WATER PRODUCTIVITY ANALYSIS FOR SMALLHOLDER RAINFED
SYSTEMS: A CASE STUDY OF MAKANYA CATCHMENT, TANZANIA
J. MUTIRO1*, H. MAKURIRA 2, A. SENZANJE 3, M.L. MUL2,4
1
WREM programme, Dept of Civil Engineering, University of Zimbabwe. P.O. Box MP 167, Mt
Pleasant, Harare, Zimbabwe
2
Dept of Civil Engineering, University of Zimbabwe. P.O. Box MP 167, Mt Pleasant, Harare,
Zimbabwe
3
Dept of Soil Science and Agricultural Engineering, University of Zimbabwe, P.O. Box MP 167,
Mt Pleasant, Harare, Zimbabwe
4
UNESCO-IHE, Institute for Water Education, PO Box 3015, 2601 DA Delft, the Netherlands
ABSTRACT
Increasing food insecurity as a result of ever-increasing population, less water availability
and soil degradation is common in countries in sub-Saharan Africa. While most of the
developed fresh water resources are heavily committed to irrigation, about 90% of subSaharan populations rely solely on rain fed agriculture for their livelihoods (Rockstrom.
et. al., 2003). The majority of the population is therefore not directly benefiting from
developed water resources but are, in fact, subsistence rain fed farmers. Thus, in subSaharan Africa, techniques which help to improve water productivity (WP) can assist in
alleviating the impacts of water scarcity especially for crop production purposes.
The Makanya catchment in northern Tanzania is one typical semi arid area where farmers
depend on rain fed subsistence farming for their livelihoods. The area receives an average
annual rainfall of about 500 mm/yr over the two rainfall seasons they experience in a
year. An assessment of the current WP in rain fed and partially supplementary irrigated
agriculture was made in Makanya catchment. The study assessed the effect on WP (of
maize) and infiltration capacity of several rainwater harvesting techniques. The crop
water requirement for maize in the study area was found to be 508 mm/season by using
the CROPWAT model compared to total received rainfall of between 276.06 mm and
383.86 mm during the same period. An attempt was made to separate transpiration from
evapotranspiration using a transpiration meter. Results indicate that currently WP for
maize in the catchment is low (0.1 to 0.6 kgm-3). Introduction of improved techniques
increased WP by between 90% and 110%. Infiltration rates also increased from 6 cm/hr
to 26 cm/hr.
The conclusion from the research is that there is room to significantly improve the water
use techniques being applied for crop productivity in Makanya catchment. A clear
understanding and quantification of the water partitioning processes is required to
maximise water used for transpiration by the plant as transpiration is directly related to
biomass production. However, scientific approaches need to be complemented by
efficient farm management practices for any technologies to be successful.
Key words: supplementary irrigation, water productivity, smallholder systems
innovations, deep ripping, rain fed.
*
Corresponding author: mutirojt@yahoo.co.uk
1
INTRODUCTION
Increasing food insecurity as a result of ever-increasing population, less water
availability, low crop yields and soil degradation is common in countries in sub-Saharan
Africa. Whilst most of the developed fresh water resources are heavily committed to
irrigation, about 90% of sub-Saharan populations rely solely on rain fed agriculture for
their livelihoods (Rockstrom et. al., 2003). In sub-Saharan Africa, techniques which help
to improve water productivity (WP) can assist in alleviating the impacts of water scarcity
for crop production purposes. There is presently an increase in water scarcity due to
increased competition for the same water from non-agricultural sectors and, also from
deteriorating standards of usable water due to pollution.
Improved water productivity, which is producing more crops per drop (van Dam. et. al,
2003), can be achieved through the introduction of improved soil, water and crop
management. Carrying out a water balance analysis reveals the gaps in knowledge
regarding best ways to increase water productivity. Most of these gaps relate to the
inability to fully quantify all flow components, their interactions with crops, agricultural
inputs and the environment in the process of producing substantive yields.
According to the Tanzania Agricultural and Livestock Policy of 1997, one of the specific
objectives is ‘to promote integrated and sustainable use and management of natural
resources such as land, water, soil and vegetation’ (United Republic of Tanzania, 1997b).
This can only be met by targeting rain fed agriculture, which accounts for about 63% of
the agricultural Gross Domestic Product (GDP) (World Bank, 1994). Low and erratic
rainfall have contributed to the low yields that threaten the contribution of rain fed
agriculture to the GDP. Therefore, water saving technologies such as conservation tillage
practices is required.
Farmers have to be encouraged to rely as much as possible on rainwater and in rain fed
systems, to introduce supplementary irrigation as per the crop needs and rainfall pattern.
