as well as the
root water uptake function (αr(h)) and root distribution and/or root growth were part of
the initial model settings.
In the field, highly resolute, automatic soil pressure head records during the study period
of two years were taken on site 1 and on the fallow site. Detailed records were taken at
42
4.2.3 Modeling soil water movement
time intervals of 15 minutes in two soil pits at soil depths of 30, 60, 90, 120, 180, 240
and 300 cm and under the fallow site additionally at 400, 500, 600 and 735 cm. These
records were used to adjust the initial model settings in a so-called 'inverse' modeling
procedure.
In an inverse modeling approach, discrepancy between observed and modeled values is
brought to a minimum, with soil hydraulic properties kept as dependent variables. This
can be done mathematically using minimum least square techniques or other adequate
methods (Marquardt, 1963; Kool et. al., 1985). Mathematical solutions, however, require
defined, controlled in-situ or laboratory conditions, such as the above mentioned internal
drainage method. As equipment was not available to establish these conditions, a different approach was chosen:
In a first step, soil hydraulic parameters were adjusted under field situations, where rootwater extraction had an almost negligible importance, e.g. in times of heavy precipitation
events in an early crop-development stage. Adjustments were made by eye-fitting corresponding graphs of the modeled pressure-head-progress with time (h(t)) to those measured at the above mentioned soil depths. To keep adjustments controllable, only the parameters with a certain degree of uncertainty, the saturated water content, θs, the saturated hydraulic conductivity, Ks, and the pore-connectivity parameter, l, or, in case of
scaled θ(h) and K(h), the scaling factors, αθ and αK, were optimized. θr, α, n and m obtained from the soil water retention curve, were kept constant.
Secondly, the root water uptake function, αr(h), i.e. its determining parameter h50 and p,
and the root distribution or the root growth parameter (Lm, r), were optimized eye-fitting
h(t) for the times of noticeable influence of root-water uptake on soil desiccation, i.e.
times preceded by several days without precipitation and with fully developed plants.
Finally, as part of the soil-water-movement validation, at certain times modeled soil moisture distributions within the soil profile were compared with in-situ soil moisture measurements. Therefore, undisturbed soil samples (cores, 250 cm3) were taken at several
depths down to 3 to 6 m and soil moisture was determined gravimetrically.
43
4.3.1 Soil-nutrient dynamics
4.3 Nutrient balance
The balance of the aboveground nutrient dynamics was calculated determining the quantities of nutrient input and output. To assess the leaching losses, concentrations of solute
nutrients were combined with water fluxes obtained by the soil water balance. Additionally, the soil nutrients were monitored throughout the study period.
4.3.1
Soil-nutrient dynamics
Soil samples for determination of soil nutrients were taken at the experimental sites from
the 20th to 23rd of January 1997, at the 9th/10th of July 1997 and at the 11th/12th of
March 1998. Four sampling depths were chosen in accordance with earlier soil samplings of the SHIFT-Project, to facilitate later comparison: 0-10 cm, 10-20 cm, 20-30 cm
and 30-50 cm. Additionally, 90-100 cm soil depth was sampled, as representative for
deeper soil layer. Four repetitions were taken at every soil depth, each of them uniting ten
single, disturbed sub-samples equally distributed over the sites and taken with a 1m-long
soil auger (Pürkhauer type). Soil samples were air-dried and sieved to < 2mm.
Chemical analyses of the soil samples were carried out by the EMBRAPA-Belém soil laboratory according to the their routine methods (EMBRAPA, 1997):
The soil pH was measured in water (1:2.5).
Organic carbon was determined with the Walkley-Black wet oxidation (potassiumdichromate) method, assuming that only 77 % of the organic carbon is oxidized and
therefore multiplying the result by 1.3 (correction factor)8.
Exchangeable cations and plant-available phosphate were determined following the
'North Carolina soil testing procedure' (Mehlich, 1953), which was taken up by Guimarães
et al. (1970) and is routinely carried out with volumetric soil samples (10 ml of soil) as
follows:
-
K and Na:
Na extraction with Mehlich I solution (0.05 N HCl + 0.025 N H2SO4) and flamephotometrical determination
-
P:
P extraction with Mehlich I solution and photometrical determination (after adding
8
Total N is routinely determined with the Kjeldahl steam-distillation and subsequent titration, but could not
be done due to lacking chemicals and equipment.
44
4.3.1 Soil-nutrient dynamics
ammonium-molybdate)
-
Al:
Al extraction with 1 N KCl-solution and titration with NaOH, indicator: bromine-timolblue
-
Sum of Ca and Mg: extraction with 1N KCl-solution; EDTA-titration, indicator eriochrome black
-
Ca:
Ca extraction with 1N KCl-solution; EDTA-titration, indicator murexide
-
Mg:
Mg (Ca +Mg) – Ca (see above)
-
effective cation exchange capacity (ECEC
ECEC):
ECEC sum of Ca, Al, Mg, K, Na
EMBRAPA laboratory results unfortunately were not calibrated with independent laboratories or national or international standards. To overcome possible (systematic) errors during the determination, EMBRAPA-results of C and plant-available P of every tenth soil
sample were compared with results of repeated determinations in the soil laboratory of
the Institute of Crop and Animal Production in the Tropics in Göttingen, Germany. To this
end, C was determined with an elemental analyzer (Carlo Erba 1500) using acetanilide
(p.a.) with known C percentage as a control (every tenth sample). The Mehlich I extraction
method used by the EMBRAPA-laboratory was repeated to determine the plant available
phosphate. No (inter)national standard was available for this method, which is typically
used only for acid (tropical) soils and thus rarely applied in Europe. Therefore, soil samples already determined by Diekmann (1997) were included in the present determinations, as results of his determinations of plant available P carried out in Rio de Janeiro in
the laboratory of the "Serviço Nacional de Levantamento e Conservação de Solos
(SNLCS)" were sufficiently calibrated with laboratory standards. Our laboratory study did
not follow the North Carolina soil testing suggestion to use volumetric samples. The proposed 10ml-soil-samples were substituted by 10g-soil-samples. Therefore, to be able to
compare results of both approaches (volumetric and gravimetric), it was necessary to estimate the density of a 10 ml soil sample. This was done filling a beaker to its 10ml-mark
and subsequently weighing the soil.
Furthermore, in May and June 1998, soil samples were taken at 30, 60, 90, 120, 180,
240 and 270 cm depth under the burned plots and under the fallow and NH4-Cl (1molar)
extractable K, Ca, Mg and Al was determined in the Institute of Soil Science and Forest
Nutrition (IBW) in Göttingen, Germany using an atom emission spectrometer (ICP-AES).
45
4.3.2 Aboveground biomass stock 4.3.3 Volatilization losses
4.3.2
Aboveground biomass stock
Aboveground plant biomass stocks were estimated on both cultivation sites during site
preparation. On each site, 5 subplots of 9 m2 were slashed and leave-biomass as well as
biomass of woody compartments was determined. Also, litter biomass including dead
branches was measured on 10 subplots of 1 m2. Furthermore, determination of aboveground biomass stocks carried out by Schmitt (1997) on site 1 and 2 (each time 5 subplots with 4.77 and 8.12 m2, respectively) were included in the evaluation. Mean biomass-stocks of the subplots then were extrapolated to account for the total site. The nutrient content (C, N, P, K, Ca, Mg, S) of three sub-samples of each compartment and site
was analyzed in the Institute of Soil Science and Forest Nutrition in Göttingen, Germany.
This was also done for samples of biomass chipped by the modified maize chopper. P, K,
Mg, Ca, and S were determined in a HNO3-pressure extract using an atom emission spectrometer (ICP-AES) and C and N were measured with the above-mentioned elemental
analyzer.
4.3.3
Volatilization losses
Volatilization losses during burning of dry aboveground biomass on the slash-and-burn
plots were measured comparing preburn nutrient stocks bound in the biomass with postburn nutrient stocks, which remained in residues and the ash. To assess the latter, 24
steel trays (46 cm x 46 cm, with 2 cm-high edge) were equally distributed among the
slashed vegetation. Soon after burning, the trays were covered with lids to avoid ash removal by wind. The next early-morning (windless time), residues collected in the trays
were sampled and separated into charcoal plus incompletely burned remains (pieces
> 2 mm diameter) and into ash. Mean post-burn residue stocks of all trays were extrapolated to account for the total plot. The nutrient content of three sub-samples (mixture of
all samples) of each compartment and each plot was determined in the same way as already mentioned above (aboveground biomass).
Thicker stems and branches (>~5 cm diameter), which remained unburned on the plots,
were quantified and then removed from the field. Its nutrient content was assumed to
equal those measured for wooden preburn biomass.
46
4.3.4 Fertilizer input and harvest exports 4.3.5 Nutrients in the soil solution - leaching losses
4.3.4
Fertilizer input and harvest exports
The nutrient input as fertilizer was considered in the nutrient balance (quantities as applied).
Fresh weight of maize-cobs, peas and pods of cowpea and cassava tubers was quantified
for the entire plots, and sub-samples of these harvest exports were taken to assess the
dry-matter. With those sub-samples (one mixed probe) the nutrient contents of maizegrain, maize-spindle, peas, pods and tubers were determined (methodology as mentioned
above). Results were extrapolated according to their individual share to account for the
nutrient export by harvested products.
4.3.5
Nutrients in the soil solution - leaching losses
Soil water (100 ml), obtained by means of suction cup lysimeter, was analyzed for its nutrient concentration. As analyses were carried out in the Institute of Soil Science and Forest Nutrition in Göttingen, Germany, the water samples had to be sterilized to avoid microbiological turnover during uncooled shipping. The sterilization was done in the field
with 2 ml chloroform, which was given into the 2-liter-suction-bottles (to which suction
cup lysimeter were connected) after taking the biweekly water samples. Added chloroform additionally inhibited the contamination of the entire lysimeter-arrangement (bottle–
tube–lysimeter). Moreover, the soil water samples were stored in a refrigerator until final
shipment.
During the first year of the study period all obtained samples of the cultivation sites were
analyzed, as in this period highest nutrient concentration and thus highest leaching
losses were expected. In the second year only selected samples were analyzed, as results
of already analyzed former samples could show that nutrient concentrations at the end of
the first year had considerably decreased.
P, K, Ca, Mg, Na, S, Mn, Al, and Fe were determined with an inductively coupled plasma atom emission spectrometer (ICP-AES), while Nt, NH4+ and NO3- were measured after UVsolution with a continuous flow colorimeter.
The leaching losses were quantified by multiplying nutrient concentrations [mg cm-3] with
daily water fluxes [cm d-1] obtained by a soil water model (see above) and accumulating
the calculated amounts.
47
4.4 Ground water – well water 4.5 Statistical analyses
4.4 Ground water – well water
The depth of the water level of nine wells of smallholdings close to the study sites was
measured monthly during one year from September 1997 to August 1998. Furthermore,
water samples of these wells were taken at the same time, and the nutrient content of
selected samples was determined (as described for soil water samples). The geographic
position of all wells was evaluated with a GPS (Garnim International, GPS 45 Personal
Navigator; accuracy: ±15 m).
4.5 Statistical analyses
Statistical analyses were carried out with SAS (Version 6.12) using the (stepwise) linear
regression procedure (PROC STEPWISE and PROC REG) and the General Linear Model
procedure (PROC GLM) (Schuemer et al., 1990). Error propagation of stochastically inde_
pendent means ( x ) was considered according to Fenner (1931):
_
_
_
_
Addition:
x1 + x 2 ± SE 12 + SE 22
Subtraction:
x1 − x 2 ± SE 12 − SE 22
_
_
_
_
Multiplication:
x1 ⋅ x 2 ±
Division:
x1/ x 2 ±
_
_
x 22 SE 12 + x12 SE 22
1
_
_
_
x 22 SE 12 + x12 SE 22 ,
x 22
whereas SE is the standard error of mean.
48
5.1.1 Precipitation
5 Results and Discussion
5.1 Water balance
5.1.1
Precipitation
Gross precipitation
Gross precipitation data for the two years of cropping were obtained from automatic
measurements of site 1 (15 minute intervals) and from daily data of EMBRAPA weather
station, which was situated close to site 2. Temporary, automatic measurements were
done on site 2.
Automatic registration of daily precipitation of site 1 and 2 did not differ statistically (Wilcoxon signed rank test of daily data, n=261), indicating that local spatial heterogeneity
(due to the 3km-distance between site 1 and 2) of precipitation was small. However, data
from the weather station were significantly different from data of site 1 and site 2 (Wilcoxon signed rank test, n=545 and n=274, respectively). But, these differences were
caused by the sampling time of EMBRAPA weather station, 9 o'clock AM. Thus, earlymorning precipitation (before 9 o'clock, which was the case at 49 days; compare probability of precipitation Appendix, Figure A-2) was registered as the previous days. However, these uncertainties of precipitation data of the EMBRAPA weather station affected
only the first three months in 1997 and December 1998, where these data entered the
water balance studies (soil water model).
Precipitation intensities ranged from 0.1 mm 15 min-1 to up to 23.0 mm 15 min-1, showing a left-skewed distribution with a median of 1.1 mm 15min-1 (lower and upper quartile
0.5 and 2.6 mm 15min-1, respectively; Appendix, Figure A-1).
Apparently, due to El Niño's influence, the seasonal distribution of gross precipitation was
quite irregular in 1997 compared to the second year of cultivation (Figure 5). The dry
season (end of August to mid-December) of the first year was exceptionally intensive with
only a single precipitation event (5.1 mm on the 22nd of September) between 28th of
August and 11th of November. About 71 % (1490 mm) of the total annual precipitation
(2104 mm) occurred in the first four months of 1997, while over the same period in
1998 this was only 52 % (1311 mm of a total 2545 mm).
49
5.1.1 Precipitation
-1
P [mm d ]
75
1997
1998
50
25
0
1.1.
1.3.
1.5.
1.7.
1.9.
1.11.
1.1.
1.3.
1.5.
1.7.
1.9.
1.11.
1.1.
Figure 5: Daily gross precipitation of the study area in 1997 and 1998
Throughfall and stemflow
Biweekly measurements of throughfall on the fallow site (Sá, unpublished data), expressed as percentage of gross precipitation, showed high spatial as well as temporary
variations (for spatial variation see Appendix, Figure A-3). No significant correlation could
be found between mean %-throughfall and (1.) biweekly gross precipitation amounts
(R=0.147, n=53), (2.) number of storm events within each two-week collecting period
(R=0.132, n=53) or (3.) a combination of both variables (multiple linear regression,
R2=0.022, n=53), respectively. Those correlations could have indicated a systematic relationship between entering variables and throughfall amounts. However, over the study
period of two years a significantly declining trend of throughfall could be detected, apparently related to the growing vegetation with increasing LAI (Figure 6).
50
5.1.1 Precipitation
Gross precipitation share [%]
100
90
80
70
60
50
40
1.1.
1.3.
1.5.
1.7.
1997
1.9.
1.11.
1.1.
1.3.
1.5.
1.7.
1998
1.9.
1.11.
1.1.
Figure 6: Relative mean throughfall under the fallow vegetation during the study period of two years (Sá,
unpublished data); dotted black line: linear regression; dotted gray lines: linear regression of 95%confidence intervals of throughfall, see Table 7
Throughfall declined from about 81 % in early 1997 to about 68 % at the end of 1998.
Ninety-five percent confidence intervals during this time were about 10 % lower or higher,
respectively. For the soil water model a linear regression was applied considering mean
values of throughfall of all measurements (Table 7).
Table 7: Linear regression for the estimate of %-throughfall under the fallow site (independent variable is
'days of year 1997', i.e. beginning with 'day 1' at the 1st of January 1997 ending with 'day 730' at the 31st of
December 1998)
Regression
Slope
Intercept
2
R
n
-------------- SE of --------------slope
intercept estimate
Mean
-0.018*
80.96
0.225*
61
0.0042
1.576
6.79
+ 95 % confidence interval
-0.019*
90.53
0.223*
61
0.0046
1.710
7.36
- 95 % confi-0.017*
71.38
0.204*
61
0.0042
1.547
6.66
dence interval
* = statistically significant; regarding slope: significantly different to zero (t-test; p ≤ 0.05)
Throughfall of maize was measured during ten weeks until maize was bent at half height
to allow better drying of ripe cobs. Measurements were started as soon as the maize
plants were tall enough to place 30cm-high gauges beneath them (36 days after sowing;
Figure 7).
51
5.1.1 Precipitation
Gross precipitation share [%]
100
80
0.5 m
60
0.56 m
0.25 m
0m
40
Mean (all)
Eye-fitted
20
Stemflow
0
0
14
28
42
56
70
84
98
112
Days after sowing
Figure 7: Mean %-throughfall (in relation to gross precipitation) under maize at four different distances towards plants, mean values of all distances, fitted progress and %-stemflow; bars denote the standard error,
SE (to avoid clutter in the figure only SE of mean throughfall is shown)
As expected, throughfall was highest at the largest possible distance (0.56 m) from a
maize plant and sequentially decreased, when approaching plants. Differences between
the four different placing-distances were statistically significant except between 0 and
0.25 m (applying a GLM and linear contrasts). This was remarkable as the difference in
placing the gauges between two rows and the mid-diagonal was only 6 cm. Spatial heterogeneity was found to be highest right beside maize plants (mean C.V. 56 %) decreasing with plant distance (mean C.V. at 0.25, 0.5 and 0.56 m, 44 %, 23 % and 29 %, respectively). For water balance studies a mean progress of %-throughfall with maizegrowth was established neglecting unreasonable deviations (see Figure 7). Intermediate
periods were linearly interpolated.
Stemflow of maize could be determined during two weeks. At the second week, however,
most of the 0.4-litre-bottles overflowed due to heavy precipitation during this week.
Therefore, only the first campaign was utilized, to draw up a simple relationship between
development stage of maize and %-stemflow. Fifty-six days after sowing, maize had
reached a plant height of about 1.80 m. At this time 17.1 % (SE 2.12 %, n= 15) of gross
precipitation reached the ground as stemflow. Thus, it was assumed that plant height
times 9.5 equals %-stemflow. Maize plant height was determined at five different times
during the cropping period (Figure 8).
52
5.1.1 Precipitation
3
Plant height [cm]
20
2
15
1.5
10
1
Stemflow [% ]
25
2.5
5
0.5
0
0
0
14
28
42
56
70
84
98
112
Days after sowing
Figure 8: Plant height of maize and related %-stemflow (bold point = determined stemflow)
Measurements of throughfall under cowpea were started as soon as plants were tall
enough to place the nine gutters beneath them (24 days after sowing). Mean throughfall,
expressed as percentage share of gross precipitation, ranged between 79.6 % and
100.0 % (Figure 9). Spatial heterogeneity of measurements was quite low, with coefficient of variation at maximum 27.9 % (1st of July 1997; mean C.V.: 18.3 %), certainly due
to the fact that cowpea growth was very homogeneous and that gutters integrated
throughfall over a large collector area. The large collector-area however made observation
impossible, when throughfall amounts were greater than 44 mm, which was the case
within the period between the 8th and 23rd of July. In these cases the connected 5-litre
120
110
100
90
80
70
60
26.8.97
19.8.97
12.8.97
5.8.97
29.7.97
22.7.97
15.7.97
8.7.97
1.7.97
50
24.6.97
Gross precipitation share [%]
bottles were to small to catch all through-falling water and overflowed.
Figure 9: Mean %-throughfall (in relation to gross precipitation) under cowpea; bars denote the SE
As no increasing or declining trend of %-throughfall was detectable, an average amount of
90 % throughfall was assumed for further water balance studies for mature cowpea. Ad53
5.1.1 Precipitation
ditionally, during early crop development stage of cowpea a linearly decrease from 95 %
to 90 % was assumed and at late season stage again an increase to 95 %.
Throughfall of cassava was observed beginning the 3rd of September 1997 until the day
of harvest (25th of June 1998). Results of minimum and maximum possible distance toward a single cassava plant, i.e. right beside one plant (0 m) and in the diagonal middle
(0.70 m) of two plants, showed a statistically significant difference during this period
(GLM; Figure 10).
Gross precipitation share [%]
120
110
100
90
80
Midst diagonal
70
Beside a plant
Mean (all)
60
50
1.11.
Fitted
1.12.
1997
1.1.
1.2.
1.3.
1.4.
1998
1.5.
1.6.
1.7.
Figure 10: Mean %-throughfall (in relation to gross precipitation) under cassava at two different distances
towards plants, mean values of both distances (all) and fitted progress; bars denote the SE
Initially, when cassava was small, the plant-leaves covered solely gauges beside them,
while throughfall of mid-diagonal gauges still reached gross precipitation amounts. This
did not alter noticeably until late May 1998. However, throughfall beside the cassava
plants increased from February 1998 to finally approach throughfall amounts of mid-diagonal gauges.
Spatial variability of throughfall under cassava was comparably high, in some cases
reaching a C.V. of 50 %. No differences in this regard could be found between results of
the two different gauge placings (mean C.V. in both cases about 24 %). For the water balance, a smoothed mean progress of %-throughfall was established, as was done already
for the maize crop.
Stemflow of mature cassava plants was determined to be 1.2 % (SE = 0.26, n=3) of gross
precipitation. The rough structure of the cassava stems basically promoted dripping of
water over their former leafstalk-bases rather then stem-flowing. Thus stemflow of cassava turned out to be quite unimportant, but still was considered in the following water
54
5.1.1 Precipitation
balance.
Finally, throughfall of the regrowing fallow vegetation from July until the end of 1998 was
assumed to be 96 % of gross precipitation, based on weekly measurements until the end
of September.
Net precipitation
To obtain daily net precipitation quantities, (bi-) weekly-accumulated net precipitation
(= amount of throughfall + stemflow) had to be scaled down on single storm events. Net
precipitation of single storm events depends on the canopy storage capacity (S; Table 8)
and on evaporation of intercepted water.
Table 8: Canopy storage capacity (S) of different vegetation
Vegetation
Terra firme forest, Pará
17-year-old secondary vegetation, Pará
Terra firme forest,
Pará
Rondônia
Terra firme forest, central Amazonia, Manaus
Tropical rain forest, Java
Tropical rain forest, Brunei
Tropical montane forest, Puerto Rico
Eucalyptus sp.
Acacia longifolia
Acacia auriculiformis
Maize
Beans (Vicia faba)
Mixed grass and legumes
Deciduous forest (Carpinus betulus), summer
winter
Coniferous forest (Picea abies)
Grass (Molinia coerulea)
Coniferous forest (Pinus nigra)
Coniferous forest (Pinus silvestris)
S [mm]
3.5
1.1
1.25
1.03
0.74
1.1
1.0
0.76-1.27
0.2-0.8
0.6
0.5-0.6
0.4-0.7
1
1.0-1.2
1.02
0.64
1.52
0.66
1.05
0.8
Data source
Jipp et al., in revision
Ubarana, 1996
Lloyd et al., 1988
Calder et al., 1986
Dykes, 1997
Scatena, 1990
Aston, 1979
Brunijnzeel & Wiersum, 1987
Stoltenberg & Wilson, 1950
Kinnersley et al., 1997
Burgy & Pomeroy, 1958
Leyton et al., 1967
Rutter et al., 1971
Gash, 1979
Therefore, those parameters were introduced into further calculations. Effectively, the
applied method was a rather simplified version of the Rutter model (Rutter et al. 1971,
1975) with the following assumptions:
-
free throughfall (=without striking the canopy) equals zero
-
leaf and stem-interception as well as their evaporation is combined
-
evaporation of the wet canopy is 1 mm h-1 and not reduced by the factor C/S (where C
is the actual amount of water on the canopy)
55
5.1.1 Precipitation
According to literature data about the canopy storage capacity (Table 8) a value of 1 mm
was chosen suitable for the fallow vegetation and all crops. Despite different cited values
for forest and herbaceous vegetation, this was justified, as there is no indication of a
separation of range between these communities (Leyton et al., 1967; Rutter, 1975).
Applying S=1 mm, on the basis of the above-shown throughfall and stemflow data, hourly
(and daily) net precipitation was calculated as mentioned in chapter 4.2.1 (Figure 11 and
Figure 12).
Calculated net precipitation on the fallow site most times exceeded the percentages of
throughfall. According to the assumption made for calculation of net precipitation this
meant that theoretical interception (gross precipitation minus throughfall) most times exceeded the canopy storage capacity. Following the requirements stated in 4.2.1 then only
S was intercepted, increasing net-precipitation. Thus, throughfall alone obviously could
not describe net precipitation dynamics.
Net precipitation as
gross precipitation share [%]
100
90
80
70
Throughfall (regression; biweekly basis)
Net precipitation (hourly basis)
60
1.1. 1.3. 1.5. 1.7. 1.9. 1.11. 1.1. 1.3. 1.5. 1.7. 1.9. 1.11. 1.1.
1997
1998
Figure 11: Calculated percentage net precipitation of the fallow site on basis of the linear regression of
throughfall measurements and a canopy storage capacity of 1 mm (hourly data from 15th of April 1997 to
30th of March 1998, others: daily data)
Not so on the cultivation sites, where only intensive storm events let net precipitation increase above the sum of throughfall and stemflow (e.g. 23rd of Nov. 1997: 16 mm within
two hours leading to net precipitation of 92 % and 100 %; Figure 12).
56
5.1.1 Precipitation
Net precipitation as
gross precipitation share [%]
100
90
80
Maize
70
Cowpea
Cassava
Regrowing
fallow
Throughfall+Stemflow (weekly basis)
Net precipitation (hourly basis)
60
1.1. 1.3. 1.5. 1.7. 1.9. 1.11. 1.1. 1.3. 1.5. 1.7. 1.9. 1.11. 1.1.
1997
1998
Figure 12: Calculated percentage net precipitation of the cultivation sites on the basis of the sum of
throughfall and stemflow and a canopy storage capacity of 1 mm for all storm events
Interception losses (gross precipitation minus net precipitation) were summed to annual
amounts. Annual interception of the fallow vegetation increased by 1.3 % to 7.9 %, which
was obviously related to the decrease of throughfall (Table 9).
Table 9: Interception during the two years of cropping on the fallow site and on the cultivation sites
Site/Vegetation
Gross precipitation
[mm]
Interception
[mm]
[%]
Fallow vegetation
1997
1998
2104
2545
139
200
6.6
7.9
52
11
9
90
19
182
86
97
3.6
11.3
6.7
4.6
2.3
4.1
4.1
3.8
Cultivation sites
Maize
1441
Cowpea
100
Cowpea + Cassava
136
Cassava
1964
Fallow regrowth
812
Sum
4454*
1997
2104
1998
2545
*not including 195 mm precipitation before maize was planted
On the cultivation sites interception losses during 1997 and 1998 were only about half of
those of the fallow vegetation. Mean percentage interception of all crops (+ regrowing
fallow) was 4.1 %. The percentage interception of cowpea mono cropped within 35 days
was about three times higher than those of maize or cassava, but on the average interception was only 0.3 mm per day, demonstrating the limited relevance of a percentage
value alone without further details on gross precipitation.
57
5.1.1 Precipitation
Numerous studies comprising rainfall partitioning and interception have been carried out
in the last years. Most considered natural forest stands, parts of them are related to forest plantations but only some deal with important agricultural crops, as assessment is
more difficult or as knowledge is simply not relevant (Table 10).
Table 10: Interception (I) and its division into throughfall (PT) and stemflow (PS) of different vegetation in
relation to gross precipitation (P)
Stand, region
P
[mm]
PT
[%]
Spontaneous fallow, central Amazonia
2588
<76.9
2-year-old fallow, Bragantina region, Brazil
1956
Terra firme forest, Pará, Peixe Boi
17-year-old secondary veg., same location
not
given
Year-old secondary vegetation, Costa Rica
Mature tropical forest, Costa Rica
567
Terra firme forest, Pará, Belém
Terra firme forest, Pará, Marabá
PS
[%]
I
[%]
Data source
>20
3.1
Schroth et al., 1999a
65
23
12
Hölscher et al., 1998
83
88.8
0.54
1.70
16.5
9.5
Jipp et al., in revision
68
52
4
9
28
39
Raich, 1983
2669
84.6
<0.4
15
Klinge et al., in revision
1650
#
3564
#
86.2
87
0.8
1.4
12.9
11.6
Terra firme forest, central Amazonia, Manaus
2721
91
1.8
7.2
Lloyd & Marques, 1988
Terra firme forest, central Amazonia, Manaus
3094♦
77.7
0.3
22
Franken et al., 1982
Trop. montane rain forest, Columbia, 2550 m
and 3370 m altitude
2115
1453
87.6
81.7
n.d.
n.d.
12.4
18.3
Veneklaas & Van Ek,
1990
Trop. montane cloud forest, Panama
3510
62.4
0.4
37.2
Cavelier et al., 1997
Trop. montane forest, Puerto Rico
5745
59
2.3
38.7
Scatena, 1990
Trop. rain forest, Sabah, Malaysia
3627
80.7
1.9
17.4
Sinun et al., 1992
Trop. rain forest, Kuala Lumpur, Malaysia
63.2*
77.1
1.2
21.7
Abas, et al., 1991
Trop. rain forest, peninsular Malaysia
2381
77.6
0.6
21.8
Manokaran, 1979
Rondônia, Jí-Paraná
ß
Trop. rain forest, Brunei
$
826
81
Tropical dry deciduous forest, India, 19751976 and 1976-1977
1196
789
76
80
6
7
12.6
17.2
Yadav & Mishra, 1985
948
77
<2
21
Okali & Furtado, 1980
1475
1863
80.9
75.4
7.9
6.6
11.2
17.9
Bruijnzeel et al., 1987
25year-old Teak plantation, Nigeria
Acacia auriculiformis plantation, Java, 4 years
and 5 years old
$$
Ubarana, 1996
1
Maize, USA
Simulation
~44
Maize, France
Simulation
40-46
Maize, Chile
§
231
sum of single days within a period of 6 months
measuring period 430 and 610 days, respectively
♦
18.5 months
72.8
18.6
18
Dykes, 1997
Bui & Box, 1992
Girardin, 1992
8.5
Ellies & Huber, 1991
*per week
measuring period 114 days
$$ not determined but assumed
§ 5 months
ß
#
$
Secondary vegetation, also subject of only a few studies, shows similar throughfall percentages as comparable (primary) rain forest. But, stemflow and thus interception are different: Interception tends to be lower (3.1 to 12 %), as stemflow of young secondary
58
5.1.1 Precipitation
vegetation has a remarkable share (20 % and more). Exceptional, not only in this regard,
are the results of Raich (1983) on a regrowing primary forest, where, contrarily, stemflow
(4 %) was low. Raich (1983), however, evaluated rainfall partitioning of 31 single days
within 6 months, not the accumulated amounts as normally done in the other cited studies, leading to some bias.
Stemflow of tropical rainforests is comparably less important comprising 0.4 % to up to
2.3 % (exception Raich, 1983: 9 %) and thus in some studies is neglected. Percentages
of rainfall partitioning of the present study lie between those cited by Schroth et al.
(1999a) and by Hölscher et al. (1998), which are the most suitable studies for comparison because of similar climatic and edaphic conditions. Their high percentage stemflow
on the one hand explains the gap, which obviously exists in the present study between
measured throughfall and finally calculated net precipitation (Figure 11). On the other
hand, their results indicate the need to include stemflow measurements into observations of young secondary vegetation, which was not done on the fallow site in the present
study under the assumption that those percentages would be negligible.
In a detailed study on stemflow and throughfall of maize comprising 7 different plant
densities, Von Hoyingen-Huene (1983) could show that stemflow of the total stand was
not really depending on the plant density, when ranging between 4 and 12 plants m-2.
Only rather high densities between 32 and 72 plants m-2 led to a doubled amount of
stemflow. Mature maize plants (4 plants m-2; LAI=1.2) 'funneled' around 16 to 19 % of
gross precipitation as stemflow. Throughfall did decrease significantly with increasing
density of maize stands. Thus, Von Hoyningen-Huene could establish a regression equation determining interception from gross precipitation and leaf area index only. Mean interception of maize with a plant density of 12 and 32 plants m-2 within the main growing
season (European summer) was 14.8 % and 23.1 % of gross precipitation (344 mm), respectively. Comparable percentages for stemflow were found by Ellies and Huber (1991,
see table). Their mean interception of 8.5 % (on the basis of 5.8 plant m-2) follows the declining trend of interception with lower plant densities. Interception in the present study
(3.6 %) by maize with 2 plants m-2 thus seems to be a realistic estimate, fitting this trend
and corresponding with Von Hoyningen-Huene's (1983) findings. Combining stemflow
with plant height of maize as was done in the present study is comparable with Von
Hoyningen-Huenes above-mentioned regression equation including LAI, as plant growth
and leaf area index are closely related. Further studies about quantities of stemflow of
maize in small-scale farming systems are certainly necessary. They should comprise the
whole cropping period of maize and additionally test a correlation with plant growth
59
5.1.1 Precipitation
and/or LAI.
Designated precipitation-interception studies about cowpea and cassava could not be
found in the literature, though their importance in a sound water balance is obvious. Assessing pollutant interception via rainfall Kinnersley et al. (1997) carried out detail laboratory studies on broad beans (Vicia faba). Canopy capacity of this crop was 1 mm and
interception fraction fell below 0.2 as soon as total amount of precipitation exceeded 2
mm. Certainly, these laboratory studies should not simply be transmitted to field studies,
but at least they can give a rough estimate of the magnitudes of rainfall interception of
beans.
Further studies on precipitation interception of fallow vegetation as well as crops should
follow, as the present study could indicate a high spatial, but more important, temporal
heterogeneity of net precipitation of these stands. Therefore, transferring weekly accumulate amounts of net precipitation to single storm events probably is the weakest point
in the net precipitation assessment. Evaluating rainfall partitioning of single storm events,
which was not done in the present study due to labor-intensity and remoteness of the
area, could improve results in this regard, particularly when including the proper Rutter
model (Rutter et al. 1971 and 1975) or the analogous Gash model (Gash, 1979).
The applied reduced form of the Rutter model with a constant evaporation rate of
1 mm h-1 after a storm in combination with a canopy storage capacity of 1 mm, on a
hourly data basis effectively meant that theoretically every single storm event could be
reduced by 1 mm (see premises in the methods chapter). Hourly evapotranspiration of
dry fallow or crop vegetation canopy would reach 1 mm h-1 only, when available energy
(net radiation) at noon (~700 W m-2) would fully enter evapotranspiration, which is however never met. Thus, it has to be questioned, whether 1 mm h-1 evaporation of wet canopy is a reasonable assumption. Details will be discussed in the following chapter.
Not considered in net precipitation calculations was the interception of water by lowgrowing weeds and by the organic or mulch layer on top of the soil. Weeds were frequently removed and the mulch layer also inhibits soil evaporation, therefore behaves as
a barrier against water fluxes in both directions and, thus, eventually cancels out. Definitely, water balances in the presence of both, weed and mulch, would be extremely difficult to assess. For more details it is referred to the study of Savabi and Stott (1994).
60
5.1.2 Potential and actual evapotranspiration
5.1.2
Potential and actual evapotranspi
evapotranspiration
Using hourly micro-meteorological data collected on the fallow site, potential evapotranspiration according to the Penman-FAO method as well as actual evapotranspiration applying the Bowen ratio energy balance were calculated over a period of about one year
(9th of April 1997 until 29th of March 1998). To complete the two-year study period Penman-Piche evapotranspiration was calculated. Furthermore, the Penman-Monteithmethod was applied, to test the option of a more appropriate method to assess reference
stand evaporation including calibration with eco-physiological parameter (porometry).
Penman-FAO and Bowen ratio – energy balance method
The exceptional dry season of 1997 (end of August until end of December) is clearly discernable, with intensified solar net radiation, higher temperature, higher vapor pressure
deficit and higher wind speed during this time (Table 11).
Table 11: Monthly mean net radiation (Rn) and temperature as well as mean daytime humidity and median
daytime wind speed measured over the fallow site (Min. and Max.-values on hourly data basis; Min.-Windspeed in all cases = 0)
Rn
Month
Mean SE
-1
[mm d ]
April 1997
May 1997
June 1997
July 1997
August 1997
September 1997
October 1997
November 1997
December 1997
January 1998
February 1998
March 1998
Overall mean
4.3
4.6
5.0
5.4
5.7
5.8
5.9
5.2
5.1
4.4
4.9
4.5
5.1
0.30
0.22
0.09
0.16
0.13
0.15
0.16
0.16
0.20
0.21
0.51
0.27
Temperature
Mean Min. Max.
----------- [°C] ----------24.7
24.9
25.8
25.2
25.2
25.8
26.9
26.6
27.1
26.5
27.4
26.9
26.1
20.2
21.0
20.3
20.2
19.4
19.6
20.4
19.3
20.6
21.9
22.2
22.6
31.9
31.7
31.8
31.4
31.8
33.7
34.4
34.4
34.3
33.8
33.2
32.5
Rel. humidity
Wind speed
Mean Min. Max. Median Max.
-1
---------- [%] ---------[m s ]
85
84
76
77
77
68
62
65
67
81
76
82
75
65
58
54
56
57
43
38
41
40
46
57
54
51
100
100
100
100
100
100
100
99
99
100
100
98
99
1.0
2.2
3.9
3.1
3.8
6.2
7.2
6.6
7.2
3.8
6.3
3.7
4.6
11.2
8.4
9.8
9.1
9.6
11.5
10.8
12.4
12.9
11.3
11.5
9.7
In October 1997, the highest net radiation (mean daily value) was measured with about
14.4 MJ m-2 d-1 (i.e. an evaporated water equivalent of 5.9 mm d-1). Also the other considered micro-climatic parameters reached extreme values. As expected, potential evapotranspiration (Penman-FAO) followed this seasonal variation (Table 12). Highest mean
daily evapotranspiration (on a monthly basis) increasing from April was reached in October with 5.1 mm d-1, then decreasing again to 3.5 mm d-1 in January 1998. The overall
61
5.1.2 Potential and actual evapotranspiration
mean potential evapotranspiration was 4.3 mm d -1.
Table 12: Monthly mean potential and actual evapotranspiration and the mean kc-value of the fallow site
(based on daily data), as well as the median Bowen ratio (n = considered hours per month for Bowen ratio;
q. = quartile)
Month
Pot. ET
Mean SE
Act. ET
Mean SE
-1
----------- [mm d ] ----------April 1997 *
3.5 0.23
May 1997
3.7 0.17
June 1997
4.1 0.08
July 1997
4.4 0.13
August 1997
4.6 0.12
September 1997
5.0 0.13
October 1997
5.1 0.14
November 1997
4.5 0.14
December 1997
4.3 0.18
January 1998
3.5 0.19
February 1998
4.6 0.38
March 1998 **
3.9 0.20
Overall mean
4.3
* Beginning the 9th of April
** Ending the 29th of March
3.5
3.7
4.0
4.2
4.7
4.6
4.3
3.8
3.7
3.3
4.8
3.2
4.0
0.23
0.18
0.09
0.14
0.12
0.12
0.11
0.13
0.17
0.18
0.43
0.17
Bowen-ratio
kc
n Median Upper q. Lower q.
[h] -------------- [-] ---------------
Mean
49
77
323
238
344
334
347
337
334
313
128
252
0.49
0.40
0.33
0.35
0.28
0.31
0.41
0.40
0.44
0.36
0.22
0.53
0.38
0.79
0.64
0.40
0.42
0.34
0.42
0.53
0.51
0.61
0.50
0.30
0.83
0.36
0.32
0.25
0.26
0.22
0.19
0.25
0.28
0.30
0.25
0.13
0.33
[-]
1.00
1.00
0.96
0.95
1.01
0.93
0.83
0.86
0.84
0.96
1.03
0.83
0.93
Actual evapotranspiration (Bowen ratio energy balance) of the fallow vegetation did only
initially, until August 1997 (4.7 mm d-1), follow the increasing trend, but than was successively reduced to finally 3.2 mm d-1 in March 1998. With proceeding dry season the available energy (net radiation) to a greater extend was turned into sensible heat (H) as can
be seen in the increasing Bowen ratio during that period. The rather low value of 0.28 in
August 1997 means that only about 22 % of the available energy left the system as sensible heat, while 78 % was turned into latent heat (evapotranspiration). In December this
ratio was 31 % sensible heat against 69 % latent heat (β = 0.44). February 1998 was
quite exceptional with altogether 13 rainless days, thus higher net radiation, mean temperature and higher median wind speed. This promoted high evapotranspiration, which
actually was 0.2 mm d-1 higher than potential ET. The Bowen ratio in February was the
lowest in the study period. Measurements of the Bowen ratio (hourly basis), which did not
meet the criteria of Ohmura (1982), were rejected. Considering also the times of malfunctioning of the psychrometer, i.e. dried-up wet-bulb thermometer, as well as data-loss
of one of the four thermometers (thermistors), a total of 20.8 % of all hourly daytime data
had to be excluded (leading to n in Table 12). Data loss was mainly responsible for the
reduced data basis in July, February and March, while low data basis in April and May was
caused by rejection of data. For further calculations, missing hourly actual ET was substituted by hourly potential evapotranspiration of the same time, multiplied by the mean
62
5.1.2 Potential and actual evapotranspiration
daily kc-value. If potential evaporation was not available, corresponding data of the preceding or following day were used. A kc-value, analogous to the Penman-FAO (crop coefficient) approach, is the ratio of actual to potential evapotranspiration. The overall mean kc
was 0.93 ranging from 0.83 (extended dry season) to 1.03 (rainy season), reflecting the
above-described behavior of actual and potential evapotranspiration.
Minimal evapotranspiration — actual as well as potential — was measured on the 21st of
December 1997, a cloudy day with 2 hours of intensive precipitation (31.9 mm), with
0.71 and 1.04 mm d-1, respectively. Maximum actual evapotranspiration was measured
on the 16th of February 1998 with 6.40 mm d-1, a brilliant (10 hours of insolation), rainless and windy day. Maximum potential ET was measured on the 27th of October 1997
with 6.52 mm d-1, a day with similar climatic condition as the 16th of February (actual ET
at that day: 4.93 mm d-1).
With regard to rainfall distribution as well as the behavior of evapotranspiration of the
fallow vegetation, three seasons could be distinguished: a transition period (beginning of
April – 21st of August 1997) characterized by frequent precipitation, a dry season (22nd of
August – 21st of December 1997) with less precipitation, and a rainy season (22nd of December '97 – end of March 1998) with very frequent and heavy precipitation. The exact
dates of distinguishing the seasons might vary annually. Based on these seasons further
comparison of hourly micro-climatic data could be made (shown in the Appendix, Figure A-4 to Figure A-8).
Beginning at the end of August 1997 the fallow vegetation was getting into water stress
due to missing rainfall. As a result, the actual evapotranspiration was reduced by about
20 %. Within the period from 22nd of August until the 8th of January 1998 the sum of actual evapotranspiration amounted to 576 mm, whereas precipitation during that time
was only 148 mm. Thus, micro-meteorological results alone suggest that the vegetation,
neglecting soil water drainage during that time, used a soil-water reservoir of 428 mm. In
the following chapter it has to be tested to what extent these findings are congruent with
the soil water model and to what depth the soil water reservoir is really depleted during
the dry season.
Penman-Piche method
Assessing the Penman-Piche evapotranspiration, first the solar term of equation (4) was
calculated including measured insolation and maximum and minimum daily temperature,
as well as values of maximum possible insolation, albedo and extraterrestrial radiation
63
5.1.2 Potential and actual evapotranspiration
(according to Maltez et al., 1986). Results of daily values of the solar term already were
highly correlated with daily Penman-FAO evapotranspiration (kc=1; Table 13).
Table 13: Regression equation to estimate daily potential evapotranspiration [mm d-1], to estimate the
aerodynamic term of the Penman-FAO equation (bold letters within dotted lines) and regression of PenmanFAO ET and Penman-Piche ET (italic letters); n=313 in all cases
Regression
R
2
ET-Penman-FAO vs.
Penman-Piche solar term
0.763**
ET-Penman-FAO vs.
Piche-Evaporation
0.644**
Penman-FAO aerodynamic
term vs. Piche-Evaporation
0.800**
Equation
§
-------------- SE of --------------slope intercept estimate
0.007
-
0.495
y=1.687ln(x)+3.082
0.071
0.060
0.609
y=0.189x-0.144
0.005
0.013
0.091
0.006
-
0.485
y=1.076x
ET-Penman-FAO vs.
§
0.773**
y=1.002x
ET-Penman-Piche
** highly significant (p≤0.01)
§ regression forced to intersect the datum (i.e. intercept = 0)
The Piche-Evaporation alone also could estimate Penman-FAO evapotranspiration accurately (second regression in Table 13), but the standard error of the estimate was higher
(0.609 mm d-1) than that of the former regression, which could be expected since the
solar term alone generally contributed more than 80 % of total Penman-FAO evapotranspiration. Separating the Penman-FAO equation into its two summands and using the regression equation between Piche evaporation and the aerodynamic term (third regression) improved the estimate of the potential evapotranspiration. The slope of the highly
significant regression equation (fourth equation) indicated that the Penman-Piche evapotranspiration, on average, was fairly congruent with the Penman-FAO evapotranspiration.
Allowing the regression curve to intersect above or below the datum did not really improve the regression (R2 increased by only 0.003), which was also true for the PenmanPiche-solar-term-regression.
Transferring the regression equation of the aerodynamic term, the Penman-Piche evapotranspiration was calculated for those times, when own measurements were missing
(January to 9th of April 1997 and 31st of March 1998 to end of 1998; Table 14).
64
5.1.2 Potential and actual evapotranspiration
Table 14: Monthly mean Penman-Piche potential evapotranspiration for the fallow vegetation
Month
Penman-Piche pot. ET
Mean
SE
-1
[mm d ]
January 1997
February 1997
March 1997
...
April 1998
May 1998
June 1998
July 1998
August 1998
September 1998
October 1998
November 1998
December 1998
Overall mean
3.2
3.7
3.3
0.15
3.5
3.8
3.6
3.5
4.3
4.6
5.1
4.2
3.9
3.9
0.18
0.19
0.17
0.13
0.10
0.10
0.10
0.12
0.09
0.18
0.20
Mean monthly Penman-Piche-evapotranspiration and its seasonal dynamics were
equivalent to the Penman-FAO results of the year before. Deviations were mostly detectable within the rainy season and thus are well explainable, as rainfall distribution and
consequently net radiation (due to cloudiness) of the two consecutive years differed considerably (see above).
The Regressions in Table 13 give additional information about (minimum) requirements
to determine potential evapotranspiration. The Piche evaporimeter alone turned out to be
an appropriate instrument to give estimates of daily potential evapotranspiration, when
sufficiently calibrated with independent measurements. More worthwhile, but also demanding better equipment, are the determination of the solar term by measuring the insolation and minimum and maximum temperature, emphasizing the need of highly reliable measurements of those parameter. Combination of both measurements only slightly
improved the results over those obtained using solely the solar-term-regression. It thus
has to be questioned, whether a Piche evaporimeter is a necessary complement to basic
temperature and insolation measurements in the humid tropics. At least in the present
study it proved to contribute only a small share (<20 %) on the estimate of total potential
evapotranspiration.
This might not be valid, however, for temperate regions. Stanhill (1962) pointed out the
simplicity of the Piche measurements to improve potential evaporation measurements.
The aerodynamic term in his study in Israel reached up to 1.9 mm d-1 that is almost one
half of the total potential evapotranspiration. The slope (a) of two independently determined regression equations (aerodynamic term vs. sheltered Piche evaporation) was
65
5.1.2 Potential and actual evapotranspiration
0.140 and 0.147, the intercept (b) 0.112 and 0.461 mm d-1, respectively and thus comparable to the intercept of the present study (b = 0.144 mm d-1). Papaioannou et al.
(1996), applying the same regression for sheltered evaporimeter data of Athens, found a
mean slope of 0.194 which is only slightly higher then the value of the present study
(a=0.189). Their intercepts, however, were higher and varied seasonally between 0.313
and 0.900. In a later study, Papaioannou et al. (1998) additionally correlated Piche
evaporation with the aerodynamic Penman-Monteith term, i.e. including also estimates of
rc and ra. They stated that one single annual relationship of those data satisfactorily could
describe daily potential evapotranspiration, as was done in the present study. Basically,
however, results did not really differ from former research, as knowledge about dynamics
of ra and rc were scarce and both were crudely approximated.
Paw and Gueye (1983) using exposed evaporimeter data (outside a weather hut and thus
also influenced by radiation) from Illinois, USA, could detect a linear relation between
those data and potential evapotranspiration. The slope was not significantly different
from 1, whereas the intercept was highly variable ranging from –1.34 to 2.84 depending
on daytime and season. The standard error of the estimate was about three times higher
then results of the present study, underlining the fact that improvement can be achieved
using a sheltered evaporimeter and correlating those data only with the Penman aerodynamic term, when the solar term can be calculated from separate measurements.
Penman-Monteith method
Finally, the Penman-Monteith equation to determine 'reference' stand (crop) evapotranspiration was applied.
The overall mean value of the aerodynamic resistance ra within the measuring period
amounted to 6 s m-1 (Table 15). The aerodynamic resistance is inversely related to wind
speed and therefore reached lowest values in the (more windy) dry season ranging between 3 and 4 s m-1. Additionally, as the term ln2(z-d/(z0) in equation (18) increased due
to growing vegetation, ra systematically declined within the measuring period (e.g. maximum values of ra achieved at zero wind speed).
The canopy resistance (rc) was calculated with the rewritten Penman-Monteith equation
incorporating results of the actual evapotranspiration determined with the Bowen ratio
energy balance method (appropriate data according to above mentioned criteria only).
Within the rainy season and the transitional period monthly daytime median rc varied between 53 and 69 s m-1. The canopy resistance increased within the dry season to reach a
66
5.1.2 Potential and actual evapotranspiration
maximum of 119 s m-1 in October 1997.
Table 15: Monthly median daytime aerodynamic resistance (ra) and canopy resistance (rc) of the fallow
vegetation on the basis of hourly data, and the calculated rc based on regression analysis
Month
-------------------------- ra -----------------------Upper Lower
quartile quartile
Median
Max.
10
9
7
7
6
4
4
4
3
5
3
5
6
21
17
6
3
21
14
7
4
20
13
5
3
19
11
6
3
18
9
5
3
18
5
4
2
17
4
3
2
16
7
3
2
16
6
3
2
15
11
4
2
14
9
3
2
13
9
4
2
--------------- rc ---------------- rc-regression
Upper
Lower
451
65
146
43
151
43
102
45
Median Quartile Quartile
Median
-1
-------------------------------------------------- [s m ] ------------------------------------------------------April 1997
May 1997
June 1997
July 1997
August 1997
September 1997
October 1997
November 1997
December 1997
January 1998
February 1998
March 1998
Overall mean
Min.
143
69
62
63
53
84
119
110
112
60
56
67
83
81
37
121
54
167
72
188
69
159
75
105
39
84
43
108
49
50
53
60
57
54
86
133
126
101
55
58
52
74
April 1997 showed an exceptionally high median daytime canopy resistance of 143 s m-1.
However, data for this month were the fewest of the measuring period, comprising only
49 hours (see n in Table 12). Thus, the possibility exists that canopy resistance was overestimated. Diurnal dynamics of the canopy resistance followed a certain pattern that was
most distinct in the dry season (Figure 13 and Appendix, Figure A-8):
200
Transitional period
Dry season
rc [s m-1]
150
Rainy season
100
50
0
6
7
8
9
10
11
12
13
14
15
16
17
18
Time of day [h]
Figure 13: Median diurnal dynamic (hourly data) of the canopy resistance during the distinguished seasons
Daytime minimum rc was reached between 7 and 8 o'clock. Field observation showed
that in the early-morning dew very frequently caused complete wetting of the vegetation67
5.1.2 Potential and actual evapotranspiration
canopy. Consequently, it was not surprising that rc at those times was close to zero, theoretically true only for a wet canopy. Subsequent desiccation of the canopy caused an increase of rc, which was most rapid in the dry season. Further diurnal patterns of rc in the
transitional period and the rainy season did not differ significantly, ranging from 40 to 80
s m-1 and rapidly increasing in the late afternoon (between 5 and 6 p.m.) to finally reach
more than 1000 s m-1. Within the dry season, however, hourly daytime canopy resistances between 6 A.M. and 4 P.M. were significantly higher than those of the other seasons, except between the dry season and the transitional period between 7 and 8 A.M.
(GLM with nested effect and comparison of least square means of all data).
A multiple stepwise linear regression analysis was applied to express canopy resistance
through micro-meteorological parameters. Initially, temperature, net radiation (Rn [W m-2])
and saturation vapor deficit (D [kPa]) entered the analysis as explainable parameters. To
comply with the required normal distribution, canopy resistance data were logtransformed. In the end, net radiation and saturation vapor deficit were the only parameter that combined could predict rc, whereas temperature did not significantly contribute to
the prediction. However, hourly canopy resistance data before 8 a.m. and after 4 p.m.
had to be excluded from the regression analysis and also extremely high values
(>330 s m-1; n=67) were not considered, to obtain reasonable daily estimates and avoid
bias towards extreme values. The canopy resistance could be predicted with the following
equation (R2 = 0.654; standard errors in Table 16):
(33)
Ln(rc) = 4.158 + (0.856 *D) - (0.00251 * Rn)
Table 16: Regression analysis to predict the log-transformed canopy resistance with the saturation vapor
deficit and the net radiation (n=2446; R2 = 0.654)
Coefficient
Std. error
Constant
4.158**
0.023
Saturation vapor deficit
0.856**
0.0132
Net radiation
-0.00251**
0.000056
Estimate
0.434
**highly significant different from zero (t-test, p≤0.01)
Contribution for
prediction
56 %
44 %
Monthly median daytime canopy resistance values obtained with this regression equation
(rc-regression in Table 15 on basis of all hourly micro-meteorological data, n=2918) did
not differ significantly from those data calculated by the above-described procedure that
included actual ET measurements (paired t-test). Moreover, based on the regression
equation (data basis: n=190 hours), the previously calculated, extraordinary high monthly
median canopy resistance of April 1997 could not be confirmed. Instead, a value of
68
5.1.2 Potential and actual evapotranspiration
50 s m-1 was calculated, which is considered reasonable for the transitional period and
the rainy season. The regression equation to determine canopy resistance, though based
on only two micro-meteorological parameters, confirmed the earlier calculations. It is,
however, certainly only valid for fallow vegetation of the study region under similar conditions. No factor directly describing soil water availability for the fallow plants is yet considered, which could be done by including soil pressure head dynamics into the regression analysis. So far, however, the above-described regression equation might serve as
an independent tool to predict stand evapotranspiration with micro-meteorological measurements only, applying the Penman-Monteith method.
Both methods that assess potential/stand evapotranspiration of the fallow vegetation,
the Penman-FAO method as well as the Penman-Monteith method (including the
achieved regression equation) were compared. To this end, hourly data of actual evapotranspiration (Bowen ratio) were plotted against hourly Penman-FAO and PenmanMonteith evapotranspiration data, respectively (Figure 14).
As could be seen already in Table 12, actual evapotranspiration frequently did not reach
potential evapotranspiration (Penman-FAO). The slope of 1.083 (significantly different
from 1; t-test, p≤0.05) indicates that potential evapotranspiration systematically was
8.3 % higher than actual ET (the reciprocal value of the slope also corresponds to the
mean kc in Table 12). However, the distribution of data points (Penman-FAO vs. Bowen ratio) was less scattered, especially at low latent heat fluxes above -100 W m-2, where actual and potential evapotranspiration fit quite well. At those flux rates the PenmanMonteith method frequently overestimated actual evapotranspiration, but also generally
estimates of hourly reference stand evapotranspiration were less accurate, as indicated
by the lower regression coefficient. Values of latent heat flux above –100 W m-2 are
mostly related to early morning or late afternoon hours. But, late in the afternoon the regression equation to predict rc was not adapted. Extending the equation to those times
resulted in an underestimated rc (see Appendix, Figure A-8), which subsequently led to an
overestimation of actual evapotranspiration. On the other hand, extending the regression
equation to early morning evapotranspiration led to a slight underestimation of actual ET
due to an overestimation of rc at those times. Thus, on a daily basis those two effects appeared to counterbalance each other. The slope of 1.006 of the Penman-Monteith regression was not significantly different from 1 (t-test, p≤0.05). Reference stand evapotranspiration calculated with the Penman-Monteith method including a regression equation describing the canopy resistance, thus met actual evapotranspiration according to
the Bowen ratio energy balance.
69
5.1.2 Potential and actual evapotranspiration
-2
λET - Penman-Monteith [W m ]
-700
-600
-500
-400
-300
y = 1.006x
-200
R = 0.841
SEy = 60.6
SEa = 0.0039
2
-100
0
0
-100
-200 -300 -400 -500
-2
λET - Bowen ratio [W m ]
-600
-700
-700
-2
λET - Penman-FAO [W m ]
-600
-500
-400
-300
y = 1.083x
-200
R = 0.910
SEy = 48.3
SEa = 0.0031
2
-100
0
0
-100
-200 -300 -400 -500
-2
λET - Bowen ratio [W m ]
-600
-700
Figure 14: Comparison of actual evapotranspiration (Bowen ratio) and potential ET according to PenmanFAO as well as stand evapotranspiration according to Penman-Monteith (hourly data; n=2963; SEy = standard error of estimate; SEa = standard error of slope)
In a second approach, data on stomata resistances and on the leaf area index of the
considered fallow vegetation were used to calculate the canopy resistance in a different
way (porometry assessment). Therefore, stomata resistances (rst) of the most abundant
species of the fallow site were measured in three campaigns on the 3rd of July and the 7th
and the 28th of August 1997 from 8 A.M. to 6 P.M. every two hours with a dynamic diffusion porometer (AP$, Delta T device, UK). The same kind of measurements was also carried out by Sá et al. (1995 and 1999) at fallow sites of different age (Table 17).
70
5.1.2 Potential and actual evapotranspiration
Table 17: Mean, minimum and maximum stomata resistances (related to two-sided unit area of leaves) of
the most abundant species at the fallow site and of species of fallows of different age in the study region;
n.g. = not given
Stomata resistance
Mean SE
Min
Max
-1
-1
[s m ]
Precent fallow vegetation
Banara guianensis
85
Davilla rugosa
115
Lacistema pubescens
83
Myrcia bracteata
96
Vismia guianensis
94
Overall mean
94
Weighted mean
90
Fallow, 1/2 year old (Sá et al., 1995)
Banara guianensis
83
Cecropia palmata
93
Davilla rugosa
121
Lacistema pubescens
111
Myrcia bracteata
136
Phenakospermum guianensis
176
Vismia guinanensis
100
Overall mean
117
Fallow, 3-4 years old (Sá et al., 1995)
Banara guianensis
83
Cecropia palmata
86
Davilla rugosa
133
Lacistema pubescens
71
Myrcia bracteata
112
Phenakospermum guianensis
126
Vismia guinanensis
92
Overall mean
100
Fallow, 6 years old (Sá et al., 1999)
Davilla rugosa
145
Lacistema pubescens
133
Myrcia bracteata
120
Phenakospermum guianensis
254
Overall mean
163
Fallow, 9-11 years old (Sá et al., 1995)
Davilla rugosa
531
Lacistema pubescens
252
Phenakospermum guianensis
381
Vismia guinanensis
188
Overall mean
338
[s m ]
9
9
7
10
9
9
8
36
52
45
39
45
196
183
163
211
191
n.g.
"
"
"
"
"
"
32
31
37
32
47
56
32
1522
11161
4274
8549
13393
893
7581
n.g.
"
"
"
"
"
"
31
33
31
32
34
36
32
2336
1119
1860
618
835
1101
1390
41
43
23
17
67
56
79
218
356
478
259
347
n.g.
"
"
"
65
53
83
69
3901
4018
2480
1674
Mean stomata resistances of species of young fallow vegetation (<4 years) ranged between 71 and 136 s m-1 (not including Phenakospermum guianensis, which was not present on the fallow site under study). However, rst of older fallow vegetation tended to increase and partially exceeded values of 200-300 s m-1 (9-11 year-old fallow). Thus, Stomata resistances with time approached values that were also measured for mature
71
5.1.2 Potential and actual evapotranspiration
(Amazonian) primary forest species (200 s m-1 by Schulze et al., 1994; 100-700 s m-1 by
Sá et al., 1996; 75-300 s m-1 by Andrade et al., 1998). The overall mean rst of five species of the present fallow site was 94 s m-1 and slightly decreased to 90 s m-1, when
weighted according to the abundance of the individual species (based on the plant survey
of Wetzel & Cordeiro, see Appendix, Table A-2).
Schmitt (1997) measured the leaf area index (LAI, referring to one leaf side) of the subject fallow vegetation. Results of his 'destructive' method (collecting all leaves above a
certain area and measuring the leaf area) ranged between 3.56 and 5.55 (exception
9.22 not considered) with a mean LAI of 4.33 (SE=0.599, n=4). Additionally, he could
confirm these findings with a second, 'non-destructive' method using a plant canopy analyzer (LiCor LAI-2000, Lincoln, Nebraska, USA), where he obtained a mean LAI of 4.17
(SE=0.127, n=5).
To finally calculate the canopy resistance, equation 20 (rc = rst/(0.5 LAI); chapter 4.2.2)
was applied9. With that, the mean canopy resistance varied between 42 and 48 s m-1
(LAI: 4.17 to 4.33; rst: 90 to 100 s m-1, see above). On the other hand, keeping the stomata resistance as the dependent variable, led to a mean rst- value of 113 to 180 s m-1
(LAI: 4.17 to 4.33; rc: 54 to 83 s m-1). In the latter case (calculating rst on basis of rc),
there would arise a difference between porometry-determined rst-values and calculated
rst-values of 23 to 90 s m-1.
When Allen (1986; see also Pereira et al., 1996) first stated the relationship between rc
and rst, in equation 20, he assumed that only one half of the canopy of a dense and hypostomatic crop is active in vapor and heat transport leading to the factor 0.5 in the denominator.
However, keeping the stomata resistance as measured porometrically (90 s m-1) and the
mean canopy resistance as determined during times, where the campaigns were carried
out (54 s m-1, median July-August-value), the factor 0.5 would be reduced to range between 0.39 and 0.40 (LAI: 4.17to 4.33). This factor would further decrease to between
0.27 and 0.32, when an overall mean value for rst of 100 s m-1 (as quoted for 3 to
4-year-old fallow) and a median value for rc of 83 s m-1 or 74 s m-1 (from Table 15), respectively, would enter the equation. These results would mean that over the whole
measuring period eventually only 27 to 32 % of the area of all leaves of the canopy were
actively contributing to vapor and heat transport.
Furthermore, it is not really justified for the studied natural vegetation to neglect the ad-
9rc
in this study is a resistance per unit area of ground of the vegetation stand
72
5.1.2 Potential and actual evapotranspiration
axial share of stomata as is implicated in equation 20. The considered species of the fallow vegetation had also adaxial ("upper") stomata, which contributed to stomata resistance. Sá et al. (1998) measured varying ratios of stomata conductance of abaxial and
adaxial leaf surfaces. They recorded the lowest for Myrcia bracteata with 9.18 (9.8 % adaxial versus 90.2 % abaxial) and highest for Davilla rugosa reaching 31.85 (3.0 % versus
97.0 %). Thus, all cited results on stomata resistance in Table 17 included the sum of
stomata conductance of both sides of the leaves (with regard to the total leaf area).
Assuming a 10 % adaxial share, the percentage of actively contributing leaves of the canopy would drop to about 25 to 29 %. Averaging these results, thus the canopy resistance
might be expressed as:
(34)
rc =
rst
0.28 ⋅ LAI
Seasonal dynamics of stomata resistance have not yet been taken into consideration. As
differences between the distinguished seasons could be found regarding daily dynamic of
the canopy resistance, it is obvious that seasonal differences should be detectable on the
leaf-level. Indeed, Sá et al. (1996) are quoting such a seasonal patterns, as they found a
correlation between rst and soil water storage for primary forest species. Generally, theoretical stomata conductance/resistance behavior is described by including ambient micro-climatic conditions such as temperature, radiation or saturation vapor deficit
(Schulze, 1994). Those relationships have to be elaborated also for stomata resistance of
secondary vegetation, which remains subject of further research.
The relationship between (single) leaf or stomata resistance and canopy resistance and
their contribution to actual or reference crop evapotranspiration is the subject of numerous studies. Stockle and Kjelgaard (1996) introduced an additional resistance term r0
(rc=r0+[rst min/0.5LAI]) accounting for inner-canopy structure of a crop (in their case corn
and potato). With that term they could improve previously overestimated (calculated) rst
values to fit with porometer data on rst. Their calculation method for rst was the same as
used in the present study, i.e. based on the rearranged Penman-Monteith equation including daytime Bowen ratio measurements and applying equation 20. Additionally, they
kept rst in their calculation as a dependent variable of saturation vapor pressure deficit
and solar radiation (linear relation), which also proved to be the best estimate for rc in our
study. Introducing an additional canopy structure resistance in our study could explain
the above-mentioned differences (23 to 90 s m-1) between porometry-rst and calculated
rst. Additional studies, however, would be necessary to validate the assumptions made in
the above calculations and, thus, to eliminate some of the alternative options for calcu73
5.1.2 Potential and actual evapotranspiration
lating rst.
Körner et al. (1979) provided numerous values for maximum leaf conductance of a variety of crops but also of natural vegetation. According to their evaluation maximum leaf
conductance of evergreen woody plants range between 0.1 and 0.5 cm s-1 (i.e. rst: 2001000 s m-1). This is at least 3 to 4 times higher the magnitude determined by Sá et al.
(1995, 1999; see Table 17). Thus, stomata resistance and with that also canopy resistance of fallow vegetation is subject to a seasonal dynamic as well as an increasing trend
with age, complicating long-term evaluation of Penman-Monteith reference stand evapotranspiration without repeated measurements of those parameter.
Aerodynamic resistances of the present study are comparable to those of forests, but are
low compared to values cited for crops. The ra of most crops range between 10 and
50 s m-1 (see several publication cited in Monteith, 1965), whereas aerodynamic resistances of forests are mostly below 10 s m-1 (2.5 s m-1 for coniferous forest California,
Monteith, 1965; 1 to~10 s m-1 for Corsican pine and Douglas fir, Robins, 1974). Low ra of
forests are caused by the comparably higher roughness length of the stand, which was
also the case in the present study (z0: 30-46 cm). But to an extent, the applied equation
(according to Thom and Oliver, 1977) was responsible for the lower ra. Thom and Oliver
(1977) modified the original equation of Monteith (1965), which resulted in a prediction
of ra at lower wind speed that did not – as was originally the case – reach extremely high
values. They claimed that their equation is "accurate enough, over a wide range in degrees of surface roughness to recommend its adoption into hydrometeorological practice", which made it preferable for the present fallow stand.
Canopy resistances of the present study of "well-watered" fallow vegetation (rainy season
and transitional period: 53-69 s m-1) are comparable to those of forests and crops, which
range between 40 and 100 s m-1 (40-80 s m-1 for clipped grass and alfalfa, Allen, 1986;
~100 s m-1 for wheat, Cajanus cajan and lentil, Wallace et al., 1981; ~50 s m-1 for barley;
Szeicz & Long, 1969; see further citations in Monteith, 1965). But also "stressed" fallow
vegetation's canopy resistances (dry season: 84-119 s m-1) are congruent with literature
values (11-110 s m-1 for rain forest, Kenya, Szeicz & Long, 1969; 67-140 s m-1 for rain
forest species in plantation, Granier et al., 1992).
In the fallow vegetation under study, the canopy resistance is a factor 4 to sometimes
more than 10 higher than the aerodynamic resistance. On this basis, evaporation from a
wet canopy – due to intercepted rainfall – should proceed faster than from a dry canopy,
74
5.1.2 Potential and actual evapotranspiration
as resistance of a wet canopy approaches zero and evaporation is regulated by the aerodynamic resistance only (Monteith, 1981). It is, therefore, reasonable to assume an
evaporation of intercepted water of 1 mm per hour as was done in the present study.
High evaporation of intercepted water was cited for pine forest in temperate regions by
Monteith (1965) and by Rutter (1975) amounting to 0.2-0.5 mm h-1, but also for tropical
forests by Bruijnzeel and Wiersum (1987) suggesting 0.5-1.2 mm h-1 and by Dykes
(1997) giving an average of 0.71 mm h-1. Also for winter wheat in Great Britain, high
evaporation rates of the wet canopy ranging between 0.43 and 1.23 mm h-1 were found
by Butler and Huband (1985), though in this case large differences between ra and rc are
not expected. Therefore, it is also justified for the cultivated crops to apply an evaporation
rate of intercepted water of 1 mm per hour.
Crop evapotranspiration (crop coefficients)
For the cultivation sites, evapotranspiration results of the Penman-FAO method obtained
from the fallow site were used. In a strict meteorological sense, transferring those data is
not permitted. As the cultivation sites were relatively small and surrounded by fallow
vegetation of the fallow site (site 1) or by fallow vegetation with comparable characteristics as the fallow site (site 2), the microclimate of these sites obviously was highly influenced by the fallow vegetation. Thus, deviation in this regard should be comparably
small.
Results of Penman-FAO evapotranspiration were multiplied with crop coefficients suitable
for maize, cowpea and cassava (according to Doorenbros & Pruitt, 1977; Figure 15).
1.2
1
kc
0.8
0.6
0.4
0.2
0
1.1.
Maize
1.3.
1.5.
Cowpea
1.7.
1997
Cassava
1.9.
1.11.
1.1.
1.3.
Fallow
1.5.
1.7.
1998
1.9.
1.11.
Figure 15: Crop coefficients (kc) for maize, cowpea, cassava and the regrowing fallow vegetation
75
1.1.
5.1.2 Potential and actual evapotranspiration
Also the regrowing fallow vegetation obtained "crop coefficients" according to field observation of its regrowth. Evaporation from bare soil preceding the maize crop was calculated according to the recommendation of Doorenbros and Pruitt (1977) using a kc-value
of 0.665. Also during intermediate times with sparse crop-cover soil evaporation was assumed. The kc value, however, was reduced as cover of crop-residues prevented excessive soil evaporation. For those times, interpolated crop coefficients of prior and following
crop (last and first kc values) were used.
Cumulative crop evapotranspiration (including evapotranspiration of regrowing fallow
vegetation) over the two years was 502 mm lower than the cumulative potential evapotranspiration (Penman-FAO, kc=1; Figure 16). However, crop evaporation as calculated
above is, per definition, only achieved with well-watered crop-stands and, thus, is considered as the 'potential' crop evapotranspiration. Applying a soil water model, where potential crop evapotranspiration is considered, it will be shown that under field conditions 'actual' crop evapotranspiration, especially within the dry season, is highly depending on soil
water availability and does not reach potential crop evapotranspiration.
3000
Cumulative ET [mm]
2500
1997:
1998:
Potential ET
1496 mm
1458 mm
Actual ET
1411 mm
n.d.
Potential ET crop
1175 mm
1277 mm
Potential ET-Piche
2000
Actual ET
1500
Potential ETcrop-Piche
Potential ET
1000
Potential ETcrop
500
0
1.1.
1.3.
1.5.
1.7.
1.9.
1.11.
1.1.
1.3.
1.5.
1.7.
1.9.
1.11.
1.1.
1997
1998
Figure 16: Cumulative evapotranspiration of the fallow vegetation over the two-year observation period according to the Bowen ratio energy balance (actual ET), the Penman-FAO method (potential ET, kc=1) and,
when considering the cultivation sites, including crop coefficients for maize, beans, cassava and regrowing
fallow vegetation (potential ETcrop, assuming well watered crops; dotted gray lines according to the PenmanPiche method)
Figure 16 also shows the cumulative actual evapotranspiration obtained with the Bowen
ratio energy balance method for the measuring period of one year until 29th of March
1998. It was assumed that actual ET would equal potential ET (Penman-FAO) within the
76
5.1.2 Potential and actual evapotranspiration
first three months of 1997 as could be found for the first three months of 1998 (paired ttest of daily actual and potential data). Considering only the measuring period of one year
(April 1997 to March 1998), potential evapotranspiration reached 1533 mm and actual
ET amounted to 1426 mm, thus ETa being 106 mm lower than potential ET. The ratio of
actual ET to potential ET, the mean kc value for the fallow site, was 0.93 as already
shown in Table 12.
For those periods, when actual evaporation data could be determined, those data were
used in the soil water model of the fallow site, whereas potential ET (Penman-FAO, kc=1
and Penman-Piche) was used for the remaining time. Under the above made assumption,
for 1997 a full data set of actual evapotranspiration was available, whereas 1998 was
represented through potential evapotranspiration.
Bowen ratio energy balance measurements are widely used to predict actual evapotranspiration. Sources of error for this method are related in the first place to inaccurate temperature determination (above all wet bulb temperature). Violation of premises of the
method such as equality of exchange coefficients (as described in the Appendix) or a not
equilibrated boundary layer due to insufficient fetch, may add to error. For further details
see Fuchs and Tanner (1970), Sinclair et al. (1975), Ohmura, (1982), Bernhofer (1992),
Stannard (1997). As a rule, for ideal conditions Foken et al. (1997) proposed an accuracy
of the Bowen ratio energy balance method of ± 10 % of every measured value. Applying
this degree of accuracy, the annual actual evapotranspiration (1426 mm, April to March)
of the fallow vegetation range between 1283 and 1569 mm.
For additional details on reference crop evapotranspiration including the PenmanMonteith equation and the FAO-Doorenbros&Pruitt approach, it is referred to Allen et al.
(1996), Smith et al. (1996) and Pereira et al. (1999).
77
5.1.3 Soil water movement
5.1.3
Soil water movement
Model adjustments
For the flow equation, the parameters describing space and time discretization, boundary
conditions and initial distribution of the pressure head within the soil profile were set as
mentioned in the methods chapter (page 36 ff). The removal of vegetation and therefore
missing transpiration at the end of November and during December 1996 on the cultivation sites caused a rather moderate desiccation of those soils compared to the soil of the
fallow site. Consequently, initial vertical pressure head distributions on the cultivation
sites and on the fallow site were different (Figure 17). On the latter pressure head in the
topsoil eventually reached –2500 cm. Only at soil depths greater than 500 cm initial
conditions at both sites were comparable.
Pressure head [cm]
0
-500
-1000
-1500
-2000
-2500
0
-100
-200
Depth [cm]
-300
-400
initial condition
'fallow'
-500
initial condition
'cultivation site'
-600
estimated intitial
conditions for
1996-modelling
-700
-800
-900
-1000
Figure 17: Estimated initial pressure head distribution for the modeling procedure of 1996 and resulting
distributions of pressure head within the soil profile at the end of year 1996 for the fallow and for the cultivation sites
Initial soil hydraulic properties expressed as Van-Genuchten parameters and fitted on the
basis of the laboratory soil-water retention data were modified to adjust modeled and
measured pressure head dynamics of the three study sites. Depending on the soil depth,
modifications (fitting) of the parameters θs, Ks and l to a certain degree were necessary
(Table 18).
78
5.1.3 Soil water movement
Table 18: Initially set and adjusted Van Genuchten parameter for soil hydraulic property (initial = initially
set; adj. = final adjustment)
Site/depth
n
--- Ks --initial
adj.
-1
--- [cm d ] ---
--- l --initial
adj.
------ [-] -----
---- θs ---initial
adj.
3
-3
-------- [cm cm ] ---------
[cm ]
[-]
Site 1
0-22.5 cm
22.5-45 cm
45-75 cm
75-105 cm
105-1000 cm
0.034
0.081
0.133
0.119
0.119
0.468
0.445
0.440
0.440
0.35
0.35
0.35
0.35
0.35
-
0.133
0.070
0.059
0.137
0.063
1.618
1.762
1.718
1.466
1.684
498
318
188
205
125
6
7
20
-
-1.4
-1.4
-1.4
-1.4
-1.37
-1
-1
-1
-
Site 2
0-22.5 cm
22.5-45 cm
45-75 cm
75-105 cm
105-1000 cm
0.114
0.156
0.154
0.136
0.136
0.450
0.450
0.443
0.471
0.35
0.35
0.35
0.35
0.35
-
0.074
0.138
0.065
0.054
0.057
1.595
1.522
1.663
1.752
1.900
91
99
130
199
125
6
6
15
-
-1.4
-1.4
-1.4
-1.4
-1.0
-1
-1
-1
-
Fallow site
0-22.5 cm
22.5-45 cm
45-75 cm
75-105 cm
105-1000 cm
0.115
0.129
0.160
0.135
0.119
0.421
0.413
0.427
0.463
0.35
0.35
0.35
0.35
0.35
-
0.070
0.111
0.059
0.059
0.067
1.762
1.515
1.766
1.878
1.578
200
161
137
216
99
6
7
20
-
-1.4
-1.4
-1.4
-1.4
-1.37
-1
-1
-1
-
θr
α
-1
No adjustments could be made for 0-22.5 cm soil depth, as no pressure head within this
profile section was measured. Furthermore, for the profile section 105-1000 cm the VanGenuchten parameters were not modified. Adjustments were realized by modifying the
scaling factors, αθ and αK (Table 19).
Table 19: Initially set and adjusted scaling factors (αθ and αK) for the soil profiles within 105 and 1000 cm,
at soil depths, where comparable (measured vs. modeled) data were available
Site/depth
αK
αθ
initial
adj.
initial
adj.
--------------- [-] ---------------
Site 1
120 cm
180 cm
240 cm
300 cm
0.94
1.04
1.05
0.97
0.94
1.04
1.05
0.97
1.10
1.65
0.58
0.68
0.40
1.65
0.90
0.68
Site 2
120 cm
180 cm
240 cm
300 cm
1.08
0.98
0.95
0.99
1.08
0.98
0.95
0.99
0.57
1.95
0.71
0.77
0.70
1.95
1.20
1.00
Fallow site
120 cm
180 cm
240 cm
300 cm
400 cm
500 cm
600 cm
0.93
1.03
1.02
0.99
0.93
0.97
1.12
0.93
1.03
1.02
0.99
0.93
0.97
0.94
1.41
1.86
1.06
0.97
0.50
0.66
0.54
1.15
1.86
1.32
1.00
0.71
1.00
0.71
79
5.1.3 Soil water movement
The saturated water content underwent major modification: While fitting the Van Genuchten parameter, it became obvious that heavy precipitation events affected deep soil layers in the model to the same extent as measured in situ only, when the effective water
content had previously been diminished in the model settings. Therefore, θs was set to
0.35 as the best fit for all soil depths, which for some depths meant a reduction of more
than 0.1. This value was suitable also for the 105-1000 cm segment, which was additionally supported by the fact that no further fitting of αθ was necessary (except at
600 cm depth on the fallow site).
Laboratory measurements of θs (= total porosity) with 100 cm3-soil-samples certainly do
not account for air-entrapments that are very often encountered in the field. And, with
bulk densities of the soil below 1 m ranging from 1.5 to sometimes more than 1.8 g cm-3,
porosity is also reduced. These two factors in combinations caused a reduction in θs to a
value suitable in the modeling procedures. Buttler and Riha (1992) also had to reduce
the saturated θs values obtained in the laboratory, when transferring them to field conditions, to appropriately describe the water flux of a maize-cultivated Oxisol of CentralBrazil. According to them, the assumption that saturated water content in the field would
equal total porosity was not valid for this soil. Finally, they reduced laboratory θs values by
30 % to obtain congruent simulated and measured water contents in the field. "Satiated"
(=field saturated) water contents never exceeded 75 % of total porosity. Additionally,
however, they found an initial increase of satiated water contents, when bulk density was
rising from 1.00 to 1.22 g cm-3. This was attributed to a loss of macroporosity and thus
an increased fraction of the mesopores containing water under satiated conditions. A further increase in bulk density under comparable conditions, however, yielded lower water
contents.
Klinge (1997) using a similar inverse modeling approach to describe the water movement
in soils of the Eastern Amazon region near Belém could not use averages of saturated
water content obtained from laboratory water retention curves, but had to reduce them.
Theoretical Rosetta neural network predictions (Schaap et al. 1998) of the saturated water content (actually not used in this study) based on textural classes of the studied soils
(loamy sands to sandy clay loams) and on a bulk density of 1.5 g cm-3 never reached θsvalues exceeding 0.4. Rising bulk density to 1.8 g cm-3, which was encountered in the
deeper soil profile, diminished θs additionally to as low as 0.3. Apparently, it is necessary
to reduce laboratory-determined saturated water contents if they are to be for soil water
movement assessments.
The saturated soil hydraulic conductivities (Ks) of the upper 105 cm soil depth also were
80
5.1.3 Soil water movement
diminished. Initial Rosetta neural network suggestions were reduced by in some cases
exceeding a factor of 20. Neural network prediction thus even failed to give rough estimates. The drop of hydraulic conductivity at lower soil depth is explainable, when considering the structure of the soil. The soil structure, influenced by (clay-) aggregation and its
stability, plays an important role in determining hydraulic conductivity (Bouma and Anderson, 1973). Soils of the study region often behave as so-called ‘pseudo-sands’, characterized by aggregation of primary clay particles to sand-like microaggregates. Despite a
considerable percentage share of clay, hydraulic conductivity of these soils would be
similar to those of sandy soils (depending on the proportion of those sand-like aggregates). Table A-1 in the Appendix gives the clay content obtained by dispersing the soil in
1N NaOH solution and the so-called 'natural' clay content, which was obtained by using
solely water for dispersion. Indeed, flocculation, the percentage of microaggregated clay
to total clay content, reached 100 % in the deeper soil layers where conductivities were
considerably increased as a result. At 120 cm depth under sites 1 and 2, where flocculation was still quite low (28 % and 61 %), αK had to be adjusted to 0.4 and 0.7, respectively, to reduce the reference hydraulic conductivity by these factors. This was not the
case for the fallow site, where at 120 cm soil depth flocculation already reached 100 %.
On site 1, however, hydraulic conductivity at 180 cm soil depth could not be explained by
flocculation alone, and it remains unclear, if conductivities of the uppermost soil layer (022.5 cm), where no adjustment could be made, are equally affected by microaggregation.
The pore-connectivity parameter l was adjusted to a lesser extent. Beginning with a value
of –1.4 based on latest results of the Riverside Salinity Laboratory working-group (Van
Genuchten et al., 1999), l received a value of -1.0 above 105 cm, as well as at greater
depth on site 2, whereas –1.37 was suitable for site 1 and the fallow site below 105 cm.
Appropriate values of l remain subject of debate even in recent publications (Table 20).
While earlier publications left l at 0.5 as proposed by Mualem (1976) as best-estimate
(Van Genuchten, 1980; Dane & Hruska, 1983; Van Genuchten & Nielsen, 1985; Kool et
al., 1987), later studies noted that l should be kept as an 'experimental unknown'. Certain correlations between l and other soil-physical parameters have been detected
(Vereecken, 1995) and it was soon clear that l might comprise a wide range including
also negative values as Mualem (1976) already had assumed.
81
5.1.3 Soil water movement
Table 20: Pore connectivity value l and its range cited in literature
Publication
Mualem, 1976
Wösten & Van Genuchten, 1988: all
coarse textured
medium textured
fine textured
----------------- l ----------------best fit/mean
range
Data set
n
45
0.5
197
105
43
49
-2.3*
0.22*
-2.47*
-7.64*
Schuh & Cline, 1990
75
0.63 **
Yates et al., 1992
36
0.39
Schaap et al., in revision
235
-1.0
-1.0 to 2.5
#
§§
-9.38 to 0.83
§§
-0.32 to 0.83
§§
-4.59 to –0.54
§§
-9.38 to –5.5
-8.73 to 14.8
-5.09 to 5.69
+
see text
** geometric mean
+ five values greater than 32 are not considered
l initially set and not independently fitted
of mean values of different soil groups
* weighted mean
#
§§ range
Earlier opinion held that Sel in equation 24 is a reduction factor that accounts for pore
discontinuity and tortuosity, and therefore should not exceed 1. This led to the assumption that negative values for l are physically not feasible. This assumption has to be reconsidered on the basis of the latest findings. Thus l should be treated 'solely' as an empirical parameter. One of the most extensive studies on the prediction of soil hydraulic
parameters focusing on the pore-connectivity parameter l and based on the Mualem-VanGenuchten approach was done by Schaap et al. (in revision). Based on a data set of 235
different samples, they claimed an optimum value for all samples of -1.0, with the databasis comprising a wide range. Splitting up the database according to the textural groups,
on the average all textural groups in their study had negative l–values, with lowest values
for loam and sand. Only l=-1 for sandy soils was statistically different from the original
Mualem (1976) value of l=0.5. In terms of best-fitted hydraulic conductivity (quantified
with the root mean square error), the variability of the other textural groups was high, with
mean l values not significantly different from 0.5.
Vogel and Císlerová (1988; see Appendix) extended the Mualem-Van-Genuchten approach to account for a pressure head air-entry value < 0 cm. Those extensions, however,
do not alter the absolute value of l (Schaap, personal communication). Thus, the poreconnectivity values adjusted for the water model settings of our study, which do include
an air-entry value of –2 cm, are comparable with the results of Schaap et al. (in revsion)
regarding sandy soils.
The graphs of the adjusted soil-water retention curves and the hydraulic conductivity
used for the model are given in Figure 18 and Figure 19.
82
5.1.3 Soil water movement
0.35
Site 1
0.3
0-22.5 cm
22.5-45 cm
Water content [-]
45-75 cm
0.25
75-105 cm
105-1000 cm
0.2
0.15
0.1
0.05
0
0.35
Site 2
0.3
0-22.5 cm
22.5-45 cm
Water content [-]
45-75 cm
0.25
75-105 cm
105-1000 cm
0.2
0.15
0.1
0.05
0
0.35
Fallow
Water content [-]
0.3
0-22.5 cm
22.5-45 cm
45-75 cm
0.25
75-105 cm
105-1000 cm
0.2
0.15
0.1
0.05
0
-1
- 10
-100
Pressure head [cm]
Figure 18: Soil water retention curves of the three experimental sites
83
-1000
-10000
5.1.3 Soil water movement
1000
0-22.5 cm
22.5-45 cm
10
45-75 cm
1
75-105 cm
0.1
0.01
45-75 cm
1
75-105 cm
0.1
0.01
0.001
- 10000
Cultivation
Site 2 site 2
105-1000 cm
10
αK=1.95
1
αK=0.70
0.1
0.01
0.0001
-1
-10
- 100
-1000
- 10000
-1
Pressure head [cm]
Hydraulic conductivity
100
0-22.5 cm
10
-1
22.5-45 cm
45-75 cm
1
- 10
- 100
- 1000
Pressure head [cm]
1000
Fallow
75-105 cm
0.1
0.01
0.001
-10000
Fallow
100
105-1000 cm
10
[cm d-1]
1000
[cm d ]
- 100
- 1000
Pressure head [cm]
0.001
0.0001
Hydraulic conductivity
- 10
100
[cm d-1]
Hydraulic conductivity
22.5-45 cm
10
[cm d ]
-1
1000
0-22.5 cm
-1
Hydraulic conductivity
- 10000
Cultivation
Site 2 site 2
100
αK=0.40
0.01
0.0001
1000
αK=1.65
0.1
0.0001
- 100
- 1000
Pressure head [cm]
105-1000 cm
1
0.001
-10
Site 1
10
0.001
-1
Cultivation site 1
100
[cm d-1]
Site 1
Hydraulic conductivity
Cultivation site 1
100
[cm d-1]
Hydraulic conductivity
1000
αK=1.86
1
α K=0.71
0.1
0.01
0.001
0.0001
0.0001
-1
-10
- 100
-1000
Pressure head [cm]
- 10000
-1
- 10
- 100
- 1000
Pressure head [cm]
Figure 19: Hydraulic conductivity in relation to the pressure head of the three experimental sites; for 1051000 cm (right side) additionally the 'scaled range' of K(h) is given (through lowest and highest values of αK
from Table 19)
Default values for h50 and p of the root water uptake function, αr(h), are suggested to be
–700 cm and 3, respectively (Van Genuchten, 1987). These values might be interpreted
as averages for most crops, but were generally subjected to adjustment in the present
study. In fact, for maize as the first crop on site 1 and site 2, these default values proved
84
-10000
5.1.3 Soil water movement
to match, while for cowpea and cassava a higher h50-value of –450 cm along with a pvalue of 5 for both crops showed the best curve-fitting results. For the fallow vegetation a
quite low h50-value of -1200 cm and p of 6 was adjusted (Figure 20). Lower h50-values
generally indicate a higher adaptation of the vegetation to resist drought through additional soil-water depletion. The value of the variable p influences the shape of the root
water-uptake function. Here, high values in general lead to a more abrupt and faster decline of αr with decreasing h.
1
Maize
Cowpea, Cassava
0.8
Fallow vegetation
αr
0.6
0.4
0.2
0
0
-500
-1000
-1500
-2000
Pressure head [cm]
Figure 20: Root water uptake function of the four different vegetation types (marked are the h50-point of
each curve)
Thus, the root water uptake function of the fallow vegetation could inhibit full (potential)
transpiration only in periods of greater soil water desiccation, which was the case only in
the dry season. The transpiration of the agricultural crops proved to be more sensitive to
light water stress situations, e.g. after a few days without precipitation.
Root-growth parameters for the two cultivation sites were kept the same with the exception of the maximum rooting depth of cassava (Table 21). On site 1 the desiccation process due to transpiration of vegetation during the dry season in 1997 affected deep soil to
a higher extent compared to site 2. Thus, the maximum rooting depth of cassava had to
be extended to 2.3 m. Compared to these results, the maximum rooting depth of maize
and cowpea were rather low. Grown in times of frequent precipitation those crops were
not dependent on the soil-water storage at greater depth.
85
5.1.3 Soil water movement
Table 21: Root growth parameter for the root-growth scenario on the two cultivation sites according to the
Verhulst-Pearl logistic growth function
Vegetation
Initial rooting
depth, L0
[cm]
Max. rooting
depth, Lm
[cm]
Growth rate,
r
-1
[d ]
Start rootgrowth
[date]
End rootgrowth
[date]
Maize
0.01
100
0.335
30/1/97
8/5/97
Cowpea
0.01
62
0.476
29/5/97
4/8/97
Cassava
0.01
230/185*
0.298
4/8/97
25/6/98
160
0.071
26/6/98
-
Fallow
0.1
*on site 1 and site 2, respectively.
However, it remained unclear, if really cassava did reach such a rooting depth or if the regrowing fallow vegetation caused deeper soil water depletion. Therefore, soil water desiccation of the cultivation sites generally has to be considered as the sum of waterconsumption of the cultivated crop and the regrowing fallow vegetation. It can be assumed, that fallow the vegetation's share on water consumption is small initially, but increases with the length of a regrowth-period.
The growth rate (r in Table 21) was adjusted according to the total growth-duration of
each crop. With these growth rates the maximum rooting depth was approximated after
about one-half of the total season. Root growth of the regrowing fallow vegetation, however, was slower. According to its growth rate, maximum rooting depth was reached at the
end of year 1998. This is based on a simplistic approach, assuming that the regrowing
fallow vegetation would start with a minimum rooting depth and that the growth would
underlie a logistic growth function. Earlier studies already showed that the deepextending rooting system of a fallow vegetation was not significantly reduced during a cultivation phase of two years (Sommer, 1996). This means, that roots of a fallow vegetation
more likely re-sprout (simultaneously over the rooting zone) rather than regrow (from the
top of the soil profile downwards). Results from the modeling procedure using a 'regrowing' fallow vegetation, however, proved to be a good approximation.
Root-mass density could be considered in the modeling procedure of the fallow site using
data of intensive earlier field studies (Sommer, 1996). The root mass density was modified to adjust measured and modeled pressure head dynamics in times of (deep) soil water depletion (Figure 21). The adjustment only led to some correction of vertical root distribution between 40 cm to 70 cm soil depth, and to a smaller extent also between
120 cm to 150 cm, but still within the range of the measured distribution. The principal
character of root distribution remained, with a sharp decline between 40 to 60 cm depth
86
5.1.3 Soil water movement
and a smaller deep-extending fraction of roots reaching 6 m soil depth.
-3
0
1
Root mass density [mg cm ]
3
4
5
2
0
-100
Depth [cm]
-200
-300
Traditional land use
Adjusted
-400
- - - - Cumulative %-distribution
-500
-600
0
20
40
60
80
[%] 100
Figure 21: Root mass density under the fallow vegetation according to earlier studies (='Traditional land
use', i.e. weighted mean of n=60, bares denote the SE; Sommer, 1996) and after adjustment in the modeling procedure, as well as the cumulative percentage distribution of root biomass (secondary x-axis at the
bottom).
Integration of the modeled root distribution over the 6 m profile resulted in a root biomass stock of 20.92 t ha-1 6m-1. Fully 33.5 % of the total root biomass was located below
0.5 m depth, still 27.1 % below 1 m and 21.5 % below 2 m depth (dotted line in
Figure 21).
After adjustment (of all model-settings) the measured and modeled pressure head dynamics over two years of cropping activities (1997-1998) reached a good fit (Figure 22,
Figure 23 and Appendix, Figure A-9 and Figure A-10).
87
5.1.3 Soil water movement
1.1.
0
1.2. 1.3.
1.4. 1.5.
1997
1.6. 1.7.
1.8.
1.9. 1.10. 1.11. 1.12. 1.1.
1.2. 1.3.
1.4. 1.5.
1998
1.6. 1.7.
-100
-200
Pressure head [cm]
-300
-400
-500
-600
-700
-800
-900
Site 1, 30 cm
Model
-1000
Figure 22: Measured and modeled pressure head dynamic at 30 cm depth on site 1 over the two-year observation period
88
1.8.
1.9. 1.10. 1.11. 1.12. 1.1.
5.1.3 Soil water movement
1.1.
0
1997
1.5.
1.7.
1.3.
1.9.
1.11.
1.1.
1.3.
1998
1.5.
1.7.
1.9.
1.11.
1.1.
1.9.
1.11.
1.1.
1.3.
1998
1.5.
1.7.
1.9.
1.11.
1.1.
-100
-200
Pressure head [cm]
-300
-400
-500
-600
-700
-800
-900
Site 1, 120 cm
Model
-1000
1.1.
0
1.3.
1997
1.5.
1.7.
-100
-200
Pressure head [cm]
-300
-400
-500
-600
-700
-800
-900
Site 1, 300 cm
Model
-1000
Figure 23: Measured and modeled pressure head dynamics at 120 cm and 300 cm depth on site 1 over
two-year observation period
Difficulties regarding model adjustment were of same nature on all sites. On the cultivation sites additional problems occurred in the phase of establishment of a new crop and
in transitional periods with mature crops, where the limited information about rooting
89
5.1.3 Soil water movement
depth and actual transpiration in addition to prescribed (and thus inflexible) root distribution led to less agreement. Interruption of periods of intensive soil water depletion by
heavy precipitation events, and vice-versa, were difficult to model on all sites. This was
likely caused by imprecise soil physical characterization (i.e. parameter). Soil hydraulic
properties (θ(h) and K(h)) were described with the Mualem-Van-Genuchten approach, but,
in fact, these steady-state relationships never fit 100 % the natural conditions. In case of
unsteady behavior of θ(h) or K(h), a tabular input of these parameters would be more appropriate. On the other hand, the modeled pressure-head dynamics systematically
showed a delayed response (increasing with soil depth) to re-wetting or desiccation,
which is related to slower conduction of water compared to field conditions. Improvement
could be achieved by further reducing the saturated water content θs and thus diminishing the effective water content of the soil, but an exact response agreement at deeper
soil depths is still lacking.
In this context it is not clear, to what extent hysteresis-effects, which were not considered
in the model procedures, are important regarding the soil water movement within the profile. Hysteresis phenomena rarely have been incorporated into modeling studies, despite
the fact that algorithms exist to solve the related flow equations (Parker & Lenhard,
1987; Lenhard & Parker, 1987). Kool and Parker (1987), based on the Van Genuchten
approach, suggested the use of two different values of αvG for the main wetting (w) and
drying (d) water-retention-curve. As a good approximation they proposed αvG(w)=2 αvG(d),
when exact data are lacking. While Vereecken et al. (1995) found an impact of hysteresis
on solute transport processes (as did Jones & Watson, 1987, and Russo et al., 1989), a
newer study conducted by Mitchell and Mayer (1998) showed that hysteresis had only
minor effects in this regard. Though evidence on the importance of hysteresis for water
dynamics is controversial, it seems a likely explanation for the retarded response of the
model to wetting or drying events in the present study. To include this effect into future
modeling, one could follow the above-mentioned relationship of Kool and Parker (1987).
However, it is doubtful that such adjustments would really have an effect on the annual
soil water balance. A whole year is starting and ending with a deeply desiccated soil profile, for which a delay of one or two days in relation to water movement and leaching is
rather insignificant.
Soil moisture distribution within the soil profile was determined gravimetrically several
times throughout the two years on the cultivation sites as well as on the fallow site. As
part of the validation of the final soil water model settings these results were compared
with modeled soil moisture contents.
90
5.1.3 Soil water movement
On the 6th of November and the 10th of December 1997, due to heavy desiccation of the
soil profile under the fallow vegetation (fallow site), the pressure head of the upper 5 m
soil profile had exceeded the measuring range of the tensiometers (see Appendix, Figure A-10). Thus, no adjustment of the model to the field observation could be made and it
had to be assumed that parameter settings would fit for this period. Results of gravimetrical determination of soil water content at these days matched well with modeled
data (Figure 24).
Water content [-]
Water content [-]
0
0.05
0.1
0.15
0.2
0
0.05
0.1
0.15
0.2
0
-100
Depth [cm]
-200
-300
-400
-500
Gravimetric, 6/Nov/1997
Gravimetric, 10/Dec/1997
Model, 6/Nov/1997
Model, 10/Dec/1997
-600
Figure 24: Measured [r32]and modeled water content of the soil profile of the fallow site on the 6th of November and on the 10th of December 1997 (bars denote SE; n=2)
Differences between modeled and measured data of the considered soil depths (0-6 m)
of the two days were insignificant (paired t-test). Stronger deviations between modeled
and measured data were present above 60 cm, where the model systematically underestimated real desiccation. This was also true on the 11th of May and on the 17th of June
1998, when similar comparisons were made on the cultivation sites: differences were
only insignificant, when data of soil water content at 30 cm soil depth (first soil depth,
where measurements were taken) were not included into the statistical analysis (paired ttest: p=0.23 including values from >30 cm to 6 m; n=66). This suggests that in models
with a root growth term (cultivated crops), model-prescribed root distribution only crudely
reproduces real root distribution, which affects also distribution and dynamics of water
content in the upper soil profile. An updated model version thus, should include more
flexibility in this regard. It could consider the exponential distribution proposed by Gale
91
5.1.3 Soil water movement
and Grigal (1987) or by Raats (1974). Raats' distribution also proved to accurately describe the uppermost vertical root distribution of fallow vegetation even under quite different agricultural management practices, as was shown by Wiesenmüller (1999).
The highest gravimetric water content was measured on the 17th of June 1998 at 30 cm
depth on the mulched plot of site 1 reaching 0.286. The corresponding pressure head
was –22.5 cm and the corresponding modeled water content was 0.280 (see Appendix,
Figure A-11 for all gravimetrically determined water contents).
Water balance of the fallow vegetation
Results of the soil water model of the fallow vegetation were distinguished in the two
consecutive years, as their water regime differed noticeably. The extreme dry season of
1997 was considered separately (Table 22).
Table 22: Water balance of the fallow site in 1997 and 1998 according to results of the soil water model
and comparable annual micro-meteorological evapotranspiration (P = gross precipitation, I = Interception;
Pn = net precipitation; Esoil = soil evaporation; Tmodel = modeled transpiration; D10m = Drainage at 10 m
depth; ET = micro-meteorological evapotranspiration: 1997 ≅ actual, 1998 ≅ potential)
Site/vegetation
P
I
Pn
E soil
Tmodel
D10m I + Tmodel
ET
---------------------------------------- [mm] ---------------------------------------------------
Fallow vegetation
1997
2104
139
1965
-
1131
897
1270
1411
1998
Sum
2545
4649
200
339
2345
4310
-
1280
2411
842
1739
1480
2750
1458
2869
148
14
134
-
288
79
302
576
22.8.97 - 8.1.98
In 1997, precipitation (2104 mm) could not balance the modeled evapotranspiration
losses (I + Tmodel, 1270 mm) in combination with drainage losses at 10 m (897 mm). Contrarily, precipitation of 1998 exceeded the sum of drainage and evapotranspiration by
223 mm, leading to an overall positive balance of +160 mm water stored in the soil profile after the two years.
In 1998, modeled evapotranspiration (I + Tmodel) and micro-climatic (potential) ET were in
remarkable agreement and differed only by 22 mm. Potential ET was used as surface
boundary condition, and the root water uptake function of the model (Figure 20) reduced
transpiration of the vegetation in case of water stress. In times of frequent precipitation
part of the micro-meteorologically obtained evapotranspiration is evaporated interception, while the soil water model only calculates transpiration, and consequently intercep92
5.1.3 Soil water movement
tion has to be added to arrive at actual evapotranspiration of the vegetation. The moderate dry season of 1998 led to only minor reduction of potential evapotranspiration. In the
rainy season and the transitional period the modeled transpiration alone reached potential ET so that adding interception led to slight overestimation of modeled ET.
In 1997, modeled evapotranspiration (Tmodel + I) was 141 mm less than micro-climatic ET.
This difference was caused by the reduction of micro-climatic ET through the root water
uptake function of the model due to soil desiccation in the dry season. This reduction was
unexpected because in this year actual ET entered the model as surface boundary condition, which already accounts for the reduction of potential transpiration of vegetation under water-stress. Considering the period of 22nd of August 1997 to 8th of January 1998,
actual evapotranspiration (576 mm) was reduced in the soil water model by 50 % (Table
22). Including interception (14 mm), evapotranspiration of the soil water model in comparison to micro-meteorological ET was still short 274 mm. This gap might partly be explained by early-morning-dew, which especially within the dry season very often caused
complete wetting of the whole vegetation canopy. During this 140-day period, assuming a
canopy storage capacity of 1 mm, up to 140 mm dew-evaporation was not included in the
soil water model but detected with the micro-meteorological measurements. Remaining
differences might be caused by uncertainties in actual evapotranspiration measurements
and partially also through underestimated topsoil desiccation in the model (about 26 mm
between 0 and 120 cm soil depth in Figure 24).
In the period of 22nd of August 1997 to 8th of January 1998, according to the soil water
model 154 mm was extracted out of the soil water reservoir (Tmodel minus Pn). Compared
to the soil water status in the rainy season (9th of April), the soil profile on the 22nd of
August was already drained to a large extent and soil water fluxes were widely reduced
(Figure 25).