This seems practical in the semi arid areas of Tanzania such as Makanya catchment,
which receives rainfall below 600 mm annually (Slaymaker et. al., 2002). However,
relying solely on rainfall for crop growth in Makanya does not achieve the best results as
witnessed by very little to zero yields currently achieved hence the need to introduce
techniques that improve water productivity. Most catchments in sub-Saharan Africa are
ungauged hence it is difficult to establish water availability in the systems. Low rainfall
usually results in very low crop yields annually, for example, maize yields are around 1
ton/ha (Rockstrom, 2001) compared to the expected 5 tons/ha.
The water productivity under the current management level is not known hence the need
to evaluate the current water productivity levels being achieved. The potential of water
system innovations to improve livelihoods while also improving water productivity
remain largely unquantified in many arid to semi arid places. Quantifying field water
productivity helps to reveal gaps in knowledge regarding the best ways to increase water
productivity. This paper reports on work that was done whose main objective was to
assess the crop and water productivity of small scale farming systems in semi-arid
Makanya catchment in Tanzania.
2
The specific objectives were to:
(i)
(ii)
Assess how much water is received at farm level.
Assess crop yield and water productivity levels being achieved by small holder
farmers using current water use technologies
(iii)
Analyse the effect of new techniques on crop and water productivity.
(iv)
Quantify transpiration as a proportion of total water available at field scale.
The work was premised on the hypotheses that:
(i)
Water supplied to the field through rainfall and supplementary irrigation is
insufficient for effective yields to be achieved under current water management
practices.
(ii)
More efficient smallholder systems innovations including on-farm soil and moisture
conservation can improve crop water productivity and increase crop yields in water
scarce areas.
METHODOLOGY
Study Area: Makanya Catchment, Tanzania.
The Makanya catchment has been selected as a case study and is located in Pangani river
basin in Tanzania. It lies between latitudes 4o15' to 4o21' S and longitudes 37o48' to 37o53' E. It
lies at an altitude ranging from 500 to 2000 m above sea level. The catchment has low and
unreliable rainfall ranging from 400-600 mm. The rainfall distribution in the catchment is
bimodal with the rainfall occurring in November - January (locally called 'Vuli') and March May (locally called 'Masika'). Vuli rains are highly unreliable and the seasonal amount exceeded
70 percent of the time is only 178 mm. The mean minimum temperature ranges from 16o C to 18o
C and mean maximum temperature ranges from 26o C to 32o C. The main crops cultivated are
maize, lablab beans, vegetables and beans. The farmers get water for supplementary irrigation
from the storage reservoir locally known as Manoo Ndiva with a capacity of 1650 m3.
Experimental Treatments
The field experiments were conducted with a total of four smallholder farmers namely Omari,
Wilson, Walter and Elizabeth. Soil samples were collected from each experimental plot and sent
to the laboratory for the analysis of the following soil properties: soil texture, soil bulk density,
porosity, pH; cation exchange capacity (CEC), organic carbon (OC), mineral nitrogen (N),
available phosphorus (P) and potash (K). Infiltration tests were done using the double ring
infiltrometer between the rip lines and in the rip lines. A flow meter was installed to measure the
amount of irrigation water entering each plot. Another flow metre was installed to measure water
going out of each field as run-off. Rain gauges were installed to measure the amount of rainfall
received in each plot except in Omari plot. There were two sources of water inflow,
supplementary irrigation and rainfall. The soil management practices analysed were deep ripping,
conventional tillage, and fanya juu.
Maize (Zea mays) was the study crop as it is widely grown in the study area. The maize variety
was TMV1 that takes about 110 to 115 days to mature. Ripping using a Magoe ripper and sowing
of seeds in the Vuli season was done on 04/10/04 (dry planting) and the recommended inter row
spacing was 75cm and the in row spacing was 30 cm. In all treatments, farmers applied manure
only (before planting) since it was their usual practice. They do not apply inorganic fertilizers
because they believe that it makes the soils barren such that if you stop using inorganic fertilizer
afterwards the yield will be low. Again organic fertilizers are very expensive
3
Weeding was done using a hand hoe. Gap filling was done from day 10 –12 after planting.
Supplementary irrigation was done on day 65. Dursban was used to control ball worm.
Supplementary irrigation was applied in Elizabeth’s plot (once) and in Wilson’s homestead plot
(twice) during the growing season. Tasseling commenced on 30/11/2004. In Elizabeth tasseling
commenced on 16/12/2004. Harvesting in all the plots was done on 06/02/2005. At harvest a
sample plot area of 30 m2 was selected and the grain yield as well as the number of plants in that
sample plot area were measured. This was then converted to per hectare basis.