93
5.1.3 Soil water movement
0.1
0.15
0.2
-0.2
0
0
-100
-100
-200
-300
Depth [cm]
-1
Water content [-]
0.25
0.3
0.35
-400
-500
-600
9.4.1997
(max. soil
water
storage)
-200
22.8.1997
(beginning
dry
season)
-400
-300
-500
-600
8.1.98
(min. soil
water
storage)
-700
-800
-0.7
Water flux [cm d ]
-1.2
-1.7
-2.2
-700
-800
-900
-900
-1000
-1000
Figure 25: Modeled soil water content and soil water fluxes over the 10 m profile under the fallow vegetation at three different times reflecting maximum soil water storage (9/4/1997), beginning dry season
(22/8/1997) and minimum soil water storage (8/1/1998); negative values designate downward oriented
fluxes
In the period considered water drainage of the soil profile at 6 m soil depth amounted to
1 mm and thus almost stagnated, while at the lower boundary at 10 m soil depth still
79 mm water was released. Almost one half (73 mm) of the total 154 mm transpired water were taken out of the soil profile below 3 m depth, below 0.9 m this was even 73.9 %
(Table 23).
Table 23: Soil water extraction within the period of 22nd of August 1997 and 8th of January 1998 and for
the year 1997 and 1998 under the fallow vegetation considering different soil layers; percentage values
are related to extraction of 0-6 m
Soil depth
22/8/97 – 8/1/98
[mm]
[%]
1997
[mm]
[%]
1998
[mm]
[%]
0 – 0.9 m
40
26.1
730
64.5
853
66.6
0.9 - 1.8 m
15
9.9
114
10.1
117
9.2
1.8 - 3 m
26
16.8
85
7.5
106
8.3
3-6m
73
47.2
202
17.8
204
15.9
6 - 10 m
79
897
842
Root water
uptake
Drainage
Annual uptake below 0.9 m in 1997 and 1998 was 400 and 427 mm, respectively. The
annual vertical percentage distribution of root water uptake strongly reflected the adjusted vertical root distribution (Figure 21) as it directly determined the percentage uptake in the model settings (through the normalized uptake distribution, b(z)). In times of
94
5.1.3 Soil water movement
soil water saturation, these model settings determined the vertical percentage distribution of root water uptake. Bias only occurred in times of water stress, when the soil water
of the profile gradually was depleted (as was true for the considered period in Table 23).
After 680 mm rainfall and 178 mm evapotranspiration since 8th of January 1998, the rewetting front reached 6 m soil depth only on the 28th of February 1998. The bottom
boundary (10 m) finally was reached one month later on the 29th of March 1998
(999 mm P and 269 mm ET since 8th of January).
The difference in the maximum and minimum amount of water stored in the soil profile of
0-6 m between 9th of April 1997 and 8th of January 1998 was 563 mm, or 94 mm per
one meter soil profile on average (Figure 26). On the 9th of April corresponding pressure
head of the profile (mean value: -44 cm) were close to the classical field capacity value of
–60 cm. Also 94 mm m-1 is only slightly higher than plant available water (amount of water between pF 1.6 and pF 4.2), which would equal 91 mm m-1.
Though upward oriented fluxes within the dry season were calculated to reach
depth greater than 6 m depth (Appendix, Table A-3), the soil water below 6 m did not
really contribute to root water uptake and was only subjected to replenishing and drainage (Figure 26). The soil water storage of 6-10 m depth varied annually between 602 mm
and 929 mm.
Soil water storage [mm]
2500
2000
1500
0-6 m profile
1000
500
6-10 m profile
0
1.1. 1.3. 1.5. 1.7. 1.9. 1.11. 1.1. 1.3. 1.5. 1.7. 1.9. 1.11. 1.1.
1997
1998
Figure 26: Soil water storage dynamics under the fallow vegetation in 1997 and 1998 separated into 0-6 m
depth and 6-10 m depth
Annual fluctuation of the soil water storage of 0-6 m depth was stronger (824-1489 mm),
as the roots of the fallow vegetation directly depleted this part of the soil profile. The
overall maximum amount of water stored in the profile (0-10 m) was 2321 mm on the
95
5.1.3 Soil water movement
9th of April 1997; the minimum was reached 9 month later on the 8th of January 1998
(1440 mm). Root water uptake gradually reduced the amounts of water, which percolated
through the soil (Figure 27). From 4310 mm net precipitation (= 0 cm depth in Figure 27)
within the two years only 2657 mm passed 0.9 m depth, and 1741 mm 6 m depth. This
equaled the cumulative drainage at the bottom boundary of 1739 mm, including a storage change of 2 mm (beginning 1997 compared to end of 1998).
1997
1998
1.1. 1.3. 1.5. 1.7. 1.9. 1.11. 1.1. 1.3. 1.5. 1.7. 1.9. 1.11. 1.1.
0
Cumulative fluxes [mm]
-500
-1000
-1500
1000 cm
600 cm
300 cm
180 cm
90 cm
0 cm
-2000
-2500
-3000
-3500
-4000
22/8/97
8/1/98
-4500
Figure 27: Cumulative water fluxes under the fallow vegetation at different soil depths over the observation
period of two years; 0 cm depth equals the net-precipitation input
Evaporation from the canopy surface of the fallow is responsible for the difference between ET-results obtained micro-meteorologically and by the soil water model, when micro-meteorologically measured evapotranspiration is used as boundary condition in the
model. Transpiration in the rainy season is systematically overestimated in the model by
the amount of water that in the micro-meteorological approach was obtained by evaporation (intercepted rainfall). In the dry season, on the other hand, evaporation of earlymorning dew may contribute to a remarkable extent to evapotranspiration, which cannot
be detected by the soil water model. In this case, modeled ET is lower than micrometeorologically obtained ET. In the model, a desiccated soil (≅ low pressure head) via
the root-water-uptake function is diminishing high ET amounts that comprise dewevaporation. Thus, annual modeled dynamics of evapotranspiration are subjected to
stronger seasonal oscillation than those micro-meteorologically obtained. However, micro-meteorological estimates of dry-season evapotranspiration are overestimating soil
desiccation, when evaporation of dew is not considered separately and falsely added to
96
5.1.3 Soil water movement
transpiration. These uncertainties could only be overcome, when evaporation would
clearly be distinguished from transpiration and only transpired amounts would enter the
soil water model as upper boundary conditions. As evaporation, however, is proceeding
much faster than transpiration, the two processes are quite difficult to separate. On the
basis of measurements in the present study it was not possible.
Transpiration during the dry season is more reliably described with a soil water model, as
it restricts soil water desiccation to reasonable amounts. For instance, had the meteorologically obtained evapotranspiration (576 mm) in the considered period (22/8/97 –
8/1/98) been actually transpired, this would have depleted the whole soil profile to an
extent, that pressure heads would have exceeded the permanent wilting point of plants
(pF 4.2). This was not measured directly and also could not be shown with gravimetrical
soil moisture determinations (10th of December, see Figure 24).
Maintaining transpiration even at a reduced level, root penetration of deep soil layers in
the present study proved to be necessary for the evergreen fallow vegetation to survive
recurring extensive dry seasons as those of 1997. This is due to the rather low plant
available soil water (PAW) within the profile. Certainly, the calculated PAW of 91 mm m-1
is not precise, as its static definition (to equal the amount of water between pF 1.6 and
pF 4.2) rarely reflects real field conditions (Ritchie, 1981). However, the amount of water,
that is not rapidly drained within some days more likely corresponds to pressure heads
below –60 cm (pF 1.6) and therefore, 91 mm m-1 is more likely to represent the upper
than the lower level of PAW.
Deep roots and their contribution to the annual water balance of the fallow vegetation are
most remarkable. Deep soil-water uptake was already proposed by Hölscher (1995)
based on micro-meteorological measurements over a young fallow vegetation in the Bragantina region. He calculated the uptake below 1 m soil depth to amount to 322 mm between June and December 1992, and additionally assumed that only in these months
deep-soil water uptake would take place. This is less than the annual uptake measured in
the present study below 0.9 m (Table 23), but exceeds the root water uptake when only
those months would be included in the present study (about 125 mm between June and
22nd of August plus 114 mm in the considered period). Hölscher et al. (1997a), however,
also suggested the possibility of dew-evaporation (167 mm per year), which would noticeably diminish the total transpiration-share and subsequently also soil water depletion
in his calculations.
Klinge (1997) using a soil-water model detected deep soil-water uptake under a primary
forest near Belém, Brazil. His results of root water uptake (365-402 mm between 1.1 m
97
5.1.3 Soil water movement
and 5 m, i.e. 26-29 % of annual modeled ET) reached amounts comparable to those of
the present study. Although the climate of Belém normally does not show such a distinct
dry season, it did in the observation period of Klinge's study.
In comparable studies, deep soil water uptake has been proved as well:
•
Poels (1987) studied the water balance on a catchment-area scale under primary and
disturbed forest in Suriname. In his hydrological model he assumed a maximum
rooting depth to 4.5 m to explain water discharge through evapotranspiration. Evapotranspiration in the dry season was reduced by up to 50 %, as was also the case in
the present study. On the other hand, Poels (1987) also stressed the possibility that
some of the primary forest trees might have roots extracting water directly from the
ground water, which in the dry-season dropped below 10 m. This could be true also in
the present study, but a different hydrological approach has to be chosen to assess
this. There is however little evidence that a water balance of the fallow vegetation obtained in such a way would differ noticeably from that obtained in the present study.
•
Hodnett et al. (1996a; 1996b) monitored the water storage by neutron probe measurements in a soil under primary forest near Manaus/Brazil, and could show that water uptake from below 2 m soil depth must have occurred to account for evaporation
demands in the dry season. According to their long-term simulation of 27 years, annual water uptake of the soil below 2 m was on average 72 mm, reaching at maximum 254 mm. Roots could be found to a depth of 6 m (Chauvel et al., 1991).
•
Nepstad et al. (1994) even claimed a soil-water uptake from down to 8 m by a primary forest in the south of Pará. They assumed that more than 75 % (380 mm) of
transpired water was taken out of 2-8 m depth during the five-month dry season
(comparable to 73.9 % in the present study). Roots were present down to 18 m.
Though these studies provide reasonable data, comparable to results of the present
study, drainage data are weak or even missing. Balances are based on evapotranspiration measurements combined with monitoring water content of the soil profile. Results
however do not exclude the possibility that evapotranspiration of the forest during the dry
season is more strongly reduced, and more soil water is drained than actually assumed.
Actual evapotranspiration of secondary/fallow vegetation was rarely measured.
Actual evapotranspiration measurements of savanna fallow bushland in the semi-arid
tropics of the Sahel (Niger) were carried out by Wallace et al. (1990) and Kabat et al.
(1997). They found ET amounts of 4-5 mm per day in the rainy season, thus comparable
to those of the present study, when also net-radiation and soil water status are similar.
But a comparison is not really helpful due to the different vegetation types.
98
5.1.3 Soil water movement
One of the first studies in the Amazon region was that carried out by Hölscher et al.
(1997a). Applying the Bowen ratio energy balance, the annual ET (April '92 - April '93) of
2-year-old fallow vegetation in the Bragantina region was determined to be 1364 mm,
which was about 75 % of the rainfall in this period (1819 mm). The absolute amount of
ET thus matches well with our results. As annual precipitation in the present study, however, was 285 mm and 726 mm higher in 1997 and 1998, respectively, drainage rates
exceeded those proposed by Hölscher et al. (1997a). They assumed a balanced soil water store (beginning – end) and thus claimed that 455 mm, i.e. the difference between
precipitation and actual ET, must have been drained in the considered period. This is only
about half of those amounts of the present study based on the soil water balance, but
seems reasonable, since the above mentioned amount of dew-evaporation (167 mm) still
has to be added and since the 1992-93 period was exceptionally dry.
Remarkably, evapotranspiration of young fallow vegetation seems not really different
from that of mature primary forests. ET values of 1319 mm a-1 are calculated by Shuttleworth (1988) for primary forest near Manaus/Brazil (annual precipitation: 2636 mm),
Bruijnzeel (1990) gives 1430 mm a-1 (n= 11; range: 1311 – 1498 mm a-1; P: 1727 –
4073 mm a-1) as an average for selected (worldwide) tropical lowland forests and Klinge
(1997) obtained 1378 mm a-1 (P: 2669 mm a-1) for a primary forest close to
Belém/Brazil. Moreover, comparing the percentages of ET to precipitation, young fallow
(58-60 %) even exceeds primary forest (50-52 %). This is likely related to the vigorous regrowth of the fallow from remaining stumps and roots that survived the fallow period. After about 2-3 years, leaf area indices are already comparable to those of primary forest,
and thus a fully installed canopy is present.
Water balance of the cultivation sites
The water balance of the cultivation sites was different from that of the fallow vegetation
(Table 24). Drainage at 10 m soil depth in both years was about 45 % higher than that of
the fallow site and on site 1 reached 1279 mm and 1190 mm in 1997 and 1998, respectively. Already at the time of planting of cowpea at the 4th of June 1997, the drainage
rate had reached the 1997-annual amount of the fallow site. In contrast, actual (modeled) crop evapotranspiration (I + Tmodel) of the cultivation sites was lower than for the
fallow, amounting to 2006 mm (site 1) over the whole period and varying noticeably between first and second year.
99
5.1.3 Soil water movement
Table 24: Water balance of site 1 for the cultivated crops and for 1997 and 1998 according to results of
the soil water model and comparable annual micro-meteorological evapotranspiration (P = gross precipitation, I = Interception; Pn = net precipitation; Esoil = soil evaporation; Tmodel = modeled transpiration; D10m =
Drainage at 10 m depth; ETcrop = potential crop evapotranspiration)
Site/vegetation
P
I
Pn
E soil
Tmodel
D10m I + Tmodel
ETcrop
------------------------------------ [mm] -------------------------------------------------------
Cultivation site 1
Before maize
195
-
195
32
0
12
0
0
Maize
1441
52
1389
22
284
826
336
326
Cowpea
100
11
89
-
77
164
88
88
Cowpea+cassava
136
9
127
-
120
129
129
128
Cassava
1964
90
1874
-
868
785
958
1264
Fallow regrowth
812
19
793
14
475
553
494
622
1997
2104
86
2018
54
719
1279
805
1139
1998
Sum
2545
4649
97
182
2448
4467
14
68
1105
1824
1190
2469
1202
2006
1289
2428
148
9
139
-
232
119
241
592
22.8.97 - 8.1.98
The water balance of site 1 and site 2 did not really differ from each other as model settings regarding net precipitation and potential crop evapotranspiration were kept the
same and cultivation-measures were synchronized. Therefore, only slight differences in
terms of total actual crop evapotranspiration could be found, caused by the stronger
desiccation of the soil profile by deeper-rooting cassava on site 1 during the dry season
(Table 25). Also the drainage rate of site 2 was negligibly lower than that of site 1 due to
the lower hydraulic conductivity below 1.05 m soil depth on this site (see Figure 19).
Table 25: Transpiration (Tmodel) and evapotranspiration (I + Tmodel) as well as drainage at 10 m soil depth
(D10 m) of site 2; remaining parameter of the water balance did not differ from site 1 (see Table 24)
Site/vegetation
Tmodel
I + Tmodel
D10 m
Before maize
0
0
12
Cultivation site 2
Maize
284
336
796
Cowpea
75
86
156
Cowpea+cassava
113
122
111
Cassava
855
945
826
Fallow regrowth
463
482
512
1997
694
780
1195
1998
Sum
1096
1790
1193
1972
1218
2413
216
225
96
22.8.97 - 8.1.98
100
5.1.3 Soil water movement
As was expected, actual crop evapotranspiration equaled potential crop evapotranspiration only in times of good water supply, which was true for the first two crops, maize and
cowpea, and also for the intercropping-phase of cowpea and cassava. With the progressing dry season, however, mono-cropped cassava was suffering water stress, and
transpiration was reduced. In the period of 22nd of August 1997 to 8th of January 1998
actual crop evapotranspiration of site 1 was only 40.7 % (241 mm) of potential ETcrop
(Table 24). Actual ETcrop on site 2 during that time was even reduced to only 38.0 % of potential ETcrop.
In the rainy season, the re-wetting front was rapidly moving downwards. Drainage rates
during the first half of the year reached highest amounts in all considered depths
(Figure 28).
1997
1.1.
1.3.
1.5.
1.7.
1998
1.9.
1.11.
1.1.
1.3.
1.5.
1.7.
1.9.
1.11.
0
-1
-1
Fluxes [cm d ]
-2
-3
-4
-5
-6
0.9 m
1.8 m
-7
-8
-1
Fluxes [cm d ]
0
-1
-2
3m
-3
6m
10 m
-4
Figure 28: Drainage rates at different soil depths under site 1 during 1997 and 1998
101
1.1.
5.1.3 Soil water movement
While in 1997 already at the beginning of July drainage rates were drastically reduced,
this was the case in 1998 only in mid-September. In the upper soil, maximum drainage
rates were almost reaching the net precipitation rates of corresponding storm events.
However, these fluxes were dampened with depth. Already at 3 m soil depth, they rarely
exceeded 2 cm per day.
Only cassava was extracting soil water below a depth of 1.8 m and thus, the accumulated
fluxes only changed below this depth after the harvest of maize and cowpea (Table 26).
Table 26: Accumulated drainage distinguished according to the cropping sequence at different soil depths
under site 1; 0 m soil depth corresponds to the net precipitation minus evaporation10
Site/vegetation
------------------------------- Drainage [mm] ---------------------------------0m
0.9 m
1.8 m
3m
6m
10 m
Cultivation site 1
Before maize
151
95
60
16
3
12
Maize
1367
1126
1097
1070
947
826
Cowpea
89
79
99
124
158
164
Cowpea+cassava
127
6
18
38
83
129
Cassava
1874
1131
942
883
838
785
Fallow regrowth
776
344
334
380
459
553
1997
1952
1332
1281
1272
1270
1279
1998
Sum
2432
4384
1449
2781
1268
2549
1239
2510
1218
2488
1190
2469
D
The whole profile was in the process of recharging at the beginning of 1997, thus drainage rates decreased with depth. During the cropping period of cowpea, the profile was
beginning to discharge (from the top to the bottom) as indicated by the increasing drainage rate with soil depth. In the remaining cropping period parts of the profile were subject
of replenishing, others of discharge.
Taking into account annual changes of soil water storage, the accumulated fluxes of different depths could be used to calculate also the root water uptake out of the respective
soil layers according to:
root uptake = incoming water – draining water - storage change
As much as 256 mm water was taken from the soil reservoir below 0.9 m depth during
the two years under site 1 (Table 27). Cassava extracted 72 % (184 mm) of this water,
and minor parts were extracted by maize (11 %, 28 mm) and by the regrowing fallow
vegetation (17 %, 44 mm).
10
Soil evaporation in the model is directly taken from the incoming net precipitation.
102
5.1.3 Soil water movement
Table 27: Changes in the soil water store of site 1 of the marked soil layers comparing the beginning and
the end of 1997 and 1998, respectively, and the root water uptake of 1997 and 1998 out of these layers
(≅ transpiration); negative values denote a store depletion
------------------------------ Soil profile ------------------------------0 - 0.9 m
0.9 - 1.8 m
1.8 - 3 m
3-6m
6 - 10 m
Store change [mm]
Beginning to end of 1997
-32
-4
-3
1
-9
Beginning to end of 1998
64
11
11
21
28
Root water uptake [mm]
1997
652
55
13
0
0
1998
917
170
18
0
0
As expected, deep-soil water use of the crops was small compared to the fallow site
(Table 23). Only the upper most layers (0 - 0.9 m and 0.9 m – 1.8 m) were depleted to a
comparable extent by the crops. In 1998, crops even exceeded root water uptake of the
fallow vegetation from these depths. As a result, also the soil water storage did not fluctuated so strongly over the year as was the case under the fallow (Figure 29).
2400
Soil water storage, 0-10 m [mm]
Fallow
Site 1
2200
Site 2
2000
1800
1600
1400
1.1. 1.3.
1.5.
1.7. 1.9. 1.11. 1.1. 1.3.
1997
1.5. 1.7.
1998
1.9. 1.11. 1.1.
Figure 29: Soil water storage dynamics under the two cultivation sites in comparison to that of the fallow
site in 1997 and 1998
Differences between maximum and minimum amount of water stored in 0-10 m soil profile in the study period of two years were 723 mm and 657 mm under site 1 and 2, respectively, instead of 881 mm under the fallow site (see also Figure 26).
103
5.1.3 Soil water movement
Vertical root distribution of conventional crops is the subject of many publications, but
maximum rooting depth is rarely determined or considered only with regard to nutrient
uptake (e.g. by Aune & Lal; 1997).
A rapid response of maize plants to water stress by increasing root growth into more favorable (deeper) soil layers was detected by Engels et al. (1994) in a pot-experiment. Only
after 6 days of drying of the topsoil, root growth noticeably had increased between 0.8
and 1.2 m, which was the maximum rooting depth considered. Comparable data on water
extraction of maize were also reported by Cabelguenne and Debaeke (1998). In their
study, maize was grown on a silty clay soil in France and extracted most water out of the
topsoil from 0 to 0.5 m depth, but also from the subsoil to a depth of maximal 1.6 m.
Moreover, they could confirm their field measurements applying a soil water model.
Though according to these studies maize seems to have a capacity to extract water from
the subsoil, this might not be necessary for an environment without water limitations:
Ayotamuno et al. (1997) estimating crop coefficients for maize under the humid tropical
climate of Nigeria (which did not really differ from those kc already recommended in the
FAO-24 paper of Doorenbros and Pruitt, 1977) found a root zone ranging from 0.29 to
0.35 m depth. Such rooting depths were also found by Hairiah and Van Noordwijk (1986),
who stated that most of the maize roots are in the top 10 cm of soil. One meter maximum
rooting depth of maize assumed in the soil water model in the present study and subsequent soil water extraction out of this layer, thus, is comparable with results of other
studies.
The maximum rooting depth of cowpea assumed in the present study (62 cm) concurs
with observations of other publications. Soil water use was measured by Singh and Singh
(1991) considering tree different varieties of cowpea, four rates of P-fertilization and
three stages of irrigation in Northwest India. About 90 % of the soil water was extracted
out of the 0 to 0.6 m depth, about 10 % below that depth down to a maximum of 0.9 m.
Consumptive water use for the whole cropping period (exact days not given) varied between 54 mm and 67 mm, which is comparable to the modeled transpiration rate of the
present study (75-77 mm). Petrie and Hall (1992) determining water use in relation to
root distribution of cowpea in a pot experiment, found roots of cowpea reaching at the
most 1 m deep. In their 'dry-treatment' with altogether 35 days without watering, root
length density and predawn leaf water potential remained almost unaffected, whereas
soil water depletion exceeded that of millet. Cowpea roots extracted water even down to
1.35 m under well-watered conditions in a field experiment conducted in California (Turk
and Hall, 1980). However, under water stress cowpea roots reached only 0.9 m depth. In
104
5.1.3 Soil water movement
a later publication of Shackel and Hall (1983), cowpea was noticeably depleting the soil
water reservoir down to 1.5 m. On the other hand, tap root length of cowpea grown in
concrete containers in Nigeria under different frequencies of irrigation and three stages
of soil compaction reached a maximum of only 19 cm (Onofiok, 1989). These results are
congruent with measurements of Kamara (1981) obtaining tap root at maximum 12.8 cm
long for cowpea grown on a gravely clay loam Ultisol in Sierra Leone. In the latter cases
rooting depth might be restricted through the gravel layers, while in the former case container were only 20 cm high and thus eventually inhibited vertical root growth.
Rooting depth of cassava according to soil water model adjustments in the present study
reached 2.3 and 1.85 m soil depth on site 1 and 2, respectively. Cassava, therefore, was
responsible for most of the root water uptake below 0.9 m depth (besides a small part of
maize and the fallow regrowth) shown in Table 27. Though the impressive potential of
cassava to survive long dry seasons is well known (Cock, 1984), rooting patterns have
rarely been studied. Still, cassava roots are commonly assumed to 'penetrate deeper'
(Hairiah and Noordwijk, 1986). Yao and Goué (1992) studying the water use efficiency of
cassava grown on a sandy soil in Ivory Coast could show that the plant-available soil water of the first 1 m soil depth was entirely depleted by cassava roots during the dry season. Deeper soil layers were not considered. Kühne (1993) mentioned a maximum rooting depth of cassava of 1.5 m in an Alley-cropping system in South-Benin.
It remains subject of further research whether really cassava did penetrate down to 1.85
to 2.3 m as was assumed in the present study, or whether re-activated fallow-vegetation
roots were responsible for this deep-soil water depletion.
Thus, both, literature citations and the results of the soil water model show that cultivated crops can use soil water from deep soil layers. However, the quantities are far less
than those of the fallow vegetation (256 mm per two years below 0.9 m soil depth under
site 1 instead of 827 mm 2y-1 under the fallow site). This result is important for the water
dynamics, but has relevance also for the solute nutrient uptake. Assuming a close relationship between both processes, it can be assumed that the capacity to take up dissolved nutrients leached out of the upper 0.9 m of soil is higher by a factor of 3 to 4 under fallow than crops. As crop cultivation and fallow, however, are traditionally in sequence, it remains to determine to what extent regrowing fallow vegetation can absorb
nutrients from the subsoil that left the rooting zone of the cultivated crops.
105
5.2.1 Soil fertility
5.2 Nutrient balance
5.2.1
Soil fertility
The soil nutrient status was determined at the beginning (20th to 23rd of January, 1997),
after half a year (9th to 10th of July, 1997) and after 15 months of cultivation (11th to 12th
of March, 1998) on all sites.
The soil organic carbon (SOC) determined by Embrapa-Belém with the Walkley-Black wet
oxidation method differed significantly from the measurements in the Institute of Agriculture in the Tropics (IAT) in Göttingen using a elemental analyzer (paired t-test, p=0.005;
Figure 30).
7
Analyses Embrapa-Belém [mg P l-1]
Analyses Embrapa-Belém [%-C]
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
y = 1.3534x
R2 = 0.883
n=32
6
5
4
3
2
1
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0
Analyses IAT-Göttingen [%-C]
1
2
3
4
5
6
7
-1
Analyses IAT-Göttingen [mg P kg ]
Figure 30: Comparing determinations of the organic carbon content and the plant-available phosphate carried out in the Embrapa-Belém soil laboratory and in the IAT-Göttingen laboratory
The elemental analyzer was frequently calibrated with acetic-anilide (p.a.). As differences
were scattering, no correction factor could be applied. Imprecise results of the WalkleyBlack method might be related to a fixed correction factor accounting for the incomplete
oxidation of organic carbon implicated in the method. Depending on the SOC fraction
(light/labile, heavy/stabile), which prevails in the soil samples, eventually the extent of
oxidation might vary and thus results also do.
The plant-available P, determined by the Embrapa soil laboratory was significantly higher
then results from IAT-Göttingen (paired t-test, p=0.023; Figure 30). As in non of the Institutes (inter-)national laboratory standards were available, IAT measurements also comprised eight soil samples of the study region, which previously had been determined for
plant-available P in Rio de Janeiro by the soil laboratory of the "Serviço Nacional de Le106
5.2.1 Soil fertility
vantamento e Conservação de Solos (SNLCS)" (Diekmann, 1997). Results from those institutes, however, did not differ (paired t-test, p=0.104)11, thus indirectly indicating overestimation in Embrapa-Belém measurements.
In the Embrapa-Belém laboratory, P-determinations and that of all other elements (except
C and N) are generally carried out with volumetric soil samples (10 cm3). Results are directly convertible into weight basis assuming a "laboratory" density of soil equal one. In
the present study this might, however, not be correct, as our measurements of the "laboratory" density of the soil resulted in a mean value of 1.12 g cm-3 (SE = 0.27 g cm-3, n=4).
A regression analysis relating results of Embrapa-Belém and of IAT, led to an even higher
slope of 1.35 (Figure 30). This value was used to recalculate Embrapa-Belém data on
weight basis.
Plant-available P was very low at soil depths below 10 cm (Table 28). Also in 0-10 cm under natural conditions (fallow), the P-concentration did not exceed 2.6 mg kg-1. In the topsoil depth, burning immediately led to a significant P-increase in July 1997 reaching as
high as 20.4 mg kg-1. The increase was still significant in March 1998. Beginning in July
1997, P-concentration increased also under mulch in 0-10 cm. Only on site 1, the influence of cultivation was apparent at 10-20 cm and less so in 20-30 cm depth, while the
deeper soil remained unaffected at all sites.
Table 28: Mean plant-available P (Mehlich I extraction) of the soils of the study sites at five different depths
and three dates and the LSD between sites (=least significant difference, p≤0.05, after one way repeated
measure GLM; dependent variable: sites; n=4); shaded cells denote significantly higher concentration in
comparison to the fallow
Element/
Site
P
Fallow
Site 1, burned
Site 1, mulched
Site 2, burned
Site 2, mulched
LSD
0-10 cm
10-20 cm
20-30 cm
30-50 cm
90-100 cm
Jan. 97 July 97 Mar. 98 Jan. 97 July 97 Mar. 98 Jan. 97 July 97 Mar. 98 Jan. 97 July 97 Mar. 98 Jan. 97 July 97 Mar. 98
-1
----------------------------------------------------------------- [mg kg ] ------------------------------------------------------------------2.6
4.9
2.5
4.0
2.5
1.7
1.5
20.4
10.9
9.6
7.4
4.9
2.2
6.9
7.8
6.7
3.7
4.1
1.6
2.0
1.7
1.5
2.0
0.5
1.5
5.4
4.1
2.2
2.2
1.5
1.5
4.1
4.3
3.7
2.4
1.9
1.5
1.5
1.5
1.0
1.5
0.2
0.9
1.5
0.7
0.7
1.1
0.4
1.1
1.5
2.0
1.5
1.5
0.6
0.7
0.7
0.7
0.7
0.7
n.s.
0.7
0.7
0.4
0.4
0.7
n.s.
0.7
0.7
0.7
1.1
0.9
n.s
0.7
0.7
0.7
0
0
0
0.2
0
0.7
0.6
0.4
0.5
0.7
0
0.7
0.7
0.7
0
Exchangeable K and Ca and ECEC of the upper soil profile (0-100 cm) determined by the
Embrapa soil laboratory were similar to results of the Institute of Soil Science and Forest
nutrition (IBW) in Göttingen (Figure 31 and Figure 32).
11
They did also not significantly differ from results of Bray-I P-determination of Diekmann (1997)
107
5.2.1 Soil fertility
0
Exchangeable K
0.025
0.05
-1
[cmolc kg ]
0.075
Exchangeable Ca
0
1
2
-1
[cmolc kg ]
3
0
50
Depth [cm]
100
150
200
250
Fallow, March 98
Fallow, March 98
Fallow, May 98 (IBW)
Fallow, May 98 (IBW)
Site 1 burned, March 98
Site 1 burned, March 98
Site 1 burned, June 98 (IBW)
Site 1 burned, June 98 (IBW)
Site 2 burned, March 98
Site 2 burned, March 98
Site 2 burned, June 98 (IBW)
Site 2 burned, June 98 (IBW)
300
Figure 31: (Mean) exchangeable K and Ca of the soil to 3 m depth of the study sites in 1998 according to
determinations of Embrapa-Belém (0-100 cm, n=4) and IBW (30-300 cm, n=1)
In the case of Al and Mg, results of both laboratories were deviating considerably (data
not shown). Differences on one hand might be related to the different sampling dates
and, therefore, to the heterogeneity encountered in the field. On the other hand, determination methods were not identical. Embrapa soil-laboratory routinely uses 1N KCl solution
for the extraction of Al, Ca and Mg (agitating the soil samples) and the Mehlich-I solution
for the extraction of K and Na, whereas at IBW extraction of all cations is done by percolating a soil column with a 1N NH4Cl solution.
As expected, exchangeable potassium was found in rather low concentration of maximal
0.06 cmolc kg-1 in 0-10 cm declining to about 0.02 cmolc kg-1 at 1 m. Thus, K saturation
was only 1 to 6 %. Different exchangeable K concentrations were found for the fallow site,
site 1 and site 2, also at greater depths (Figure 31). This could be confirmed by the Embrapa soil laboratory determinations, including also the mulched plots (0-100cm; Table
29). Especially the upper soil depths (0-20 cm) showed increased exchangeable K concentrations at all cultivation sites. In 0-10 cm (and partly 10-20 cm) these increases had
reverted in March 1998.
108
5.2.1 Soil fertility
-1
0
1
2
3
0
1
2
3
0
1
2
[cmolc kg ]
3
0
50
Depth [cm]
100
150
200
Fallow,
March 98
Site 1 burned,
March 98
Site 2 burned,
March 98
250
Fallow,
May 98
(IBW)
Site 1 burned,
June 98 (IBW)
Site 2 burned,
June 98 (IBW)
300
Figure 32: (Mean) ECEC of the soil to 3 m depth of the study sites in 1998
Ca was the predominant cation (saturation 42 to 99 %) in the upper 30 cm, ranging from
2.0 to 2.6 cmolc kg-1 in 0-10 cm and declining to 0.17 to 0.43 cmolc kg-1 in 90-100 cm.
Significant differences in Ca could not be detected in comparing cultivation sites with the
fallow site, apart from some inconsiderable shifts under the burned and mulched plot of
site 2 (Table 29). However, Ca concentrations did slightly increase in 0-10 cm on the cultivation sites during cultivation.
Al did not vary significantly during cultivation (Table 29). Aluminum was the predominant
exchangeable cation below 30 cm (22 to 70 % saturation). Though Al concentration had
significantly dropped in 90-100 cm on site 2, this eventually was a peculiarity of the site
and not related to cultivation.
At 90-100 cm Mg did slightly increase under the burned plot of site 1 (January 1997) and
site 2 (July 1997 and March 1998). But also in 0-10 cm depth Mg concentration under
the burned (less under the mulched) plot of site 2 was higher than measured under the
fallow site.
The pH of the soil was significantly higher in 0-30 cm depth in response to the burning on
site 1. This effect was quickly reverted and already in July 1997 differences were insignificant. Mulching did not affect the soil pH. On site 2, the pH of the upper 1 m soil apparently was generally lower than under the fallow site but, also here, the pH increased in
0-10 cm due to burning.
109
5.2.1 Soil fertility
Table 29: Mean exchangeable cations, ECEC [cmolc kg-1] and pH of the soils of the study sites at five different depths and three dates and the LSD between sites (see description of table 28); lightly shaded cells
denote significantly higher and dark-shaded cells lower concentrations compared to the fallow
Element/
Site
Ca
Fallow
Site 1, burned
Site 1, mulched
Site 2, burned
Site 2, mulched
LSD
0-10 cm
10-20 cm
20-30 cm
30-50 cm
90-100 cm
Jan. 97 July 97 Mar. 98 Jan. 97 July 97 Mar. 98 Jan. 97 July 97 Mar. 98 Jan. 97 July 97 Mar. 98 Jan. 97 July 97 Mar. 98
-1
--------------------------------------------------------------- [cmolc kg ] ----------------------------------------------------------------2.2
2.0
1.3
2.5
1.4
1.6
2.1
2.2
1.6
2.3
2.0
0.5
2.1
2.2
2.0
2.6
2.5
0.4
1.5
1.3
1.1
0.9
1.4
0.3
1.3
1.4
1.0
1.2
1.3
0.4
1.3
1.3
1.1
1.8
1.6
0.4
0.9
0.9
0.6
0.8
0.6
0.2
0.8
0.8
0.6
0.6
0.7
0.2
0.9
0.6
0.6
0.9
1.1
0.3
0.5
0.6
0.3
0.5
0.4
0.2
0.4
0.4
0.3
0.4
0.4
0.2
0.4
0.4
0.3
0.7
0.6
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.2
0.2
0.2
0.3
0.2
0.1
0.2
0.2
0.2
0.4
0.4
0.1
0.07
0.10
0.07
0.16
0.16
0.02
0.05
0.12
0.12
0.11
0.14
0.03
0.05
0.04
0.04
0.06
0.07
0.01
0.04
0.10
0.06
0.10
0.09
0.02
0.04
0.09
0.08
0.06
0.07
0.02
0.04
0.03
0.04
0.05
0.07
0.01
0.03
0.07
0.04
0.05
0.06
0.02
0.03
0.04
0.04
0.03
0.05
0.02
0.03
0.03
0.04
0.04
0.06
0.01
0.02
0.04
0.03
0.03
0.04
0.02
0.02
0.03
0.03
0.02
0.03
0.01
0.02
0.02
0.03
0.04
0.04
0.02
0.02 0.01 0.01
0.01 0.02 0.01
0.01 0.02 0.01
0.01 0.01 0.02
0.02 0.01 0.02
0.004 0.004 0.005
0.22
0.44
0.32
0.32
0.42
0.21
0.23
0.26
0.24
0.52
0.54
0.17
0.37
0.35
0.41
0.57
0.43
0.17
0.21
0.25
0.35
0.22
0.27
0.06
0.31
0.39
0.28
0.31
0.33
0.19
0.35
0.26
0.31
0.39
0.44
0.16
0.17
0.25
0.22
0.27
0.20
0.07
0.20
0.22
0.37
0.30
0.35
0.11
0.24
0.24
0.24
0.30
0.44
0.16
0.14
0.12
0.25
0.22
0.15
0.09
0.26
0.20
0.19
0.24
0.28
0.14
0.30
0.17
0.33
0.33
0.37
0.13
0.10
0.37
0.17
0.15
0.07
0.22
0.12
0.15
0.11
0.30
0.19
0.10
0.19
0.19
0.17
0.30
0.28
0.10
0.05
0
0.02
0.12
0.22
0.08
0.02
0
0.13
0
0.06
0.06
0
0
0.09
0
0
0.12
0.06
0
0.20
0.40
0.17
0.14
0.16
0
0.20
0.13
0.22
0.15
0.09
0.04
0.17
0.04
0.07
0.15
0.26
0.15
0.37
0.30
0.52
0.13
0.35
0.30
0.28
0.33
0.26
0.13
0.11
0.19
0.24
0.24
0.19
0.19
0.52
0.42
0.67
0.57
0.69
0.14
0.58
0.57
0.43
0.48
0.44
0.19
0.28
0.56
0.48
0.31
0.31
0.21
0.72
0.64
0.81
0.59
0.62
0.14
0.79
0.87
0.61
0.44
0.46
0.14
0.59
0.56
0.61
0.43
0.30
0.16
0.07
0.07
0.07
0.12
0.12
0.01
0.06
0.06
0.06
0.05
0.06
0.01
0.05
0.03
0.02
0.06
0.06
0.01
0.05
0.07
0.06
0.09
0.07
0.01
0.04
0.06
0.05
0.03
0.04
0.01
0.03
0.03
0.03
0.04
0.04
0.01
0.05
0.06
0.05
0.06
0.07
0.01
0.04
0.04
0.04
0.02
0.03
0.01
0.03
0.02
0.02
0.04
0.04
0.01
0.032
0.044
0.033
0.042
0.046
0.010
0.03
0.03
0.03
0.02
0.03
0.01
0.02
0.02
0.03
0.03
0.03
0.01
0.02
0.02
0.01
0.02
0.02
0.01
0.02 0.01
0.02 0.01
0.02 0.02
0.01 0.02
0.02 0.02
0.005 0.004
2.63
2.59
1.82
3.24
2.33
0.42
2.45
2.63
2.16
3.00
2.83
0.52
2.52
2.63
2.60
3.34
3.02
0.52
1.87
1.72
1.75
2.01
1.67
0.26
1.86
1.91
1.65
1.70
1.92
0.36
1.82
1.69
1.64
2.28
2.18
0.33
1.43
1.39
1.30
1.49
1.46
0.16
1.40
1.37
1.30
1.27
1.35
0.16
1.29
1.09
1.12
1.52
1.80
0.29
1.24
1.20
1.30
1.33
1.32
0.16
1.26
1.25
0.98
1.18
1.23
0.18
1.02
1.11
1.13
1.38
1.40
0.22
1.17
1.29
1.21
1.02
0.98
0.27
1.18
1.24
0.98
1.04
0.91
0.14
0.97
0.99
0.99
1.11
1.04
0.11
5.7
6.1
5.7
5.7
5.3
0.3
5.7
6.0
5.5
5.7
5.2
0.3
5.7
4.9
5.7
5.6
5.4
0.2
5.5
5.8
5.5
5.0
5.4
0.2
5.5
5.7
5.2
5.1
4.8
0.2
5.4
5.0
5.5
5.5
5.5
0.4
5.1
5.4
5.1
5.0
4.8
0.1
5.2
5.1
4.9
4.8
4.6
0.2
5.3
5.3
5.3
5.1
5.2
0.3
4.8
5.0
4.8
4.7
4.7
0.1
4.9
4.8
4.6
4.6
4.4
0.2
5.4
5.0
4.9
5.0
5.1
0.2
4.8
4.9
4.8
4.9
4.7
0.1
4.9
4.7
4.6
4.6
4.6
0.2
4.9
5.0
4.7
4.8
5.1
0.2
K
Fallow
Site 1, burned
Site 1, mulched
Site 2, burned
Site 2, mulched
LSD
Mg
Fallow
Site 1, burned
Site 1, mulched
Site 2, burned
Site 2, mulched
LSD
Al
Fallow
Site 1, burned
Site 1, mulched
Site 2, burned
Site 2, mulched
LSD
Na
Fallow
Site 1, burned
Site 1, mulched
Site 2, burned
Site 2, mulched
LSD
ECEC
Fallow
Site 1, burned
Site 1, mulched
Site 2, burned
Site 2, mulched
LSD
pH
Fallow
Site 1, burned
Site 1, mulched
Site 2, burned
Site 2, mulched
LSD
ECEC remained constant throughout cultivation and was only higher on site 2, again possibly a site-specific peculiarity and, furthermore, associated with the pH increase. ECEC
110
5.2.1 Soil fertility
declined drastically from the topsoil to 1 m depth, which is obviously caused by the decreasing SOC content. Below 1 m soil depth, however, ECEC decreased less pronounced
from about 1 cmolc kg-1 to 0.5 cmolc kg-1 at 3 m depth. Consequently, it was not surprising, that considerable amounts of exchangeable cations were present in deeper layers
(Table 30). On site 1 for instance, about 2500 kg ha-1 exchangeable Ca was present
down to 3 m soil depth, whereby about one third was located below 90 cm. In the case of
K and Mg, as much as 44 % of the sum of cations was present in 90-300 cm.
Table 30: Exchangeable amounts of cations of the soils to 3 m depth under the study sites and under different other site of the study region (range of n=8; Fölster unpublished data); 0-30 cm based on EmbrapaBelém measurements, 30-300 cm based on IBW-measurements; soil density in the field assumed to be
1.5 g cm-3 (0-10 cm: 1.0 g cm-3); ECEC not including Na, except for "study region"; italic values based on
Embrapa determinations
Element/
Depth
--------- Fallow ----------1
[kmolc ha ]
-1
[kg ha ]
Cultivation site 1 Cultivation site 2
-1
-1
[kmolc ha ] [kg ha ]
-1
-1
[kmolc ha ] [kg ha ]
Study region
-1
[kmolc ha ]
K
0-30 cm
30-60 cm
60-90 cm
90-300 cm
Sum
Ca
0-30 cm
30-60 cm
60-90 cm
90-300 cm
Sum
Mg
0-30 cm
30-60 cm
60-90 cm
90-300 cm
Sum
Al
0-30 cm
30-60 cm
60-90 cm
90-300 cm
Sum
ECEC
0-30 cm
30-60 cm
60-90 cm
90-300 cm
Sum
1.4
0.4
0.1
0.5
2.5
53
3
2
10
68
12.6
0.9
0.6
2.6
16.7
0.5
0.5
0.7
3.2
13
13
13
92
124
1.4
1.2
0.6
2.5
5.6
55
45
22
98
220
2.0
2.3
1.5
2.4
8.3
79
91
59
94
323
1069
54 259
36 259
207 264
1366 1852
51
19
15
40
125
1027
378
306
792
2503
66
14
12
46
138
1333
279
234
927
2774
134
42
41
171
389
16.0
3.8
3.0
13.9
36.8
195
46
37
169
495
11.0
3.5
3.4
14.1
32.0
28
405 176
369 176
1432 1372
2234 1752
3
29
34
184
251
30
261
306
1657
2255
4
37
34
132
207
38
333
306
1189
1866
57
16
5
20
98
6
153
11
7
31
41
202
11
11
3
45 20
41 20
159 152
248 195
72
49 45
44 45
173 173
337 334
21
21
26
132
132
79
68
53
53
240
414
90
57
50
195
392
2.0 - 4.7
27 -
85
8 -
22
447
93 - 214
212 - 273
Drastically fewer amounts of K, Ca and Mg were present in the deeper soil layers under
the fallow site, which is also visible in Figure 31. Amounts increased in the upper 1 m,
when data of Embrapa measurements were used for calculation instead of IBW-data
111
5.2.1 Soil fertility
(italic values in Table 30). In the case of Mg they were even exceeding data of both cultivation sites, demonstrating the limited relevance of single measurements (IBW) and/or
indicating possible uncertainties regarding Mg-measurements. In both calculations, however, the sum of K and Ca (0-3 m) under the fallow did not reach amounts encountered
under the cultivation sites. This complies with the common assumption that during a fallow period the soil exchange-complex is impoverished in bases, at first discharged by protons (of rainwater), which themselves may subsequently be displaced by dissolving aluminum (Rowell, 1994).
Higher plant-available P concentrations and increased K (less pronounced for Ca and Mg)
concentrations after burning and initiation of cultivation are in agreement with other
studies (Stromgaard, 1984; Tulaphitak et al., 1985; Eden et al., 1991; Romanya et al.,
1994). In the study region this was previously observed and discussed in detail by Kato
(1998a and b) and Hölscher (1995; see also Hölscher et al, 1997b).
Distinct differences of soil nutrient status between the burned and the mulched treatment on the basis of the present data could only be detected for the soil pH. Obviously,
large amounts of biomass applied as a mulch-layer did not affect the other soil chemical
properties considered. Differences might be expected regarding soil organic matter and
soil nitrogen content, but they were not tracked in this study. In this regard, further detailed studies are necessary.
The soil pH in 0-10 cm increased by about 0.4 units in response to the burning. This, despite the fact that exchangeable Ca, as one of the important mineralization products of
burning (basic active as CaO), was not significantly elevated, and also the increase of Mg
and K (mineralized as K2O and MgO) was small. This indicates a low buffering capacity of
these soils.
Higher amounts of plant available nutrients set free by mineralization through burning
and/or by decomposition of organic matter can be taken up by the crops. The rapid reversal of initially increased K and less pronounced also of P concentrations after about
15 months of cultivation (March 1998) apparently, at least partly, effects a decline of soil
fertility, which finally brings about the abandonment of the cultivation sites. For further
details on chemical, but also biological parameter determining soil fertility and productivity one can refer to Diekmann (1997).
112
5.2.2 Aboveground fluxes
5.2.2
Aboveground fluxes
Aboveground nutrient inputs and nutrient exports on both cultivation site were determined. They included burning losses (volatilization and particle transfer), export of firewood and harvest, as well as input of fertilizer. Nutrient inputs through deposition were
not measured in the present study but were assumed to equal those determined by Hölscher (1995) for the study region.
Biomass stock
For evaluation of volatilization losses, preburn nutrient stocks bound in the standing biomass and in dead biomass as litter and dead branches were determined.
The sum of leaves and wooden dry matter (DM) of the 7-year-old fallow vegetation growing on site 2 of 40.6 t ha-1 was almost twice as high as at the 3.5-year-old fallow vegetation of site 1 (21.4 t ha-1; Table 31).
Table 31: Mean aboveground biomass of the initial (before cropping) fallow vegetation on the cultivation
sites distinguished into leaves, wood and litter compartment (litter also comprising dead branches; n=10,
including results of Schmitt, 1997)
Compartment
Leaves
Site 1
Site 2
-1
--------------------------- [t DM ha ] -----------------------------SE
SE
Mean
Mean
5.5
10.0
0.77
1.43
Wood
15.9
2.35
30.6
6.61
Litter
7.2
0.63
6.0
0.40
Sum
28.7
2.56
46.5
6.77
Litter contributed 13 – 25 % to total biomass. The overall aboveground biomass of the
fallow vegetation of site 2 was a factor 1.62 larger than of the fallow vegetation on site 1.
The 7-year-old fallow vegetation was more heterogeneous, which is indicated by the
rather high standard error of the woody biomass. This might reflect a natural characteristic of this type of regrowing vegetation, where after a few years some fast-growing plant
species (e.g. Cecropia palmata) dominate the succession, and lead to deviating high
stock estimations, when incidentally included in determination of biomass.
The mean nutrient concentrations of the vegetation compartments did not differ significantly between sites (t-test; Table 32).
113
5.2.2 Aboveground fluxes
Table 32: Mean nutrient concentration of the compartments of the fallow vegetation on site 1 and 2 before
cropping (n=3)
Site/
Compartment
C
N
P
K
Ca
Mg
S
-1
--------------------------------------------------- [mg g DM] -----------------------------------------------Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE
Site 1
Leaves
Wood
Litter
504 3.8
491 1.2
493 9.0
14.3 1.18
5.8 0.12
11.1 0.71
0.52 0.02
0.25 0.04
0.26 0.02
5.7 1.20
3.5 0.84
1.7 0.06
10.6 1.26
7.0 0.55
12.3 1.59
2.2 0.28
1.0 0.18
1.8 0.17
2.4 0.42
1.0 0.09
1.4 0.14
Site 2
Leaves
Wood
Litter
498 4.5
489 3.6
466 12.1
15.8 3.32
5.2 0.83
11.5 1.17
0.62 0.04
0.29 0.07
0.29 0.02
7.7 1.05
3.5 0.47
1.5 0.25
6.7 0.88
7.5 1.75
13.7 1.25
2.3 0.49
0.9 0.02
2.3 0.36
2.1 0.03
1.1 0.12
1.5 0.07
For evaluation of nutrient stocks of standing biomass, the concentration of leaves and
wooden compartments were multiplied with corresponding biomass amounts. Results of
those calculations together with nutrient stocks present in the litter layer built the aboveground biomass stocks (Table 33).
Table 33: Mean nutrient stocks bound in the biomass of the initial fallow vegetation on the cultivation sites
and its percentage distribution in wood (wo.), leaves (le.) and litter (lit.)
C
N
P
K
Ca
Mg
S
-1
----------------------------------------------------- [kg ha ] ------------------------------------------------Mean
SE
Mean
SE
Mean
SE
Mean
SE
Mean
SE
Mean
SE
Mean
SE
Site 1
% - wo./le./lit.
14157 1259.7
55/20/25
251 20.7
37/31/32
9 1.0
46/33/21
100 17.6
56/32/13
258 25.6
43/23/34
41 4.7
39/29/32
40 4.3
41/34/26
22716 3317.0
66/22/12
387 59.3
41/41/18
17 3.0
53/37/10
194 31.4
56/40/5
378 74.6
61/18/22
65 8.8
43/35/21
65 9.0
54/33/14
Site 2
% - wo./le./lit.
Due to biomass differences, nutrient stocks on site 2 were 1.46 (Ca) to 1.94 (K) times
higher than those of site 1. On site 2 nutrient stocks showed a rather high degree of
variation expressed in their standard error. This was mostly affected by the highly varying
woody biomass stocks on this site. In case of Ca the woody biomass of site 2 contained
61 % of the total. Also for the other elements considered, the woody compartment on this
site always contained the largest amounts (between 41 % [N] and 66 % [C]). This was
also the case on site 1, but percentages were more equally distributed among the three
components, wood, leaf and litter, as may be expected for younger vegetation. The woody
compartments held between 37 % (N) and 56 % (K) of the total amounts.
114
5.2.2 Aboveground fluxes
Volatilization losses
Postburn residues were heterogeneously distributed over the burned plots. The mean
amount of all residues was 818 kg ha-1 on site 1 and 1519 kg ha-1 on site 2 (Table 34).
For calculation of the means, all extreme values were rejected which exceeded the 4sigma-range. Nevertheless, normal distribution according to the Kolmogorov-Smirnov test
(p ≤ 0.1) could not be achieved for the skewed data set of the ash-amounts on site 2, expressed in the difference between the mean and the median. On both sites mean ash
amounts exceeded mean charcoal amounts, indicating that the burning intensity (≅ quality) in both cases was high. Charcoal showed an even higher variance, because thicker
and incompletely dried stems, the potential source for charcoal, were not evenly distributed over the sites.
Table 34: Mean postburn residues distinguished into charcoal (+ incompletely burned remains, >2 mm)
and ash of the fallow vegetation on site 1 and 2; n=24 minus those rejected, when exceeding the 4-sigmarange; for median n=24
Mean
SE
-1
----------- [kg ha ] ---------
n
Minimum Maximum
Median
-1
--------------- [kg ha ] ---------------
Site 1
Ash
Charcoal
Site 2
Ash
Charcoal
584
234
62.3
37.3
23
21
26
15
1219
602
564
226
1049
471
133.4
77.7
24
21
207
62
2264
1376
791
457
Beside those burned residues caught with the steel trays, 312 kg ha-1 and 1736 kg ha-1
unburned residues (DM) were manually collected from site 1 and site 2, respectively.
Normally, these residues are piled up and burned once more or they are used by the
farmers as firewood for drying and roasting of cassava-flower (farinha) or cooking. In the
latter case they are removed from the fields, which was also done in the present study.
Amounts of unburned residues on site 2 were more than 5 times higher than those of
site 1, which was obviously related to the presence of thicker stems on that site.
Nutrient concentrations of ash and charcoal showed considerable differences between
both cultivation sites (Table 35). P-concentration in the ash was 4.7 times higher on site
2 than on site 1 (statistically highly significant, t-test). P-concentration of charcoal differed
significantly by a factor 2. Smaller, but still significant (t-test) differences in ash and charcoal could also be detected for K, Mg and S. Significantly differing concentration in the
charcoal could be found for C and Ca. N-concentration of both sites did not differ.
115
5.2.2 Aboveground fluxes
Table 35: Mean nutrient concentration of postburn residues distinguished into charcoal (+ incompletely
burned remains >2 mm) and ash (n=3)
Site/
C
N
P
K
Ca
Mg
S
-1
Compart- ----------------------------------------------------- [mg g DM] --------------------------------------------------Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE
ment
Cultivation site 1
Ash
99.0 7.86
1.8 0.52
1.06 0.020
Charcoal
736.0 5.68
8.7 0.74
0.59 0.003
1.5 0.03
8.0
66.6 1.32 172.3 9.74
19.5 0.41
6.7 0.17
17.8 0.08
2.1 0.01
1.1 0.01
4.94 0.300
61.8 0.87 172.4 1.75
26.4 0.22
9.0 0.08
1.16
14.8
9.0 0.04
Cultivation site 2
Ash
Charcoal
94.5 1.33
668.3
7.31
0.29
0.010
0.08
29.0
0.21
5.2
0.04
2.0 0.01
Taking element (and compound) vaporization temperatures (Tv) into the consideration,
two different processes can be distinguished:
•
C and N are more volatile (Tv below 400 °C; Lide, 1998) as is S (Tv = 445 °C). Thus,
these elements are already set free at lower burning intensities apparently met on
both site. Consequently their concentration in the ash did not or – in the case of S –
only slightly differ between both sites.
•
Vaporization temperatures of P (Tv inorganic P: 774 °C, but sublimation temperature
of P4O10= 360 °C), K (Tv = 774 °C) and Mg (Tv = 1104 °C) are higher, so that considerable amounts are only set free, when burning intensities are high. As the postburn
concentrations on site 1 were lower than those on site 2, temperatures during burning on this site appear to have been higher.
Calcium is assumed to volatilize rarely or never during vegetation fires as its vaporization
temperature of 1484 °C (and also that of inorganic compounds of Ca) is very high. Therefore, it can be used to assess the degree of volatilization of other elements considering
preburn and postburn ratios of Ca to those elements. Comparing the Ca:element-ratios of
leaves, wood and litter with those found in the ash (Appendix, Table A-4), an increase was
especially noticeable for C, N, S and also P indicating that those elements were mostly
volatilized. An increase was found on both sites, but it was higher on site 1. No or only a
slight increase was detectable for K and Mg, which concurred with their vaporization
temperatures.
Relating postburn nutrient stocks (Table 36) with those bound in the preburn biomass
(Table 33) resulted in 90 % P-volatilization losses on site 1 and in 63 % on site 2
(Table 37).
116
5.2.2 Aboveground fluxes
Table 36: Mean nutrient stocks remaining in the postburn residues on site 1 and 2
C
N
P
K
Ca
Mg
S
-1
--------------------------------------------------- [kg ha ] ---------------------------------------------Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE
Mean SE
Burned residues (ash + charcoal)
Site 1
230 28.6
3.1 0.49
Site 2
414 53.6
5.3 0.67
0.7 0.07
5.7 0.74
41 4.2
72 8.4
105 12.2
194 23.2
12 1.2
30 3.6
4 0.4
10 1.2
2 0.3
13 1.4
0.3 0.03
1.7 0.14
0.3 0.02
1.9 0.13
Unburned residues (thicker stems)
Site 1
Site 2
153 0.5
851 3.0
1.7 0.09
9.7 0.52
0.08 0.01
0.47 0.06
1
6
0.1
0.7
Table 37: Mean percentages of aboveground dry matter and nutrient loss due to volatilization
DM
C
N
P
K
Ca
Mg
S
------------------------------------------------- [%] ------------------------------------------------Site 1
Mean
SE
Site 2
Mean
SE
96
97
98
90
58
59
70
89
9
9
8
11
18
11
12
11
93
94
96
63
60
45
51
81
15
15
15
19
17
21
15
14
But, all elements considered were volatilized or at least removed by particle flight to a
high extent. Proportionally, site 1 lost more nutrients than site 2, except for K, underlining
the assumption that burning intensity on site 1 was higher. Percentage losses decreased
in the order N > C > P > S > Mg > Ca > K on site 1 and N > C > S > P > K > Mg > Ca on
site 2. As expected, losses in terms of stocks decreased on both sites in the order C > N >
Ca > K > S > Mg > P.
Assuming that Ca was removed only by particle export and with that other less volatile
elements to the same extent, then, differences between percentages of Ca and those
elements would account for their percentage volatilization. This would be 0-15 % in the
case of K and 6-11 % Mg.
Amounts of N (3.1–5.3 kg ha-1) and P (0.7–5.7 kg ha-1) in Table 36 remaining in the
postburn residues are limited in terms of direct fertilization effect on the subsequent
crops. On the other hand, amounts of K (41–72 kg ha-1), Ca (105–194 kg ha-1) and Mg
(12–30 kg ha-1) are considerable, as is the positive effect of rising soil-pH.
Unburned residues (Table 36), which were removed from the field, did not contain many
nutrients, though the amount of N in this form almost equaled or even exceeded amounts
remaining on the field as ash and charcoal.
117
5.2.2 Aboveground fluxes
Fertilizer input and harvest exports
With fertilization a total of 70 kg N ha-1, 48 kg P ha-1 and 66 kg K ha-1 was applied on the
cultivation sites. Phosphate was given as triple super-phosphate, and therefore additionally about 31 kg Ca ha-1 was applied (calculated on basis of the formula).
Maize grain-yields varied between 1.82 and 2.69 t ha-1 (mean: 2.33 t ha-1) depending on
site and land preparation (Figure 33).
9
8
7
-1
[t ha ]
6
5
4
Maize
Spindle
Grain
Cowpea
Pods
Peas
Cassava
Tuber
3
2
1
0
lot
lot
lot
lot
lot
lot
lot
lot
lot
lot
lot
lot
d p ed p ed p ed p
d p ed p ed p ed p
d p ed p ed p ed p
e
e
e
rn urn lch lch
rn urn lch lch
rn urn lch lch
u
u
u
u
u
u
bu
b
bu
b
bu
b
1, e 2, 1, m 2, m
1, e 2, 1, m 2, m
1, e 2, 1, m 2, m
e
e
e
Sit
Sit Site Site
Sit
Sit Site Site
Sit
Sit Site Site
Figure 33: Maize, cowpea and cassava yields on site 1 and 2 on the slash-and-bun and slash-and-mulch
plots (13 % moisture for grains, but oven-dry for cassava)
The maize grain-yield on the mulched plots was slightly less then those of the burned
plots and, yields on site 1 exceeded those of site 2. These differences, though not statistically significant (two-way ANOVA), possibly were related to differences in concentrations
of plant-available P on those sites (see chapter 5.2.1).
Site-related variations also were insignificant for the cowpea yield. Yields varied between
1.45 and 1.88 t ha-1 (mean: 1.69 t ha-1).
The cassava yields of the burned plots of 8.69 t DM ha-1 exceeded those of the mulched
plots (7.60 t DM ha-1) by 1.09 t DM ha-1. This difference on basis of a two-way-ANOVA
again was not statistically significant (p=0.053). Mean cassava yield was 8.15 t DM ha-1
and mean moisture-content was 59.6 %.
Statistical analyses, nevertheless, based on only one repetition per treatment (site and
land preparation), as was the case for analyses of all crops, should be taken very cautiously.
118
5.2.2 Aboveground fluxes
Nutrient concentrations were determined on only one pooled subsample for each site
and crop-compartment, without repetition. Therefore, results of those data (Appendix, Table A-5) are not discussed in detail. Based on these results nutrient stocks of harvested
goods leaving the sites were calculated (Table 38).
Table 38: Nutrient stocks withdrawn by harvest goods and its mean percentage extraction by each crop
Site/Compartment
Site 1, burned plot
Site 2, burned plot
Site 1, mulched plot
Site 2, mulched plot
Mean: all
Maize
Cowpea
Cassava
C
N
P
K
Ca
Mg
S
----------------------------------- [kg ha-1] -----------------------------------5987
5978
5126
5123
125
127
112
119
22
25
22
20
77
83
83
76
14
15
14
14
14
13
12
11
7
7
7
7
5554
121
22
80
14
13
7
-------------------------------------- [%] --------------------------------------21
27
43
19
2
30
33
16
52
27
31
34
37
42
63
21
30
50
64
33
25
Due to higher yields of cassava after slash-and-burn more C was removed from these
plots. Higher cassava yields, however, did not affect K or Ca extraction, as the concentrations of those elements in tubers of the burned plots were lower. N-withdrawal from the
mulched plot of site 1 was lower then on the other plots due to the lower cowpea yields.
Apart from these exceptions, extraction of the remaining elements did not really differ
among sites and plots.
Maize was responsible for most P-withdrawal (43 %), cowpea for most N (52 %) and S
(42 %; Table 38). Most K and Ca (besides C) were removed from the field with the cassava-tubers. Withdrawn Mg-stocks were almost equally distributed among the crops.
The aboveground biomasses of the fallows preceding the cultivation phase in the present
study are slightly higher then comparable determination of other studies carried out in
the study region in the SHIFT-project (Table 39). Bünemann (1998) determined the biomass of a 7-year-old fallow to be 39.8 t ha-1. This is 6.6 t ha-1 less than evaluations of the
present study, but still comparable, when considering the standard error of determination
of Bünemann (1998) reaching 8.0 t ha-1 for wooden compartments alone. The biomass of
the 3.5-year-old fallow on site 1 had already reached amounts given by Denich (1989) for
a 4 to 5 years old fallow.
119
5.2.2 Aboveground fluxes
Table 39: Dry matter and nutrient stocks as well as nutrient concentrations of green matter (leaves), woody
compartments and litter (including dead branches) of fallow vegetation of different age in the study region;
sulfur was not determined (n.d.) in the cited studies
Age/Com-
DM
partment
1.25 years
Leaves
Wood
Litter
2 years
Leaves
Wood
Litter
4 years
Leaves
Wood
Litter
4-5 years
Leaves
Wood
Litter
7 years
Wood+leaves
Litter
7 years
Leaves
Wood
Litter
Chopped veg.
10 years
Leaves
Wood
Litter
[t ha ]
-1
N
P
-1
-1
K
-1
-1
Ca
-1
-1
Mg
-1
-1
Author
-1
-1
[kg ha ] [mg g ] [kg ha ] [mg g ] [kg ha ] [mg g ] [kg ha ] [mg g ] [kg ha ] [mg g ]
3.2
5.7
1.3
51.7
21.3
50.9
13.3
4.3
7.8
2.1
1.5
2
0.5
0.3
0.3
36.9
31.4
17.3
9.5
6.3
2.7
28.8
34
49.9
8.9
5.9
39.5
14
8
24
4.2
1.3
18.8
7.5
13.6
2.2
98.6
91.2
21
16.4
6.2
12.7
6.6
4.6
0.6
0.9
0.3
0.4
80.9
93.5
6.9
10.7
6.2
3.8
54.2
106.4
16.9
7.2
7.8
7.8
23
22
6
3.0
1.6
2.7
57
52
34
11.4
3.5
8.5
3
4
2
0.6
0.3
0.5
26
41
5
5.2
2.7
1.3
48
47
45
9.6
3.1
11.3
14
18
10
2.8
1.2
2.5
4.6
15.3
7.8
69.8
67.5
83
15.2
4.4
10.6
2.8
3.3
2
0.6
0.2
0.3
29.9
48.6
8
6.5
3.2
1.0
29.6
72.2
61
6.4
4.7
7.8
12
15
9
2.7
1.0
1.2
22.2
9.0
113
101
5.1
11.2
6
3
0.3
0.3
75
6
3.4
0.7
164
143
7.4
15.9
29
16
1.3
1.8
8.5
23.8
7.5
28.3
128.6
85.5
15.1
3.6
5.9
5.6
0.7
0.2
64.3
97
7.6
4.1
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
175.5
6.2
8.7
0.3
125.1
4.4
n.d.
n.d.
n.d.
n.d.
94
181
57
11.8
4.1
8.1
4
3
1
0.5
0.1
0.1
74
106
7
9.3
2.4
1.0
54
312
64
6.8
7.1
9.1
20
56
11
2.5
1.3
1.6
Gehring, 1997
(and pers. com.)
Gehring, 1997
(and pers. com.)
Kato, 1999
5
15
4
Denich, 1989
Hölscher, 1995
Bünemann, 1998
Kato, 1999
8
44
7
Results of nutrient concentrations differ remarkably in the cited studies and with that
also the nutrient stocks. Ranges of results include those of the present study. Gehring
(1997) for instance calculated P-stocks for a 2-year-old fallow (unfertilized control), which
exceeded those cited by Hölscher (1995) for a 7-year-old fallow and even those calculated by Kato et al. (1999) for a 10-year-old fallow. Similar examples are also found for
other elements. Besides uncertainties due to the extrapolation of small subsamples to
bulk biomass, such deviations are likely to reflect site-specific parameters, such as landuse history and cultivation frequency as well as edaphic conditions.
Preburn vegetation-biomass of the 7-year-old fallow examined by Hölscher (1995) was
lower and drying period was longer (31 days with only 9 mm rainfall) than in case of the
present 7-year-old fallow (site 2). Nevertheless, amounts of remaining debris (ash and
unburned trunks) in his experiment exceeded those found in the present study. Ash remaining was 1.7 t ha-1 (including charcoal) and 2.8 t ha-1 remained unburned (Mackensen et al. 1996). Thus, the preburn vegetation-biomass was reduced by about 86 %, instead of 93 % (7-year-old fallow) or even 96 % (3.5-year-old fallow) of the present study.
120
5.2.2 Aboveground fluxes
As detected already in the present study, the burning intensity here also influenced the
extent of volatilization.
Mackensen et al. (1996) reported that only 16 %, 9 % and 17 % of K, Ca, and Mg, respectively, were exported with the burning of the understory biomass (33.5 t ha-1) of a logged
secondary forest, where burning quality was considered to be medium to poor. Percentages increased to 48 %, 35 % and 40 % (K, Ca, Mg), when they burned the abovementioned 7-year-old fallow, where burning was considered good to excellent. Moreover,
results of the present study indicate that up to 60 %, 59 % and 70 % of K, Ca and Mg, respectively, might be transferred to the atmosphere, when burning quality was excellent.
The burning temperatures on the ground in the study of Mackensen et al. (1996) reached
953 °C. Thus, temperature had exceeded the vaporization temperatures (according to
Lide, 1998) of C, N, P, S and K, but not of Ca and Mg. Comparable burning quality has to
be assumed for the present study.
On site 2, the order of percentage losses of all elements followed that of the vaporizationtemperature sequence. On site 1, this was not the case, as more Mg was removed. However, high standard errors of mean percentages of the cations prevented clear distinction
between the sites. Furthermore, in the case of Mg, organically bound plant materials basically might be more susceptible to volatilization than the free cation alone (Raison et al.,
1985). This may also be true for the organic compounds of P, which might explain its high
percentage losses.
High nutrient losses as were found for the slash-burning in the Bragantina region (present
studies as well as those of Mackensen et al., 1996), have rarely been cited for comparable land use systems. Burning a secondary tropical dry forest (Caatinga) in the Brazilian
Northeast with 74 t ha-1 aboveground biomass, Kauffman et al. (1993) found comparable
losses of 96 % of preburn C and N stocks, but also 56 % of the P stocks. Additionally, they
observed that part of the remaining ash was quickly removed via wind erosion. This is
also occurring in the Bragantina region, but was not tracked in the present study12. During Amazonian primary forest conversion into agricultural land for instance, only 29.3 %
of the biomass (264.6 t ha-1) was burned and with it 27.5 % C (35.9 t C ha-1) was released into the atmosphere (Fearnside, et al., 1993).
Mean grain-yield of maize (2.33 t ha-1) in the present study was comparable to the
2.2 t ha-1 obtained by Kato (1998) for the same cultivar under comparable (mulched)
land preparation and fertilization. Mean cowpea yield (1.69 t ha-1) was negligibly higher
12
After the burning the ashtrays were immediately covered.
121
5.2.2 Aboveground fluxes
than those of Kato et al. (1999; 1.54 t ha-1; same cultivar, mulched or burned and fertilized). In both cases, yields achieved with moderate fertilizer input irrespective of land
preparation (burning or mulching) exceeded by far yields normally obtained under traditional slash-and-burn land use without fertilizer application. According to statistical data
for the municipality of Igarapé-Açu, these are 0.7-0.8 t ha-1 maize grain and 0.7 t ha-1
cowpea (IBGE; 1994b). For both crops, Bünemann (1998) could show that in the first
place P availability is limiting plant growth, when land preparation is done by slash-andmulch. Kato et al. (1999) showed that this is also true for the slash-and-burn land preparation, though in a more moderate way, as residual nutrients in the ash have a limited
fertilizing effect. For details on crop-nutrient aspects and the importance of fertilizer in
modified slash-and-mulch land use systems in the Bragantina region is made to Kato,
(1998a), Kato (1998b) and Gehring et al. (1998).
Cassava fresh yield (DM + 59.6 % moisture) in the present study ranged from 18.8 t ha-1
(mulched) to 21.5 t ha-1 (burned) and thus was about 10 t ha-1 less than comparable
yields given by Kato et al. (1999; mulched: 27.8 t ha-1, burned: 30.1 t ha-1 ). On the other
hand, yields were in the range given by Kato (1998a and 1998b) for a screening experiment where the Pretinha-cultivar was tested against other regionally used cultivars
(~17.5 t ha-1 for mulched + unfertilized to ~23.5 t ha-1 for mulched + fertilized). Thus,
both comparisons indicate that yields of cassava might vary considerably, and possibly
depend on the residual effect of fertilizer applied to maize and cowpea. In the present
study, as in the studies of Kato (1998a) and Kato (1998b) and Kato et al. (1999) burning
in all cases resulted in higher cassava yields, though statistically not significant. Higher
yields are explainable, when considering the high demand on K and Ca (see export, Table 38). Those amounts were highest in the remaining ash. Thus, K and Ca could be yieldlimiting, in the case of cassava not covered by the residual fertilizer amounts. Once again,
however, yields exceeded those normally achieved in the municipality (~10 t ha-1; IBGE,
1997b).
122
5.2.3 Soil-water-solute nutrient fluxes
5.2.3
Soil-water-solute nutrient fluxes
Nutrient concentrations in the soil solution and their dynamics
Nutrient concentrations in the soil solution were measured at three different depths
(0.9 m, 1.8 m and 3 m) in both cultivation sites and both treatments. Samples were
taken during the two years of cultivation. In the second year, the number of samples
analyzed was reduced to one out of six repetitions on site 2, which had provided the most
representative data in the first year. Under the fallow site (reference) concentrations of
nutrients were determined at selected times only. Concentrations as well as their annual
(site 1) or two-year (site 2) dynamics are shown for all relevant nutrients and for the pH
(Figure 34 to Figure 41).
8
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 600 cm
Site 1, burnt, 90 cm
Site 1, burnt, 180 cm
7.5
Site 1, burnt, 300 cm
7
pH
6.5
6
5.5
5
4.5
4
8
7.5
Site 1, mulched, 90 cm
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
7
pH
6.5
6
5.5
5
4.5
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
4
Figure 34: Annual or two-year dynamics of the pH of the soil water samples taken at different soil depths
under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under the fallow vegetation;
bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
123
5.2.3 Soil-water-solute nutrient fluxes
3
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 600 cm
Site 1, mulched, 90 cm
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
-1
[mg K l ]
2.5
Site 1, burnt, 90 cm
Site 1, burnt, 180 cm
Site 1, burnt, 300 cm
2
1.5
1
0.5
0
3
-1
[mg K l ]
2.5
2
1.5
1
0.5
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure 35: Annual or two-year dynamics of potassium concentrations in the soil water samples taken at different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under the
fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
Generally, treatment-specific (burned–mulched) differences in nutrient dynamics did exceed those, which were site related. Nutrient concentrations were higher and fluctuated
more after burned land preparation. Burning also had a greater impact on the pH of the
soil water, which increased by about 1 unit at both sites compared to pH of the fallow
site. But, this was also detectable on the mulched plot of site 2. Soil solution pH of the
burned plots was to some extent correlated to the concentration of nitrate (r = 0.60),
magnesium (r = 0.51) and calcium (r = 0.46; Appendix, Table A-7). Absolute values of the
pH, however, have to be taken with caution, as CO2-release after exposing soil water to
atmospheric CO2-partial-pressure inevitably leads to an increase of pH.
124
5.2.3 Soil-water-solute nutrient fluxes
12
Site 1, burnt, 300 cm
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 600 cm
Site 1, mulched, 90 cm
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
Site 1, burnt, 90 cm
Site 1, burnt, 180 cm
-1
[mg Ca l ]
10
8
6
4
2
0
12
-1
[mg Ca l ]
10
8
6
4
2
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure 36: Annual or two-year dynamics of calcium concentrations in the soil water samples taken at different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under the fallow vegetation, bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
The dynamic of nutrient concentration and pH at 0.9 m depth were generally sensitive to
cultivation-measures, again, more pronounced under the burned treatments. Three main
cropping activities (see also Table 4) led to higher nutrient concentrations:
1. Land preparation, sowing of maize and NPK-fertilization in January 1997
2. Weeding, bending of maize, sowing of cowpea and NPK-fertilization in May 1997
3. Harvesting of cassava at the end of June 1998
An additional nutrient-peak in the soil solution at 0.9 m depth was present after rewetting of the soil subsequent to the intensive 1997-dry-season.
The concentration peak after the first cultivation measure (land preparation) was most
pronounced in the case of calcium and nitrate, and was only exceeded by the peak appearing after re-wetting in the case of potassium, magnesium and chloride. This is of
special importance for nutrient leaching, as soil water fluxes at the beginning of a year
(rainy season) are at their annual maximum (see Figure 28).
125
5.2.3 Soil-water-solute nutrient fluxes
3
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 600 cm
Site 1, burnt, 90 cm
Site 1, burnt, 180 cm
Site 1, burnt, 300 cm
-1
[mg Mg l ]
2.5
2
1.5
1
0.5
0
3
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
-1
[mg Mg l ]
2.5
Site 1, mulched, 90 cm
2
1.5
1
0.5
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure 37: Annual or two-year dynamics of magnesium concentrations in the soil water samples taken at
different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under
the fallow vegetation, bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
Desiccation of the soil profile during the dry season 1997 did not increase the concentration of nutrients (concentration effect), as the soil water at the considered depth under
the cultivation sites was not as strongly depleted as under the fallow site (see previous
chapter).
Concentration peaks in 0.9 m soil depth caused by the above-mentioned cultivation
measures could also be found for some elements under the mulched plots, namely for
chloride and - more pronounced on site 2 – for nitrate, calcium and magnesium. However, only chloride reached levels encountered under the burned plots.
The propagation of nutrient flushes through the soil profile showed two major characteristics:
1. Concentration peaks were delayed if they occurred at all
2. Concentrations of nutrients were strongly reduced
126
5.2.3 Soil-water-solute nutrient fluxes
8
-1
Site 1, mulched, 90 cm
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
Site 1, burnt, 180 cm
7
[mg N l ]
Site 1, burnt, 300 cm
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 600 cm
Site 1, burnt, 90 cm
6
5
4
3
2
1
0
8
-1
[mg N l ]
7
6
5
4
3
2
1
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure 38: Annual or two-year dynamics of nitrate concentrations in the soil water samples taken at different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
While at 1.8 m depth most nutrients showed a noticeable increase, nutrient concentrations flattened out at 3 m depth. In the case of nitrate on the burned plot of site 2, for example, the concentration reached almost 8 mg N l-1 at 0.9 m soil depth after land preparation and sowing of maize at the end of February 1997. This pulse reached 1.8 m depth
two months later with < 5 mg N l-1. Concentration peaks of nitrate reaching 3 m soil depth
did not exceed 1.4mg N l-1. Similar observations could be made for nitrate on the burned
plot of site 1, and on both mulched plots, though concentrations in the latter were generally lower.
Also chloride concentrations followed this vertical pattern, apparently rather independent
of treatment.
127
5.2.3 Soil-water-solute nutrient fluxes
30
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 300 cm
Fallow, 600 cm
Site 1, burnt, 90 cm
Site 1, burnt, 180 cm
Site 1, burnt, 300 cm
-1
[mg Cl l ]
25
20
15
10
5
0
30
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
-1
[mg Cl l ]
25
Site 1, mulched, 90 cm
20
15
10
5
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure 39: Annual or two-year dynamics of chloride concentrations in the soil water samples taken at different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
Similar characteristics could also be found for calcium, magnesium and to a lesser extent
for potassium. However, in the first year, concentrations at 3 m depth remained almost
unchanged under both sites and treatments, comparable to the concentration measured
under the fallow. For potassium this was already the case at 1.8 m soil depth. At 0.9 m
depth, the low K-concentration on site 1 was highly variable as indicated by the high
standard error (the standard error equaled the range as n = 2). This suggested that dissolved potassium did not reach 0.9 m soil depth everywhere, as was the case under the
mulched plot on site 1. In the second year of observation, the potassium concentration at
3 m soil depth under the burned plot increased continuously. Surprisingly, this phenomenon could not be observed at 1.8 m soil depth of the same profile nor under the mulched
plot. But, this interpretation deserves some reservation as only a single soil sample was
analyzed on each date.
128
5.2.3 Soil-water-solute nutrient fluxes
Site 1, burnt, 180 cm
Site 1, burnt, 300 cm
1.2
-1
[mg S l ]
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 600 cm
Site 1, burnt, 90 cm
1.4
1
0.8
0.6
0.4
0.2
0
1.4
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
-1
[mg S l ]
1.2
Site 1, mulched, 90 cm
1
0.8
0.6
0.4
0.2
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure 40: Annual or two-year dynamics of sulfate concentrations in the soil water samples taken at different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under the fallow vegetation, bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
Like potassium also Ca and Mg-concentrations in the second year increased slightly but
continuously at 3 m soil depth.
Sulfate was rather low and its dynamics did not follow the above-described characteristics for chloride or nitrate. After burning of site 1, the concentration of one of two repetitions at 0.9 m soil depth increased which was not observed on site 2. Sulfate concentrations at 3 m depth under both mulched plots exceeded those measured in 0.9 m or
1.8 m soil depth.
Phosphate in the soil solution was barely detectable, and its concentrations generally declined over the observation period, reaching the AES-measuring limit of 0.02 mg P l-1.
Concentration under the cultivation sites did not differ from P-concentration under the
fallow site. Fluctuations of P-concentrations were present at all depths at the same time,
indicating uncertainties of laboratory determinations rather than a systematic impact.
129
5.2.3 Soil-water-solute nutrient fluxes
0.14
Site 1, burnt, 180 cm
0.12
-1
[mg P l ]
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 600 cm
Site 1, burnt, 90 cm
Site 1, burnt, 300 cm
0.1
0.08
0.06
0.04
0.02
0
0.14
-1
[mg P l ]
0.12
Site 1, mulched, 90 cm
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
0.1
0.08
0.06
0.04
0.02
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure 41: Annual or two-year dynamics of phosphate concentrations in the soil water samples taken at different soil depths under the burned and mulched plots of site 1 and 2, respectively, and at 6 m under the
fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
Sodium concentration (Appendix, Figure A-12) did not follow patterns found for the other
cations. High concentrations were detected in all depths, and differences were more sitespecific than treatment-specific. After initial high concentrations at 0.9 m and 1.8 m soil
depth, beginning in May 1997, concentrations at 3 m exceeded those at 0.9 m and 1.8 m
depths. This was only reversed on the burned plot between February and May 1998.
Aluminum was only detectable in about 20 % of the soil water samples (Appendix, Figure A-13). The rather low solubility products for gibbsite (Al(OH)3) and the high affinity of
the soil for Al3+, AlOH2+ or Al(OH)2+ results in undetectable concentrations of these solute
aluminum forms at pH of the soil solution above ~5.5. The pronounced pH-dependence
(exponential form) of dissolved aluminum was also visible in the present study
(Figure 42). However, concentrations of dissolved aluminum, when measurable, were often far above those predicted by the gibbsite solubility products. At a pH above ~4.7 the
130
5.2.3 Soil-water-solute nutrient fluxes
concentration of all species theoretically should be below 0.04 mg l-1 (Rhodes & Lindsay,
1978). Thus, it had to be assumed that aluminum was present in polymeric form or organically bound. On the other hand, this result implies that saturation of (exchangeable)
aluminum in all soil water samples is presumable, and thus unknown amounts of gibbsite
are involved in soil chemical processes.
200
Electrical balance [µmol c l -1]
1.4
1.2
-1
Al [mg l ]
1
0.8
0.6
0.4
0.2
0
150
100
50
0
-50
-100
3
4
5
pH
6
3
7
Figure 42: pH dependence of Al in the soil solution;
data with concentration > 0 are shown, dotted line
indicates AES-measuring limit (0.04 mg l-1)
4
5
pH
6
Figure 43: pH dependence of the electrical balance
Fe and Mn were barely detected and if, at insignificant concentrations (Appendix, Table A-6). This was also true for ammonium, while dissolved organic nitrogen (DON) contributed with 7.2 % (median) to total N concentration and was present in 97 % of all samples.
The mean electrical balance (sum of molc of cations and anions) of all samples was
51.2 µmolc l-1 (SE 1.48 µmolc l-1, n=989), slightly varying with soil depth (higher in soil
samples of deeper soil layers; Appendix, Table A-6), but highly dependent on pH (Figure
43). A positive and pH-dependent electrical balance may be related to solute CO2 (mostly
as HCO3-), which was not determined and is set free upon exposing soil water to (low) atmospheric CO2-partial-pressure. The higher the increase of pH, the more concentrated
was the initial dissolved CO2, and the more positive the electrical balance becomes. Besides carbonic acid, however, also other not-determined organic acids might balance the
positive surplus of charge.
As concentration of dissolved nutrients generally declined with soil depth, so did the electrical conductivity. Mean conductivity (both sites and treatments) decreased linearly from
60 µS cm-1 at 0.9 m over 47 µS cm-1 at 1.8 m to 29 µS cm-1 at 3 m soil depth (Appendix,
Table A-6).
131
7
5.2.3 Soil-water-solute nutrient fluxes
Correlation coefficients comparing all relevant elements as well as pH, EC and the electrical balance were calculated (Appendix, Table A-7). Comparisons were carried out separately for the burned and mulched treatments. Site, depth or season-specific comparison
did not further contribute to explanation. Ca, Mg, nitrate and Cl were highly correlated in
the burned treatments. This was similar in mulched treatment, though correlation between Cl and nitrate as well as between Mg and nitrate were weak. Concentrations of Cl
and nitrate (less, when mulched), however, were also correlated with sodium concentration. Those correlations on one hand indicate preferential appearance and movement of
solute salts, such as NaCl or CaNO3, on the other hand may denote competition of ions in
anion or cation exchange. Above-mentioned nutrients also did correlate with the electric
conductivity (EC), i.e. in this case did determine EC. Aluminum did correlate with the electrical balance, but this might be indirectly, as appearance of Al depended on pH, which
also influenced the electrical balance (see above), and, thus, indicating the limited relevance of correlation coefficients alone.
(Annual) fluxes at different depths
Nutrient fluxes and consequently leaching losses were calculated multiplying daily soil
water fluxes (see chapter 5.1.3) with nutrient concentrations of the soil water samples of
each depth. It was assumed that samples taken bi-weekly comprised the mean nutrient
concentration prevailing during this period. Cumulative nutrient fluxes of the observation
period were computed for each soil depth (Appendix Figure A-14 to Figure A-16).
Within the first five months of 1997, more than 80 % of total annual nutrient flux at 0.9 m
soil depth had been leached. This was caused by high water fluxes combined with generally high nutrient concentrations during this period. Also at 1.8 m depth most leaching
occurred until June 1997. At 3 m nutrient fluxes generally were comparably low as nutrient concentrations at this depth were reduced. Additionally, retarded occurrence of nutrient peaks at 3 m depth did not lead to high leaching losses, as at these times (second
half of the year) water fluxes were greatly reduced due to the onset of the dry season (see
Figure 28). At the beginning of the rainy season of 1998, a second nutrient flush was
caused by high water fluxes, analogous to the year 1997. At this time, considerable
amounts of nutrients were also leached below 3 m soil depth, due to increased concentrations at the end of 1997. However, leaching did not reach the level of the upper soil
zones.
To be able to assess the leaching balance for the year 1998, concentrations of nutrients
132
5.2.3 Soil-water-solute nutrient fluxes
at the last sampling time (15/9/98) were extrapolated for the rest of the year. This is feasible as water fluxes from September to the end of 1998 were small.
In accordance with the vertical distribution of the nutrient concentrations, the amounts
percolating through the soil profile are diminishing with depth (Table 40 and Table 41).
Table 40: Nutrient losses through leaching considering different soil depths under the burned and mulched
plots of site 1 and 2; negative values denote losses (1997: site 1 n=2, site 2 n=6; 1998: n=1; values of
single sample of 1998 also shown separately in 1997)
Element/depth
Site 1
1997
burned
mulched
mean SE mean SE
-1
[kg ha ]
Site 2
1997
n=1
burned
mulched
mean SE n=1 mean SE
-1
[kg ha ]
1998
burned mulched
n=1
n=1
-1
[kg ha ]
Nitrate-N
90 cm
180 cm
300 cm
-50 5.2
-35 2.9
-8 2.3
-9 0.4
-2 0.3
-0.6 0.2
-83
90 cm
180 cm
300 cm
-55 3.9
-37 3.1
-9 1.5
-10 0.6
-3 0.3
-1.1 0.4
-89
90 cm
180 cm
300 cm
-0.7 0.004
-0.8 0.01
-0.7 0.005
-0.7 0.06
-0.7 0.001
-0.7 0.03
-0.9
90 cm
180 cm
300 cm
-11 9.3
-2 0.1
-2 0.4
-2
-2
-2
90 cm
180 cm
300 cm
-65 4.6
-69 12.0
-19 7.1
-11 4.7
-10 1.3
-5 2.3
90 cm
180 cm
300 cm
-12 0.1
-9 2.7
-3 0.1
-4
-2
-1
0.6
-29
0.4
-14
0.5
-9
90 cm
180 cm
300 cm
-6
-1
-2
-1
-1
-4
0.1
-3
0.1
-4
0.3
-5
90 cm
180 cm
300 cm
-61 8.4
-45 7.4
-27 1.2
-57 3.2
-33 6.2
-27 2.4
-88
90 cm
180 cm
300 cm
-105 17.5
-77 20.8
-15 0.6
-110 4.0
-38 3.7
-27 2.1
-229
-38
-7
-66 5.9
-26 2.5
-3 1.0
-13
-5
-0.05
-11 3.5
-4 2.1
-1 0.7
-11
-10
-2
-6
-6
-8
-13
-5
-2
3.6
-13
-13
2.2
-3
-7
0.9
-7
-9
-0.9
-0.8
-0.8
0.06
-0.4
-0.3
0.03
-0.2
-0.2
0.04
-0.2
-0.1
-7
-3
-6
1.6
-16
-12
0.4
-2
-2
1.1
-9
-1
-69
-38
-23
9.4
-59
-35
6.6
-51
-31
4.2
-29
-20
-15
-7
-12
-6
-6
-5
0.5
-4
-8
0.5
-5
-4
1.5
-3
-9
-82 16.4
-84 19.1
-47 7.8
-26
-34
-16
-26
-14
-49
-173 19.0
-89 16.1
-37 7.5
-157
-66
-86
-90
N total
-41
-8
-71 6.7
-28 2.8
-4 1.1
-18
-0.9 0.02
-1.0 0.04
-0.7 0.03
-0.8
-5
-1
P
-0.8
-0.6
-0.7
-0.7
K
0.1
-12
0.4
-4
0.01
-6
-9
-3
-4
2.2
-13
0.4
-4
0.5
-9
-90 8.6
-37 6.6
-16 2.6
-76
-19 2.4
-10 1.9
-4 1.2
-15
Ca
-121
-59
-19
-35
-20
Mg
-10
-3
-12 1.1
-7 1.3
-4 0.8
S
5.5
0.3
0.1
-3
-4
-3
0.8
-3.7
0.5
-5.1
0.6
-12
-70 8.0
-89 5.1
-27 2.4
-72
-162 16.1
-72 9.4
-16 1.1
-228
-3
-3
-5
Na
-44
-32
-52
-30
Cl
-96
-18
133
-116
-39
-116
-64
5.2.3 Soil-water-solute nutrient fluxes
Table 41: Reduction of elements during percolation from 0.9 m to 3 m soil depth under the burned and
mulched plots of site 1 and 2, respectively, of the year 1997 and 1998; negative values indicate an increase (=release of this elements out of the considered profile)
Element
------------ Site 1 -----------1997
burned
mulched
SE
mean SE
mean
-1
42.7 5.68
8.2
0.46
62.8 5.94
9.6 3.61
4.6
2.7
-1
3.0 0.41
0.6
0.03
4.5 0.42
0.7 0.26
0.3
0.2
0.06
0.07
0.16 0.04
0.13 0.07
0.13
0.12
0.006 0.007
0.016 0.003
0.013 0.007
0.013
0.012
[kg ha ]
Nitrate-N
[kmolc ha ]
-1
0.05 0.01
-1
0.005 0.001
[kg ha ]
P
[kmolc ha ]
-1
9.3 9.32
-1
0.2 0.24
-1
46.2 8.45
6.9
-1
2.3 0.42
0.3
-1
9.4 0.13
-1
0.8 0.01
-1
-1
[kg ha ]
K
0.10
5.1 2.24
0.6 1.97
6.5
10.8
-0.001 0.003
0.1 0.06
0.02 0.05
0.2
0.3
5.23
74.6 8.96
46.2 10.30
29.2
15.6
0.26
3.7 0.45
2.3 0.51
1.5
0.8
2.9
0.79
14.6 2.67
8.6 1.36
8.9
1.3
0.2
0.06
1.2 0.22
0.7 0.11
0.7
0.1
4.3 5.47
-3.7
0.31
-0.4 0.99
-2.5 1.57
0.7
-1.0
0.3 0.34
-0.2
0.02
-0.03 0.06
-0.2 0.10
0.05
-0.1
-1
34.4 8.49
29.4
3.98
43.5 8.33
34.7 18.17
12.0
-15.4
-1
1.5 0.37
1.3
0.17
1.9 0.36
1.5 0.79
0.5
-0.7
89.8 17.47
83.7
4.57
145.6 16.10
135.2 20.38
2.5 0.49
2.4
0.13
4.8 0.61
1.9
0.32
5.9 0.72
2.7
0.13
[kmolc ha ]
[kg ha ]
Ca
[kmolc ha ]
[kg ha ]
Mg
[kmolc ha ]
[kg ha ]
S
[kmolc ha ]
[kg ha ]
Na
[kmolc ha ]
-1
[kg ha ]
Cl
-1
[kmolc ha ]
Σ cations
Σ anions
------------------------- Site 2 --------------------------1997
1998
burned
mulched burned mulched
n=1
n=1
mean SE
mean SE
-1
[kmolc ha ]
-1
[kmolc ha ]
-0.05
93.4
-24.6
0.57
2.6
-0.7
6.9 0.62
4.5 0.95
2.9
0.5
8.6 0.62
4.4 0.64
3.0
-0.6
4.1
0.45
3.8
At 0.9 m depth considerable amounts of nitrate, Ca, Mg, Na and Cl were leached during
the first year of cultivation. N, Ca and Mg were more readily lost under the burned plots,
whereas Cl varied more between sites. Na leaching was rather independent of both, land
preparation or site. However, only 5–18 % of this N and 10–24 % of the Cl measured at
0.9 m was leached below 3 m soil depth in 1997, leading to a net retention of
62.8 kg nitrate-N ha-1 a-1 and 145.6 kg Cl ha-1 a-1 between 0.9 and 3 m depth on the
burned plot of site 2. Also the cations Mg, Ca and Na were reduced during percolation,
however, proportionally less drastic. About 16–31 % of Mg, 16–40 % of Ca, and 37–58 %
of Na of the 0.9m-leaching-fraction arrived 3 m depth. Mostly, the percentage reduction
of leached elements was more pronounced under the burned plots. In 1998, not only the
percolating amounts of nutrients at 0.9 m depth but also the percentage reduction during
percolation were smaller then in the first year. The result were higher nutrient concentrations at 3 m.
Leaching of phosphate (0.2–0.7 kg P ha-1 a-1) was hardly measurable. Phosphate at all
soil depths was not affected by any of the above-mentioned factors. P is strongly ad134
5.2.3 Soil-water-solute nutrient fluxes
sorbed on the soil surface and thus less mobile.
Percolating potassium under the burned plots in 1997 decreased between 0.9 m to
1.8 m but then remained on that level also at 3 m soil depth. On site 1 and 2, reduction
was 84 % (9.3 kg ha-1 a-1) and 66 % (5.1 kg ha-1 a-1), respectively (Table 41). Under the
mulched plots, the K-levels remained rather constant. In 1998 a clear K-reduction with
soil depth (6.5 and 10.8 kg ha-1 a-1) on both plots was detectable.
Sulfate leaching under the mulched plots slightly increased with depth in both years, as
already indicated by the concentration dynamics. Under the burned plots leaching remained unchanged on site 2, whereas 4.3 kg S ha-1 a-1 were retained on site 1, but highly
varying as indicated by the large SE.
Between 1.9 and 6.9 kmolc ha-1 cations were retained between 0.9 and 3 m depth in the
first year depending on site and treatment. The positive charge was more than counterbalanced by the sum of retained anions of 2.7–8.6 kmolc ha-1. Only the soil of the
mulched plot of site 2 in 1997, and that of the burned plot in 1998, was balanced regarding retention of cations and anions. In the other cases apparently more anions than
cations were retained. However, differences are within the range of variation expressed in
the standard error. Moreover, the positive charge of aluminum was not included, as the
precipitation of gibbsite due to rising pH (CO2-release) prevented exact determination. For
instance, adding the aluminum as determined under the mulched plot of site 1 would add
another 0.25 kmolc ha-1 positive charge assuming tri-valent Al.
The magnitude of retention of anions and cations apparently depends on their concentration (≅ ionic strength). Relating the mean annual concentration at 0.9 m depth of both
sites and treatments as well as of both years to the magnitude of retention (kmolc in
Table 41) shows a positive relation of both parameters for most elements (Figure 44).
Statistically, both years should not be treated independently as nutrient dynamics of the
first year might influence that of the following year. However, this was only the case for
sodium, where dependence of retention on concentration could not be found if data of
the second year (filled in points in the figure) were excluded.
Retention of nitrate and Cl at mean concentrations above 0.15 mmolc l-1 were of the
same order of magnitude. Below this concentration however Cl was released, while nitrate was still retained. This was the case under the mulched plot in 1998, where
0.7 kmolc Cl ha-1 a-1 was released, when the mean annual Cl-concentration in 0.9 m
depth was still 0.12 mmolc l-1. In this year, 0.2 kmolc nitrate ha-1 a-1 was retained at a
mean N-concentration of 0.05 mmolc l-1.
135
5.2.3 Soil-water-solute nutrient fluxes
5
-1
-1
[kmolc ha a ]
Retention, 0.9 m to 3 m
4
3
Cl
Nitrate-N
K
Ca
Mg
Na
S
2
1
0
-1
0
0.05
0.1
0.15
0.2
0.25
0.3
-1
Mean annual concentration, 0.9 m [mmolc l ]
Figure 44: Magnitude of annual retention (0.9 – 3 m soil depth) of cations and anions in relation to their
mean annual concentration at 0.9 m soil depth; relation comprises the pooled data of site 1 and 2, both
treatments and both years; filled in circles: Na data of 1998
For the cation retention, comparisons of this kind are difficult as concentrations of each
of the cations were of rather different orders of magnitude. However, proportionally
higher concentration of Ca did not lead to a net-release of Mg or K through competitive
displacement, indicating that the preference of retention of Mg and K is probably higher
than of Ca.
Adsorption sites of the soil particle surfaces were dominantly charged with Al (chapter
5.2.1; Table 42). Aluminum could account for up to 93 % of the bound cations. Nevertheless, also considerable amounts of Ca, Mg, K were found.
Table 42: Exchangeable amounts of cations of the soil profile of 0.9 m to 3 m soil depths under the study
sites and under different other sites (range of n=8) and their percentages saturation (compare Table 30),
as well as anion exchange capacity (AEC); n.d. = not determined; ECEC-calculation based on results of
NH4Cl-extraction and a soil density of 1.5 g cm-3; AEC calculated based on determination (2 mM CaCl2percolation) of Anurugsa (1998) on soil samples out of 30-50 cm depth and different pH ranging from 6.5
to 3.1
Element
K
Ca
Mg
Al
Na
ECEC
AEC
Site 1
-1
[kmolc ha ]
[%]
2.5
40
14
184
n.d.
240
1
16
6
77
Site 2
-1
[kmolc ha ] [%]
2.4
46
14
132
n.d.
195
Fallow
-1
[kmolc ha ] [%]
1
24
7
68
§ Na not considered
136
0.5
12
3
202
n.d.
218
0.2
6
2
93
Other sites
-1
[kmolc ha ]
[%]
2.0
27
8
93
17
191
25
- 4.7
- 85
- 22
- 214
21
§
- 253
- 126
1
11
3
48
- 2
- 41
- 11
- 84
not consid.
5.2.3 Soil-water-solute nutrient fluxes
Relating cations retained from solution to those on the exchange complex shows that
amounts are proportionally small. The two-year sum of retained cations of the burned plot
of site 2 (6.9+2.9=9.8 kmolc ha-1) would claim 5 % of the total ECEC of 195 kmolc ha-1.
Retained anions (11.6 kmolc ha-1) would at least claim 9 % of AEC depending on the "effective" capacity.
In this calculations, however, it was tacitly assumed that retained cations and anions are
adsorbed on the soil surface involving desorption of previously bound ions. But in this
case, the sum of anions and the sum of cation in Table 41 should have been close or
equal to zero. This was not the case. Moreover, all quantitatively important elements
were retained by the soil in the first year. In the second year of land use, only under the
mulched treatment more anions were released than entered the profile. Here, equal
leaching amounts of Cl and Na (both -0.7 kmolc ha-1) suggested that Na + Cl were percolating jointly.
Nevertheless, cations could have displaced the prevailing exchangeable aluminum, which
then would have precipitated as gibbsite and, hence, would not have been detectable.
This seems feasible and is confirmed by two facts:
-
Aluminum saturation decreases in the transition from fallow to cultivation (Table 42).
-
Ca, Mg and Al concentrations in the soil solution correlated negatively with pH, which
might be related to the precipitation-reaction of the Ca/Mg-displaced free aluminum,
in which according to the valence of Al, the same amount of protons are released.
Aluminum saturation, nonetheless, varied widely over the different sites (Table 42) and,
thus, exchange reactions with other cations are difficult to track even on the same site.
Furthermore, pH-decrease and the appearance of Ca and Mg was not strongly correlated,
and not at all on the mulched plots. Therefore, it remains questionable, whether cations
did really displaced aluminum on the exchange complex.
No desorption of quantitatively adequate amounts of anions was detectable, nor could
they have been exchanged and subsequently precipitated. Generally, precipitation of
salts comprising elements like Cl, Na, K, Ca, Mg, and compounds like nitrate is not conceivable at the prevailing concentrations. Also, uptake of nutrients by roots or temporary
fixation by microorganisms below 0.9 m soil depth seems unlikely, especially not for great
amounts of Na or Cl. Also, the water balance showed less importance of deep soil-water
uptake for maize and cowpea and below 1.8 m also for cassava (chapter 5.1.3).
The prevailing soils of the study region are highly weathered/desilicated Oxisols or, as
was the case in the present study, Ultisols. They predominantly contain kaolinitic clay
137
5.2.3 Soil-water-solute nutrient fluxes
minerals and sesquioxides. These are characterized by low cation exchange capacity
(CEC), but have also an anion exchange capacity (AEC). They are so-called "variablecharge soils" (Uehara & Gillman, 1981; Sollins et al. 1988), which means that exchange
capacities depend on pH and on the ionic strength of the equilibrated solution (Okamura
& Wada, 1983). The pH-dependence is routinely considered when determining the effective CEC and AEC, and quite often, consecutive batch experiments in the laboratory are
performed (Van Raij & Peech, 1972). Anurugsa (1996) determined AEC and CEC of subsoil (30-50 cm soil depth) of the present study region at different pH. He could show that
AEC decreased from initially 0.4 cmolc kg-1 at pH 3.1 to 0.08 cmolc kg-1 at pH 6.5 (entering Table 42), whereas the CEC increased from 0.39 to 1.69 cmolc kg-1 within this pHrange.
The ionic strength should also be considered especially for determination of the AEC. But,
very often AEC is measured with leaching solutions with much higher concentrations than
encountered in the field. Wong et al. (1990) studying the retardation of nitrate-leaching in
a number of tropical soils, supposed that this might result in an overestimation of "real"
AEC, as sulfate ions are displaced by the leaching solution containing Cl or nitrate ions,
which may normally not occur in situ. On the other hand, their data were not really consistent in this regard, and they had to admit that the chemistry of sulfate is not well understood (as previously stated by Mott, 1988).
Describing mechanisms of salt absorption by Andisols, Wada (1984) stated that a soil
equilibrated with a dilute solution and then brought into contact with a more concentrated solution might increase nearly equivalently its cation and anion adsorption capacity. Wada (1984) argued stoichiometrically that a –SiOH surface in this case releases a
proton that is transferred to a –AlOH surface. This concept was taken up by Katou et al.
(1996) demonstrating that the ionic strength of an invading CaCl2-CaNO3 solution determined the retardation of Cl and nitrate by an Andisol. Nitrate showed a slightly smaller affinity for adsorption than Cl, but adsorption of Cl and NO3 much exceeded the sulfate desorption. Katou et al. (1996) suggested that two steps are involved in the anion adsorption process: 1.) an increase in AEC in response to an increase in ionic strength of the
bulk (salt) solution counterbalanced by an increase in CEC, i.e. net proton surface charge
density remains constant; 2.) a displacement of sulfate by the monovalent anions. The
second step, in their view, is generally of minor importance and to what extent it occurs,
depends on the ionic strength of the bulk solution in relation to indigenous sulfate.
Certainly, those results cannot simply be transferred to conditions of the present
study as Andisols and Oxisols chemically as well as physically differ markedly. But an
138
5.2.3 Soil-water-solute nutrient fluxes
increase in AEC is the only feasible explanation for a joint retention of all quantitatively
important anions. Since the CEC increases simultaneously and equally, the retention
of more or less the same amounts of cations in the present study are thereby explainable.
Indeed, retardation of nitrate and Cl in tropical soils comparable to those of the study region is often reported (Kinjo & Pratt, 1971a, b; Black & Waring, 1979; Wong et al., 1990;
Schroth et al., 1999b). Black and Waring (1976a, b, c) studied nitrate adsorption of a cultivated Australian Oxisol considering also the deeper soil. Initially, fully 72 % of fertilized
201 kg N ha-1 were retained between 40 and 120 cm depth decreasing with time. AEC of
the soil increased with depth from 0.16 cmolc kg-1 in the 45-90 cm to 0.45 cmolc kg-1 in
360-600 cm depth. This appears related to the decrease of organic matter and soil pH
and the increase of kaolinite and sesquioxide content. Comparable results were also
found by Toner et al. (1989) and Vogeler et al. (1996). They are in agreement with findings in the present study leading to the assumption that also AEC of the soils of the study
region might increase with depth and might exceed those values determined by Anurugsa
(1996) for a soil depth of 30-50 cm.
The following processes can be surmised to take place: Nutrients were released in the
topsoil of the cultivation sites due to land preparation increasing the concentration of the
soil solution. This solution percolates through the soil profile and displaces the existing
dilute soil solution, which causes an increase in the exchange capacity of the soil-particle
surface. Additional anions as well as cations can thereupon be adsorbed. Additionally, a
limited displacement of Al and sulfate might occur. The latter appeared in the soil solution at 3 m depth. Once this process is reversed through dilution of the soil solution
(rainwater), delayed leaching of the nutrients can begin. This was observed for Cl and Na
under the mulched plot in the second year.
A different kind of nutrient retention is found in well-structured soils, i.e. soils with nonhomogeneous physical and chemical properties. Here, non-unimodal pore-size distribution cause non-uniform velocity fields ("non-ideal behavior") which results in a two-domain
system: 1.) a mobile domain, where water and solute transport occurs by advection and
dispersion and 2.) a immobile domain with minimal advective flow. Diffusive mass transfer occurs between both domains, and thus, the immobile domain behaves as a sink or
source even for non/less-sorbing solutes like Cl or nitrate (Brusseau & Rao; 1990; Kung,
1993). The coarse-textured soils of the study region, however, are uniformly developed,
thus bimodal flow seems improbable, though generally cannot be excluded. At least theoretically, clay aggregation (≅ pseudosand, see chapter 5.1.3) might lead to a "spatially
139
5.2.3 Soil-water-solute nutrient fluxes
emphasized retardation" within these aggregates.
Quite independent on the particular responsible process of retention, as a result, nutrients are temporarily adsorbed or retained, respectively, but subsequently released.
Therefore, one might use the term 'retardation' of leaching as an apt definition as already
done above.
A quantitative description for retardation in the break-through of anions in a soil column,
albeit not accounting for an increase in AEC as described above, was given by Wild
(1981). Retardation is measured as number of pore volumes of water Vp with a certain
anion concentration necessary to displace the anion through the soil column, according
to
(35)
Vp = 1 +
bp
,
θ
where b is the adsorption coefficient, which equals anions adsorbed [mmolc kg-1] per anions in the soil solution [mmolc l-1], p is the soil density [g cm-3] and θ the volumetric water
content [-]. If there is no capacity for anion adsorption (AEC=0), then b becomes zero and
thus Vp equals one, indicating that the anion is not retarded.
Wong et al. (1990) measured NO3-retardation of several tropical soils on laboratory soil
columns. Nitrate retardation of soil samples out of 60-80 cm soil depth of a Brazilian red
yellow Latosol (Oxisol; AEC = 0.22 cmolc kg-1) was 2.3 pore volumes. However, for a sample of the same soil taken at 80-100 cm depth (AEC = 0.29 cmolc kg-1) retardation increased to 2.7 pore volumes. Though this soil is comparable with the soil of the study region, results are not easily transferable. With a drainage of 2510 mm water within the
two years at 3 m soil depth (Table 26 in chapter 5.1.3) no increased output of nitrate or
Cl (except the mulched plot on site 2 in the second year) was detected. Based on the
concept of Wild (1981), this amount of water would equal at least 3.4 pore volumes between 0.9 m and 3 m depth (θs ≤ 0.3513).
Net-adsorption of 11.6 kmolc ha-1 anions within the soil profile of 0.9 to 3.0 m depth under the burned plot of site 1 must have increased the AEC over this depth by
0.037 cmolc kg-1 (soil density = 1.5 g cm-3). This would equal a percentage increase of
almost 20 % assuming an initial AEC of 0.2 cmolc kg-1.
Cation adsorption is more often studied than that of anions: Ludwig et al. (1997) investigated sorption/desorption processes in subsoil (80-100 cm) of the study region. Their
model indicated that gibbsite precipitation/dissolution was the most important proton
13
One pore volume = profile*θs = 2100 mm * 0.35 = 735 mm
140
5.2.3 Soil-water-solute nutrient fluxes
buffer reaction. Thus, large amounts of free Al at low pH might be present in the soil solution. Furthermore, they calculated cation selectivity coefficients based on sequential
batch experiments. Results indicated that potassium was preferentially bound over aluminum. Generally, selectivity coefficients decreased in the order K > Al > Na > Ca > Mg.
These results explain the high K-retention measured in the present study, but also confirm that Al-displacement and precipitation are likely to be important soil-chemical reactions. The low Mg-preference is conflicting with results of the present study, where Mg
was retained despite relatively higher concentration of Ca. Indeed, Anurugsa (1998), repeating these sequential batch experiments with comparable soil samples, could not reproduce the results of Ludwig et al. (1997). He ranked Mg-preference for adsorption
above that of Ca, as seemed to be the case in the present study. Actually, preferential
adsorption of potassium was reported already in an early publication (Hoover, 1944) and
was confirmed later (Nye et al., 1961; Pleysier et al., 1979; Levy et al., 1988).
Nutrient concentrations in the present study were measured beginning in 0.9 m depth
only, because nutrient dynamics in shallower depths were studied earlier within the
SHIFT-project by Hölscher (1995), as well as, especially related to N and P, by Kato
(1998a) and Kato (1998b). A related study was carried out by Klinge (1997), who considered the nutrient balance after primary forest conversion and installation of a forest
plantation.
Concentrations of all quantitatively important nutrients were measured by Hölscher
(1995) in the soil solution at 105 cm soil depth under a burned cultivation site, which
had been in fallow for 7 years. The nutrient levels concurred remarkably well with concentrations of the present study at 0.9 m. Only Ca in his study did not exceed 5 mg l-1 and,
therefore, was only half of maximum concentration of Ca of the burned plots of the present study. Nutrient concentrations determined by Klinge (1997) were partly similar to
those of the present study but not so under the area, where higher initial biomass stocks
(92 t ha-1) were burned for land preparation. Here, maximum concentrations were generally doubled except for sodium and magnesium.
Cumulative leaching below 105 cm soil depth in the study of Hölscher (1995) did not fit
with results of the present study (0.9 m depth), although nutrient concentrations were
comparable. N, K, Ca and Mg amounts of Hölscher (1995) were two to six times smaller,
most pronounced in the case of N with a loss of 13 kg ha-1 within the traditional cultivation period of 1.5 to 2 years. Our results were 84 kg N ha-1 (Table 40). Only Na was quite
comparable (78 versus 108 kg N ha-1). P and S of Hölscher's study were substantially
141
5.2.3 Soil-water-solute nutrient fluxes
higher, though still negligible. However, Hölscher (1995) was calculating nutrient leaching
losses on the basis of a micro-meteorological water balance of a fallow vegetation (compare chapter 5.1.3) without knowledge of actual soil-water fluxes at the cultivation sites.
The above-mentioned studies show a vertical decrease of concentrations in the soil solution from 25 cm down to 110 cm depth (Klinge, 1997) or 120 cm (Hölscher, 1995), especially for NO3, Ca, K, less for Cl and Mg. This could be also due to retention, though in
these depths considerable plant uptake of nutrients might be involved. Nevertheless, the
upper soil profile would have to be taken into consideration to improve the assessment of
adsorption/discharge and retardation processes of nutrients.
Soil water samples were obtained by suction cup lysimeters. It was assumed that with
these instruments representative samples of percolating soil water can be taken. This
was shown by Hetsch et al. (1979), who could not find significantly deviating nutrient
concentration in soil solutions obtained by ceramic cups of P80 type (which were used in
this study) compared to the input solution, with the exception of phosphate sorbed on the
ceramic surface. In tropical soils, however, P is strongly adsorbed on the soil particle surface and not assumed to be leached in considerable amounts. Also other authors under
the premises of equally applied tensions on all samplers and same (possibly short) sampling frequency could achieve reasonable results (Hansen & Harris, 1975; Grossmann &
Udluft, 1991). Nevertheless, they stressed a variety of sources of errors and pointed out
that "there is no quick and easy solution to obtain representative samples" (Litaor, 1988).
It is recommended to include appropriate convection-dispersion models for proper description of nutrient flow processes. In the present context, this approach could not be
pursued as chemical parameters (e.g. adsorption coefficients) and biological processes
like mineralization were unknown. It is doubtful whether such a modeling approach would
be feasible under field conditions.
142
5.2.4 Net-balance
5.2.4
Net-balance
Considering all relevant nutrient inputs and outputs a balance was drawn up for both cultivation sites using complementary data about deposition and biological nitrogen fixation
(BNF) from literature, most likely to be relevant the study region and vegetation
(Table 43).
Table 43: Nutrient balance of site 1 and 2, burned and mulched land preparation, considering the complete
cropping cycle (3.5 and 7 years of fallow, respectively and 2 years of cultivation); deposition according to
Hölscher (1995), BNF according to Thielen-Klinge (1997); leaching based on measurements at 3 m depths,
amounts in the second year of site 2 were assumed to describe also those of site 1
Site/Treatment
Site 1, burned
Deposition
Fertilizer
B(N)F
Burning
Firewood
Harvest
Leaching, 1997
Leaching, 1998
Σ
Site 1, mulched
Deposition
Fertilizer
B(N)F**
Harvest
Leaching, 1997
Leaching, 1998
Σ
Site 2, burned
Deposition
Fertilizer
B(N)F
Burning
Firewood
Harvest
Leaching, 1997
Leaching, 1998
Σ
Site 2, mulched
Deposition
Fertilizer
B(N)F
Harvest
Leaching, 1997
Leaching, 1998
Σ
C
-1
[t ha ]
14.2
-13.8
-0.2
(-6.0)
14.2
(-5.1)
22.7
-21.5
-0.9
(-6.0)
22.7
(-5.1)
N
P
K
Ca
Mg
S
-1
------------------------ [kg ha ] ---------------------------14
70
12
-246
-1.7
-125
-9
-7
-292
14
70
12
-112
-1
-9
-26
23
70
24
-372
-10
-127
-4
-7
-403
23
70
24
-119
-2
-9
-13
4
48
12
66
30
31
15
0
22
0
-8
-0.1
-22
-0.7
-0.2
22
-58
-1
-77
-2
-9
-69
-151
-2
-14
-19
-29
-155
-29
-0.3
-14
-3
-6
-36
-35
-0.3
-7
-2
-3
-26
4
48
12
66
30
31
15
0
22
0
-22
-0.7
-0.1
30
-83
-2
-1
-8
-14
-5
-20
23
-12
-1
-5
-3
-7
-4
-9
2
7
48
19
66
50
31
25
0
36
0
-11
-0.5
-25
-0.7
-0.2
18
-116
-6
-83
-4
-9
-132
-171
-13
-15
-16
-29
-163
-33
-2
-13
-4
-6
-33
-53
-2
-7
-3
-3
-32
7
48
19
66
50
31
25
0
36
0
-20
-0.8
-0.1
34
-76
-6
-1
2
-14
-23
-20
24
-11
-4
-5
5
-7
-5
-9
14
Volatilization losses and losses by particle export proved to be the major outputs on the
143
5.2.4 Net-balance
burned plots, more pronounced on site 2 which had more biomass. Nutrient exports with
harvested products were the most important losses for the mulched plots, but they also
constituted a great share of the losses on the burned plots. In the case P and K (solely
site 1) they even exceeded losses caused by burning.
The nutrient balance on the burned plots was highly negative for all elements except P.
On the mulched plots only minor losses of N were incurred, and on site 1 also for K and
Mg, whereas the other elements even had a slightly positive balance. The Phosphate balance was positive because of the fertilizer input of 48 kg P ha-1.
Between 292 and 403 kg N ha-1 were estimated to be removed from the (agricultural-)
system during a cropping cycle of 5.5 and 9 years on the burned plots of site 1 and 2, respectively. K losses differed considerably on both burned plots (69 and 132 kg ha-1),
while this was not the case for the other elements.
However, for the proper comparison of the nutrient balances between both sites, element
fluxes have to be related to the total number of years of the cropping cycle (Figure 45).
N
P
K
Ca
Mg
S
5
-5
[kg ha-1 a-1]
-15
-25
Site 1, burned
-35
Site 2, burned
Site 1, mulched
-45
Site 2, mulched
-55
Figure 45: Mean annual nutrient balance on both sites and both land preparations
Indeed, the calculations of mean annual balances offer a different view. Despite higher
total nutrient losses on the burned plot of site 2, losses per time unit were lower than on
site 1. This has important consequences in long-term effects of different fallow lengths of
traditional slash-and-burn agriculture. Diminishing the fallow length from 7 to 3.5 years
will result over time in higher nutrient losses, especially in the case of N and Ca, but also
of Mg and S. In the short run, yields were not affected by this reduction of fallow length.
Within about 50 years, the small-farmer could harvest 9 to 10 times assuming a constant
144
5.2.4 Net-balance
fallow length of 3.5 years, but only 5 to 6 times with a fallow length of 7 years. This comparison does not account for an eventual yield-decrease due to a continuously negative
nutrient balance. The situation was totally different under the mulched plots. Shortening
fallow did not affect the nutrient balance, as burning losses were avoided.
Leaching losses were determined at 3 m soil depth. More nutrients were retained within
the upper soil. As was discussed in the previous chapter, these retained nutrients might
also be leached in the first year(s) under the regrowing fallow vegetation. This would increase the total losses in the present study. But, this possibility was not studied and after
3 years of fallow, dissolved nutrient concentrations in the leachates were negligible.
The carbon dynamics could not really be tracked in the present study, as decomposition
of organic matter applied on the soil surface (mulch) and of soil organic matter (SOM)
was not measured. Field observation of the mulch surface indicated that most of the
mulch layer had disappeared after around 1.5 years of cropping. Biological incorporation
and turnover of the mulched biomass (but also of decaying roots) might positively influence SOM.
All nutrients, which were not lost via burning, should positively influence the crops, assuming that they are at a certain time plant-available. Based on soil nutrient analyses
and on crop yields, this could not be proved. At least, an increased leaching of nutrients
out of the mulched biomass was not detected and a surplus of e.g. 8 to 11 kg P ha-1 and
58 to 116 kg K ha-1 not lost through burning should be noticeable in the long run.
Indeed, Kato et al. (1999) could prove this positive effect on crop yields, when after traditional land use subsequently a second cropping sequence was initiated. However, a prolonged land use has long been identified to be responsible for worsening regrowth of the
fallow vegetation (Denich, 1989; Baar, 1997) and, thus, may not be desirable in shifting
cultivation.
The nitrogen balance was the most negative of all nutrients. With 24 kg N ha-1 of BNF after 7 years of fallow and a fertilizer input of 70 kg N ha-1, only marginal improvements
were seen in the balance. Thielen-Klinge (1997) stated that the calculated BNF was
comparably small and stressed the possibility that non-symbiotic nitrogen-fixing bacteria,
which were not considered, might contribute considerable amounts of N. Also the BNF of
cowpea (legume) was not assessed. To what extent these processes could positively alter
the balance is not known. Paparcikova (personal communication) compared the N-store
of the upper meter of soil under primary forest and under fallow and found a decrease
under fallow. This would rather support the concept of a continuously negative N-balance
145
5.2.4 Net-balance
of shifting cultivation.
Phosphate gave a positive balance in all cases, which gains importance as P is considered to be the most limiting nutrient in the cropping system. This was shown by Bünemann (1998) in an experiment with increasing P-fertilization for maize and cowpea, and
by Gehring et al. (1999) in a minus-one-trial for the (re-) growing fallow vegetation.
A nutrient balance for the traditional slash-and-burn system had already been carried out
by Hölscher (1995), though with less accuracy regarding leaching losses (see previous
chapter). His results based on a land use after 7 years of fallow, are comparable to results regarding site 1 preceded by 3.5 years of fallow. The biomass stocks of both fallows
were of the same magnitude (31.2 and 28.7 t ha-1, respectively), which indicates that not
fallow length but cumulative biomass of vegetation determines nutrient losses when volatilization losses are predominant. In Hölscher's (1995) calculations 285 kg N, 75 kg K,
125 kg Ca, 16 kg Mg and 13 kg S per hectare were withdrawn from the system, while P
was positively balanced with 11 kg P ha-1. Hölscher (1995) also found a considerable increase of yields by applying small amounts of NPK-fertilizer.
In both studies (Hölscher's and our) the fertilizer-P-input could more than balance the Pexports. This was not the case for N and K fertilizer, though the differences between fertilizer input and harvested exports are moderate, not supporting expectations of accelerated nutrient depletion and declining fertility due to fertilization.
In the modified, fire-free shifting cultivation, fertilization is basically necessary to overcome nutrient deficiency due to immobilization by microorganisms, and the resultant
yield reduction.
The situation in the system is most precarious for K (Table 44). Already Hölscher (1995)
found the highest rate of depletion of the total ecosystem store to be potassium.
Table 44: Amounts of potassium present on site 1 and 2 in different compartments, their percentages of
the total and withdrawal through slash-and-burn agriculture; root-K: assuming a root biomass of 25 t ha-1
(Sommer et al., 2000) with a K concentration of 3.5 mg g-1 (≅ concentration of wooden above-ground biomass)
Compartment
Site 1
-1
[kg K ha ]
[%]
Site 2
-1
[kg K ha ]
[%]
Above-ground biomass
100
24
194
32
(Burning remains)
(42)
(10)
(78)
(13)
Roots
87
21
87
14
Soil (exchangeable K, 0-3 m)
220
54
323
54
Total
407
100
604
100
Withdrawal
69
17
132
22
146
5.2.4 Net-balance
Out of a total of 407 and 607 kg K ha-1 of site 1 and 2, respectively, 17 and 22 % was
lost during one cropping cycle. In the case of Ca, Mg and N, these had been only 5 %, 6–
8 % and 2–4 %, respectively (Appendix, Table A-8) and thus less concerning. Certainly, percentages only relate to the present status quo of the (agro-eco) system. Long-term relationships are subject of a floating equilibrium, where potential withdrawals of nutrients
are driven by the available stocks. Therefore, it has to be assumed that, especially Kstocks, either must have been considerably higher in earlier times of shifting cultivation,
or that the study soils (still) contain considerable amounts of non-weathered minerals
with the potential to release K.
Percentages of P brought into the system in relation to available stocks are not easily determined, as plant-available P in the soil is replenished by considerable amounts of less
available P forms. A surplus of 18 and 22 kg P ha-1 on the burned plots, therefore, has to
be seen in this context. Nevertheless, a balanced phosphate budget remains important in
shifting cultivation, as P is assumed to be growth limiting. Potassium is on the point of
achieving this status, when shifting cultivation is continued in its recent form using fire as
land-preparation tool.
147
5.3 Ground water – well water
5.3 Ground water – well water
Water levels of nine wells of small-farmers close to the study sites were monitored
monthly during one year. These measurements were carried out as a first assessment of
the impact of the dry-season on soil water depletion and discharge on a micro-regional
scale. Despite of all uncertainties concerning the environmental/hydrological settings and
also not accounting for the consumption by the small-farmer, which obviously influence
the well-water charge and discharge, some basic results will be presented.
Mean annual well-water levels ranged from 3.46 m (Sebastião) to 10.6 m (Bosco) below
the soil surface (Figure 46). Though the altitudes of the wells could not be measured, localization by GPS on a topographic map (chapter 3.1) indicated that mean well-water levels were positively correlated with the altitudes.
1.5
Change of well-water level [m]
0.15
Sebastiao (3.46 m)
Tiao (6.77 m)
Comunidade (8.06 m)
Antonio (8.20 m)
Manoel (8.44 m)
Gonzaga (8.59 m)
Raimundo (8.62 m)
Fransisco (9.72 m)
Bosco (10.60 m)
Soil water store, 6-10 m
1
0.5
0
0.1
0.05
0
-0.5
-0.05
-1
-0.1
-1.5
98
1.
9.
98
8.
1.
98
7.
1.
98
6.
1.
98
5.
1.
98
1.
4.
98
3.
1.
98
2.
1.
7
98
1.
1.
.9
7
12
1.
.9
11
1.
10
.9
7
-0.15
1.
1.
9.
97
-2
Change of soil water store (fallow), 6-10 m [m]
2
Figure 46: Changes of well-water levels from September 1997 to August 1998 and corresponding soil water store change in 6-10 m depth (right axis) as modeled for the fallow site (compare Figure 26 chapter 5.1.3); shown are the relative changes in relation to mean annual well-water levels/soil water store
(= depths in parentheses)
The amplitude of discharge and replenishing ranged from maximal -1.75 m (9/2/98; Bosco) to +1.87 m (5/8/98; Raimundo) relative to mean well-water level. The single amplitudes were highly correlated with the mean well-water levels (R = 0.715).
The lowest well-water levels were reached on the 9th of February 1998 due to the dry
season and desiccation of the soil. However, the well of Sr. Sebastião at this time already
was beginning to be replenished. The re-wetting front of the beginning rainy season had
148
5.3 Ground water – well water
already reached the rather high water table of this well at the end of December 1997.
Consequently, depending on the (mean) depth of the water levels, individual replenishment was delayed up to one month. The water levels of the wells of Sr. Fransisco and Sr.
Antonio (reached/) passed their annual mean only in April 1998 (intersection of curve
with x-axis in Figure 46), though their mean levels were not the lowest. Just these two
wells, however, were located closest to site 2, where soil water drainage rates had been
found to be comparably low (chapter 5.1.3) causing a delayed replenishment.
Figure 46 also shows the change in soil water storage of 6-10 m soil depth, which was
modeled for the fallow site and given in Figure 26. The mean two-year storage in this
layer was 716 mm, fluctuating in the above-considered period between 602 mm (equal
to -0.115 m difference on the 4/3/98) and 834 mm (equal to +0.118 m on the 4/5/98).
The mean store was reached on the 16th of March 1998, coinciding with the period in
which replenishment of the wells reached their mean levels. In this regard, the units of
well-water level change and soil-water storage change are not comparable, as both processes are driven by rather different processes. In the first case for instance this involves
two or three dimensional water movement of aquifers.
On the 26th of November 1997 and the 23rd of April 1998 soil water samples of the nine
wells were analyzed for nutrient content. The first date represented the time of well/soil
water desiccation before major slash-and-burn activities. On the 23rd of April wells already
had begun to recharge, with the probability that nutrient concentrations had changed due
to the release of nutrients through cultivation activities. Nutrient concentrations at both
times were considerably higher than expected on the basis of the concentrations found in
the soil water under the fallow site at 6 m depth (Table 45).
Nutrient concentrations on the 23rd of April 1998 were much higher than on the previous
sampling date. The increase was the lowest for Cl (plus 25 %) and the highest for Ca (+
534 %). Also pH increased by about 0.5 units, while Al concentration decreased. The pH
of 6.9 at the 23rd of April 1998 was 2 units higher than measured 6 weeks earlier in the
soil solution from 6 m depth under the fallow site.
149
5.3 Ground water – well water
Table 45: Median, minimum and maximum nutrient concentrations, pH and EC of the well water of 26th of
November 1997 and 23rd of April 1998 and the percentage increase within these dates as well as the nutrient concentration in the soil solution of 6 m depth under the fallow on the 4th of March 1998; n=8, the
well water of Sr. Fransisco was not considered due to extremely biasing concentrations (contamination)
Element
Ca
K
Mg
Na
Al
Cl
Nitrate-N
S
P
pH [ ]
-1
EC [µS cm ]
---------- 26/11/97 ---------------------- 23/04/98 -----------Median
Median
Min
Max
Min
Max
-1
--------------------------------- [mg l ] -----------------------------------0.67
0.29
0.33
1.55
0.06
3.16
0.51
0.32
0.07
6.4
26.7
0.20
0
0.15
1.23
0
2.13
0
0.08
0.05
4.3
14.1
4.55
0.86
1.22
3.34
0.42
4.60
3.07
0.38
0.09
6.8
55.3
4.22
0.70
0.71
3.47
0.03
3.95
2.27
0.47
0.13
6.9
49.7
0.32
0.16
0.15
1.12
0
2.62
0.17
0.08
0.06
4.4
17.7
Increase
36.17
7.48
1.75
7.88
0.35
8.69
3.42
2.04
0.30
8.2
230.0
[%]
4/3/98,
6m
-1
[mg l ]
534
144
116
124
-54
25
345
47
95
0.17
0
0.17
0.43
0.15
2.12
0
0.10
0.03
86
4.9
13.7
A close relationship, as was established between seasonal soil water movement and well
water recharge, could not be found for the chemical properties of soil-water and well water. Interpretation of these results is hampered, as the data set is limited to two times of
the year. Moreover, at least two unknown factors are to be considered:
1. Though well water is fed by groundwater, which itself is fed by soil water, these three
fluxes are not necessarily the same. For instance, contributions of lateral moving water of different origin might add to the groundwater flux. The different origin might include geologically different strata present in deeper horizons enriched in dissolved
nutrients.
2. The well water might be contaminated e.g. by inattentive users or by dripping surfacewater
Contamination of well water might occur, but only in one case (Sr. Fransisco) this was
quite obvious (e.g. nitrate concentration 15.1 mg l-1, Cl-concentration 39.9 mg l-1), while
concentrations of the other well waters did not fluctuate as much as would be expected if
caused by contamination. Furthermore, a general increase in the nutrient concentration
in all wells at the second sampling can only be explained by an intensified input of nutrient-enriched surface water dripping into the wells. This is imaginable in the wake of heavy
storm events within the rainy season.
Higher nutrient concentrations in the well water could possibly be explained by an extensive leaching of nutrients out of the soil profile under cultivated sites. However, it could
be shown that those leaching losses occur with a delay if ever. It is then unclear, which
150
5.3 Ground water – well water
cultivation activities and in which year caused an increase of nutrients in the well water,
and why not all nutrient concentrations (especially Cl) were noticeably higher, and the pH
not lower. Furthermore, only a small part of smallholder land is temporary cultivated,
while most parts are in the state of fallow. It is questionable, whether 5-10 % of temporarily cultivated land can noticeable change the nutrient concentration of well water.
More plausible is a geologically and geochemically different stratum underlying the
uppermost stratum. Already in 1951 Sioli (1951) pointed out that in the Bragantina region three different geological formations are found: the so-called "Pará formation" (Quaternary-Pleistocene), secondly the so-called "series of the Barreiras" (Tertiary-Pliocene),
both of continental origin, and finally, the so-called "Pirabas formation“ of marine origin
(Tertiary-Miocene). The latter occurs over at least one-third of the total Bragantina region,
probably including the present study sites. It possesses deposits of limestone and is often
overlain by the Pará or the Barreiras formation. The Pirabas formation outcrops in some
parts, for instance about 30 km east of the location of the study sites, near the town Capanema. Sioli (1951) could prove that the creek water out of this formation has a considerably higher pH (around 7) and higher Ca concentration (25 to 42 mg l-1) than encountered in creek water not influenced by this stratum.
Both characteristics are also prevailing in the well waters of the present study, suggesting
that Pirabas is also underlying the study sites. Increased concentrations of nutrients in
well water at the second sampling date might thus be caused by an intensified nutrient
exchange (due to reduced water fluxes) between ground water and the Pirabas stratum
during the dry season. With the onset of the rainy season the nutrient enriched groundwater would be transported into the wells.
151
6.1 Methodology and concepts
6 General Discussion
6.1 Methodology and concepts
The quality of results obtained by a soil water model based on the Richards equation
generally depends upon three factors, to which the present study gave major attention:
1.) To which extent the soil hydraulic parameters (the relationships between soil moisture, pressure head and hydraulic conductivity) reflect real field conditions.
2.) The reliability of the boundary conditions.
3.) The realistic incorporation of the sink-term (root water uptake).
Ad 1) The soil hydraulic properties of the studied soils were obtained by inverse modeling
using continuous in-field pressure head readings, laboratory water retention curves
and pedo-tranfer functions (neural network predictions of Ks). The laboratory water retention curves proved to be a good estimation of the θ(h)-relationship for pressure
heads <~-100 cm, but in-field θ(h)-relationships under water-saturated conditions
could not be described with the laboratory curves. In this case, the saturated water
content θs had to be reduced to a satiated (=field-saturated) value. In accordance with
various other studies (Flühler et al., 1976; Buttler & Riha, 1992; Kühne, 1993; Klinge,
1997), this peculiarity seems to be more the rule than the exception and is apparently
caused by air-entrapment.
Also saturated hydraulic conductivity values (Ks), in the present study estimated by
neural network predictions, had to be modified, when parameter-optimization was
done. This is in agreement with findings of Tomasella and Hodnett (1994), who had to
highly increase Ks of a Brazilian Oxisol obtained by in-situ permeability measurements,
when optimized according to an internal-drainage experiment. But, also theoretical,
laboratory-obtained, relationships of the (unsaturated) hydraulic conductivity to water
retention K(h) might deviate from actual behavior (Johnson et al., 1999). This is especially true for the so-called "hybrid" Oxisols. Already Sanchez (1976) stated that these
soils are acting like sands in terms of water movement at low soil-water tensions but
hold water like clays at higher tensions. In our study, the magnitude of unsaturated
conductivity was optimized by the pore connectivity parameter l, which theoretically
should account for effects of discontinuity and tortuosity. Results were deviating from
the original suggestions of Mualem (1976; l = 0.5), but were in agreement with recent
suggestions for sandy soils of Schaap et al. (in revision, l =-1).
152
6.1 Methodology and concepts
Ad 2) The upper boundary of the model was fed by net-precipitation. When hourly micrometeorological data were available, the model was run with this frequency of data input. This was done to track fast responses to heavy storm-events. However, contrary to
gross precipitation, net precipitation, i.e. its components throughfall and stemflow,
could not be measured with the same frequency. It was therefore calculated based on
data of (bi-)weekly cumulative throughfall in combination with a canopy storage capacity term accounting for interception of rainfall by the vegetation. With that, calculated net precipitation and the complementary component interception achieved percentages, which were comparable to literature data. Interception of the fallow vegetation in 1997 was 6.6 % (139 mm) of an annual gross precipitation of 2104 mm, in the
second year amounting to 7.9 % (200 mm) of a total of 2545 mm. Nevertheless, netprecipitation assessment remained a critical point in the water balance study, as distinct relationships between gross precipitation and throughfall could not be proved,
even though a large number of collectors were used, as spatial heterogeneity of the
throughfall component was considerable. However, at no time the dynamics of modeled and measured soil water pressure head were deviating because of incorrect netprecipitation. This suggests a good fit between the calculated and real net precipitation.
Ad 3) The two-year sum of rainfall interception and modeled root water uptake by plants
(≅ transpiration) was comparable to the evapotranspiration obtained by micrometeorological measurements. However, within the observation period some deviations between both methods occurred, which were methodology-related. Evapotranspiration, not solely transpiration entered into the model to mark off (maximum) amounts
of root water uptake in the soil water model. Thus, in the rainy season (unrestricted
transpiration), the model overestimated root water uptake by the amount of evaporation. This was not the case in the dry season, where water-constraints (desiccated soil)
diminished root water uptake in the model. In this period, however, dew-evaporation is
likely to contribute to evapotranspiration to a remarkable extent, which is difficult to
quantify and to separate from root water uptake in micro-meteorological measurements. Therefore, in the dry season, the soil water model gives more reasonable estimates of soil desiccation, than the micro-meteorological measurements (alone).
Vertical root distribution can enter the model in any shape, when root distribution is
assumed to remain constant. This is adequate for the assessment of water uptake by
the stable deep-rooting fallow vegetation. When root growth is involved as in the culti153
6.1 Methodology and concepts
vation cycle, vertical root distribution is restricted by the model to a prescribed shape,
which results in slight deviations between modeled and measured soil water content
under the cultivation sites.
On the other hand, vertical root distribution in the model determines vertical distribution of soil water extraction in times without water stress, when maximum transpiration
is split between soil layers according to the normalized root distribution (equaling a
percentage distribution). This might not reflect natural behavior, as plant water uptake
e.g. out of 5 m soil depth may need to be induced by a desiccation of the upper soil
layer. In this case, the distribution of water uptake according to the root distribution
might be misleading, i.e. overestimating the annual water uptake from deeper soil layers. Therefore, interpretation of model results in this regard should be taken cautiously.
Actually, the root-uptake-inaccuracy is related to the difficulty of clearly separating rootactivity-induced and drainage-induced alterations of the soil water pressure head and
soil water content. A perfectly adapted feedback model, however, is never achievable
for field-conditions, because of the heterogeneity of the soil hydraulic parameters and
root distribution in the field. This point was stressed by Ehlers (1976), who detected a
considerable overestimation of the unsaturated conductivity determined in situ, when
root water extraction was not considered under plots, where the vegetation had to remain undisturbed. Water input and soil water movement in the rainy season exceeds
root water uptake by at least a factor of 10, which prevents statistically precise differentiation of both processes.
Clearly different is the situation in the dry season, when water fluxes are negligibly
small and root water uptake is predominant. Then, only an exorbitant raise of the unsaturated hydraulic conductivity, to equal the rate of transpiration (i.e. 2-4 mm d-1),
could effect a deep soil desiccation by drainage alone. This was not apparent in the
present study.
Validation of models is essential before their application. This was done with a limited
number of independent gravimetric determinations of the soil water content, which
proved to be in agreement with modeled soil moisture even under extreme desiccation.
Additionally, satiated water contents, approximated in situ, did not reach the laboratorysaturated values. Determination of both "ends" of soil water retention also gave valuable
information about the effective water content encountered in the field, which is most important regarding amounts of water involved in the flow processes (Tomasella & Hodnett,
1996).
154
6.1 Methodology and concepts
Bypassing or short-circuit flow, as well as channeling or preferential flow, also generally
known as nonideal flow processes (Brusseau & Rao, 1990), are certainly contributing to
noticeable extent to the water movement in structured soils. This was shown in a number
of publications (Bouma et al., 1982; Beven & Germann, 1981 and 1982; German &
Beven, 1981; Germann, 1986; Mallants et al., 1997). In the present study, however, the
soils were homogeneously structured with a medium to coarse texture, where K-θ-models
generally fit best (Schuh & Cline, 1990). Thus, a nonideal flow cannot simply be introduced to explain deviations of modeled to measured pressure head and/or soil moisture
dynamics, when detailed knowledge of soil hydraulic characteristics is missing as in the
present study.
Nonetheless, it was possible on the basis of the Mualem-Van-Genuchten approach, to
apply a soil water model, which could describe prevailing pressure head dynamics, as
well as soil moisture status at selected times, with sufficient accuracy. Additional proximity of model-results to the real dynamics, especially with regard to the time-lag of rewetting fronts, might be achieved by including hysteresis into the model (Kool & Parker,
1987) or relaxing Van Genuchten's (1980) closed-form equation to gain more flexibility
regarding unsteady hydraulic behaviors. The latter was done by Klinge (1997), optimizing
θ(h) and K(h) relationships of an Brazilian Oxisol on a tabular-basis, achieving impressive
accordance with measured pressure head dynamics under primary forest using a unimodal, one-dimensional soil water model. The current version of the Hydrus-1D model did
not allow a tabular modification of the soil hydraulic parameters, but this is planned to be
incorporated in the following version (Simunek, personal communication).
It is uncertain to which degree the pore-space of aggregated clay particles of the loamy
sandy (to sandy clay loamy) soil of the present study is excluded from quantitative important water movement (immobile water domains). This could promote macro-porous structure and rapid, bypassing water movement (Young & Leeds-Harrison, 1990). Such an effect was shown to be important for an Indonesian clayey Kanhapludult (Ultisol; clay content 50-80 %) by Arya et al. (1999). In their study, only native, deep-rooting (fallow) vegetation was able to extract water out of immobile water domains of meso and micropores
of the subsoil, possibly facilitating nutrient recycling.
Retention of percolating nutrients under the cultivation sites, as was observed in the present study, might be explained by diffusion of these nutrients into the immobile water
domains and thus by spatial exclusion from further convection. But, nutrient retention
was not consistent with this concept, as some nutrients were preferentially retained (potassium). Moreover, the vertical decrease in concentration was not uniform for all nutri155
6.2 Deep soil water uptake
ents, and small amounts of sulfate were released. Finally, the break-through curves of Ca,
Cl or nitrate did not follow nonideal solute transport with early initial break through and
relative "tailing", i.e. delayed approach of a fraction of those elements and compounds.
It appears plausible that retardation in solute transport was caused by a temporary increase of AEC and CEC due to an increase in ionic strength of the soil solution, affecting
low leaching amounts in 3 m soil depth under the cultivation sites in the first two years. If
true, leaching has to be expected in the following years under fallow.
6.2
Deep soil water uptake
The present study demonstrated that the deep-penetrating roots are crucial for the fallow
vegetation to maintain an evergreen canopy during the dry-season. In the four-monthslasting dry season of 1997, 74 % of the transpired water was taken up from the soil reservoir below 0.9 m depth. In 1997 and 1998, 400 mm and 427 mm, respectively, originated from 0.9 m to 6 m depth. These results show that the fraction of about 30 % of
roots of the fallow vegetation found in the deep layers below 0.9 m depth plays an important role in the water balance. The results confirm also findings of Hölscher (1995), who,
based on a micro-meteorological approach, calculated a substantial contribution of deepsoil water to transpiration of a fallow vegetation in the Bragantina region. The results are
also comparable to those of hydrological and micro-meteorological studies of Amazonian
primary forests. Also for these forests, a deep root system was proven to provide water
for transpiration in the dry season (Poels, 1987; Nepstad et al., 1994; Hodnett et al.,
1996; Klinge, 1997; Ashby, 1999).
In comparison to the fallow, deep-soil water usage of the crops was low. In the study period only 256 mm 2 a-1 were taken up from below 0.9 m, including the contribution of the
regrowing fallow vegetation at the end of the second year. Thus, the capacity of the fallow
vegetation for deep soil-water use exceeds that of crops 3 to 4 times.
6.3 Nutrient uptake
Van Noordwijk (1989) established a leaching model, in which he supposed that a deeprooting fallow vegetation is capable to take up all nutrients that are leached below the
rooting zone of the crops, as long as the leaching front has not passed the maximum effective rooting depth of the fallow.
The root biomass of the upper 1 m of the fallow vegetation in the Bragantina region was
156
6.3 Nutrient uptake
significantly reduced during the two-year cropping phase, but the deeper root biomass
remained unaffected (Sommer, 1996). Therefore, analogous to Van Noordwijk's leaching
model, the deep root system might provide a safety net against leaching losses. The root
system of a fallow vegetation, however, might not be active during the cropping period, in
times, when the aboveground biomass of the fallow is frequently weeded. If so, nutrient
recycling depends on the velocity of re-activation of the root system after abandonment of
cropping, but also on the velocity of movement of the leaching front. Furthermore, the
"mesh-width" of the safety net of the roots is important. In our study, the rather low rootmass density in deeper soils (~0.1 mg cm-3) raised question about a 100 % recycling.
On the other hand, retarded leaching found in the present study, especially for highly mobile elements and compounds (e.g. nitrate), increases the probability of uptake due to an
increased importance of the diffusion component in solute transport.
In the modified system of slash-and-mulch, the nutrient recycling capacity of the fallow
vegetation is generally even more important, as the non-combusted nutrients are in danger of being leached. However, as most of the nutrients of the mulched biomass are released slowly and additionally are retained by the soil, the deep-rooting of the fallow
vegetation may be less important. Then, uptake of nutrients might be predominantly accomplished by the dense superficial rooting system of the fallow, as after the cropping
period most nutrients are still not leached out of the upper soil layer.
Only some attempts were made to assess the capacity of nutrient uptake by deep-rooting
fallow vegetation or trees (Van Noordwijk, 1989; Shepherd et al., 1996). Apart from the
fact that results are rather contradictory, direct quantification of nutrient uptake by deeproots was not achieved. Therefore, the role of deep roots in nutrient cycling in the tropics
is still unknown as Nepstad et al., already pointed out in 1991.
In the present study, the apparently slow decrease in soil fertility, in spite of the century
long cultivation-history on these nutrient-poor soils, might be due to an uptake of deepsoil nutrients. Nevertheless, though the nutrient recycling from deep soils seems likely
assuming a nutrient uptake along with water uptake, results of the present study do not
prove it unequivocally.
If we assume that dissolved nutrients are taken up by a re-establishing fallow vegetation,
the magnitude of leaching losses is low compared to volatilization losses. Therefore,
avoidance of burning should have priority about improvement of the nutrient-recycling
capacity of the cropping system.
157
6.4 Sustainability of slash-and-mulch agriculture
6.4 Sustainability of slash-and-mulch agriculture
Soil fertility deterioration cannot clearly be shown by comparison of former and recently
carried out soil fertility analyses or studies on crop yields. However, (soil) nutrient depletion was clearly indicated in the present study by the negative nutrient balance of the
slash-and-burn cycle. Nitrogen losses were highest with 292 to 403 kg N per hectare and
cropping cycle (5.5 and 9 years, respectively). Viewed in relation to available stocks in the
system, especially mining of potassium of 69 to 132 kg K per hectare and cycle is of concern. Phosphate, though positively balanced in the present study due to fertilization, has
already reached a critical limit for crop production in the Bragantina region. This was already demonstrated in previous studies of Bünemann (1998), Gehring et al. (1999) and
Kato et al. (1999). In the present situation it is only a question of time, until potassium
also will become a growth-limiting element.
Therefore, avoiding fire as a means of land preparation is a necessary step towards ecological stabilization of shifting cultivation. The present study showed that the input-output
budget for the slash-and-mulch system was balanced for all nutrients (negligibly negative
only for N and K). Mulching, however, requires fertilization, to avoid unacceptable yield
reduction due to the immobilization of soil nutrients by microorganism. But, already the
moderate fertilization in the present study could more than double the regular (slash-andburn) yields without fertilization. Thus, the results show that even under conditions of
shortened fallow (3.5 years) crop production can be enhanced without causing deterioration of soil fertility.
The feasibility of slash-and-burn agriculture is commonly explained by the input of major
quantities of nutrients by the ash (Sanchez, 1982; Smyth & Bastos, 1984). In the present
study, however, the ash contained only small amounts of N (3.1 to 5.3 kg ha-1) and P (0.7
to 5.7 kg P ha-1), as the largest amounts bound in the fallow biomass (~97 % N and 63 to
90 % P) were lost during intensive burning. But, considerable amounts of K (41 to
72 kg ha-1) and Ca (105 to 195 kg ha-1) remained in the ash, which raised the soil pH.
The latter stimulates the decomposition of organic matter (Rowell, 1994). Therefore, it
may be assumed that decomposing organic matter, not the ash, provides most N and P
for the crops. This was also detected by Stromgaard (1984) and Lessa et al. (1996). Indeed, contents of plant-available P in 0 to 10 cm soil depth on the burned and mulched
plots were significantly higher after 7 months of cultivation.
Mulching might primarily lead to an immobilization of nutrients by microorganisms. But, in
the long run, after 1.5 years, as was shown by Kato et al. (1999), decomposition of the
158
6.4 Sustainability of slash-and-mulch agriculture
mulch leads to higher Nmin-concentration in the soil solution at 40 cm depth compared to
burned sites and control plots. This is a peculiarity that gives new options for the cropping-management. For instance, one might change the sequence of crops to match
(peaks of) nutrient demands with available stocks in the soil. Details, however have to be
evaluated.
The overall leaching losses did not increase under the mulched plots during the observation period, neither at 0.9 m depths nor below. This was apparently due to immobilization
of nutrients by microorganisms and plant uptake. Fertilization, in combination with the
rate of decomposition, satisfied crop demand and did not lead to increased leaching.
Though less likely, it cannot be excluded, that leaching might still occur in the first years
of the fallow following the cultivation, which were not included in the observation period.
The initial concentrations of dissolved nutrients upon cultivation were diminished during
percolation. Two-year sums of nutrients leached below 3 m depth were rather insignificant in relation to overall nutrient fluxes. For instance, during the two years of cropping
10 to 16 kg N ha-1 were leached, but 246 to 372 kg N ha-1 were removed by the burning
and 112 to 127 kg N ha-1 were withdrawn by the harvested goods. Williams and Melack
(1997) measured a comparably negligible increase in nutrient exports via leaching on a
catchment scale in the central Amazon region. After forest conversion, about 9, 4, 5, 1,
and 0.07 kg ha-1 Ntot, Ca, K, Mg and P, respectively, were found to be leached out of the
catchment over one year, predominantly by solute base flow (94 %).
Hydrological measurements on a catchment scale are not always comparable to leaching
losses estimated for the rooting zone. This was indicated in the present study considering
nutrient concentrations of well water. These were higher than expected if calculated on
the basis of the distribution of recently cultivated fields in the catchment. A catchment
approach is subject to different system-boundaries (e.g. aquifers in different geological
strata), which is only partly captured by field-scale determinations.
Based on our results, slash-and-mulch in combination with maintaining a deep-rooting
fallow vegetation is seen as a feasible alternative to overcome deterioration of soil fertility
in shifting cultivation under pressure of land use. (Socio-) economic studies should now
follow, to describe the conditions, under which a mechanized slash-and-mulch landpreparation will be practicable for smallholders. Such studies should also consider the
recent trend in smallholdings towards establishment of perennial cash crops such as
passion fruit. Such trends in intensification imply the extinction of natural deep-rooted
fallow vegetation and, thus, a loss in the capacity for recycling of nutrient from deep soil
layers.
159
7 Conclusions
7 Conclusions
The application of a soil water model provided a highly differentiated insight into the water dynamics of the two cultivation sites and the fallow. High-resolution data on the soil
water movement is essential for subsequent distinct description of solute transport processes. Soil water model predictions of annual actual evapotranspiration were comparable
with those micro-meteorologically obtained. The comparison of daily evapotranspiration
generated by both methods indicated times, where the micro-meteorological assessment
overestimated root water uptake due to the inclusion of dew evaporation. Further studies
should quantify the contribution of dew evaporation to total evapotranspiration.
The characteristic of the studied soils to carry variable positive and negative charges influenced the transport of dissolved ions. It was likely that in response to elevated concentrations of solutes, the subsoil cation and anion exchange capacity increased causing a
retention of the dissolved ions. A subsoil accumulation of nutrients, especially of the
highly mobile anions as nitrate and chloride, has to be confirmed by the determination of
concentration of exchangeable ions. The description of transport processes might additionally be improved applying the classical convection-dispersion equation, when necessary adsorption coefficients/isotherms and biological processes are known.
The ability for nutrient retention in the subsoil emphasizes the importance of a deeprooting fallow vegetation to recycle the retained nutrients. The considerable capacity for
the depletion of subsoil water of this vegetation was proven. Assuming an equivalent accompanying (passive) uptake of dissolved nutrients, a nutrient recycling capacity of the
deep-rooting fallow vegetation is likely. Consequently, the "mesh width" of this safety net
against leaching should now be evaluated by determining the extent, to which natural
deep-rooted vegetation contributes to the nutrient balance of traditional as well as modified shifting cultivation. This might be done by identifying natural tracers, with a distinct
vertical gradient in the soil. One might also introduce labeled fertilizer (e.g. with 15N) into
the subsoil. In both cases the main pathways of allocation of such elements subsequently
have to be traced.
The nutrient balance showed that slash-and-burn agriculture with a short fallow period, as
practiced in the Bragantina region, is a soil-nutrient-depleting form of agriculture. Looking
only at plant-available soil nutrient stocks, which are not as reduced, as expected from
land-use history, only tells half of the story. Total weatherable minerals and, thus, poten160
7 Conclusions
tially plant-available nutrient stocks should also be considered in future.
Slash-and-mulch cultivation with a moderate fertilization is an ecological sound option.
However, the interaction or interference of the large amounts of mulched biomass with
the soil-matrix and with the planted crops is hardly understood yet. Therefore, further
studies should analyze the organic matter and nutrient turnover, which are likely to be
greatly modified through the new land-preparation technique. It is commonly expected
that mulching increases the soil organic matter stock. If so, soil fertility would be improved and, additionally, carbon would be sequestered. These effects need to be ascertained by field measurements.
In short – for an intensified, but ecologically sound land use in the Bragantina region two
components appear crucial:
1. a natural, deep-rooting (fallow) vegetation to attenuate leaching losses (additionally
also maintaining a high degree of biodiversity) and
2. land preparation by slash-and-mulch to avoid soil nutrient depletion by burning (eventually also improving soil fertility and sequestering carbon).
161
8 Summary
8 Summary
Small-farm shifting cultivation predominates the northeast of Pará state in the Eastern
Amazon of Brazil. Slashing and burning a three to eight-year-old fallow vegetation is followed by cultivation of maize, beans and cassava for a period of one and a half to two
years. In the subsequent fallow period the secondary (fallow) vegetation regenerates from
roots and stumps, which survived the cropping period. Besides maintaining a relatively
high biodiversity the growing vegetation accumulates nutrients in the biomass and soil
fertility is successively restored, enabling limited crop production on basically nutrientpoor soils. Additionally, the fallow vegetation successfully shades out and, thus, suppresses herbaceous weeds.
Though the interaction of fallow (vegetation) and cropping is well known, the subsoil water and nutrient dynamic of this vegetation has not yet been studied. The need for such a
study is evident, as the fallow vegetation maintains a deep-reaching root system to at
least 6 m depth. Also a previous study assumed deep soil water depletion during the dry
season in September to December. Therefore, the first objective of this thesis was to assess the subsoil water dynamic of a fallow vegetation.
Burning as land preparation technique releases the major part of the C and N accumulated in the aboveground biomass, but also large quantities of P, K, Ca, Mg and S to the
atmosphere. To counterbalance this nutrient loss, the length of the fallow period would
have to be extremely long (> 70 years). The contrary is the practice, as smallholders
rather reduce the fallow length due to the pressure on land. Therefore, a fire-free land
preparation through slashing, chipping and mulching is suggested as a promising alternative. The second objective of this thesis was to compare the nutrient dynamics of this alternative land preparation with those of traditionally burned plots. The fallow length and
its effect on the nutrient balance was additionally considered.
It was anticipated that applying large amounts of biomass to the soil surface would lead
to higher losses of nutrients by leaching. Therefore, the movement of dissolved nutrients
in the soil profile was especially considered.
For determinations of deep soil water uptake a three-year-old secondary vegetation was
selected. For the study of the cultivation impact two sites with three-and-a-half and sevenyear-old secondary vegetation were selected (henceforth site 1 and site 2). The age of the
preceding secondary vegetation represents the minimum and maximum fallow length of
slash-and-burn agriculture in its recent form. Starting the agricultural phase in December
1996, the fallow vegetation was slashed, one half of each field was burned and the other
162
8 Summary
was mulched with a tractor-force-driven modified maize chopper.
Following site preparation, on both sites maize (Zea mays) was sown at the end of January 1997, cowpea (Vigna unguiculata) followed at the end of May and cassava (Manihot
esculenta) at the end of June. Maize was fertilized with 60 kg N ha-1 (urea), 26 kg P ha-1
(triple-super-phosphate) and 25 kg K ha-1 (KCl). Beans received 10 kg N ha-1, 22 kg P ha-1
and 41 kg K ha-1 of the same kind of fertilizer, on both cultures broadcasted by hand.
Maize-cobs were harvested in mid-June 1997, beans (+pods) at the beginning of August
1997. The last (sixth) weeding was done in mid-March 1998, and then fallow vegetation
was allowed to regrow. The cropping phase was terminated with harvesting of the cassava-tubers at the end of June 1998.
The aboveground nutrient balance was calculated measuring the nutrient input and output-quantities (fertilizer, atmospheric deposition, gaseous losses and extraction of harvest products and of firewood). To investigate the belowground leaching losses, concentrations of dissolved nutrients were determined in samples of soil solution taken biweekly
using suction-cup lysimeter.
Precise quantification of the soil water fluxes in the rooting zone is only possible applying
a soil water model. The annual dynamics of the soil water pressure head at different
depth in the soil profile were recorded with tensiometers. The soil water movement then
was modeled (inversely) using laboratory soil-water retention curves, pedo-transfer functions and applying the soil water model, Hydrus-1D.
To assess net precipitation, which is a necessary input into the soil water model, its two
components, throughfall and stemflow, were measured biweekly on the cultivation sites
and the fallow. Further microclimatic parameters (net-radiation, air temperature, vapor
pressure, wind speed) were determined to predict the potential evapotranspiration (ETp)
according to Penman, the so-called 'sink-term' in the soil water model. The actual evapotranspiration (ETa) of the fallow vegetation was determined according to the PenmanMonteith method as well as the Bowen ratio – energy balance method. ETa-results were
compared with the outcomes of the soil water model on root water uptake. All measurements were taken over the 1.5 years of traditional agricultural land use.
Water dynamics
Rainfall interception of the fallow vegetation in the first year amounted to 139 mm, i.e.
6.6 % of the annual gross precipitation (P) of 2104 mm. In the second year this was
200 mm or 7.9 % of P equaling 2545 mm; the percentage slightly increased, apparently
163
8 Summary
due to a closing canopy of the growing vegetation. Rainfall interception of the crops was
less amounting to 4.1% in 1997 and 3.8 % in 1998.
Actual evapotranspiration (Bowen ratio energy balance) of the 3 to 4-year-old vegetation
was sensitive to the pronounced dry season in 1997 reaching 1411 mm and exceeding
soil water model prediction of ETa of that year (1270 mm) by 141 mm. Differences were
apparently related to the addition of dew evaporation to actual evapotranspiration, which
could be determined micro-meteorologically but not by the soil water model.
Soil-water drainage according to model results was 897mm in 1997 (43 % of gross precipitation), but only 842 mm (33 %) in 1998 due to the influence of an intensive soil water store depletion in the former year, which was not fully compensated by the higher
rainfall in the second year. The crops had a lower evapotranspiration resulting in higher
drainage rates of 1190 to 1279 mm a-1, exceeding those of natural vegetation by 348 to
382 mm.
The deep roots of the fallow vegetation were crucial for the soil water uptake. As much as
35.4 % (427 mm) and 33.4 % (400 mm) of the water transpired in 1997 and 1998, respectively, were extracted out of the soil layer of 0.9 to 6 m depth. In the pronounced dry
season of 1997 this fraction was even exceeding 70 %. Even though within the dry season the fallow vegetation gets into a stress situation, visible through a reduced transpiration and increased canopy resistance, most fallow species are able to maintain an evergreen canopy due to the fact that they deplete the deep soil water storage.
On the cultivation sites the soil water store below 0.9 m depth was only marginally depleted. Mostly cassava, but also the regrowing fallow vegetation after abandonment of
the sites, was responsible for a water extraction out of the soil layer of 0.9-1.8 m depth
during the second year of cultivation.
Nutrient balance
Burning-losses of C and N bound in the aboveground biomass on both sites were considerable. At least 93 % of both element-stocks, corresponding to 13.8 and 21.5 t C ha-1 and
246 and 372 kg N ha-1 on site 1 and 2, respectively, were volatilized. Additionally, over
80 % of S, i.e. 35 and 53 kg S ha-1 (site 1 and 2) were lost by the fire. Also 45 % to 70 %
of the generally less volatile cations K, Ca and Mg were lost, apparently mostly by particle
flight. The export of the growth/yield-limiting phosphate was alarming, as it comprised
90 % of the aboveground stocks on site 1. The absolute amounts of 8 and 11 kg P ha-1
volatilized were offset by the subsequent fertilizer input of 48 kg P ha-1.
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8 Summary
Slash-and-mulch prevented these losses and, supported by the moderate NPKfertilization, yields of maize, beans and cassava were not different from those of the
burned treatment. The yields exceeded those commonly achieved by smallholders of the
Bragantina region by a factor of two to three. About 2.3 t ha-1 maize-grains, 1.7 t ha-1
cowpeas and 20.2 t ha-1 cassava tubers were harvested. The harvest was responsible for
most of the withdrawal of nutrients on the mulched sites, but was generally only of secondary importance on the burned sites.
Due to the high burning losses of nutrients, the overall nutrient balance of the burned
sites was negative for all elements, with the exception of phosphate, which was balanced
by the P-fertilization. Losses amounted to: 292 to 403 kg N ha-1, 69 to 132 kg K ha-1, 155
to 163 kg Ca ha-1, 36 to 33 kg Mg ha-1 and 26 to 32 kg S ha-1 (site 1 and 2; referring to
the total cropping cycle of 5.5 and 9 years).
Relating overall nutrient losses of the burned sites to the rotation-period of 5.5 and
9 years indicated that shortening the fallow period led to an increase in nutrient-mining.
This means that, besides the general fact that slash-and-burn agriculture in its traditional
form is ecologically unsustainable, soil nutrient mining is even accelerated by intensified
land use and shortened fallows. An important contribution apparently is the increase of
burning intensity of young fallow slash, which consists only of small stems and branches.
This increases also the percentages of volatilization of nutrients, presumably due to the
more intensive fire.
In contrast, under slash-and-mulch practice even intensified land use (3.5 years of fallow
only) seems to be feasible, as the nutrient balance of this site was in balance, which was
anyway the case after 7 years of fallow.
The leaching losses measured at a reference depth of 3 m were comparably low for both
sites and treatments. Mulching, i.e. the application of high amounts of biomass, did not
promote leaching.
The concentration of nutrients in the soil solution at 0.9 m depth temporarily increased in
response to the following measures:
1.) initial land preparation, sowing of maize and first fertilization in January 97
2.) weeding, sowing of cowpea and second fertilization in May 97
3.) desiccation and rewetting of the soil profile at the end of the year 1997
4.) harvest of cassava at the end of June 98
The nutrient concentrations under the mulched plots generally did not reach the values
found under the burned plots. On the other hand, the length of the preceding fallow period had no significant influence on the concentration.
165
8 Summary
The transport dynamics of the leachates were apparently highly influenced by the ion exchange capacities of the soil. Comparing the nutrient fluxes at the reference depths of
0.9 m, 1.8 m and 3 m depth, the quantity of all mobile nutrients, but also chloride and
sodium, were reduced during percolation and must have been retained. Considering both
sites and treatments, more than 80 % of the nitrate and more than 75 % of the chloride
measured in 0.9 m depth was retained in the underlying soil profile and did not reach
3 m soil depth in the first year (1997). Also, all important cations were retained, though to
a lesser extent. The retention capacity was more pronounced in the burned plots. Within
the two-year observation period, 67.4 kg nitrate, 103.8 kg Ca, but also 11.6 kg K and
23.5 kg Mg per hectare were retained on site 2 between 0.9 m and 3 m depth.
As all quantitatively important ions were retained during percolation, "simple" exchange
processes at the soil matrix do not explain the nutrient retention. This would require
equivalent amounts of exchanged ions in the soil solution in 3 m depth, which actually
were not found. Possibly, an increase in the cation as well as anion exchange capacity
due to an increased ionic strength of the soil solution is responsible for this retention.
This, however, would imply a release of the retained nutrients after the abandonment of
the area, when percolating rainwater with extremely low ionic strength leads to a decrease of the exchange capacities towards initial (natural) conditions. If so, a rapid reestablishment of the deep-rooting secondary vegetation is crucial for an efficient deepsoil nutrient uptake. This would limit the scope of prolonged cropping and any agriculture
that reduces the vitality of the fallow vegetation.
166
8 Zusammenfassung
Zusammenfassung
Kleinbäuerliche Feldumlagewirtschaft ist das vorherrschende Landnutzungssystem im
östlichen Amazonasgebiet von Brasilien. Nach dem Roden und Brennen einer in der Regel drei bis acht Jahre alten Sekundärvegetation wird über den Zeitraum von eineinhalb
bis zwei Jahren Mais, Bohnen und Maniok angebaut. In der anschließenden Brache regeneriert sich die Sekundärvegetation (Brachevegetation) durch Wiederaustrieb aus verbliebenen Wurzelstöcken. Die Sekundärvegetation behält dabei ein relativ hohes Maß an
Biodiversität bei, sie akkumuliert zudem Nährstoffe in der Biomasse und kann erfolgreich
Unkräuter ausschatten und unterdrücken. Außerdem erholt sich die Bodenfruchtbarkeit
während der Brache, womit im begrenzten Maße Nahrungsmittel auf eigentlich nährstoffarmen Böden erzeugt werden können.
Obwohl die Interaktionen zwischen Brache(vegetation) und Bewirtschaftungspotential gut
verstanden sind, wurde die Wasser- und Nährstoffdynamik tiefer Böden in diesem System noch nie untersucht. Dies ist aber notwendig, da sich gezeigt hat, dass die Brachevegetation ein Wurzelsystem ausbildet, das zumindest 6 m tief reicht. Außerdem wurde
auf Grundlage einer früheren Untersuchung vermutet, dass in der Trockenzeit, die von
September bis Dezember andauert, die Brachevegetation tieferen Bodenschichten Wasser entzieht. Das erste Ziel dieser Arbeit war es folglich, die Wasserdynamik tiefer Bodenschichten unter einer Brachevegetation zu untersuchen.
Das Brennen zu Beginn des Anbaus führt zur Freisetzung des größten Teils des in der
oberirdischen Biomasse gebundenen Kohlenstoffs und Stickstoffs, aber auch von großen
Mengen an Phosphat, Kalium, Calcium, Magnesium und Schwefel in die Atmosphäre. Um
diese Verluste alleinig durch atmosphärische Deposition auszugleichen, sind extrem lange (> 70 Jahre) notwendig. Das Gegenteil ist aber der Fall, da Kleinbauern aufgrund von
steigendem Landnutzungsdruck die Brachezeiten reduzieren. Deshalb wird die feuerfreie
Landnutzung mittels Roden, Häckseln und Mulchen als eine vielversprechende Alternative gesehen. Das zweite Ziel dieser Arbeit war es, die Nährstoffbilanz dieser alternativen
Landnutzungsform mit dem traditionellen Brandrodungsfeldbau zu vergleichen. Dabei
wurde zusätzlich der Einfluss der Brachezeit berücksichtigt.
Es wurde angenommen, dass das oberflächliche Ausbringen von großen Mengen von
gemulchter Biomasse zu erhöhten Nährstoffausträgen durch Versickerung führt. Deshalb
wurde der Transport von gelösten Nährstoffen im Boden besonders beobachtet.
Zur Quantifizierung der Pflanzenaufnahme von Wasser aus tiefen Bodenschichten wurde
eine dreijährige Brachevegetation ausgewählt. Zur Untersuchung des Einflusses der ver167
8 Zusammenfassung
schiedenen Landnutzungsmethoden auf die Nährstoffdynamik waren dies zwei Brachevegetationen im Alter von dreieinhalb bzw. sieben Jahren (im folgenden Fläche 1 und Fläche 2). Die Brachezeit dieser beiden Flächen stellt die minimale bzw. maximale Dauer einer Brache unter momentanen Bedingungen dar. Zu Beginn der landwirtschaftlichen
Nutzung im Dezember 1996 wurde die Sekundärvegetation gerodet und auf einer Hälfte
jeder Fläche gebrannt. Der jeweils verbliebene Teil wurde mit einem traktorgetriebenen
modifizierten Maishäcksler zerkleinert und als Mulch auf diesem Teil der Flächen verteilt.
Im Anschluss an die Flächenvorbereitung wurde im Januar 1997 auf beiden Flächen Mais
(Zea mays) gesät, Ende Mai folgten Augenbohnen (Vigna unguiculata) und Ende Juni Maniok (Manihot esculenta). Mais wurde mit 60 kg N ha-1 (Harnstoff), 26 kg P ha-1 (Trippelsuperphosphat) und 25 kg K ha-1 (Kaliumchlorid) gedüngt. Die Bohnen erhielten 10 kg N
ha-1, 22 kg P ha-1 und 41 kg K ha-1 in identischer Düngerform. Mitte Juni 1997 wurden
der Mais (Kolben) geerntet, Anfang August 1997 die Bohnen und im Juni 1998 die Maniokknollen. Bereits im März 1998 fand die letzte der insgesamt sechs Unkrauthackungen statt. Im Anschluss konnte die Brachevegetation wieder aufwachsen.
Zur Berechnung der oberirdischen Nährstoffbilanz wurden alle Ein- und Austräge quantifiziert. Dies waren Düngung, atmosphärische Einträge, Volatilisationsverluste, Ernteentzüge sowie Brennholzentnahme. Um die unterirdischen Versickerungsverluste zu bestimmen, wurden die Konzentrationen von gelösten Nährstoffen im Sickerwasser bestimmt,
das mit Saugkerzen im vierzehntägigen Turnus entnommen wurde.
Die exakte Bestimmung der Bodenwasserflüsse im Wurzelraum ist nur durch die Anwendung eines Bodenwassermodells möglich. Dafür wurde mit Tensiometern die jährliche
Dynamik der Saugspannung des Bodens in verschiedenen Tiefen gemessen. Die Bodenwasserflüsse wurden aufbauend auf Labor pF-Kurven und Pedotranferfunktionen anschließend mit dem Bodenwassermodell Hydrus-1D (invers) modelliert.
Der zur Modellierung benötigte Bestandsniederschlag wurde in seinen beiden Komponenten Stammabfluss und Kronendurchlass erfasst. Letzterer wurde in den Kulturflächen
und der Brachefläche im wöchentlichen bzw. vierzehntägigem Turnus gemessen. Weitere
mikrometeorologische Parameter (Strahlungsbilanz, Lufttemperatur, Wasserdampfdruck
und Windgeschwindigkeit) wurden zur Bestimmung der potentiellen Evapotranspiration
(Penman-FAO) erfasst. Diese ging als 'sink-term' in das Bodenwassermodell ein. Die aktuelle
Verdunstung
über
der
Brachevegetation
wurde
mittels
Bowen-ratio-
Energiebilanzmethode und mittels Penman-Monteith-Methode bestimmt und mit den Ergebnissen des Bodenwassermodells zu den Wurzelwasserentzügen verglichen. Alle Messungen wurde im Zeitraum der eineinhalbjährigen Bewirtschaftung durchgeführt.
168
8 Zusammenfassung
Wasserdynamik
Die Niederschlagsinterzeption der Brachevegetation im ersten Jahr betrug 139 mm, d.h.
6.6 % des jährlichen Freilandniederschlags (P) von 2104 mm. Im zweiten Jahr erhöhte sie
sich leicht auf 200 mm bzw. 7.9 % (P = 2545 mm) bedingt durch den vermehrten Kronenschluss der Vegetation. Die Kulturpflanzen interzipierten weniger Wasser mit 4.1 % in
1997 und 3.8 % in 1998.
Die aktuelle Verdunstung (Bowen-ratio-Energiebilanz) der dreijährigen Brachevegetation
reagierte deutlich auf die ausgeprägte Trockenzeit in 1997 und belief sich auf 1411 mm
a-1. Dies waren 141 mm mehr als mit dem Bodenwassermodell ermittelt (1270 mm) und
hing offensichtlich mit der frühmorgendlichen Bildung von Tau zusammen, der anschließenden evaporiert nur mikrometeorologisch erfasst wurde, nicht aber im Bodenwassermodell.
Die Versickerung gemäß Bodenwassermodell belief sich in 1997 auf 897 mm (43 % des
Freilandniederschlags). In 1998 waren dies nur 842 mm aufgrund der intensiven Ausnutzung des Bodenwasserspeichers im Vorjahr, ein Defizit, das trotz höherer Niederschläge
im zweiten Jahr nicht völlig ausgeglichen wurden. Die Kulturpflanzen verdunsteten weniger Wasser, wodurch die Versickerung auf 1190 bis 1279 mm a-1 anstiegen. Sie waren
damit 348 bis 382 mm höher als die der Brachefläche. Der entscheidende Faktor für die
Ausnutzung der tiefen Bodenwasservorräte waren folglich das tiefreichende Wurzelwerk
der Brachevegetation. Ganze 35.4 % (427 mm) bzw. 33.4 % (400 mm) des transpirierten
Wassers von 1997 bzw. 1998 stammten aus 0.9 bis 6 m Tiefe. In der Trockenzeit von
1997 stieg dieser Anteil auf über 70 %. Selbst wenn also die Brachevegetation während
der Trockenzeit unter Wasserstress gerät, sich der Kronendachwiderstand erhöht und die
Transpiration verringert, sind die meisten Arten doch dazu in der Lage, ein immergrünes
Laubwerk aufrecht zu erhalten, indem sie das Wasser tiefer Bodenschichten ausnutzen.
Auf den beiden Kulturflächen wurde der Bodenwasservorrat unterhalb von 0.9 m Tiefe
nur peripher genutzt, wobei hauptsächlich Maniok, nach Aufgabe der Fläche im zweiten
Jahr aber auch die wiederaufwachsende Brachevegetation für die Entnahme aus 1.8 m
bis maximal 3 m verantwortlich war.
Nährstoffbilanz
Die Brandverluste von C und N aus der oberirdischen Biomasse waren auf beiden Kulturflächen beachtlich. Mindestens 93 % der Vorräte wurden volatilisiert, das entspricht 13.8
bzw. 21.5 t C ha-1 sowie 246 bzw. 372 kg N ha-1 auf Fläche 1 bzw. 2. Zusätzlich wurden
mehr als 80 % der S-Vorräte (35 bzw. 53 kg ha-1), aber auch 45-70 % der weniger volatili169
8 Zusammenfassung
sierbaren K-, Ca- und Mg-Vorräte ausgetragen, im letzteren Fall hauptsächlich durch Partikelflug. Der Austrag des wachstumslimitierenden Phosphats war alarmierend, da es auf
Fläche 1 ganze 90 % der oberirdischen Vorräte ausmachte. Die Gesamtmengen von 8
bzw. 11 kg P ha-1 wurden jedoch durch die Düngung von 48 kg P ha-1 mehr als ausgeglichen.
Mulchen als Flächenvorbereitung vermied diese Austräge. Durch die moderate NPKDüngung unterschieden sich die Erträge von Mais, Bohnen und Maniok nicht von denen,
die unter gebrannten Bedingungen erzielt wurden. Sie übertrafen Erträge, die von Kleinbauern in der Region regulär erzielt werden, um den Faktor 2 bis 3. Im Durchschnitt wurden 2.3 t Mais (Körner), 1.7 t Bohnen und 20.2 t Maniokknollen pro Hektar erzielt. Die
Erntegüter waren für den größten Teil der Nährstoffentzüge auf den gemulchten Flächen
verantwortlich. Auf den gebrannten Flächen waren sie aber generell nur von zweitrangiger
Wichtigkeit.
Aufgrund der hohe Nährstoffausträge durch das Brennen war die gesamte Nährstoffbilanz auf beiden gebrannten Flächen negativ, mit Ausnahme von P, kompensiert durch
den Düngeeintrag. Die Verluste betrugen 292-403 kg N ha-1, 69-132 kg K ha-1, 155163 kg Ca ha-1, 36-33 kg Mg ha-1 und 26-32 kg S ha-1 (Fläche 1 bzw. Fläche 2).
Wurden die gesamten Nährstoffverluste auf den Nutzungszeitraum von 5.5 und 9 Jahren
bezogen, so zeigte sich, dass eine Verkürzung der Brachezeit zu einer intensiveren Nährstoffausbeutung führte. Abgesehen davon, dass Brandrodungsfeldbau generell ökologisch nicht nachhaltig ist, bedeutet dies folglich, dass eine zusätzliche Intensivierung des
Landbaus mit verkürzten Brachezeiten zu beschleunigter Bodendegradierung führt. Einen
wichtige Rolle spielt dabei, dass die jüngere Brachevegetation vornehmlich aus dünnen
Stämmen und Ästen besteht, die intensiver, d.h. bei höheren Temperaturen, verbrennen,
was zu erhöhten prozentualen Volatilisationsverlusten führt.
Im Gegensatz dazu ist unter gemulchten Bedingungen auch intensiverer Landbau (dreieinhalb Jahre Brache) möglich, da selbst dann die Nährstoffbilanz ausgeglichen war. Dies
war sowieso nach siebenjähriger Brachezeit der Fall.
Die Nährstoffausträge durch Versickerung gemessen in 3 m Tiefe waren in beiden Behandlungen auf beiden Flächen vergleichsweise gering. Das Mulchen von hohen Mengen
an Biomasse erhöhte die Versickerungsverluste nicht.
In 0.9 m Tiefe waren die Nährstoffkonzentration durch die folgenden Bewirtschaftungsmaßnahmen zeitweise erhöht:
1.) Flächenvorbereitung, Maissaat und erste Düngung im Januar 1997
2.) Unkrauthackung, Bohnensaat und zweite Düngung im Mai 1997
170
8 Zusammenfassung
3.) Das Austrocknen und Wiederbefeuchtung des Bodenprofils am Ende der Trockenzeit
4.) Maniokernte Ende Juni 1998
Die Nährstoffkonzentrationen unter den gemulchten Behandlungen erreichten generell
nicht die Werte, wie sie unter den gebrannten Varianten gefunden wurden. Andererseits
hatte die Länge der vorangegangenen Brachezeit keinen signifikanten Einfluss auf die
Konzentrationen.
Die Transportdynamik der gelösten Nährstoffe wurde offensichtlich stark durch die Ionenaustauschkapazität des Boden beeinflusst. Der Vergleich der Nährstoffflüsse in den
Referenztiefen 0.9 m, 1.8 m und 3 m Tiefe zeigte, dass alle Nährstoffe, aber auch Natrium und Chlorid während des Perkolierens adsorbiert wurden. Auf beiden Flächen in beiden Behandlungen wurden im ersten Beobachtungsjahr (1997) mehr als 80 % des Nitrat
und 75 % des Chlorids, das in 0.9 m Tiefe gemessen wurde, im darrunterliegenden Bodenprofil zurückgehalten und erreichte 3 m Tiefe nicht. Dies traf auch auf alle wichtigen
Kationen zu, wenn auch im geringeren Maße. Im gesamten Beobachtungszeitraum von
zwei Jahren wurden unter den gebrannten Behandlungen mehr Nährstoffe zurückgehalten als unter den gemulchten. In dieser Zeit wurden auf Fläche 2 (gebrannte Behandlung)
67.4 kg Nitrat, 103 kg Ca, sowie 11.6 kg K and 23.5 kg Mg pro Hektar zwischen 0,9 und
3 m Tiefe zurückgehalten.
Dass alle quantitativ wichtigen Ionen adsorbiert wurden, kann durch einfache Austauschprozesse an der Bodenmatrix nicht erklärt werden, da in diesem Falle äquivalente
Mengen an ausgetauschten Ionen in der Bodenlösung in 3 m Tiefe hätten gefunden werden müssten, was nicht der Fall war. Wahrscheinlicher, scheint eine Erhöhung der Austauscherkapazität zu sein ausgelöst durch die erhöhte Äquivalentkonzentration der perkolierenden Bodenlösung. Dies würde aber bedeuten, dass die adsorbierten Nährstoffe
wieder freigesetzt würden, wenn die Flächen erst einmal aufgegeben sind und Regenwasser mit geringerer Ionenkonzentration perkoliert und eine Verminderung der Austauchkapazität zu "natürlichen" Bedingungen hin bewirkt. Trifft dies zu, so ist ein schneller Wiederaufwuchs der tiefwurzelnden Brachevegetation für eine effiziente Nährstoffaufnahme aus tiefen Bodenschichten zwingend notwendig. Dies würde eine Verlängerung der Anbauzeiten bzw. jedwede Art der Landbewirtschaftung ausschließen, die die Vitalität der Brachevegetation beeinträchtigt.
171
8 Resumo
Resumo
O sistema de produção agrícola de pousio, tradicionalmente usado por pequenos
produtores, prevalece na região nordeste do Pará, no leste da Amazônia brasileira. O
processo de derruba-e-queima de uma vegetação secundaria em pousio (Capoeira) de 3
a 8 anos é seguido por um ciclo de cultivo, variando de 1.5 ate 2 anos, incluindo as
culturas do milho, do feijão e da mandioca. Depois deste ciclo, a regeneração da
vegetação é principalmente regenerativa das raízes que sobrevivem ao ciclo cultivo. A
vegetação secundaria preserva um alto grau de biodiversidade, e acumula nutrientes na
biomassa. Durante a fase de pousio a fertilidade do solo é sucessivamente recuperada,
facilitando uma produção limitada de grãos a um solo basicamente de baixa fertilidade.
Além disso, a vegetação secundaria, quando predomina, elimina as ervas daninhas.
Embora a interação entre a (vegetação em) fase de pousio e o potencial agrícola seja
bem conhecido, a dinâmica d’água e dos nutrientes das camadas profundas do solo
nunca foi estudada. A necessidade é evidente, pois foi provado que a vegetação
secundaria mantém um sistema de raízes profundas, chegando a pelo menos 6 metros.
A esse respeito, um estudo anterior supôs um uso d’água das camadas profundas do
solo pela vegetação secundaria durante a época seca, nos meses de setembro até
dezembro. Neste sentido, o primeiro objetivo do presente trabalho foi estabelecer um
balanço da dinâmica d’água das camadas profundas do solo.
A queima, como técnica de preparação da terra, é responsável por liberar na atmosfera
tanto a maior parte do C e do N acumulada na biomassa superficial, como grandes
quantidades de P, K, Ca, Mg e S. Para contrabalançar esta perda dos nutrientes, a fase
de pousio deveria ser aumentada extremamente (> 70 anos). Na prática acontece o
contrario: os pequenos produtores reduzem a fase de pousio em virtude da pressão
demográfica sobre a terra. Por isso, uma preparação da terra sem fogo, através de
derruba-e-tritura ('mulching'), vem sendo sugerido como uma alternativa viável. O
segundo objetivo do presente trabalho foi comparar a dinâmica dos nutrientes deste
sistema com o sistema tradicional (derruba-e-queima). A duração da fase de pousio foi
particularmente considerada com relação aos efeitos do balanço dos nutrientes.
A suposição era que, aplicar grande quantidades de biomassa na superfície do solo,
levaria a um aumento da perda dos nutrientes através de lixiviação. Por isso, no presente
estudo o movimento de nutrientes solúveis foi especialmente considerado.
Para a determinação do uso d’água das camadas profundas, foi escolhido uma
vegetação secundaria de 3 anos de idade. Para o estudo do impacto do ciclo de cultivo,
172
8 Resumo
usou-se duas áreas com vegetação de pousio de 3.5 e 7 anos (a partir daqui chamadas,
respectivamente, de área 1 e de área 2), correspondentes, respectivamente, a mínima e
máxima idade nos ciclos de pousio, observadas mais recentemente na região. O
experimento de campo começou em dezembro 1996, onde a vegetação foi derrubada e
metade queimada e a outra triturada.
Foi plantado milho (Zea mays) no final de janeiro de 1997, em seguida caupi (Vigna
unguiculata) no final de maio, e mandioca (Manihot esculenta) no final de junho. O milho
foi adubado com 60 kg N ha-1 (uréia), 26 kg P ha-1 (superfosfato triplico) e 25 kg K ha-1
(KCl). Caupi recebeu 10 kg N ha-1, 22 kg P ha-1 e 41 kg K ha-1 da mesma formula. A
aplicação para estas duas culturas se deu da forma de lanço. Os grãos de milho
(com espigas) foram coletados na metade de junho de 1997 e os do caupi (com a casca)
no inicio de agosto do mesmo ano. A ultima capina nas áreas foi feita em março de
1998, depois foi deixado rebrotar a vegetação secundaria. O ciclo de cultivo terminou
com a colheita da mandioca no final de junho de 1998.
O balanço superficial de nutriente foi calculado medindo a quantidade de input e output
(adubação, deposição atmosférica, perdas gasosa, e extração da colheita e da lenha).
Para investigar as perdas através de lixiviação, foram determinadas as concentrações de
nutrientes solúveis em amostras de solução de solo, que foram tiradas quinzenalmente
com lisímetros de copo de sucção.
Medidas exatas dos fluxos de água de solo na zona das raízes só são possíveis com a
aplicação de um modelo. As dinâmicas anuais da tensão de água de solo foram medidas
em diferentes profundidades, através de tensiômetros. Consecutivamente, o movimento
d’água no solo foi modelado (inversamente), usando curvas pF laboratoriais e funções de
pedotransferência, aplicando o modelo Hydrus-1D.
Uma das entradas necessárias no modelo é a chuva liquida, que foi determinada em
suas duas componentes, a chuva sob dossel e a chuva desaguada aos troncos
(stemflow), quinzenalmente nas áreas cultivadas e na vegetação secundaria em pousio.
Adicionalmente, parâmetros micro-meteorológicos (radiação solar, temperatura e
umidade do ar e velocidade do vento) foram determinadas para predizer a
evapotranspiração potencial (ETp), através da metodologia de Penman, assim chamado o
'sink-term' no modelo. A evapotranspiração atual (ETa) da vegetação secundaria foi
determinada através da metodologia de Penman-Monteith e através do balanço
energético (Bowen ratio). Resultados de ETa foram comparados com resultados do
modelo sobre o uso da água das raízes. Todos as medidas foram feitas no período do
ciclo cultivo (1.5 anos).
173
8 Resumo
Dinâmica d’ água
A interceptação de chuva pela vegetação secundaria no primeiro ano foi equivalente a
139 mm, ou 6.6 % da chuva bruta de 2104 mm. No segundo ano foi 200 mm, ou 7.9 %
da chuva bruta anual (2545 mm). A percentagem neste período cresceu levemente,
aparentemente causado pelo o dossel da vegetação, que foi fechando durante o
crescimento. A interceptação de chuva pelos cultivos chegou a somente 4.1 % no ano de
1997 e 3.8 % no ano de 1998.
A evapotranspiração atual (Bowen ratio) da vegetação em pousio foi sensitiva á extensiva
época seca de 1997, chegando a 1411 mm, e assim excedendo a determinação do
modelo a respeito ao ETa (1270 mm) do mesmo ano por 141 mm. Aparentemente, esta
diferencia foi relacionada à evaporação do orvalho no dossel da vegetação de manhã
cedo, que foi incluído na determinação meteorológica, mas não considerado no modelo
d'água de solo.
A drenagem d'água de solo segundo o modelo foi 897 mm em 1997 (43 % da chuva
bruta), mas somente 842 mm (33 %) em 1998, em virtude da influencia de um despejo
intensivo no ano anterior, que não foi a todo compensado com o maior input de chuva no
ano seguido. Os cultivos mostraram uma evapotranspiração relativamente menor,
causando uma maior drenagem d'água chegando a 1190 até 1279 mm a-1, e assim
excedendo aquela da vegetação secundaria por 348 até 382 mm.
As raízes profundas da vegetação em pousio foram crucial para a absorção d'água de
solo.
Ao
todo,
35.4 %
(427 mm) e
33.4
%
(400 mm)
d'água
transpirada,
respectivamente, em 1997 e 1998, foram tiradas das camadas profundas de 0.9 m a
6 m de profundidade. Na extensiva época seca do ano 1997 esta fração chegou a
exceder 70 %. Isso significa que, mesmo que, a vegetação secundaria ecofisicamente
estava estressada na época seca, visualizado por uma transpiração reduzida e com uma
resistência do dossel foliar aumentada, a maioria das espécies era capaz de manter um
dossel sempre-verde, retirando água das camadas profundas de solo.
Nas áreas cultivadas o estoque d'água do solo a baixo de 0.9 m só insignificantemente
foi explorado. Na maioria dos casos a mandioca, mas também a vegetação secundaria,
que recresceu depois da fase cultivo, foi responsável por retirar água das camadas de
0.9 a 1.8 m de profundidade durante o segundo ano de cultivação.
Balanço de nutrientes
As perdas de C e N localizados na biomassa superficial, através da queima, foram
graves. No mínimo 93 % do estoque destes elementos correspondente a 13.8 e
174
8 Resumo
21.5 t C ha-1 e 246 e 372 kg N ha-1, respectivamente, na área 1 e na área 2, foram
volatilizados. Adicionalmente, mais de 80 % do enxofre, i.e. 35 e 53 kg S ha-1 (área 1 e 2)
foram perdidos através da queima. Também, 45-70 % daqueles elementos que
basicamente são pouco combustíveis, foram retirados do campo, geralmente através de
partículas de fuligem. A exportação de fosfato, que limita o crescimento, alertou, pois
chegou a 90 % do estoque superficial na área 1. Cerca de 8 a 11 kg P ha-1 foram
volatilizados; em total contudo, balanceados por o input de 48 kg P ha-1 pelo adubo.
A derruba-e-tritura evitou estas perdas e, suportada com a adubação de NPK, a colheita
de milho, caupi e mandioca não se distinguiu daquela do tratamento derruba-e-queima.
As colheitas basicamente superaram a um fator 2 até 3 daquelas comumente
alcançadas na região do estudo. Foram coletadas entre 2-3 t ha-1 grãos de milho,
1.7 t ha-1 caupi e 20.2 t ha-1 raízes de mandioca. A colheita foi responsável pela maior
subtração de nutrientes nas áreas trituradas, mas geralmente só foi de segundo
importância nas áreas queimadas.
Nas áreas queimadas, causado pelas grande perdas dos nutrientes através da queima, o
balanço dos nutrientes foi negativo para todos os elementos considerados, com a
exceção de P balanceado por a adubação. As perdas chegaram a 292 e 403 kg N ha-1;
69 e 132 kg K ha-1; 155 e 163 kg Ca ha-1; 36 e 33 kg Mg ha-1; e, 26 e 32 kg S ha-1,
respectivamente, para a área 1 e 2, com respeito a ciclo total de 5.5 e 9 anos.
Relacionando a perda total dos nutrientes nas áreas queimadas ao ciclo da rotação de
5.5 e 9 anos, foi possível mostrar que a redução da fase de pousio levou à uma
exploração aumentada de nutrientes. Isso significa que – alem do fato geral que o uso da
terra com a técnica de derruba-e-queima é ecologicamente insustentável – a exploração
da terra até é acelerada com a fase de pousio reduzida. Uma contribuição
aparentemente importante, neste sentido, é o fato que a intensidade do fogo cresce, no
caso de uma vegetação secundaria de pouca idade ser queimada. Esta vegetação na
maioria consiste em troncos e galhos finos. Como conseqüência cresce também a
percentagem da volatilização dos nutrientes.
Em contrapartida, na pratica de derruba-e-tritura aparece como praticável até mesmo um
uso da terra intensificado (apenas 3.5 anos de pousio), visto que o balanço dos
nutrientes desta área foi equilibrado, que em todo o caso estava certo com uma fase de
pousio de 7 anos.
As perdas pela lixiviação medidas numa profundidade referencial de 3 m foram
comparavelmente baixas pelos dois tratamentos e as duas áreas. "Mulching", i.e. aplicar
grandes quantidades de biomassa triturada na superfície da terra não promoveu
175
8 Resumo
lixiviação.
As concentrações dos nutrientes na solução de solo de 0.9 m de profundidade
temporariamente aumentaram, devido aos seguintes acontecimentos:
1.) preparação inicial da terra, plantação de milho e primeira aplicação de adubo em
janeiro de 1997;
2.) capina, plantação de caupi e segunda aplicação de adubo em maio de 1997;
3.) secagem e re-humedecimento do perfil de solo no final do ano 1997; e,
4.) colheita de mandioca no final de junho de 1998.
As concentrações de nutrientes na solução de solo nas áreas tratadas com detritos
vegetais triturados em geral não chegaram aos valores encontrados nas areas
queimadas. Por outro lado, a duração da fase de pousio não influenciou distintamente as
concentrações.
A dinâmica do transporte dos nutrientes solúveis aparentemente foi altamente
influenciada pela capacidade de troca de ions do solo. A comparação dos fluxos dos
nutrientes nas profundidades de 0.9 m, 1.8 m e 3 m, mostrou que as quantidades de
todos os nutrientes móveis, e também de sódio e clorido, foram reduzidas durante a
percolação pelo perfil. Independente da área e do tratamento, acima de 80 % do nitrato
e de 75 % do clorido, medidos na solução de solo na profundidade de 0.9 m, foram
retiradas na camada de solo a baixo, e não chegaram a 3 m no primeiro ano (1997). Do
mesmo modo, também os predominantes cations foram retirados, ainda que de um grau
menos marcado. A capacidade para a retenção foi mais expressada nas areas
queimadas. Durante o período de observação de 2 anos na área 2, 67.4 kg nitrato;
103.8 kg Ca; 11.6 kg K; e, 23.5 kg Mg por hectare foram retiradas no perfil de 0.9 a 3 m.
Como todos os ions quantitativamente importantes foram retirados, um processo de
troca de ions na matriz do solo não pode ser responsável. Isso precisaria de equivalentes
quantidades de ions trocados na solução na profundidade de 3 m, que na realidade não
foram achadas. Possivelmente, um aumento temporal da capacidade de troca dos
cations e dos anions foi responsável pela retenção, causada pela concentração da
solução de solo aumentada durante o ciclo de cultivo. Isto, todavia, significaria uma
liberação dos nutrientes retirados depois do ciclo de cultivo, quando água da chuva, com
extremamente baixa concentração de solúveis, percola no solo, implicando de novo uma
queda da capacidade de troca de ions. Se for assim, um re-estabelecimento rápido da
vegetação secundaria com raízes profundas será crucial para uma recepção destes
nutrientes liberados. Isto limitaria a envergadura de qualquer atividade de cultivação
(prolongada), que reduzisse a vitalidade da vegetação secundaria.
176
9 References
9 References
Abas, M.R.; Ahmad-Shad, A.; Awang, M.N.; Cape, J.N.; Fowler, D. (1991). Fluxes of ions in
precipitation, throughfall and stemflow in an urban forest in Kuala Lumpur, Malaysia. Environ. Pollut., 75 (2): 209-213.
Ahuja, L.R.; Green, R.E.; Chong, S.-K.; Nielsen, D.R. (1990). A simplified functions approach for determining soil hydraulic conductivities and water characteristics in situ.
Water Resour. Res., 16: 947-953.
Alburquerque, M. de (1961). Notas sobre mandioca. Boletim Técnico N. 41. Instituto
Agronômico Do Norte, Belém-Pará-Brazil. 92 pp.
Alexandratos, N. (1995). Pressure on the environment from agriculture. In: Alexandratos,
N. (ed.). World agriculture towards 2010. An FAO study. pp. 350-364. Chichester.
John Wiley & Sons. 488 pp.
Allen, R.G. (1986). A Penman for all seasons. J. Irrig. Drain. Eng.-ASCE, 112: 348-368
Allen, R.G.; Smith, M.; Pruitt, W.O.; Pereira, L.S. (1996). Modifications to the FAO crop coefficient approach. In: Evaporation and irrigation scheduling. Proceedings of the International Conference, San Antonio, Texas, USA, Nov. 3-6 1996. pp. 124-132. St.
Joseph, ASAE.
Andrade, J.L.; Meinzer, F.C.; Goldstein, G.; Holbrook, N.M.; Cavelier, J.; Jackson, P.; Silvera, K. (1998). Regulation of water flux through trunks, branches and leaves in
trees of lowland tropical forest. Oecologia, 115: 463-471.
Andriesse, J.P.; Schelhaas, R.M. (1987). A monitoring study of nutrient cycles in soils
used for shifting cultivation under various climatic conditions in tropical Asia: II. Nutrient store in biomass and soil: Results of baseline studies. Agr. Ecosyst. Environ.,
19: 285-310.
Anurugsa, B. (1998). Experimentelle Untersuchungen und Modellierung bodenchemischer Reaktionen in ferallitischen Böden unter Bedingungen traditioneller
Feldumlagewirtschaft. Ph.D. Thesis. University of Göttingen. 101 pp.
Araújo, T.M.; Carvalho, J.A.Jr.; Higuchi, N.; Brasil, A.C.P. Jr.; Mesquita, A.L.A. (1999). A
tropical rainforest clearing experiment by biomass burning in the state of Pará, Brazil. Atmos. Environ., 33: 1991-1998.
Arya, L.M.; Dierolf, T.S.; Sofyan, A.; Widjaja-Adhi, I.P.G.; Van Genuchten, M.T. (1999). Significance of macroporosity and hydrology for soil management and sustainability of
agricultural production in humid-tropical environment. Soil Sci., 164(8): 586-601.
Ashby, M. (1999). Modelling the water and energy balance of Amazonian rainforest and
pasture using Angol-Brazilian climate observation study data. Agr. Forest Meteorol.,
94 (2): 79-101.
Aston, A.R. (1979). Rainfall interception by eight small trees. J. Hydrol., 42: 382-396.
Aune, J.B.; Lal, R. (1997). Agricultural productivity in the tropics and critical limits of properties of Oxisols, Ultisols, and Alfisols. Trop. Agr., 74 (2): 96-103.
Ayotamuno, J.M.; Akor, A.J.; Teme, S.C.; Essiet, E.W.U.; Isirimah, N.O.; Idike, F.I. (1997).
Computing maize crop coefficients in the Port Harcourt area, Nigeria, using a class
A pan evaporimeter. Outlook Agr., 26 (3): 185-189.
177
9 References
Baar, R. (1997). Vegetationskundliche und ökologische Untersuchungen der Buschbrache in der Feldumlagewirtschaft im östlichen Amazonasgebiet. Ph.D. Thesis.
University of Göttingen. 202 pp.
Beckerman, S. (1987). Swidden in Amazonia and the Amazon rim. In: Turner, B.L.; Brush,
S.B. (eds.). Comparative Farming systems. pp. 55-93. New York. Guilford. 428 pp.
Bernhofer, C. (1992). Estimating forest evapotranspiration at a non-ideal site. Agr. Forest
Meteorol., 60: 17-32.
Beven, K. Germann, P. (1981). Water flow in soil macropores II: combined flow model. J.
Hydrol., 32: 15-29.
Beven, K.; Germann, P. (1982). Macropores and water flow in soils. Water Resour. Res.,
18 (5): 1311-1325.
Billot, A. (1995). Agriculture et systèmes d'élevage en zone bragantine (Pará-Brésil). Diagnostic des systèmes de production familiaux a forte composant élevage. Unpublished Diploma Thesis. CNEARC-EITARC, Montpellier. 140 pp.
Black, A.S.; Waring, S.A. (1976a). Nitrate leaching and adsorption in a krasnozem from
Redland Bay, Qld. I. Leaching of banded ammonium nitrate in a horticultural rotation. Aust. J. Soil Res., 14 (2): 171-180.
Black, A.S.; Waring, S.A. (1976b). Nitrate leaching and adsorption in a krasnozem from
Redland Bay, Qld. II. Soil factors influencing adsorption. Aust. J. Soil Res., 14 (2):
181-188.
Black, A.S.; Waring, S.A. (1976c). Nitrate leaching and adsorption in a krasnozem from
Redland Bay, Qld. III. Effect of nitrate concentration on adsorption and movement in
soil columns. Aust. J. Soil Res., 14 (2): 189-195.
Black, A.S.; Waring, S.A. (1979). Effect of nitrate leaching in Oxisol columns on 15N abundance and nitrate breakthrough curves. Commun. Soil Sci. Plant Anal., 10 (3): 521529.
Bouma, J.; Anderson, J.L. (1973). Relationship between soil structure characteristics and
hydraulic conductivity. In: Bruce, R.R. (ed.). The soil water regime. pp. 77-105. SSSA
Spec. Publication 5. Madison WI.
Bouma, J.; Belmans, C.F.M.; Dekker, L.W. (1982). Water infiltration and redistribution in a
silt loam subsoil with vertical worm channels. Soil Sci. Soc. Am. J., 46: 917-921.
Bouchet, R.J. (1963). Evapotranspiration réelle et potentielle: Signification climatique.
IAHS-Publication, 62: 134-142.
Bowen, I.S. (1926). The ratio of heat losses by conduction and by evaporation from any
water surface. Physiol. Rev., 27: 779-787.
Brand, J.; Pfund, J.L. (1998). Site- and watershed-level assessment of nutrient dynamics
under shifting cultivation in eastern Madagascar. Agr. Ecosyst. Environ., 71: 169183.
Brunel, J.P. (1989). Estimation of sensible heat flux from measurements of surface radiative temperature and air temperature at two meters: application to determine actual
evaporation rate. Agr. Forest Meteorol., 46: 179-191.
Bruijnzeel, L.A. (1990). Hydrology of moist tropical forests and effects of conversion: A
state of knowledge review. International Hydrological Programme: 1224. UNESCO,
Paris.
178
9 References
Bruijnzeel, L.A.; Wiersum, K.F. (1987). Rainfall interception by a young Acacia auriculiformis (A. Cunn) plantation forest in West Java, Indonesia: application of the Gash's
analytical model. Hydrol. Process., 1: 309-319.
Brusseau, M.L.; Rao, P.S.C. (1990). Modeling solute transport in structured soils: a review. Geoderma, 46: 169-192.
Bui, E.N.; Box, J.E. Jr. (1992). Stemflow, rain throughfall, and erosion under canopies of
corn and sorghum. Soil Sci. Soc. Am. J., 56: 242-247.
Bünemann, E. (1998). Einfluß von Mulch und mineralischem Dünger auf Zea mays und
Vigna unguiculata in der Feldumlagewirtschaft Ostamazoniens. Unpublished Diploma Thesis. University of Göttingen. 79 pp.
Burgy, R.H.; Pomeroy, C.R. (1958). Interception losses in grassy vegetation. Trans. Amer.
Geophys. Union, 39: 1095-1100.
Butler, D.R.; Huband, N.D.S. (1985). Throughfall and stem-flow in wheat. Agr. Forest Meteorol., 35: 329-338.
Buttler, I.W.; Riha, S.J. (1992). Water fluxes in oxisols: a comparison of approaches. Water Resour. Res., 28 (1): 221-229.
Cabelguenne, M.; Debaeke, P. (1998). Experimental determination and modelling of the
soil water extraction capacities of crops of maize, sunflower soybean, sorghum and
wheat. Plant Soil, 202 (2): 175-192.
Calder, I.R.; Wright, I.R.; Murdiyarso, D. (1986). A study of evaporation from tropical rain
forest – West Java. J. Hydrol., 89: 13-31.
Cardon, G.E.; Letey, J. (1992). Plant water uptake terms evaluated for soil water and solute movement models. Soil Sci. Soc. Am. J., 32: 1876-1880.
Cavelier, J.; Jaramillo, M.; Solis, D.; de Leon, D. (1997). Water balance and nutrient inputs
in bulk precipitation in tropical montane cloud forest in Panama. J. Hydrol., 193: 8396.
Celia, M.A.; Bouloutas, E.T.; Zarba, R.L. (1990). A general mass-conservative numerical
solution for the unsaturated flow equation. Water Resour. Res., 26 (7): 1483-1496.
Chauvel, A.; Grimaldi, M.; Tessier, D. (1991). Changes in soil pore-space distribution following deforestation and revegetation: an example from the Central Amazon Basin,
Brazil. Forest Ecol. Manage., 38: 259-271.
Clausing, G. (1994). Frühe Regeneration und Wiederbesiedlung auf Kulturflächen der
Wald-Feld-Wechselwirtschaft im östlichen Amazonasgebiet. Diploma Thesis, University of Göttingen. Göttinger Beiträge zur Land- und Forstwirtschaft in den Tropen
und Subtropen No. 97. 151 pp.
Cock, J.H. (1984). Strategies of the cassava plant for resisting drought. Cassava News letter, 8: 4-5+10.
Contreras-Hermosilla, A. (2000). The underlying causes of forest decline. CIFOR Occasional Paper No. 30, June 2000.
Dane, J.H.; Hruska, S. (1983). In-situ determination of soil hydraulic properties during
drainage. Soil Sci. Soc. Am. J., 47: 619-624.
Dambrine, E.; Loubet, M. Vega, J.A.; Lissarague, A. (1997). Localization of mineral uptake
by roots using Sr isotopes. Plant Soil, 192: 129-132.
Darcy, H. (1856). Les fontaines publiques de la ville de Dijon. Dalmont, Paris.
179
9 References
Denich, M. (1989). Untersuchungen zur Bedeutung junger Sekundärvegetation für die
Nutzungssystemproduktivität im östlichen Amazonasgebiet, Brasilien. Ph.D. Thesis.
University of Göttingen. 265 pp.
Denich (1996). Ernährungssicherung in der Kleinbauernlandwirtschaft Ostamazoniens –
Probleme und Lösungsansätze. Göttinger Beiträge zur Land und Forstwirtschaft in
den Tropen und Subtropen No. 115: 78-88.
Denich, M.; Block, A.; Lücke, W.; Vlek, P.L.G. (1998). A bush chopper for mulch production
in fallow-based agriculture and resource conservation. In: Lieberei, R.; Voß, K.; Bianchi, H. (eds.). Proceedings of the Third SHIFT-Workshop Manaus, March 15-19,
1998. pp. 61-66. Geesthacht. GKSS. 625 pp.
Denich, M.; Lücke, W. (1998). Buschhäcksler – eine Entwicklung zur nachhaltigen Resourcennutzung durch Mulchproduktion als Alternative zur Brandrodung in tropischen Brachesystemen. Landtechnik, 53: 250-251.
Diekmann, U. (1997). Biologische und chemische Bodencharakteristika zur Beurteilung
der nachhaltigen Produktivität von Landnutzungssystemen in der Zona Bragantina,
Ost-Amazonien. Ph.D. Thesis. University of Göttingen. 189 pp. Internetpublication:
http://www.sub.uni-goettingen.de/f_digbib.htm
Diniz, T. D. de A.S.; Cardon, D.A.; Bastos T.X.; Maltez, M.G.L. (1986). Relação entre
radiação solar global e insolação para a região de Belém. In: 1. Simpósio do Trópico
Úmido , Belém, 1984. Anais. Volume 1: Clima e solo. pp. 68-74. Belém. EMBRAPACPATU. 512 pp.
Doorenbros, J.; Pruitt, W.O. (1977). Guidelines for predicting crop water requirements.
FAO Irrigation and Drainage Paper No. 24. 144 pp.
DVWK (1986). Ermittlung des Interzeptionsverlustes in Waldbeständen bei Regen. DVWK
Merkblätter: 211. Hamburg. Parey.
Dykes, A.P. (1997). Rainfall interception from a lowland tropical rainforest in Brunei. J.
Hydrol., 200: 260-279.
Eden, M.J.; Furley, P.A.; McGregor, D.F.M.; Milliken, W.; Ratter, J.A. (1991). Effect of forest
clearance and burning on soil properties in northern Roraima, Brazil. Forest Ecol.
Manage., 38: 282-290.
Ehlers, W. (1976). Zur Bestimmung der ungesättigten Wasserleitfähigkeit im Felde. Z.
Pflanz. Bodenk., 139: 417-427.
Ehrhardt, O. (1983). Einsatz von Thermistoren zur Temperaturmessung am Meßturm Göttinger Wald. Wetter und Leben, 35: 235-239.
Ehui, S.K.; Hertel, T.W.; Preckel, P. (1990). Forest resources depletion, soil dynamics, and
agricultural productivity in the tropics. J. Environ. Econ. Manage., 18: 136-154.
Ellies, A.; Huber, A. (1991). Wasserhaushalt und Bodenwasserverteilung bei einer Futtermaiskultur in Südchile. Z. Pflanz. Bodenk., 154: 9-12.
EMBRAPA (1997). Manual de métodos de análises de solo.– 2. ed. rev. atual. Centro
Nacional de Pesquisa de Solos (Rio de Janeiro, RJ). EMBRAPA-CNPS Documentos.
Rio de Janeiro. 212 pp.
EMBRAPA-CPATU (1977-1988). Boletim Agrometeorológico Ano 1977 – Ano 1988. Embrapa Cpatu - Belem-PA.
180
9 References
Engels, C.; Mollenkopf, M.; Marschner, H. (1994). Effect of drying and rewetting the topsoil on root growth of maize and rape in different soil depths. Z. Pflanz. Bodenk.,
157 (2): 139-144.
Ewel, J.; Berish, C.; Brown, B.; Price, N.; Raich, J. (1981). Slash and burn impacts on a
Costa Rican wet forest site. Ecology, 62 (3): 816-829.
Falesi, I.C. (1972). Solos da Rodovia Transamazônica. Boletim Técnico N. 55. Instituto De
Pesquisa Agropecuária Do Norte. 196pp.
Fearnside, P.M. (1993). Deforestation in Brazilian Amazonia: the effect of population and
land tenure. Ambio, 22 (8): 537-545.
Fearnside, P.M.; Leal, N.; Fernandes; F.M. (1993). Rainforest burning and the global carbon budget: biomass, combustion efficiency, and charcoal formation in the Brazilian
Amazon. J. Geophys. Res.-Atmos, 98 (D9): 16733-16743.
Feddes, R.A.; Bresler, E.; Neuman, S.P. (1974). Field test of a modified numerical model
for water uptake by root systems. Water Resour. Res., 10 (6): 1199-1206.
Feddes, R.A.; Kowalik, P.J.; Zaradny, H. (1978). Simulation of field water use and crop
yield. New York. John Wiley & Sons.
Feller, M.C. (1988). Relationship between fuel properties and slash-burning-induced nutrient losses. Forest Sci., 34 (4): 998-1015.
Fenner, G. (1931). Das Genauigkeitsmaß von Summen, Produkten und Quotienten der
Beobachtungsreihen. Die Naturwissenschaften, 19: 310.
Fleagle, R.G.; Businger, J.A. (1980). An introduction to atmospheric physics. Second Edition. New York. Academic. 432 pp.
Flühler, H.; Germann, P.; Richard, F.; Leuenberger, J. (1976). Bestimmung von hydraulischen Parametern für die Wasserhaushaltsuntersuchungen im natürlich gelagerten Boden. Ein Vergleich von Feld- und Labormethoden. Z. Pflanz. Bodenk., 139:
329-342.
Fölster, H. (1994). Stability of forest ecosystems in the humid tropics. Interciencia, 19(6):
291-196.
Förster, M. (1970). Einige Beobachtungen zur Ausbildung des Wurzelsystems tropischer
Waldbäume. Allg. Forst Jagdztg., 141: 185-188.
Foken, T.; Richter, H.; Müller, H. (1997). Zur Genauigkeit der Bowen-ratio Methode. Wetter und Leben, 49 (2): 57-77.
Franken, W.; Leopoldo, P.R.; Matsui, E.; Ribeiro, M. de N.G. (1982). Interceptação das
precipitações em floresta amazônica de terra firme. Supl. Acta Amazonica, 12 (3):
15-22.
Fuchs, M.; Tanner, C.B. (1970). Error analysis of Bowen ratios measured by differential
psychrometry. Agr. Meteorol., 7: 329-334.
Gale, M.R.; Grigal, D.F. (1987). Vertical root distributions of northern tree species in relation to successional status. Can. J. Forest. Res., 17: 829-834.
Gardener, W.R. (1958). Some steady-state solutions of the unsaturated moisture flow
equation with application on evaporation from a water table. Soil Sci., 58: 228-234.
Gash, J.H. (1979). An analytical model of rainfall interception by forests. Quart. J. Roy.
Meteorol. Soc., 105: 43-55.
181
9 References
Gehring, C. (1997). Die Bedeutung der Nährstoffversorgung für die Regeneration einer
Sekundärvegetation im östlichen Amazonasgebiet. Unpublished M.Sc. Thesis. University of Göttingen. 83 pp.
Gehring, C.; Denich, M.; Kanashiro, M.; Vlek, P.L.G. (1999). Response of secondary vegetation in Eastern Amazonia to relaxed nutrient availability constraints. Biogeochemistry, 45: 223-241.
Germann, P.F.; Beven, K. (1981). Water flow in soil macropores. I. An experimental approach. J. Soil Sci., 32: 1-13.
Germann, P.F. (1986). Rapid drainage response to precipitation. Hydrol. Process., 1: 313.
Girardin, P. (1992). The 'funnel effect' of a maize canopy. In: Proceedings of the second
congress of the European Society of Agronomy, Warwick University, August 23-28,
1992. pp. 76-77.
Granier, A.; Huc, R.; Colin, F. (1992). Transpiration and stomatal conductance of two rain
forest species growing in plantations (Simarouba amara and Goupia glabra) in
French Guyana. Ann. Sci. Forest, 49: 17-24.
Green, R.E.; Ahuja, L.R.; Chong, S.K. (1986). Hydraulic conductivity, diffusivity, and
sorbtivity of unsaturated soils: Field Methods In Klute, A.(ed.): Methods of Soil
Analysis Part 1: Physical and Mineralogical Methods. Agronomy Series, 2nd Edition.
Greenland, D.J.; Okigbo, B.N. (1983). Crop production under shifting cultivation and maintenance of soil fertility. In: Smith. W.H. and Banta, S.J. (eds.). Potential productivity
of field crops under different environments. pp. 505-524. IRRI. Los Banos.
Gross, D.R.; Giten, G., Flowers, N.M. Leoi, F.M.; Ritter, M.L.; Werner, D.W. (1979). Ecology
and acculturation among native peoples of central Brazil. Science, 206: 10431050.
Grossmann, J.; Udluft, P. (1991). The extraction of soil water by the suction-cup method: a
review. J. Soil Sci., 42: 83-93.
Guimarães, G. de A.; Bastos, J.B.; Lopes, E. (1970). Métodos de análise física, química e
instrumental de solos. Ipean. Química de solos, 1 (1): 1-112.
Hairiah, K., Van Noordwijk, M. (1986). Root studies on a tropical ultisol in relation to nitrogen management. Report of field work at IITA's high rainfall substation at Onne
(Port Harcourt, Nigeria) in 1985. Rapport Instituut voor Bodemvruchtbaarheid,
Netherlands, No. 7. 122 pp.
Hansen, E.A.; Harris, A.R. (1975). Validity of soil-water samples collected with porous ceramic cups. Soil Sci. Soc. Am. J., 39: 528-536.
Harrison, L.P. (1963). Fundamental concepts and definitions relating to humidity. In:
Wexler, A. (ed.). Humidity and Moisture : measurement and control in science and
industry. pp. 3-80. New York. Reinhold Publishing Co.
Hartemink, A.E.; Buresh, R.J.; Jama, B.; Jansen, B.H. (1996). Soil nutrient and water dynamics in Sesbania fallows, weed fallows, and maize. Soil Sci. Soc. Am. J., 60: 568574.
Haverkamp, R.; Parlange, J.-Y. (1986). Predicting the water-retention curve from particlesize distribution: 1. Sandy soils without organic matter. Soil Sci., 142 (6): 325-339
182
9 References
Hecht, S.B. (1989). Indigenous soil management in the Amazon basin: some implications
for development. In: Browder, J.O. (ed.). Fragile lands of Latin America: strategies for
sustainable development. pp. 166-181. Boulder. Westview. 301 pp.
Helmuth, L. (1999). A shifting equation links modern farming and forests. Science, 286:
1283.
Hetsch, W.; Beese, F.; Ulrich, B. (1979). Die Beeinflussung der Bodenlösung durch Saugkerzen aus Ni-Sintermetall und Keramik. Z. Pflanz. Bodenk., 142; 29-38
Hillel, D. (1998). Environmental soil physics. San Diego. Academic. 771 pp.
Hillel, D.; Krentos, D.; Stylianou, Y. (1972). Procedure and test of an internal drainage
method for measuring soil hydraulic characteristics in situ. Soil Sci., 114 (5): 395400.
HMSO (1982). Handbook of meteorological instruments. 2nd edition. 6. Measurement of
solar and terrestrial radiation. Meteorological Office. London. Government Publication. 45 pp.
Hodnett, M.G.; Oyama, M.D.; Tomasella, J.; Marques Filho, A. de O. (1996a). Comparisons
of long-term soil water storage behavior under pasture and forest in three areas of
Amazonia. In: Gash, J.H.C.; Nobre, C.A.; Roberts, J.M. Victoria, R.L. (eds.). Amazonian
deforestation and climate. pp. 57-77. Chichester. John Wiley. 661 pp.
Hodnett, M.G.; Tomasella, J.; Marques Filho, A. de O. Oyama, M.D. (1996b). Dee soil water uptake by forest and pasture in central Amazonia: predictions from long-term
daily rainfall data using a simple water balance model. In: Gash, J.H.C.; Nobre, C.A.;
Roberts, J.M. Victoria, R.L. (eds.). Amazonian deforestation and climate. pp. 79-99.
Chichester. John Wiley. 661 pp.
Hölscher, D. (1995). Wasser- und Nährstoffhaushalt eines Agrarökosystems mit Waldbrache im östlichen Amazonasgebiet. Ph.D. Thesis. University of Göttingen. 134 pp.
Hölscher, D., Möller, R.F.; Denich, M. and Fölster, H. (1996). Nutrient input-output budget
of shifting agriculture in Eastern Amazonia. Nutr. Cycl. Agroecosyst., 47(1): 49-57.
Hölscher, D.; Sá, T.D. de A.; Bastos, T.X.; Denich, M.; Fölster, H. (1997a). Evaporation
from young secondary vegetation in eastern Amazonia. J. Hydrol., 193: 293-305.
Hölscher, D.; Ludwig, B.; Möller, R.F.; Fölster, H. (1997b). Dynamic of soil chemical parameters in shifting agriculture in the Eastern Amazon. Agr. Ecosyst. Environ., 66:
153-163.
Hölscher, D.; Sá, T.D. de A.; Mölller, R.R.; Denich, M.; Fölster, H. (1998). Rainfall partitioning and related hydrochemical fluxes in a diverse and in a mono specific
(Phenakospermum guyannense) secondary vegetation stand in eastern Amazonia.
Oecologia, 114: 251-257.
Hoover, C.D. (1944). The fixation of potash by a kaolinitic and smectitic soil. Soil Sci. Soc.
Am. Proc., 9: 66-71.
Hougthon, R.A.; Lefkowitz, D.S.; Skole, D.L. (1991). Changes in the landscape of Latin
America between 1850 and 1985. I. Progressive loss of forests. Forest Ecol. Manage., 38: 143-172.
Huang, K.; Mohanty, B.P.; Van Genuchten, M.T. (1996). A new convergence criterion for
the modified Picard iteration method to solve the variably saturated flow equation.
J. Hydrol., 178: 69-91
IBGE (1991). Censo demográfico Pará, n° 7. Rio de Janeiro. Brasil.
183
9 References
IBGE (1994a). Nova divisão territorial do Brasil – 1993. Rio de Janeiro, Brasil.
IBGE (1994b). Produção agrícola municipal Pará. Rio de Janeiro, Brasil.
IBGE (1997a). Censo agropecuário 1995-1996. No. 5, Pará. Rio de Janeiro, Brasil.
IBGE (1997b). Levantamento sistemático da produção agrícola. Ministério do Planejamento e Orçamento. Belém, Brasil.
IBGE (1998). Contagem da População 1996. Rio de Janeiro, Brasil.
Idso, S.B.; Jackson, R.D. (1969). Thermal radiation from the atmosphere. J. Geophys.
Res., 74: 5397-5403.
Instituto Nacional de Pesquisas Espaciais, INPE (1997). Desflorestamento 1995-1997.
São José dos Campos. São Paulo. Brazil.
Jensen, M.E.; Robb, D.C.N.; Franzoy, C.E. (1970). Scheduling irrigations using climatecrop-soil data. J. Irrig. Drain. Eng.-ASCE, 96: 25-38.
Jensen, M.E.; Burman, R.D.; Allen, R.G. (1990). Evapotranspiration and irrigation water
requirements. ASCE manuals and reports on engineering practice No. 70.
Jipp, P.H.; Oren, R.; Nepstad, D.C.; Sá, T.D. de A. (in revision). Impacts of disturbance on
rainfall partitioning and water-use in seasonally-dry Amazonian forests. Submittted
to Agr. Forest Meteorol.
Jones, M.J.; Watson, K.K. (1987). Effects of soil water hysteresis on solute movement
during intermittent leaching. Water Resour. Res., 23: 43-54.
Johnson, A.D.; Cabrera, M.L.; McCracken, D.V.; Radcliffe, D.E. (1999). LEACHN simulations of nitrogen dynamics and water drainage in an Ultisol. Agron. J. 91: 597-606.
Jordan, C.F. (1989). An Amazonian rainforest. The structure and function of a nutrient
stressed ecosystem and the impact of slash-and-burn agriculture. Man and Biosphere, Series Vol. 2. UNESCO. Paris. 176 pp.
Juo, A.S.R.; Manu, A. (1996). Chemical dynamics in slash-and-burn agriculture. Agr., Ecosyst. Environ., 58: 49-60.
Kabat, P.; Dolman, A.J.; Elbers, J.A. (1997). Evaporation, sensible heat and canopy conductance of fallow savannah and patterned woodland in the Sahel. J. Hydrol., 188:
494-515.
Kamara, C.S. (1981). Effects of planting date and mulching on cowpea in Sierra Leone.
Exp. Agr., 17: 25-31.
Kammesheidt, L. (1999). Forest recovery by root suckers and above-ground sprouts after
slash-and-burn agriculture, fire and logging in Paraguay and Venezuela. J. Trop.
Ecol., 15: 143-157.
Kato, M.S.A. (1998a). Fire-free land preparation as an alternative to slash-and-burn agriculture in the Bragantina region, eastern Amazonia: Crop performance and phosphorus dynamics. Ph.D. Thesis. University of Göttingen. 144 pp.
Kato, O.R. (1998b). Fire-free land preparation as an alternative to slash-and-burn agriculture in the Bragantina region, eastern Amazonia: Crop performance and nitrogen
dynamics. Ph.D. Thesis. University of Göttingen. 132 pp.
Kato, M.S.A., Kato, O.R.; Denich, M.; Vlek, P.L.G. (1999). Fire-free alternatives to slashand-burn for shifting cultivation in the eastern Amazon region: the role of fertilizers.
Field Crop. Res., 62: 225-237.
184
9 References
Katou, H., Clothier, B.E.; Green, S.R. (1996). Anion transport involving competitive adsorption during transient water flow in an Andisol. Soil Sci. Soc. Am. J., 60: 1368-1375.
Kauffman, J.B.; Sanford, R.L.-Jr.; Cummings, D.L.; Salcedo, I.H.; Sampaio, E.V.S.B. (1993).
Biomass and nutrient dynamics associated with slash fires in neotropical dry forests. Ecology, 74 (1): 140-151.
Kinjo, T.; Pratt, P.F. (1971a). Nitrate adsorption: I. In some acid soils of Mexico and South
America. Soil Sci. Soc. Am. Proc. 35: 722-725.
Kinjo, T.; Pratt, P.F. (1971b). Nitrate adsorption: II. In competition with chloride sulfate
and phosphate. Soil Sci. Soc. Am. Proc. 35: 725-728.
Kinnersley, R.P.; Goddard, A.J.H.; Minski, M.J.; Shaw, G. (1997). Interception of cesiumcontaminated rain by vegetation. Atmos. Environ., 31(8): 1137-1145.
Kleinman, P.J.A.; Pimentel, D.; Bryant, R.B. (1995). The ecological sustainability of slashand-burn agriculture. Agr. Ecosys. Environ., 54: 235-249.
Kleinman, P.J.A.; Bryant, R.B.; Pimentel, D. (1996). Assessing ecological sustainability of
slash-and-burn agriculture through soil fertility indicators. Agron. J., 88: 122-127.
Klinge, R. (1997). Wasser- und Nährstoffdynamik im Boden und Bestand beim Aufbau
einer Holzplantage im östlichen Amazonasgebiet. Ph.D. Thesis. University of Göttingen. 256 pp.
Klinge, R.; Schmidt, J.; Fölster, H. (in revision). Simulation of water budgets of a rain forest
and forest conversion plots using a soil water model. Submitted to J. Hydrol.
Koch (1930). Klimakunde von Südamerika. In: Köppen, W., Geiger, R. (eds.). Handbuch
der Klimatologie in fünf Bänden. Bd. 2, Teil G. Berlin. Gebrüder Borntraeger.
Kohlhepp, G. (1994). Raum und Bevölkerung. In: Briesemeister, D.; Kohlhepp, G.; Mertin,
R.-G.; Sangmeister, H.; Schrader, A. (eds.). Brasilien heute. Politk, Wirtschaft, Kultur.
pp. 9-109. Bibliotheca Ibero-Americana. Bd. 53. Frankfurt. Vervuert. 664 pp.
Kool, J.B.; Parker, J.C. (1987). Development and evaluation of closed-form expressions
for hysteretic soil hydraulic properties. Water Resour. Res. 23: 105-114.
Kool, J.B.; Parker, J.C.; Van Genuchten, M.Th. (1985). Determining soil hydraulic properties from one-step outflow experiments by parameter estimation: I. Theory and numerical studies. Soil Sci. Soc. Am. J. 49: 1348-1354.
Kool, J.B.; Parker, J.C.; Van Genuchten, M.Th. (1987). Parameter estimation for unsaturated flow and transport models - a review. J. Hydrol., 91: 255-293.
Körner, C.; Scheel, J.; Bauer, H. (1979). Maximum leaf diffusive conductance in vascular
plants. Photosynthetica, 13 (1): 45-82.
Klute, A. (1952). A numerical method for solving the flow equation for water in unsaturated material. Soil Sci., 73: 105-116.
Kühne, R.F. (1993). Wasser- und Nährstoffhaushalt in Mais-Maniok-Anbausystemen mit
und ohne Integration von Alleekulturen ("Alleycropping") in Süd-Benin. Ph.D. Thesis
University of Hohnenheim. Hohenheimer Bodenkundliche Hefte, 13. 244 pp.
Kung, K.-J.S. (1993). Laboratory observation of funnel flow mechanism and its influence
on solute transport. J. Environ. Qual., 22: 91-102.
Lal, R.; Kimble, J.M. (1997). Conservation tillage for carbon sequestration. Nutr. Cycl.
Agroecosyst., 49: 243-253.
185
9 References
Lang, A.R.G. (1973). Measurement of evapotranspiration in the presence of advection, by
means of a modified energy balance procedure. Agr. Meteorol., 12: 75-83.
Lanly, J.P. (1985). Defining shifting cultivation. Contribution to the IX. World Forestry Congress Mexico 1985. Unasylva No. 147.
Lenhard, R.J.; Parker, J.C. (1987). A model for hysteresis constitutive relations governing
multiphase flow: 2. Permeability-saturation relations. Water Resour. Res. 23: 21972206
Lessa, A.S.N.; Anderson, D.W.; Moir J.O. (1996). Fine root mineralization, soil organic matter and exchangeable cation dynamics in slash and burn agriculture in the semi-arid
northeast of Brazil. Agr. Ecosys. Environ. 59 (3): 191-202.
Levy, G.J.; Van der Watt, H.v.H.; Shainberg, I.; Plessis, H.M. du (1988). Potassium-Calcium
and Sodium-Calcium exchange on kaolinite and kaolinitic soils. Soil Sci. Soc. Am. J.;
52: 1259-1264.
Leyton, L.; Reynolds, E.R.C.; Thompson, F.B. (1967). Rainfall interception in forest and
moorland. In: W.E. Sopper and H.W. Lull (eds.). International Symposium of Forest
Hydrology. pp. 163-178. Oxford. Pergamon.
Libardi, P.L.; Reichardt, K.; Nielsen, D.R.; Biddar, J.W. (1980). Simple field methods for
estimating soil hydraulic conductivity. Soil Sci. Soc. Am. J., 44: 3-7.
Lide, D.R. (1998). CRC handbook of chemistry and physics: a ready-reference book of
chemical and physical data. 79th ed. Boca Raton. CRC. 2556 pp.
Litaor, M.I. (1988). Review of soil solution samplers. Water Resour. Res., 24 (5): 727733.
Lloyd, C.R.; Marques, A. de O. (1988). Spatial variability of throughfall and stemflow
measurements in Amazonian rainforests. Agr. Forest. Meteorol., 42: 63-73.
Lloyd, C.R.; Gash, J.H.C.; Shuttleworth, W.J.; Marques, A. de O. (1988). The measurement
and modelling of rainfall interception by Amazonian rain forests. Agr. Forest. Meteorol, 43: 277-294.
Longman, K.A.; Jenik, J. (1987). Tropical forest and its environment. 2nd edition. New
York. John Wiley. 347 pp.
Luckner, L.; Van Genuchten, M.Th.; Nielsen, D.R. (1989). A consistent set of parametric
models for the two-phase flow of immiscible fluids in the subsurface. Water Resour.
Res., 25 (10): 2187-2193.
Ludwig, B.; Hölscher, D.; Khanna, P.; Prenzel, J. Fölster, H. (1997). Modelling of sorption
experiments and seepage data of an Amazonian Ultisol subsoil under cropping fallow. Z. Pflanz. Bodenk., 160: 447-454.
Lumbanraja, J.; Syam, T.; Nishide, H.; Mahi, A.K.; Utomo, M.; Sarno; Kimura, M. (1998).
Deterioration of soil fertility by land use changes in South Sumatra, Indonesia: from
1970 to 1990. Hydrol Process., 12: 2003-2013.
Mackensen, J.; Hölscher, D.; Klinge, R.; Fölster, H. (1996). Nutrient transfer to the atmosphere by burning of debris in eastern Amazonia. Forest Ecol. Manage., 86: 121-128.
Maklouf C., E.J.; Costa, M.P. da; Costa Veloso, C.A. (1997). Caracterização físico hídrica
de um Podzólico vermelho-amarelo textura arenosa/média sob diferentes usos, em
Igarapé-Açu, Pará. Boletim de Pesquisa, 174. EMBRAPA.
186
9 References
Mallants, D.; Tseng, P.H.; Toride, N; Timmerman, A.; Feyen, J. (1997). Evaluation of multimodal hydraulic functions in characterizing a heterogeneous field soil. J. Hydrol.,
195: 172-199.
Maltez, H.T.; Maltez, M.G.; Bastos, T.X.; Diniz, T.D. de A.S. (1986). Avaliação da
evapotranspiração potencial da região de Belém, Pará. In: 1. Simpósio do Trópico
Úmido , Belém, 1984. Anais. Volume 1: Clima e solo. pp. 56-67. Belém. EMBRAPACPATU. 512 pp.
Manokaran, N. (1979). Stemflow, throughfall and rainfall interception in a lowland tropical rain forest in Peninsular Malaysia. Malaysian forester, 42 (3): 174-201.
Marquardt, D.W. (1963). An algorithm for least-squares estimation on nonlinear parameters. SIAM J. Appl. Math., 11: 443-441.
Mehlich, A. (1953). Determination of P, Ca, Mg, K, Na and NH4. North Carolina Soil Testing Division Publication 1. Raleigh NC. 195 pp.
Metzger, J.P.W. (1997). Dinâmica da paisagem, tempo de pousio e estrutura espacial da
vegetação secundária numa área de agricultura de corte e queima (Igarapé-Açu).
Relatório de atividades. Unpublished Report. 24 pp.
Miller, E.E.; Miller, R.D. (1956). Physical theory for capillary flow phenomena. Journal of
Appl. Phys., 27 (4): 324-332.
Mitchell R.J.; Mayer, A.S. (1998). The significance of hysteresis in modeling solute transport in unsaturated porous media. Soil Sci. Soc. Am. J. 62: 1506-1512.
Monteith, J.L. (1965). Evaporation and environment. Symposium Soc. Exp. Biol., 19: 205234.
Monteith, J.L. (1973). Principles of environmental physics. New York. Elsevier. 183 pp.
Monteith, J.L. (1981). Evaporation and surface temperature. Quart. J. Roy. Meteorol. Soc.,
107:1-27.
Mott, C.J.B. (1988). Surface chemistry of soil particles. In: Wild, A. (ed.). Russell's Soil
condition and plant growth, 11th ed. pp. 239-281. Essex. Longman. 981 pp.
Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated
porous media. Water Resour. Res., 12 (3): 513-522.
Nepstad, D.C.; Uhl, C.; Serrão, E.A.S. (1991). Recuperation of a degraded Amazonian
landscape: forest recovery and agricultural restoration. Ambio, 20 (6): 248-255.
Nepstad, D.C.; Carvalho, C.R. de; Davidson, E.A.; Jipp, P.H.; Lefebvre, P.A., Negrelos, G.H.;
Silva, E.D. da ; Stone, T.A.; Trumbore, S.E.; Vieira, S. (1994). The role of deep roots
in the hydrological and carbon cycles of Amazonian forests and pastures. Nature,
372: 666-669.
Nepstad, D.C.; Veríssimo, A.; Alencar, A.; Nobre, C.; Lima E.; Lefebvre, P.; Schlesinger, P.;
Potter, C.; Moutinho, P. Mendonza, E.; Cochrane, M.; Brooks, V. (1999). Large-scale
impoverishment of Amazonian forests by logging and fire. Nature, 398: 505-508.
Neuman, S.P.; Feddes, R.A.; Bresler, E. (1974). Finite element simulation of flow in saturated-unsaturated soils considering water uptake by plants. Third Annual Report,
Project No. A10-SWC-77. Hydraulic Engineering Lab.. Haifa.
Nye, P.H.; Greenland, D.J. (1960). The soil under shifting cultivation. Technical Communication No. 51. Common Wealth Bureau of Soils, Harpenden, UK. 156 pp.
187
9 References
Nye, P.; Craig, D.; Coleman, N.T.; Ragland, J.L. (1961). Ion exchange equilibria involving
aluminum. Soil Sci. Soc. Am. Proc., 25: 14-17.
Ohmura, A. (1982). Objective criteria for rejecting data for Bowen ratio flux calculations. J.
Appl. Meteorol. 21: 595-598.
Okali, D.U.U.; Furtado, J.I. (1980). Estimating water use by tropical forests: an example
from a plantation teak forest. In: Tropical ecology and development Proceedings of
the Vth International Symposium of Tropical ecology. International Society of Tropical
Ecology Kuala Lumpur, Malaysia. pp. 581-591.
Okamura, Y.; Wada, K. (1983). Electric charge characteristics of Ando (B) and Red-Yellow
(B) soils and weathered pumices. J. Soil Sci., 34: 287-295.
Oliver, S.A. (1982). Some aspects of the water and energy balances of vegetation. Proc.
Symp. Hydrol. Res. basins, Bern: 229-238.
Oliver, S.A.; Oliver , H.R.; Wallace, J.S.; Roberts, A.M. (1987). Soil heat flux and temperature variation with vegetation, soil type and climate. Agr. Forest Meteorol., 39: 257269.
Onofiok, O.E. (1989). Effect of soil compaction and irrigation interval of the growth and
yield of cowpea on a Nigerian Ultisol. Soil Till. Res., 13: 47-55.
Pachepsky, Y.A.; Timlin, D.; Varallyay, G. (1996). Artificial neural networks to estimate soil
water retention from easily measurable data. Soil Sci. Soc. Am. J. 60: 727-733.
Papaioannou, G.; Vouraki, K.; Kerkides, P. (1996). Piche evaporimeter data as a substitute for Penman equation's aerodynamic term. Agr. Forest Meteorol., 82: 83-92.
Papaioannou, G.; Kaloudis, S.; Kerkides, P. (1998). On the proper employment of Piche
evaporimeters in estimating evapotranspiration. Int. J. Climatol., 18: 1247-1260.
Parker, J.C.; Lenhard, R.J. (1987). A model for hysteresis constitutive relations governing
multiphase flow: 1. Saturation-pressure relations. Water Resour. Res., 23: 21872196.
Paw, U.K.T.; Gueye, M. (1983). Theoretical and measured evaporation rates from an exposed Piche atmograph. Agr. Meteorol., 30: 1-11.
Penman, H.L. (1948). Natural evaporation from open water, bare soil and grass. Proc.
Roy. Soc. London, A, 193: 120-146.
Penman, H.L. (1963). Vegetation and Hydrology. Tech. Publication No. 53. Commonwealth Bureau of Soils. Harpenden. 125 pp.
Pereira, O.G. (1971). A cultura do milho na Amazônia. Instituto de Pesquisas e
Experimentação Agropecuárias do Norte (IPEAN), Serie Fitotécnia, Vol. 1, No. 5,
Belém-Pará-Brazil. 28 pp.
Pereira, L.S.; Perrier, A.; Allen, R.G.; Alves, I. (1996). Evapotranspiration: Review of concepts and future trends. In: Evaporation and irrigation scheduling. Proceedings of
the International Conference, San Antonio, Texas, USA, Nov. 3-6 1996. pp. 109115. St. Joseph, ASAE.
Pereira, L.S.; Perrier, A.; Allen; R.G.; Alves, I. (1999). Evapotranspiration: concepts and future trends. J. Irrig. Drain. Eng.-ASCE, 125 (2): 45-51.
Perrier, A. (1975). Étude de l'évapotranspiration dans les conditions naturelles. III: Evapotranspiration réelle et potentielle des couverts végétaux. Ann. Agron., 26: 229245.
188
9 References
Petrie, C.L.; Hall, A.E. (1992). Water relations in cowpea and pearl millet under soil water
deficits. II. Water use and root distribution. Aust. J. Plant Physiol., 19: 591-600.
Pleysier, J.L.; Juo, A.S.R., Herbillon, A.J. (1979). Ion exchange equilibria involving aluminum in a kaolinitic Ultisol. Soil Sci. Soc. Am. J., 43: 875-880.
Poels, R.L.H. (1987). Soils, water and nutrients in a forest ecosystem in Suriname. Ecology and management of tropical rain forests in Suriname. Report No. 3. Agricultural
University Wageningen 253 pp.
Pruitt, W.O.; Morgan, D.L.; Lourence, F.J. (1973). Momentum and mass transfers in the
surface boundary layer. Quart. J. Roy. Meteorol. Soc., 99: 370-386.
Pye-Smith, C. (1997). Friendly fire. New Sci., Nov. 97: 24-25.
Raich, J.W. (1983). Throughfall and stemflow in mature and year-old wet tropical forest.
Trop. Ecol., 24 (2): 234-243.
Raats, P.A.C (1974). Steady flows of water and salt in uniform soil profiles with plant
roots. Soil Sci. Soc. Am. Proc., 38: 717-722
Raison, R.J.; Khanna, P.K.; Woods, P.V. (1985). Mechanisms of element transfer to the
atmosphere during vegetation fires. Can. J. Forest. Res., 15: 132-140.
Rajkai, K.; Kabos, S.; Van Genuchten, M.Th.; Jansson, P.-E. (1996). Estimation of waterretention characteristics from the bulk density and particle-size distribution of
Swedish soils. Soil Sci., 161 (12): 833-845.
Rawls, W.J.; Brakensiek, D.L. (1985). Prediction of soil water properties for hydrological
modeling. Watershed Management in the Eighties. Proceedings of the conference
of the American society of civil engineers, New York. pp.293-299.
Rego, R.S., da Silva, B.N.R., Junior, R.S.O. (1993). Detailed soil survey in an area in the
municipality of Igarapé-Açu. In: Summaries of lectures and posters presented at the
1st SHIFT-Workshop in Belém, March 8-13, 1993. pp. 146. EMBRAPA-CPATU. 202
pp.
Repetto, R. (1990). Deforestation in the Tropics. Sci. Amer., 262 (4): 18-24.
Rhodes, E.R.; Lindsay, W.L. (1978). Solubility of aluminum in soils of the humid tropics. J.
Soil Sci., 29: 324-330.
Richards, L.A. (1931). Capillary conduction of liquids through porous media. Physics 1:
318-333.
Richards, L.A.; Gardner, W.R.; Ogata, G. (1956). Physical processes determining water
loss from soil. Soil Sci. Soc. Am. Proc., 20: 310-314.
Ritchie, J.T. (1981). Soil water availability. Plant Soil, 58, 327-338.
Robins, P.C. (1974). A method of measuring the aerodynamic resistance to the transport
of water vapour from forest canopies. J. Appl. Ecol., 11: 315-325.
Romanya, J.; Khanna, P.K.; Raison, R.J. (1994). Effects of slash burning on soil phosphorus fractions and sorption and desorption of phosphorus. Forest Ecol. Manage., 65:
89-103.
Rose, C.W.; Stern, W.R.; Drummond, J.E. (1965). Determination of hydraulic conductivity
as a function of depth and water content for soil in situ. Aust. J. Soil Res. 3: 1-9.
189
9 References
Rosell, R.A.; Galantini, J.A. (1998). Soil organic carbon dynamics in native and cultivated
ecosystems of South America. In: Lal, R.; Kimble, R.F.; Steward, B.A. (eds.). Advances in Soil Science. Management of carbon sequestration in soils. pp. 11-33.
Boca Raton. CRC. 457 pp.
Rouw, A. de (1995). The fallow period as a weed-break in shifting cultivation (tropical wet
forests). Agr., Ecosys. Environ., 54: 31-43.
Rowell, D.L. (1994). Soil Science: Methods and Applications. London. Longman. 614 pp.
Russo, D.; Jury, W.A.; Butters, G.L. (1989). Numerical analysis of solute transport during
transient irrigation: 1. The effect of hysteresis and profile heterogeneity. Water Resour. Res., 25: 2109-2118
Rutter, A.J.; Kershaw, K.A.; Robins, P.C.; Morton, A.J. (1971). A predictive model of rainfall
interception in forests 1. Derivation of the model from observations in a plantation
of Corsican pine. Agr. Meteorol., 9: 367-384.
Rutter, A.J.; Morton, A.J.; Robins, P.C. (1975). A predictive model of rainfall interception in
forests 2. Generalization of the model and comparison with observations in some
coniferous and hardwood stands. J. Appl. Ecol., 12: 367-380
Rutter, A.J. (1975). The hydrological cycle in vegetation. In: Monteith, J.L. (ed.). Vegetation
and Atmosphere. Vol. 1: Principles. pp. 111-150. London. Academic. 278 pp.
Sá, T.D. de A.; Weber Neto, O.; Oliveira, V. C. de; Carvalho, C.J.R. de (1995). Ökophysiologische Untersuchungen an ausgewählten Arten der Sekundärvegetation des nordöstlichen Pará. In: P.L.G. Vlek, M. Denich, H. Fölster (eds.). Sekundärwald und Brachevegetation in der Kulturlandschaft des östlichen Amazonasgebietes – Funktion
und Manipulierbarkeit -. pp. 97-105. Unpublished report of the 1st Phase of the
Shift-Capoeira German-Brasilian bilateral Research Project. 139 pp.
Sá, T.D. de A.; Costa, J.de P.R. da; Roberts, J.M. (1996). Forest and pasture conductance
in southern Pará, Amazonia. In: Gash, J.H.C.; Nobre, C.A.; Roberts, J.M. Victoria, R.L.
(eds.). Amazonian deforestation and climate. pp. 242-263. Chichester. John Wiley.
661 pp.
Sá, T.D. de A.; Oliveira, V.C. de; Coimbra, H.M.; Carvalho, C.J.R. de; Dias-Filho, M.B.; Sommer, R.; Brienza Jr. S. (1998). Diurnal and seasonal patterns of leaf water relations
in spontaneous and enriched secondary vegetation components. In: Lieberei, R.;
Voß, K.; Bianchi, H. (eds.). Proceedings of the Third SHIFT-Workshop Manaus, March
15-19, 1998. pp. 61-66. GKSS Geesthacht. 625 pp.
Sá, T.D. de A.; Oliveira, V. C. de; Araújo, A.D. de; Brienza Jr., S. (1999). Spectral irradiance
an stomatal conductance of enriched fallows with fast–growing trees in eastern
Amazonia, Brazil. Agroforest. Syst., 47: 289-303.
Sanchez, P.A. (1976). Properties and management of soils in the tropics. New York, John
Wiley. 618 pp.
Sanchez, P.A. (1982). Nitrogen in shifting cultivation systems of Latin America. Plant Soil,
67(1): 91-103.
Sanchez, P.A. (1993). Alternatives to slash and burn agriculture. ASA Special Publication
No. 56: 33-39.
Sanchez, P.A., Villachica, J.H.; Bandy, D.E. (1983). Soil fertility dynamics after clearing a
tropical rainforest in Peru. Soil Sci. Soc. Am. J., 47: 1171-1178.
190
9 References
Savabi, M.R.; Stott, D.E. (1994). Plant residue impact on rainfall interception. Trans.
ASAE, 37 (4): 1093-1098.
Scatena, F.N. (1990). Watershed scale rainfall interception on two forested watersheds in
the Luquillo mountains of Puerto Rico. J. Hydrol., 113: 89-102.
Schaap, M.G.; Bouten, W. (1996). Modeling water retention curves of sandy soils using
neural networks. Water Resour. Res., 32: 3033-3040.
Schaap, M.G.; Leij, F.J.; Van Genuchten, M.T. (1998). Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Sci. Soc. Am. J. 62: 847-855.
Schaap, M.G.; Leij, F.J.; Van Genuchten, M.T. (1999). A bootstrap-neural network approach to predict soil hydraulic parameters. In: Van Genuchten, M.T.; F.J. Leij; L. Wu
(eds.) Int. Workshop: Characterization and measurements of the hydraulic properties of unsaturated porous media. pp. 1237-1250. University of California, Riverside, CA.
Schaap, M.G.; Leij, F.J. (in revision). Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten model. Submitted to Soil Sci. Soc. Am. J.
Schmitt, D. (1997). Untersuchungen über nichdestruktive Methoden zur Bestimmung von
Biomasse junger Sekundärvegetation im östlichen Amazonien. Unpublished Master
Thesis. University of Göttingen. 87 pp.
Schöningh, (1985). Die Wirkung von Mulch auf Ertrag und Faktoren der Bodenfruchtbarkeit im östlichen Amazonasgebiet Brasiliens. Ph.D. Thesis. Wissenschaftliches
Zentrum Tropeninstitut, University of Giessen. 189 pp.
Schroth, G.; Silva, L.F. da; Wolf, M.A.; Teixeira, W.G.; Zech, W. (1999a). Distribution of
throughfall and stemflow in multi-strata agroforestry, perennial monoculture, fallow
and primary forest in central Amazonia, Brazil. Hydrol. Process., 13 (10): 14231436.
Schroth, G.; da Silva, L.F.; Seixas, R.; Teixeira, W.G., Macêdo, J.L.V.; Zech, W. (1999b).
Subsoil accumulation of mineral nitrogen under polyculture and monoculture plantations, fallow and primary forest in a ferralitic Amazonian upland soil. Agr. Ecosys.
Environ., 75: 109-120.
Schuemer, R.; Ströhlein, G.; Gogolok, J. (1990). Datenverarbeitung und statistische
Auswertung mit SAS. Vol. II: Komplexe statistische Analyseverfahren. Stuttgart. Gustav Fischer. 437 pp.
Schuh, W.M.; Cline, R.L. (1990). Effect of soil properties on unsaturated hydraulic conductivity pore-interaction factors. Soil Sci. Soc. Am. J. 54: 1509-1519
Schulze, E.D. (1994). The regulation of plant transpiration: Interaction of feed-forward,
feedback and futile cycles. In: E.D. Schulze (ed.) Flux control in biological systems.
pp. 203-237. San Diego. Academic. 487 pp.
Schulze, E.D.; Kelliher, F.M.; Körner, C.; Lloyd, J.; Leuning, R. (1994). Relationships
among maximum stomatal conductance, ecosystem surface conductance, carbon
assimilation rate, and plant nitrogen nutrition: A global ecology scaling exercise.
Annu. Rev. Ecol. Syst., 25: 629-660.
Shackel, K.A.; Hall, A.E. (1984). Effect of intercropping on the water relations of sorghum
and cowpea. Field Crop. Res., 8: 381-387.
191
9 References
Shepherd, K.D.; Ohlsson, E.; Okalebo, J.R.; Ndufa, J.K. (1996). Potential impact of agroforestry on soil nutrient balances at the farm scale in the East African Highlands.
Fertizer Res., 44: 87-99.
Shuttleworth, W.J. (1988). Evaporation from Amazonian rainforest. Proc. Roy. Soc. London B, 233: 321-346.
Siegmund-Schultze, M.; Rischkowsky B.; Nielsen, S.N.; da Veiga, J.B.; Tourrand, J.-F.; King,
J.M. (2000). The response of the smallholder farm to the introduction of cattle in
eastern Amazonia: The case of the Bragantina region. In: Summaries of Lectures
and Poster of the German-Brazilian Workshop on Neotropical Ecosystems held in
Hamburg Sept. 3-8. In press.
Silva-Forsberg, M.C.; Fearnside, P.M. (1997). Brazilian Amazonian caboclo agriculture: effect of fallow period on maize yield. Forest Ecol. Manage., 97: 283-291.
Simmons, C.S.; Nielsen, D.R.; Biggar, J.W. (1979). Scaling of field-measured soil-water
properties. I. Methodology. II. Hydraulic conductivity and flux. Hilgardia, 47: 77-174.
Sinclair, T.R.; Allen, L.H.; Lemon, E.R. (1975). An analysis of errors in the calculation of
energy flux densities above vegetation by a Bowen-ratio profile method. Boud-Lay.
Meteorol., 8: 129-139.
Singh, G.; Singh, R. (1991). Water use studies in three varieties of cowpea (Vigna unguiculata L.) as influenced by moisture regimes and phosphorus levels during
summer. Narendra Deva J. Agr. Res., 6 (1): 32-35.
Sinun, W.; Meng, W.W.; Douglas, I.; Spencer, T.; Marshall, A.G., Swaine, M.D. (1992).
Throughfall, stemflow overland flow and throughfall in the Ulu Segama rain forest,
Sabah, Malaysia. Phil. Trans. Roy. Soc. London B, 335: 389-395.
Sioli, H. (1951). Estudo preliminar das relações entre a Geologia e a Limnologia da Zona
Bragantina (Pará). In: Boletim Técnico do Instituto Agronômico do Norte, No. 24. pp.
67-79.
Sioli, H. (1968). Zur Ökologie des Amazonasgebietes. In: Fittkau, E.J.; Illies, J. (eds.). Biography and ecology in South America, Vol. 1. pp. 130-170. The Hague. NV. Publication.
Sisson, J.B.; Van Genuchten, M.T. (1991). An improved analysis of gravity experiments for
estimating the unsaturated soil hydraulic functions. Water Resour Res., 27: 569575.
Skole, D.; Tucker, C. (1993). Tropical deforestation and habitat fragmentation in the Amazon using satellite data from 1978 to 1988. Science, 260: 1905-1910.
Slichter, C.S. (1899). U.S. Geol. Sur. Ann. Rep., 19-II: 295-384.
Smith, M.; Allen, R.G.; Pereira, L. (1996). Revised FAO methodology for crop water requirements. In: Evaporation and irrigation scheduling. Proceedings of the International Conference, San Antonio, Texas, USA, Nov. 3-6 1996. pp. 116-123. St. Joseph, ASAE.
Smith, S.J. and Sharpley, A.N. (1990). Soil nitrogen mineralization in the presence of surface and incorporated crop residues. Agron. J., 82: 112-116.
Smyth T.J.; Bastos, J.B. (1984). Soil fertility changes in a Typic Acrorthox by slash-andburn clearing of the standing vegetation. Revista Brasileira de Ciencia do Solo 8 (1):
127-132.
192
9 References
Sollins, P.; Robertson, P.; Uehara, G. (1988). Nutrient mobility in variable- and permanentcharge soils. Biogeochemistry, 6: 181-199.
Sommer, R. (1996). Kohlenstoffvorräte unter intensiv genutzten Sekundärwaldflächen im
östlichen Amazonasgebiet, Brasilien. Unpublished Diploma Thesis. University of Göttingen. 115 pp.
Sommer, R., Denich, M.; Vlek, P.L.G. (2000). Carbon storage and root penetration in deep
soils under small-farmer land-use systems in the Eastern Amazon region, Brazil.
Plant Soil, 219: 231-241.
Stannhard, D. (1997). A theoretically based determination of Bowen-ratio fetch requirements. Bound.-Lay. Meteorol., 83 (3): 375-406.
Stanhill, G. (1962). The use of Piche evaporimeter in the calculation of evaporation.
Quart. J. Roy. Meteorol. Soc., 88: 80-82.
Stockle, C.O.; Kjelgaard, J. (1996). Parameterizing Penman-Monteith surface resistance
for estimating daily crop ET. In: Evaporation and irrigation scheduling. Proceedings
of the International Conference, San Antonio, Texas, USA, Nov. 3-6 1996. pp. 697703. St. Joseph, ASAE.
Stolte, J.; Freijer, J.I.; Bouten, W.; Dirksen, C.; Halbertsma, J.M.; Van Dam, J.C.; Van den
Berg, J.A.; Veerman, G.J.; Wösten, J.H.M. (1994). Comparison of six methods to determine unsaturated soil hydraulic conductivity. Soil Sci. Soc. Am. J., 58: 15961603.
Stoltenberg, N.L.; Wilson, T.V. (1950). Interception storage of rainfall by corn plants.
Trans. Amer. Geophys. Union, 31 (3): 443-448.
Stromgaard, P. (1984). The immediate effect of burning and ash-fertilization. Plant Soil
80, 307-320.
Szeicz, G.; Long, I.F. (1969). Surface resistance of crop canopies. Water Resour. Res., 5
(3): 622-633.
Tetens, O. (1930). Über einige meteorologische Begriffe. Z. Geophys., 6: 297-309
Thielen-Klinge, A. (1997). Rolle der biologischen N2-Fixierung von Baumleguminosen im
östlichen Amazonasgebiet, Brasilien – Anwendung der 15N natural abundance
Methode. PhD Thesis, University of Göttingen. 202 pp. Internet Publication:
http://www.sub.uni-goettingen.de/f_digbib.htm
Thom, A.S.; Oliver, H.R. (1977). On Penman's equation for estimating regional evaporation. Quart. J. Roy. Meteorol. Soc., 103: 345-357.
Thom, A.S.; Thony, J.L.; Vauclin, M. (1981). On the proper employment of evaporation
pans and atmometers in estimating potential transpiration. Quart. J. Roy. Meteorol.
Soc., 107: 711-736.
Thurston, H.D. (1997). Slash/mulch systems. Sustainable methods for tropical agriculture. London. Westview. 196 pp.
Tiersch, G. (1988). Die Bestimmung der aktuellen Evapotranspiration landwirtschaftlicher
Nutzbestände mit Hilfe mikrometeorologischer Verfahren. Ph.D. Thesis. Berlin.
Technische Universität. 215 pp.
Tillotson, P.M.; Nielsen, D.R. (1998). Scale factors in soil science. Soil Sci. Soc. Am. J., 48
(5):953-959
193
9 References
Tomasella, J.; Hodnett, M.G. (1994). Soil hydraulic properties and van Genuchten parameters for an oxisol under pasture in Central Amazonia. In: Gash, J.H.C.; Nobre,
C.A.; Roberts, J.M. Victoria, R.L. (eds.). Amazonian deforestation and climate.
pp. 101-124. Chichester. Wiley. 661 pp.
Toner, C.V.; Sparks, D.L., Carski, T.H. (1989). Anion exchange chemistry of middle Atlantic
soils: Charge properties and nitrate retention kinetics. Soil Sci. Soc. Am. J., 53:
1061-1067.
Trumbore, S.E. (1993). Comparison of carbon dynamics in tropical and temperate soils
using radiocarbon measurements. Global Biogeochem. Cycle., 7: 275-290.
Tulaphitak, T.; Pairintra, C.; Kyuma, K. (1985). Changes in soil fertility and tilth under
shifting cultivation. II. Changes in the soil nutrient status. Soil Sci. Plant Nutr., 31:
239-249.
Turk, K.J.; Hall, A.E. (1980). Drought adaptation of cowpea. IV. Influence of drought on
water use, and relations with growth and seed yield. Agron. J., 72: 434-439.
Ubarana, V.N. (1996). Observations and modelling of rainfall interception at two experimental sites in Amazonia. In: Gash, J.H.C.; Nobre, C.A.; Roberts, J.M. Victoria, R.L.
(eds.). Amazonian deforestation and climate. pp. 151-162. Chichester. John Wiley.
661 pp.
Uhl, C.; Nepstad, D.; Buschbacher, R.; Clark, K.; Kauffman, J.B.; Subler, S. (1989). Disturbance and regeneration in Amazonia: Lessons for sustainable land-use. The Ecologist, 19 (6): 235-240.
Uhl, C.; Jordan, C.F. (1984). Succession and nutrient dynamics following cutting and
burning in Amazonia. Ecology, 65 (5): 1476-1490.
Uehara, G.; Gillman, G. (1981). The mineralogy, chemistry, and physics of tropical soils
with variable charge clays. Westview Tropical Agriculture Series, No. 4. Boulder.
Westview. 170 pp.
Van Genuchten, M.Th. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J., 44: 892-898.
Van Genuchten, M.Th. (1987). A numerical model for water and solute movement in and
below the root zone. Research Report No. 121. U.S. Salinity Laboratory, USDA, ARS,
Riverside. CA.
Van Genuchten, M.Th.; Nielsen, D.R. (1985). On describing and predicting the hydraulic
properties of unsaturated soils. Ann. Geophys., 3: 615-628.
Van Genuchten, M.Th.; Leij, F.J.; Yates, S.R. (1991). The RETC code for quantifying the
hydraulic function of unsaturated soils. EPA/600/2-91/065. Roberts, S. Kerr. Environmental Research Laboratory. U.S. Environmental Protection Agency, Ada. OK.
Van Genuchten, M. Th.; Šimůnek, J.; Poeter, E. (1999). Unpublished handout of the compact-course "Advanced modeling of water flow and solute transport in variably saturated media", held in Hannover 23 –25 April 1999.
Van Noordwijk, M. (1989). Rooting depth in cropping systems in the humid tropics in relation to nutrient use efficiency. In: Van der Heide, J. (ed.). Nutrient management for
food production in tropical farming systems. pp. 129-144. Haren. Institute for Soil
Fertility. 394 pp.
Van Raij, B.; Peech, M. (1972). Electrochemical properties of some oxisols and alfisols of
the tropics. Soil Sci. Soc. Am. Proc., 36: 587-593.
194
9 References
Van Reuler, H.; Janssen, B.H. (1993a). Nutrient fluxes in the shifting cultivation system of
south-west Cote d'Ivoire. I. Dry matter production, nutrient contents and nutrient
release after slash and burn for two fallow vegetations. Plant Soil, 154: 169-177.
Van Reuler, H.; Janssen, B.H. (1993b). Nutrient fluxes in the shifting cultivation system of
south-west Cote d'Ivoire. II. Short-term and long-term effects of burning on yield and
nutrient uptake of food crops. Plant Soil, 154: 179-188.
Veneklaas, E.; Van Ek, R. (1990). Rainfall interception in two tropical montane rain forests, Colombia. In: E. Veneklaas (ed.). Rainfall interception and aboveground nutrient fluxes in Colombian montane tropical rain forests. pp. 23-44. Proefschrift
Utrecht. 109 pp.
Vereecken, H. (1995). Estimating the unsaturated hydraulic conductivity from theoretical
models using simple soil properties. Geoderma, 65: 81-92.
Vereecken, H.; Diels, J.; Viaene, P. (1995). The effect of soil heterogeneity and hysteresis
on solute transport: A numerical experiment. Ecol. Model. 77: 273-288.
Verhulst, F. (1996). Nonlinear differential equations and dynamical systems. 2nd reviewed and expanded edition. Berlin. Springer. 303 pp.
Verma, S.B.; Rosenberg, N.J.; Blad, B.L. (1978). Turbulent exchange coefficients for sensible heat and water vapor under advective conditions. J. Appl. Meteorol., 17: 330338.
Viera, L.S.; dos Santos, W.H.; Falesi, I.C. Filho, J.P.S.O. (1967). Levantamento do reconhecimento dos solos da região Bragantina, Estado do Pará. Pesquisa Agropecuária
Brasileira, 2: 1-63.
Vogel, T.; Císlerová, M. (1988). On the reliability of unsaturated hydraulic conductivity calculated from the moisture retention curve. Trans. Porous Media, 3: 1-15.
Vogel, T.; Císlerová, M.; Hopmans, J.W. (1991). Porous media with linearly variable hydraulic properties. Water Resour. Res., 27 (10): 2735-2741
Vogel, T.; Huang, K.; Zhang, R.; Van Genuchten, M.T. (1996). The Hydrus code for simulating one-dimensional water flow, solute transport and heat movement in variably
saturated media, Version 5.0. Research Report No. 140. U.S. Salinity Laboratory
USDA, ARS, Riverside, CA.
Vogeler, I.; Clothier, B.E.; Green, S.R.; Scotter, D.R., Tillman, R.W. (1996). Characterizing
water and solute movement by Time Domain Reflectometry and disk permeametry.
Soil Sci. Soc. Am. J., 60: 5-12.
Von Hoyningen-Huene, J. (1983): Die Interzeption des Niederschlags in landwirtschaftlichen Pflanzenbeständen. In: Einfluß der Landnutzung auf den Gebietswasserhaushalt. pp. 3-53. DVWK-Schrift. Berlin. Parey. 298 pp.
Wada, S.I. (1984). Mechanism of apparent salt absorption in ando soils. Soil Sci. Plant
Nutr., 30: 77-83.
Wallace, J.S.; Batchelor, C.H.; Hodnett, M.G. (1981). Crop evaporation and surface conductance calculated using soil moisture data from Central India. Agr. Meteorol., 25:
83-96.
Wallace, J.S.; Gash, J.H.C.; Sivakumar, M.V.K. (1990). Preliminary measurements of net
radiation and evaporation over bare soil and fallow bushland in the Sahel. Internat.
J. Climatol., 10: 203-210.
195
9 References
Walter, H., (1990). Vegetation und Klimazonen. 6., verbesserte Auflage. Stuttgart. Ulmer.
382 pp.
Watrin, O. do S. (1994). Estudo da dinâmica na paisagem da Amazônia Oriental através
de técnicas de geoprocessamento. M.Sc. Thesis. Instituto de Pesquisas Espaciais.
São José dos Campos, Sao Paulo. 153 pp.
Wiesenmüller, J. (1999). Einfluß landwirtschaftlicher Flächenvorbereitung auf die Dynamik des Wurzelsystems und die oberirdische Regeneration der Sekundärvegetation Ostamazoniens, Pará, Brasilien. Ph.D. Thesis. University of Göttingen. 228 pp.
Williams, M.R.; Melack, J.M. (1997). Solute export from forested and partially deforested
catchments in the central Amazon. Biogeochemistry, 38: 67-102.
Wild, A. (1981). Mass flow and diffusion. In: Greenland, D.J. and Hayes, M.H.B. (eds.) The
chemistry of soil processes. pp. 37-80. Chichester. John Wiley. 714 pp.
Wong, M.T.F., Hughes, R., Rowell, D.L. (1990). Retarded leaching of nitrate in acid soils
from the tropics: measurements of the effective anion exchange capacity. J. Soil
Sci., 41: 655-663.
Wösten, J.H.M.; Van Genuchten, M.T. (1988). Using texture and other soil properties to
predict the unsaturated hydraulic functions. Soil Sci. Soc. Am. J., 52: 1762-1770.
Wright, J.L. (1982). New evapotranspiration crop coefficients. J. Irrig. Drain. Div., 108: 5774.
Wright, J.L.; Jensen, M.E. (1972). Peak water requirements of crops in Southern Idaho. J.
Irrig. Drain. Div., 96: 193-201.
Yao, N.R.; Goué, B. (1992). Water use efficiency of a cassava crop as affected by soil water balance. Agr. Forest Meteorol., 61: 187-203.
Yadav, A.K.; Mishra, G.P. (1985). Distribution of precipitation under a tropical dry deciduous forest stand of central India. J. Trop. Forest., 1 (3): 182-197.
Yates, S.R.; Van Genuchten, M.Th.; Warrick, A.W.; Leij, F.J. (1992). Analysis of measured,
predicted and estimated hydraulic conductivity using the RETC Computer Program.
Soil Sci. Soc. Am. J. 56: 347-354.
Young, E.G.; Leeds-Harrison, P.B. (1990). Aspects of transport processes in aggregated
soils. J. Soil Sci., 41: 665-675.
196
10 Appendix
10 Appendix
Bowen ratio – energy balance method
To be able to solve the Bowen ratio β =
H
, certain assumptions have to be made:
λET
The evapotranspiration (=latent heat flux) physically is described as:
ET = −ρ ⋅ K ET ⋅
(A-1)
ET = latent heat flux [mm s-1]
ρ = ("rho") density of the air [kg m-3]
KET = coefficient of eddy diffusivity of latent
heat flux [m2 s-1]
∂q/∂z = gradient of the specific humidity (q)
along the distance z [m-1]
∂q
∂z
The analog expression for sensible heat flux is:
H = sensible heat flux [ W m-2]
Cp = coefficient of specific heat for moist air
at constant pressure [Ws kg-1 °K-1]
KH = coefficient for eddy diffusivity of sensible
heat flux [m2 s-1]
∂θ/∂z = gradient of the potential temperature (θ)
along the distance z [K m-1]
∂θ
H = −C p ⋅ ρ ⋅ K H ⋅
∂z
(A-2)
Thus, dividing both equations leads to:
H
=
ET
(A-3)
∂θ
∂z
∂q
⋅
∂z
− Cp ⋅ ρ ⋅ K H ⋅
− ρ ⋅ K ET
The potential temperature (θ) can be substituted through the air temperature (T) and the
specific humidity through the actual vapor pressure. Their relations are:
(A-4)
θ≈T
(A-5)
q ≈ ε⋅
ε = ratio of mole weight of water vapor compared to the mole weight of air [-]
= 0.622
ea = actual vapor pressure [kPa]
p = atmospheric pressure [kPa]
ea
p
Furthermore, the psychrometric equation can be introduced as is:
(A-6)
γ =
γ = psychrometric coefficient [kPa °C-1]
Cp = coefficient of specific heat for moist air
at constant pressure [J kg-1 K-1]
λ = latent heat of vaporization [MJ kg-1]
Cp ⋅ p
ε⋅λ
γ⋅ε⋅λ
⇔p =
,
Cp
so that
197
10 Appendix
(A-7)
H
=
ET
∂T
∂z
∂ ((e a ⋅ C p ) /(γ ⋅ λ))
− Cp ⋅ ρ ⋅ K H ⋅
− ρ ⋅ K ET ⋅
∂z
Cp, γ and λ do not show considerable variation within a vertical profile of several meters
and, therefore, can be excluded from the differential quotient. Then, several reduction
can be made, so that:
(A-8)
H
=γ
λET
∂T
∂z
∂e a
⋅
∂z
− KH ⋅
− K ET
Finally, Bowen (1926) assumed that the coefficients for the eddy diffusivities of sensible
and latent heat fluxes are equal and thus KH/KET is unity. Then the Bowen ratio can be
solved after substituting the differential quotient through gradient measurements:
(A-9)
β=
H
∆T
=γ
λET
∆e a
The latter assumption (KH=KET) might be violated at labile or stabile conditions, or when
sensible heat is entering the system (oasis effects) and, thus, gradients of sensible heat
and latent heat are opposite. Then KH might exceed KET by a factor of up to three (Pruitt
et al., 1973; Verma et al. 1978). In case KH≠KET, both coefficients have to determined
separately combining flux gradients with results of independently determined ET. Therefore the Bowen ratio energy balance requires criteria for rejection of inappropriate data
(e.g. drawn up by Ohmura, 1982) or appropriate modification, such as done by Lang
(1973) installing two-dimensional gradient measurements.
198
10 Appendix
Richards equation
Already in the 19th century Henry Darcy (1856) formulated the common flow equation for
saturated steady or stationary flow in porous media (as a soil can be considered), known
as Darcy's law:
(A-10)
q=
q = flux density (or simply flux) [cm d-1]
V = volume of water [cm3]
A = cross-sectional Area [cm2]
K = hydraulic conductivity [cm d-1]
∆H/L = drop of hydraulic head (or hydraulic potential) per unit distance L in the direction of flow [cm cm-1]
V
∆H
=K⋅
A⋅t
L
Generalized by Slichter (1899) into a three-dimensional differential equation, Darcy's law
became:
∂H x ∂H y ∂H z
(A-11) q = −K ⋅ ∇H
+
+
∇H= {∂xH, ∂yH, ∂zH} =
∂x
∂y
∂z
= gradient of the hydraulic potential in a
three-dimensional space (with the dimensions x, y and z; rectangular system)
Or in case of a one-dimensional (vertical) system:
(A-12)
q = −K ⋅
∂H z
∂z
The hydraulic potential, H, is the sum of two heads (or potentials), namely the pressure
head, hp, and gravitational head, hg (other heads, e.g. the osmotic head, are of minor importance and therefore not considered).
To describe unsteady or transient flow processes Darcy's law is not sufficient. For this
purpose it has to be extended by including the law of conservation of matter, which
states that within a defined soil volume a change in water content can be explained
through the sum of fluxes into and out of this volume and extraction through designated
sinks14 (e.g. root water uptake). The law of conservation of matter thus is given by the
equation:
(A-13)
θ = volumetric water content [cm3 cm-3]
∂θ
= −∇ q − S
∂t
∇q = {∂xq, ∂yq, ∂zq} =
∂q x ∂q y ∂q z
+
+
∂y
∂z
∂x
= hydraulic flux gradient in a threedimensional space, in this case it is also
called 'divergence'
S = sink term [cm3 cm-3 d-1]
14 Of course not only sinks are possible, but also sources (e.g. through sub-soil irrigation). In the present
study, however, only sinks were present.
199
10 Appendix
Or in case of a one-dimensional (vertical) system:
(A-14)
∂q
∂θ
= − z −S
∂z
∂t
Extending Darcy's law with equation (A-13) leads to:
(A-15)
∂θ
= ∇ ⋅ (K ∇ H) − S
∂t
The one-dimensional form is:
(A-16)
∂  ∂H 
∂θ
⋅ K
=
−S
∂t ∂z  ∂z 
Splitting the hydraulic potential into its summands,
(A-17)
∂
∂θ
=
∂t ∂z
  ∂h
∂hg
⋅  K  p +
∂z
  ∂z

  − S .

Gravitational potential can be related to some reference datum (soil surface, or bottom of
the soil profile) and then be expressed as ∂z, so that:
(A-18)
∂
∂θ
=
∂t ∂z
  ∂h

⋅  K  p + 1  − S

  ∂z
Unsaturated soil conditions require an extension of the latter flow equation. In this case
the hydraulic conductivity is highly variable and dependent on the volumetric water content or the pressure head, respectively. The equation can, thus, be written in three different forms, h-based, θ-based or in a mixed form:
∂ 
∂h

 ∂h
+ 1  − S ,
⋅  K (h)
=
∂z 
∂t

 ∂z
h-based: (A-19)
C (h)
θ-based: (A-20)
∂θ
∂ 
∂θ

+ K ( θ)  − S ,
⋅  D ( θ)
=
∂z
∂t ∂z 

mixed form: (A-21)
∂ 
∂θ

 ∂h
+ 1  − S ,
⋅  K (h)
=
∂t ∂z 

 ∂z
where
C(h) = ∂θ/∂h is the specific moisture capacity function [cm-1]
and
D(θ) =K(θ)/C(θ) is the hydraulic diffusivity [cm2 d-1].
Equations (A-15) to (A21) are various forms of the so-called Richards Equation (Richards,
1931).
200
10 Appendix
Vogel-van-Genuchten equation
The following modifications of the original van Genuchten equation were done by Vogel
and Císlerová (1988):
(A-22)
θ(h) = θr +
θm − θr
,
(1 + (α vG ⋅ h)n )m
θ(h) =θs ,
θr = residual water content [cm cm-1]
θm = fictitious, extrapolated water content
slightly higher then θs [cm cm-1]
θs = saturated water content [cm cm-1]
αvG, n, m = empirical constants [cm-1],[-],[-]
n>1
for h<hs
for h≥hs
The parameter hs herein is a non-zero minimum capillary height, or the so-called 'air-entry
value', up to which saturation of soil is still maintained or is reached, respectively. θm is,
therefore, a necessary extrapolated point of water content, resulting in a shift of the retention curve, so that h(θm)=hs. While this modification has little effect on the retention
curve, its effect on the hydraulic conductivity function is noticeable. The hydraulic conductivity becomes:
(A-23)
 1 − F(θ) 

K(h) = K s ⋅ S e 
 1 − F(θs ) 
2
se =
"
θ − θr
= effective water content
θ s − θr
  θ−θ
r
F(θ) = 1 − 
  θ m − θr

Ks
l



1/ m




m
= saturated hydraulic conductivity [cm d-1]
= pore-connectivity parameter
The purpose of this modification is mostly to add flexibility in describing flow processes
near saturation, where macro-porous flow dominates the flow processes corresponding
with high hydraulic conductivities.
Vogel and Císlerová (1988) included two more fictitious values, θk and θa, which should
promote additional adjustment parameters. For details see their publication and Luckner
et al. (1989).
201
10 Appendix
Textural analyses
Textural analyses were carried out in the EMBRAPA/Belém soil laboratory according to
the Embrapa soil-analyses manual (Embrapa, 1997). Soil samples were dispersed in a
1N NaOH solution and subsequently wet-sieved (sand fraction). The clay fraction then
was determined by the method of sedimentation. The silt fraction was calculated as the
complementary part to 100 %. Additionally the 'natural clay' content was determined using water as dispersion solution (Table A-1).
Table A-1: Textural distribution of the three study sites (n=1)
Site/Soil depth
------- Sand -------
3
4
Clay
Nat. clay
Flocculation
total
-------------------------------------------- [%] -------------------------------------------------1
2
coarse
fine
15
30
60
90
120
180
240
300
400
500
600
55
16
48
19
47
18
46
18
48
18
49
17
52
16
49
19
49
21
46
24
45
27
15
30
60
90
120
180
240
300
63
21
58
18
51
15
52
15
50
16
50
16
52
15
51
16
[cm]
Silt
Fallow
71
67
65
64
66
66
68
68
70
70
72
6
5
7
8
6
8
6
8
6
6
8
20
28
28
28
28
26
26
24
24
24
20
8
10
14
14
0
0
0
0
0
0
0
60
64
50
50
100
100
100
100
100
100
100
84
76
66
67
66
66
67
67
8
8
8
7
6
12
11
11
8
16
26
26
28
22
22
22
2
6
18
18
20
10
0
0
75
62
31
31
28
54
100
100
49
32
81
9
10
0
15
35
28
63
11
26
18
30
36
26
62
10
28
22
60
36
26
62
8
30
18
90
33
27
60
14
26
10
120
35
25
60
14
26
0
180
36
25
240
61
15
24
0
36
26
62
14
24
0
300
1 2 mm>particle>0.2 mm; 2 0.2>p.>0.053 mm ; 3 53 μm>p.>2 μm ; 4 2 μm>p.
100
31
21
40
61
100
100
100
Site 1
Site 2
202
10 Appendix
Gross precipitation
Considering all precipitation events during 577 days (April 1997 to December 1998,
where records could be made), about 30 % of the 15-minutely records had an intensity
between 0.5 and 1 mm 15 min-1 (Figure A-1). Still 31 % of all events were exceeding
2 mm 15min-1 intensity, underlining the fact that the precipitation in the study region is
intensive, short (median duration of precipitation per day = 1 hour) and occurs mostly at
the afternoon (Figure A-2).
40
35
P, mm/15 min
P, mm/h
Share [%]
30
25
20
15
10
5
>12
11.5
10.5
9.5
8.5
7.5
6.5
5.5
4.5
3.5
2.5
1.5
0.5
0
-1
Gross precipitation intensity classes [mm time ]
Figure A-1: Histogram of precipitation intensity divided up into 0.5mm-classes, considering 15minutely and
hourly data and their total share (considering only P>0)
10
9
Probability [%]
8
7
6
5
4
3
2
1
22:00
20:00
18:00
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
0:00
0
Time (15 minute interval)
Figure A-2: Daily probability of precipitation (including also rainless times, n=54261, 15minutely records)
203
10 Appendix
Throughfall of the fallow site
Throughfall of the fallow site of all observation within the 2 years study period (n=3200)
showed a slightly left-skewed distribution (skewness=1.108) with a mean value of 75.3 %
(SD 33.34 %; median of 73.4 %; Figure A-3). The shape of the distribution equaled those
typical for tropical forests as published e.g. by Lloyd and Marques (1988).
450
400
Frequency
350
300
250
200
150
100
50
> 200
%-class (gross precipitation share)
190
170
150
130
110
90
70
50
30
10
0
Figure A-3: Histogram of throughfall as percentage share on gross precipitation of the fallow site
204
10 Appendix
Wind speed
10
10
Transition phase
9
8
7
7
7
5
4
3
Wind speed [m s-1]
8
6
6
5
4
3
6
5
4
3
2
2
2
1
1
1
0
0
0
2
4
6
8 10 12 14 16 18 20 22
Time of day [h]
Rainy season
9
8
Wind speed [m s-1]
Wind speed [m s-1]
9
10
Dry season
0
0
2
4
6
8 10 12 14 16 18 20 22
Time of day [h]
0
2
4
6
8 10 12 14 16 18 20 22
Time of day [h]
Figure A-4: Daily median wind speed (based on hourly data) over the fallow vegetation of the three distinguished seasons (dotted gray lines are the lower and upper quartile)
205
10 Appendix
Potential and actual daily evapotranspiration 6
5
-1
[mm d ]
4
3
2
1
0.5
Potential LET (Penman-FAO)
Actual LET (Bowen ratio)
kc
0
1.4.97
1.5.97
1.6.97
1.7.97
1.8.97
1.9.97
1.10.97
1.11.97
1.12.97
1.1.98
1.2.98
1.3.98
1.4.98
Figure A-5: Dynamic of daily actual (Bowen ratio) and potential (Penman-FAO) evapotranspiration of the fallow vegetation and their relation kc within the measuring period
206
10 Appendix
Energy balance
600
600
600
Dry season
Transition phase
Rainy season
200
200
200
0
6
-200
-400
-600
12
18
24
0
0
0
6
12
18
24
-200
LET
-400
Time of day [h]
-2
Time of day [h]
-2
-2
Time of day [h]
0
[W m ]
400
[W m ]
400
[W m ]
400
0
6
-200
LET
-400
LET
H
H
H
Rn
Rn
Rn
-600
-600
Figure A-6: Energy balance (based on mean hourly data) of the fallow vegetation of the three distinguished seasons
207
12
18
24
10 Appendix
Diurnal Bowen ratio
1
Transition phase
0.8
β [-]
0.6
0.4
0.2
0
6
7
8
9
10
11
12
13
14
15
16
17
18
13
14
15
16
17
18
14
15
16
17
18
1
Dry season
0.8
β [-]
0.6
0.4
0.2
0
6
7
8
9
10
11
12
1
Rainy season
0.8
β [-]
0.6
0.4
0.2
0
6
7
8
9
10
11
12
13
Time of day [h]
Figure A-7: Median diurnal change of the Bowen ratio (hourly data) of the fallow vegetation of the three distinguished seasons (dotted lines are the lower and upper quartile)
208
10 Appendix
Diurnal canopy resistance
200
Transition period
-1
rc [s m ]
150
100
50
0
6
7
8
9
10
11
12
13
14
15
16
17
18
10
11
12
13
14
15
16
17
18
11
12
13
14
15
16
17
18
200
Dry season
-1
rc [s m ]
150
100
50
0
6
7
8
9
200
Rainy season
-1
rc [s m ]
150
100
50
0
6
7
8
9
10
Time of day [h]
Figure A-8: Median diurnal change of the canopy resistance (hourly data; bold line) of the fallow vegetation
of the three distinguished seasons; dotted lines are the lower and upper quartiles; thin lines are calculated
rc based on the regression equation (extrapolation is dotted)
209
10 Appendix
Plant survey
A plant survey (%-cover of species) was carried out on the fallow site in January 1997 by
Wetzel and Cordeiros (unpublished) at 20 plots every 6 m along a transect diagonalcrossing the site (Table A-2).
Table A-2: Percentage cover of species on the fallow site and steadiness (all plants included with height
> 50 cm; plot size: 2 m2)
Nr. Name of species
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Lacistema pubescens
Davilla rugosa
Rollinia exsucca
Banara guianensis
Myrciaria floribunda
Vismia guianensis
Myrcia bracteata
Myrcia sylvatica
Cassia chrysocarpa
Myrcia deflexa
Rourea ligulata
Aegiphila racemosa
Casearia arborea
Tabernaemontana heterophylla
Annona montana
Inga heterophylla
Ocotea opifera
Ocotea linifolia
Abarema cochleata
Bernadinia fluminensis
Cordia nodosa
Macherium madeirense
Myrciaria tenella
Inga macrophylla
Memora allamandiflora
Memora flavida
Sabicea aspera
Virola calophylla
Borreria verticillata
Cecropia palmata
Coccoloba sp.
Cordia exaltata
Desmodium canum
Dioclea virgata
Lecythis lurida
Macherium froesii
Miconia eriodonta
Moutabea guianensis
Ormosia paraensis
Poecilanthe effusa
Tabernaemontana angulata
Doliocarous brevipedicellatus
Steadiness
20
16
14
12
10
10
9
9
8
8
8
6
6
6
5
5
5
4
3
3
3
3
3
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
40
6
3
3
2
20
10
10
2
5
8 4
5
6
4
3 4 5
15 8 8
8 4 5
2
3 3 2
2 12
5
2
3
4 4
13 3
2 2
1
2 2
5
30
2 3
2
Plot Nr.
6 7 8 9 10 11 12
10 50 70 3 18 6 30
3 2 40 10
1 5 1 3 3 3
2
1 4 3 2 2
2
15
8
5 4
8
20 2 5
15
1
1
2 2 1
2
3
2
4
1
4 3
1
5
8
2
3
2
2
1
6
26
5
13 14 15 16 17 18 19
20 40 25 30 25 15 20
7 5
4 2 6 4
3
7 2
3 3
1
3 4 8 20 25
10 3
20 5 11
6
7 3 2
1
6 3
2
1
5
4 3
6
3
2
4 3
3
10 10
7
2 4 3
7
6 18
2
6 7
4
3
4 1
15 8
2
1
2
3
1
2
60
2
12
8
40
25
12
210
1
3
1
3
3
40 15
6
8
5
9
10 35
6
5
6
2
3
4 25
12
20
14
6
10 Appendix
Measured and modeled pressure head dynamics under the fallow site
1997
1.1.
1.3.
1.5.
1.7.
1998
1.9.
1.11.
1.1.
1.3.
1.5.
1.7.
1.9.
1.11.
0
1.1.
0
-100
-20
-200
Pressure head [cm]
-300
-40
-400
-500
-60
-600
-80
-700
-800
Fallow vegetation, 30 cm
-100
Model
-900
-1000
1.1.
-120
1.3.
1997
1.5.
1.7.
1.9.
1.11.
1.1.
0
1.3.
1998
1.5.
1.7.
1.9.
1.11.
1.1.
0
-100
-20
-200
Pressure head [cm]
-300
-40
-400
-500
-60
-600
-80
-700
-800
Fallow vegetation, 120 cm
-100
Model
-900
-1000
-120
Figure A-9: Measured and modeled pressure head dynamics at 30 cm and 120 cm depth in the observation
period of 1997 and 1998 on the fallow site
211
10 Appendix
1.1.
1.3.
1.5.
1997
1.7.
1.9.
1.11.
1.1.
1.3.
1998
1.5.
1.7.
1.9.
1.11.
0
1.1.
0
-100
-20
-200
Pressure head [cm]
-300
-40
-400
-500
-60
-600
-80
-700
-800
-900
Fallow vegetation, 300 cm
-100
Model
-1000
1.1.
-120
1.3.
1997
1.5.
1.7.
1.9.
1.11.
1.1.
1.3.
1998
1.5.
1.7.
1.9.
1.11.
1.1.
0
0
Pressure head [cm]
-100
-20
-200
-40
-300
-60
-400
-80
-500
Fallow vegetation, 600 cm
-600
Model
-100
-700
1.1.
-120
1.3.
1997
1.5.
1.7.
1.9.
1.11.
1.1.
1.3.
1998
1.5.
1.7.
1.9.
1.11.
1.1.
Pressure head [cm]
0
0
-20
-100
-40
-200
-60
-300
-80
-400
Fallow vegetation, 735 cm
-100
Model
-500
-120
Figure A-10: Measured and modeled pressure head dynamics at 300 cm, 600 cm and 735 cm depth
212
10 Appendix
Gravimetrically determined soil water content
The soil water content was determined gravimetrically under the fallow site on the 6th of
November and on the 10th of December 1997 as well as under the cultivation sites on
the 11th of May and on the 17th of June 1998 (Figure A-11).
0.35
Water content [-]
0.30
0.25
0.20
0.15
30 cm
0.10
60 cm
90 cm
0.05
120-600 cm
0.00
0.1
1
10
100
1000
Pressure head [-]
Figure A-11: Gravimetrically determined soil water content in relation to corresponding pressure head at
different soil depths; values with pressure heads > 200 cm are reflecting the desiccated profile under the
fallow vegetation (6th of Nov. and 10th of Dec. '97), remaining values are those of the cultivation sites (11th
of May and 17th of June 1998)
213
10 Appendix
Upward orientated fluxes
Table A-3: Times and amounts of upward orientated fluxes under the three experimental sites
----------- Fallow -----------Depth/period
Flux [mm]
90 cm
21/9/97 - 4/12/97
30/10/98 - 24/11/98
90 cm
0.2
0.5
180 cm
24/9/97 - 14/11/97
15/11/98 - 27/11/98
0.3
0.2
0.2
0.9
18/9/97 - 22/1/98
3/11/98 - 3/12/98
20/12/98 - 31/12/98
0.4
0.001
8.4
23/10/97 - 25/1/98
-
16/9/97 - 1/1/98
15/10/98 - 24/11/98
18/12/98 - 22/12/98
13.3
5.8
1.0
19/9/97 - 21/1/98
2/11/98 - 30/11/98
22/12/98 - 28/12/98
11.2
3.3
0.2
300 cm
2.7
1/12/97 - 24/1/98
-
600 cm
600 cm
-
-
214
0.4
1.8
0.1
180 cm
300 cm
600 cm
13/10/97 - 28/2/98
29/9/97 - 1/12/97
19/10/98 - 26/11/98
----------- Site 2 -----------Depth/period
Flux [mm]
90 cm
180 cm
300 cm
24/9/97 - 3/12/97
11/1/98 - 25/1/98
------------ Site 1 ------------Depth/period
Flux [mm]
0.4
10 Appendix
Ca:element-ratio of pre- and postburn vegetation biomass
Table A-4: Mean ratios of Ca to each of the considered elements of the distinguished pre- and postburn
compartments
Site/
Compartment
Site 1
Leaves
Wood
Litter
Chopped veg
Ash
Charcoal
Site 2
Leaves
Wood
Litter
Chopped veg
Ash
Charcoal
C
N
P
Mean SE Mean SE Mean
0.02
0.01
0.03
0.01
0.003
0.001
0.004
0.002
0.76
1.20
1.10
1.02
0.13
0.07
0.08
0.11
1.77 0.228 108.7 28.5
0.02 0.000
2.2 0.07
0.01
0.02
0.03
0.01
0.002
0.003
0.002
0.001
0.41
1.12
1.21
1.36
0.01
0.10
0.13
0.13
1.83 0.008 116.6 3.82
0.04 0.0002
3.6 0.15
20.5
29.0
47.4
27.7
K
SE
2.34
4.73
2.32
1.38
162.7 6.26
30.2 0.15
10.7
28.5
47.3
24.7
0.80
7.78
2.68
2.55
35.1 1.88
25.0 0.31
Mg
S
Mean SE Mean SE Mean SE
1.9
2.2
7.1
2.3
0.33
0.49
0.78
0.35
4.9
7.1
6.9
6.5
0.25
0.63
0.66
1.00
2.6 0.15
2.0 0.01
8.9 0.50
8.6 0.03
0.9
2.2
9.5
1.5
3.3
8.1
6.1
5.2
0.22
0.62
0.79
0.15
2.8 0.01
2.0 0.02
215
0.98
1.72
0.81
0.61
6.5 0.03
5.6 0.07
4.5
6.9
8.7
6.6
0.29
0.27
0.40
0.95
25.9 1.41
16.9 0.20
3.2
6.5
9.1
6.9
0.43
1.06
0.39
0.24
19.1 0.21
14.6 0.09
10 Appendix
Nutrient concentration of exported harvest goods
Table A-5: Nutrient concentration of harvested crops of site 1 and 2 of both treatments
Site/compartment
Site 1, burned plot
Maize grain
Maize spindle
Cowpeas
Cowpea pods
Cassava tuber
Site 1, mulched plot
Maize grain
Maize spindle
Cowpeas
Cowpea pods
Cassava tuber
Site 2, burned plot
Maize grain
Maize spindle
Cowpeas
Cowpea pods
Cassava tuber
Site 2, mulched plot
Maize grain
Maize spindle
Cowpeas
Cowpea pods
Cassava tuber
DM
C
N
P
K
Ca
Mg
S
-1
-1
[t ha ] ------------------------------- [mg g DM] -----------------------------------------2.45
0.49
1.50
0.40
8.73
465.5
472.2
448.9
445.6
431.8
13.7
6.2
37.6
9.2
3.2
4.4
0.8
3.6
0.8
0.6
6.1
7.0
11.4
15.3
4.1
0.1
0.2
1.0
7.4
1.0
1.66
0.32
1.60
4.97
0.58
0.94
0.42
1.70
0.54
0.20
2.04
0.40
1.32
0.35
7.54
462.2
469.2
450.6
443.9
429.5
14.5
7.4
37.3
8.9
3.6
4.4
1.1
3.5
0.7
1.0
5.8
6.3
11.5
15.4
6.4
0.1
0.2
1.2
7.0
1.3
1.69
0.58
1.66
5.13
0.58
1.03
0.47
1.78
0.53
0.27
2.33
0.48
1.71
0.50
8.66
462.5
469.9
451.0
450.6
425.3
14.2
6.0
38.9
12.6
2.1
4.8
0.6
3.7
0.9
0.8
6.0
7.1
11.5
15.1
4.4
0.1
0.3
1.0
8.5
1.0
1.84
0.49
1.58
5.21
0.41
1.05
0.42
1.79
0.70
0.14
1.65
0.36
1.61
0.47
7.66
460.8
468.0
447.4
448.5
425.7
14.7
5.1
38.7
8.5
3.5
3.9
0.3
3.7
0.6
0.9
5.6
7.5
11.6
16.4
11.8
0.1
0.2
1.1
7.3
2.9
1.55
0.48
1.56
4.42
0.49
1.12
0.42
1.76
0.52
0.27
216
10 Appendix
Nutrient concentrations in the soil solution
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 300 cm
Site 1, burnt, 90 cm
14
Site 1, burnt, 180 cm
Site 1, burnt, 300 cm
-1
[mg Na l ]
12
10
8
6
4
2
0
14
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
-1
[mg Na l ]
12
Site 1, mulched, 90 cm
10
8
6
4
2
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure A-12: Annual or two-year dynamics of sodium concentrations in the soil water samples taken at different soil depths under the burned and mulched plots of cultivation site 1 and 2, respectively, and at 3 m
under the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
217
10 Appendix
-1
[mg Al l ]
0.5
Site 2, burnt, 90 cm
Site 2, burnt, 180 cm
Site 2, burnt, 300 cm
Fallow, 300 cm
Site 1, burnt, 90 cm
0.45
Site 1, burnt, 180 cm
0.4
Site 1, burnt, 300 cm
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.5
0.45
-1
[mg Al l ]
0.4
Site 1, mulched, 90 cm
Site 2, mulched, 90 cm
Site 1, mulched, 180 cm
Site 2, mulched, 180 cm
Site 1, mulched, 300 cm
Site 2, mulched, 300 cm
0.35
0.3
0.25
0.2
0.15
0.1
0.05
1.9.98
1.7.98
1.5.98
1.3.98
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
1.1.98
1.11.97
1.9.97
1.7.97
1.5.97
1.3.97
1.1.97
0
Figure A-13: Annual or two-year dynamics of aluminum concentrations in the soil water samples taken at
different soil depths under the burned and mulched plots of cultivation site 1 and 2, respectively, and at
3 m under the fallow vegetation; bars denote the standard error (1997: site 1 n=2, site 2 n=6; 1998: n=1)
218
10 Appendix
Table A-6: Mean, minimum and maximum concentration of iron, manganese, aluminum, ammonium and
organic N and the conductivity and electrical balance in the soil water solution distinguished according to
site, treatment and depth
Site/Treatment/Depth
Site 1
burned
Conductivity
-1
-1
-------------------------------- [mg l ] ------------------------------ [µS cm ]
Fe
0.9 m Mean
Min
Max
n
1.8 m Mean
Min
Max
n
+
Al
NH4
Norg.
0.0003
0.062
0.046
0.108
5
0.960
0.267
0.020
0.940
37
69
24
132
39
49
-24
182
39
0.159
0.010
0.680
34
49
15
122
37
75
13
200
37
0.115
0.010
0.420
28
35
11
79
37
65
-54
132
37
0.124
0.010
0.400
36
37
10
96
38
9
-88
96
39
1
0.0008
0.0006
0.0009
2
1
0.160
1
3.0 m Mean
Min
Max
n
mulched 0.9 m Mean
Min
Max
n
0.027
1.8 m Mean
Min
Max
n
0.016
0.012
0.019
3
3.0 m Mean
Min
Max
n
0.001
0.9 m Mean
Min
Max
n
0.021
0.011
0.054
6
1.8 m Mean
Min
Max
n
Electrical
balance
-1
[µmolc l ]
Mn
0.304
0.045
0.523
15
1
0.158
0.048
0.282
15
1.200
0.160
2.500
3
0.145
0.010
0.400
26
21
7
42
37
17
-23
55
38
0.044
0.040
0.051
3
0.490
1
0.120
0.010
0.290
21
28
8
53
38
21
2
72
38
0.0005
0.0003
0.0006
2
0.200
0.040
1.177
45
0.570
0.150
1.840
8
0.227
0.010
1.360
121
69
13
177
133
28
-97
176
134
0.032
0.001
0.087
11
0.0184
0.0006
0.0363
2
0.154
0.040
0.391
36
0.230
0.220
0.240
2
0.149
0.010
0.760
105
58
8
127
127
37
-30
178
128
3.0 m Mean
Min
Max
n
0.026
0.001
0.119
8
0.0006
0.160
1
0.096
0.040
0.381
8
1
0.108
0.010
0.590
88
24
10
40
129
63
15
186
129
mulched 0.9 m Mean
Min
Max
n
0.024
0.010
0.043
11
0.0328
0.0003
0.0873
4
0.126
0.040
0.665
16
0.698
0.200
1.220
6
0.180
0.010
1.100
114
55
15
185
124
64
-27
186
124
1.8 m Mean
Min
Max
n
0.021
0.011
0.051
6
0.0002
0.0002
0.0003
2
0.148
0.040
0.406
42
0.240
0.200
0.280
2
0.121
0.010
0.530
84
44
9
140
121
59
-57
187
122
3.0 m Mean
Min
Max
n
0.018
0.001
0.044
11
0.0004
0.0003
0.0005
2
0.219
0.041
0.732
17
0.266
0.150
0.500
5
0.139
0.010
0.600
76
33
10
91
123
80
-56
195
124
Site 2
burned
0.0049
0.0003
0.0134
3
1
219
10 Appendix
Table A-7: Correlation coefficients (Pearson) of concentration of solute elements in the soil water under the
two cultivation sites distinguished according to burned (upper right triangle) and mulched (lower left triangle) land preparation; also given: significance (second line) and n (third line); bold number ≅ most pronounced
pH
Ca
Mg
K
Na
Al
Cl
Nitrate
Norg
P
burned
-0.463 -0.512 -0.347 -0.312 -0.427 -0.429 -0.600 -0.388 -0.009
**
**
**
**
**
**
**
**
n.s.
504
504
472
504
94
503
425
413
468
pH
S
EC
El. bal.
0.105
*
487
-0.607
**
502
0.503
**
504
-0.074
n.s.
482
Mg
-0.274
**
481
0.748
**
484
K
0.098
*
452
0.231
**
455
0.333
**
454
Na
0.013
n.s.
482
0.172
**
485
0.011
n.s.
484
-0.020
n.s.
455
Al
-0.572
**
106
0.131
n.s.
108
0.376
**
108
0.043
n.s.
98
0.075
n.s.
108
-0.285
**
482
0.772
**
485
0.711
**
484
0.080
n.s.
455
0.528
**
485
0.410
**
108
Nitrate
-0.108
n.s.
295
0.504
**
297
0.370
**
296
0.364
**
273
0.197
**
297
0.221
*
82
0.152
**
297
Norg
-0.073
n.s.
355
0.098
n.s.
357
0.141
**
356
0.302
**
334
0.035
n.s.
357
0.125
n.s.
100
-0.030
n.s.
357
0.449
**
288
P
0.157
**
439
0.163
**
442
0.155
**
441
0.117
*
419
-0.015 -0.089
n.s.
n.s.
442
94
0.099
*
442
0.045
n.s.
265
0.142
*
318
S
0.178
**
460
-0.379 -0.311
**
**
463
462
0.098
*
434
0.196
**
463
-0.170 -0.223 -0.237
n.s.
**
**
93
463
280
0.035
n.s.
337
-0.086
n.s.
420
EC
-0.255
**
481
0.834
**
481
0.698
**
480
0.216
**
451
0.592
**
481
0.235
**
103
0.121
*
354
0.093
n.s.
438
-0.213
**
459
-0.444
**
502
El. bal.
0.653
**
482
pH
-0.013 -0.204
n.s.
**
485
484
Ca
Mg
0.171
**
455
K
0.095
*
485
Na
-0.465 -0.232 -0.058 0.236
**
**
n.s.
**
108
485
297
357
Al
Cl
Nitrate Norg
0.208
**
442
P
0.043
n.s.
463
S
-0.127
**
481
EC El. bal.
Cl
mulched
Ca
0.868
**
504
0.236
**
472
0.287
**
504
-0.001
n.s.
94
0.821
**
503
0.778
**
425
0.346
**
413
0.163
**
468
-0.126
**
487
0.874
**
502
-0.324
**
504
0.269
**
472
0.243
**
504
-0.041
n.s.
94
0.819
**
503
0.685
**
425
0.308
**
413
0.148
**
468
-0.105
*
487
0.818
**
502
-0.373
**
504
0.133
**
472
0.216
*
90
0.231
**
471
0.339
**
393
0.328
**
381
0.048
n.s.
441
0.366
**
456
0.331
**
470
-0.183
**
472
0.293
**
94
0.511
**
503
0.573
**
425
0.235
**
413
0.079
n.s.
468
0.074
n.s.
487
0.638
**
502
-0.285
**
504
0.068
n.s.
94
0.275
*
80
0.167
n.s.
77
0.148
n.s.
87
-0.168
n.s.
90
0.275
**
92
-0.320
**
94
0.653
**
425
0.278
**
413
0.136
**
467
-0.006
n.s.
486
0.868
**
501
-0.463
**
503
0.459
**
385
0.183
**
398
-0.070
n.s.
411
0.893
**
424
-0.470
**
425
0.092
n.s.
384
-0.001
n.s.
399
0.409
**
411
0.004
n.s.
413
-0.035
n.s.
451
0.159
**
466
0.176
**
468
220
0.916
**
481
0.504
**
295
-0.052 -0.013
n.s.
n.s.
485
487
10 Appendix
Accumulative solute nutrient fluxes
Site 1
Site 2
Nitrate
1.5.97
1.9.97
0
-10
1.1.98
3 m, mulched
1.8 m, mulched
3 m, burned
0.9 m, mulched
1.5.97
1.9.97
1.1.98
1.5.98
1.9.98
0
3 m, mulched
3 m, burned
1.8 m, mulched
0.9 m, mulched
-10
-20
-20
[kg N ha-1]
1.1.97
[kg N ha-1]
1.1.97
1.8 m, burned
-30
-30
1.8 m, burned
-40
-40
-50
0.9 m, burned
-50
-60
-60
-70
-70
-80
-80
0.9 m, burned
Phosphate
1.5.97
1.9.97
1.1.97
1.1.98
-0.1
-0.1
-0.3
-0.3
-0.9
1.9.97
1.1.98
1.5.98
1.9.98
-0.5
-0.5
-0.7
1.5.97
[kg P ha-1]
[kg P ha-1]
1.1.97
3 m, mulched
3 m, burned
1.8 m, mulched
0.9 m, mulched
0.9 m, burned
1.8 m, burned
-0.7
-0.9
-1.1
-1.1
-1.3
-1.3
3 m, mulched
3 m, burned
1.8 m, mulched
0.9 m, mulched
1.8 m, burned
0.9 m, burned
Figure A-14: Accumulative nitrate and phosphate fluxes on site 1 and site 2 on the burned and mulched
plots at 0.9, 1.8 and 3 m depth
221
10 Appendix
Potassium
1.5.97
1.9.97
0
1.1.98
1.1.97
1.8 m, burned
0.9 m, mulched
3 m, burned
3 m, mulched
1.8 m, mulched
[kg K ha-1]
-5
1.5.97
1.9.97
1.1.98
1.5.98
1.9.98
0
1.8 m, burned
1.8 m, mulched
-5
3 m, mulched
[kg K ha-1]
1.1.97
-10
-10
0.9 m, burned
-15
3 m, burned
-15
0.9 m, mulched
-20
-20
-25
-25
0.9 m, burned
Calcium
1.1.97
0
-20
1.5.97
1.9.97
1.1.97
1.1.98
3 m, mulched
1.8 m, mulched
0.9 m, mulched
-40
1.8 m, burned
[kg Ca ha-1]
[kg Ca ha-1]
-80
0.9 m, burned
1.9.97
1.1.98
1.5.98
1.9.98
-20
3 m, burned
-40
-60
1.5.97
0
3 m, mulched
3 m, burned
-60
1.8 m, mulched
-80
-100
-100
-120
-120
-140
-140
-160
-160
1.8 m, burned
0.9 m, mulched
0.9 m, burned
Figure A-15: Accumulative potassium and calcium fluxes on site 1 and site 2 on the burned and mulched
plots at 0.9, 1.8 and 3 m depth
222
10 Appendix
Magnesium
1.5.97
1.9.97
0
1.8 m, burned
-10
0.9 m, burned
1.5.97
1.9.97
1.1.98
1.5.98
1.9.98
0
3 m, mulched
1.8 m, mulched
3 m, burned
0.9 m, mulched
-5
[kg Mg ha-1]
1.1.97
1.1.98
-5
3 m, mulched
-10
3 m, burned
[kg Mg ha-1]
1.1.97
1.8 m, mulched
-15
-15
0.9 m, mulched
-20
-20
-25
-25
-30
-30
-35
-35
1.8 m, burned
0.9 m, burned
Sulfate
1.1.97
-1
1.5.97
1.9.97
1.1.97
1.1.98
0.9 m, mulched
1.8 m, burned
1.8 m, mulched
3 m, burned
1.1.98
1.5.98
1.9.98
-3
3 m, mulched
[kg S ha-1]
[kg S ha-1]
1.9.97
-1
-3
-5
1.5.97
-5
-7
3 m, burned
1.8 m, mulched
0.9 m, burned
-9
-9
1.8 m, burned
-11
-11
0.9 m, mulched
-13
-13
-15
-15
0.9 m, burned
-7
3 m, mulched
Figure A-16: Accumulative magnesium and sulfate fluxes on site 1 and site 2 on the burned and mulched
plots at 0.9, 1.8 and 3 m depth
223
10 Appendix
(Plant-available) nutrient stocks
Table A-8: Amounts of nutrients present on site 1 and 2 in different compartments, their percentages of the
total and amounts withdrawn under slash-and-burn; assuming a root biomass of 25 t ha-1 with nutrient
concentrations ≅ those of wooden aboveground biomass
Compartment
Site 1
-1
[kg ha ]
[%]
Site 2
-1
[kg ha ]
[%]
Ca
Above-ground biomass
258
9
378
Burning remains
107
4
207
6
174
2503
2936
155
6
85
100
5
174
2774
3326
163
5
83
100
5
41
9
65
12
12
3
32
6
25
389
455
36
6
85
100
8
25
447
537
33
5
83
100
6
251
2
387
3
5
0.04
15
0.1
144
11413
11808
292
1
97
100
2
144
10664
11195
403
1
95
100
4
9
30
17
45
1
3
6
17
6
14
29
-22
22
48
100
-74
6
14
37
-18
17
38
100
-49
Roots
Soil (exchangeable 0-3m)
Sum
Withdrawal
Mg
Above-ground biomass
Burning remains
Roots
Soil (exchangeable 0-3m)
Sum
Withdrawal
N
Above-ground biomass
Burning remains
Roots
§
Soil (total 0-3m)
Sum
Withdrawal
P
Above-ground biomass
Burning remains
Roots
§§
Soil (plant-available 0-3m)
Sum
Withdrawal
S
Above-ground biomass
Burning remains
Roots
Soil
Sum
Withdrawal
§
40
65
4
12
26
?
65
26
26
?
90
32
11
based on data of Kato (1998b) and own N-determination (elementary analyzer)
based on own P data, and assuming 0.1 mg P Kg-1 below 1m depths
§§
224
Acknowledgements
I express my sincere gratitude to Prof. Horst Fölster for his encouragement, support and
the fruitful discussions during all phases of the Ph.D. study.
I would like to thank Prof. Paul L.G. Vlek for giving me the opportunity for this study, for
his constructive contributions during the phase of the field work and the helpful comments on the draft.
Particular thanks are due to Dr. Konrad Vielhauer and Dr. Tatiana Sá, and also to Dr.
Eduardo Maklouf as well as to all colleagues from Embrapa-Cpatu for their support during
the phase of the field work in Belém.
I am grateful to Dr. Manfred Denich and Dr. Roland Kühne for the constructive discussions on the topic.
Special thanks to Roberta Pantoja, Reginaldo Frazão, Alessandro Carioca de Araújo,
Hubertus Schnurbein and to Antonio Ferreira, but also to the numerous Cumaru-workers
for helping me carry out the field work. Without the help of Luis Bentes, the bureaucracy
of Brazilian customs would never have been overcome – thanks a lot!
I wish to thank Dr. Heiner Kreilein for his valuable remarks on the micrometeorology as
well as for providing the sensor calibrations, and Prof. Gode Gravenhorst for his comments on the draft.
Thanks to Dr. Jochen Schmidt (Silvaq GmbH) for his support in installing the automatic
data-logger station, and also to Dr. Rudolf Klinge for the information on soil water modeling.
I am grateful to Cornelia Conrad, who gave valuable support in carrying out the chemical
analyses in IAT in Göttingen.
I would like to thank Prof. Daniel Hillel for giving me his precious Saturday early-morning
breakfast time to appraise the soil water model outcomes.
Thanks to Dr. Jirka Šimůnek and Dr. Marcel Schaap for the information on the Hydrus-1D
model.
To the German Federal Ministry of Education, Science, Research and Technology (BMBF)
I am grateful for financing this study.
Special thanks to my Brazilian and German fellows Alda, Alessandro, Christine, Dieter,
Else, Goreti, Luba, Parahyba, Roberta, Ronaldo, Sabine, Val and Wanda for their moral
support.
Sincere thanks to my dear parents for all their support and confidence.
Very special and sincere thanks to my dearest friend and partner Barbara for all her patience, encouragement and support.
225
Curriculum vitae
Name:
Rolf Sommer
Nationality:
German
Date of birth:
27.12.68
Place of birth:
Marburg – Germany
Civil status:
unmarried
Address:
Obere Mühle 1A,
37077 Göttingen
Germany
Education
1975–1979:
Primary School Fronhausen
1979–1988:
Grammar School Elisabethschule Marburg
1988 –1990:
Civil Service at the Lebenshilfewerk Marburg
1990–1996:
Study of Biology at the Georg-August-University of Göttingen
1996–1999:
Junior fellow at the Institute of Agriculture in the Tropics (IAT) at the
University of Göttingen.
1999–2000:
Junior fellow at the Center for Development Research (ZEF) – Department of Ecology and Resource Management at the University
of Bonn
226