In the Masika season the same treatments that were applied in the Vuli season were repeated. The
only difference was that there was also run off diversion on the rain fed plots (Walter and Iddi).
In Elizabeth plot planting was done on 20/03/05, Iddi plot – 08/03/05, Walter plot – 08/03/05,
Wilson chini – 09/03/05 and in Wilson home plot planting was done on 06/03/05.
Experimental design
Below is the table (Table 1) that shows the different experimental treatments in each of the plots.
Table 1: Treatments for the Vuli season
PLOT
TREATMENT
Supplementary irrigation Ripping
Fanya juu
Elizabeth
x
x
x
Wilson Home
x
x
x
Wilson chini
x
x
x
Walter
x
x
Omari
x
x
x
Transpiration Measurement
Measurement of transpiration was done using the Sap Flow Meter that was installed in
Elizabeth’s plot 64 days after planting to measure sap flow. Water lost as direct evaporation
during this period was calculated by subtracting the transpiration measured from
evapotranspiration measured over the same period.
The water lost as deep percolation was also monitored using the TDR tubes installed in the
experimental plots. The water applied to the field as run-on, rainfall and supplementary irrigation
minus the water lost from the field as run-off and deep percolation is equal to the water
evapotranspired. To calculate the WP following equation was used;
Yield (kgha1 )
WP 
WaterEvapoTranspired (m 3 ha 1 )
Equation 1
The paired samples t-test was carried out using SPSS Statistical program for Windows to find
whether there was any significant difference between the means of the different parameters
among different treatments.
RESULTS
Soil physical and chemical analysis
The results of soil chemical and physical properties before the commencement of the Vuli season
are shown in Table 2 below. Most of the soils in Makanya catchment are alkaline in nature with
pH ranging from 7.4 to 8.8. Total nitrogen ranged from 0.14 to 0.26 with the lowest being
recorded in Wilson chini’s plot. The available phosphorus ranged from 12.8 mg/kg to 47.2 mg/kg
with the highest being recorded in Elizabeth’s plot. Soils with available phosphorus greater than
4
6 mg/kg are considered to be good soils. Organic matter and available potassium ranged from
2.93% to 4.91% and 1.74 to 8.9 Cmols/kg, respectively. Results show that the manure applied is
rich in available phosphorus, %N, K and organic matter. Porosity and bulk density ranged from
42 to 45% and 1.456 to 1.534 g/cm3, respectively. Soil depths were as follows; Elizabeth plot (50
cm), Wilson home (56 cm), Wilson chini (72 cm), Omari (130 cm), Walter (20 cm).
Table 2: Soil analysis results.
Farmer/plot Depth pH %N
Available
(cm)
P- mg/kg
Elizabeth
0-20 8.8 0.19
47.2
Manure
8.5 11.82
712.0
Wilson
0-20 8.2 0.14
30.4
Manure
8.7 12.73
524.0
Iddi
0-20 7.4 0.15
21.8
Manure
9.3 16.13
754.0
K
Cmol/kg
6.46
26.58
4.1
28.68
4.12
36.47
%OC
%OM
2.07
130.0
1.52
140.0
1.64
177.4
3.57
13.79
2.62
13.67
2.83
13.72
BD
(g/cc)
1.534
%
Porosity
42
1.456
45
1.504
43
Crop yield results:
The tables below Tables 3, 4, 5, 6 and 7) show the maize grain yield from each experimental plot
during the Vuli season. The grain yield results for the Masika season could not be collected
because of time constraint.
Table 3: Maize yield in Elizabeth’s plot (received only one water allocation)
Number of Planting
Sample plot plants
in density
2
PLOT number & treatment
area (m )
sample plot
(plants/ha)
A (Ripping & irrigation)
30
116
38667
B (Irrigation & no ripping)
30
108
36000
39667
C (No irrigation & no ripping)
30
119
Yield (t/ha)
1.67
0.80
1.00
Table 4: Maize yield in Wilson Homestead’s plots (received two water allocations)
Sample plot No. of plants Plant density Yield
PLOT
area (m2)
in sample plot (plants/ha)
(t/ha)
A (Irrigation & ripping)
30
75
25000
1.83
14460
B (with irrigation & no ripping)
69.85
101
0.21
44333
C (No irrigation & no ripping)
30
133
2.08
Table 5: Maize yield in Wilson Shamba chini plots (received no water allocation)
Sample plot No. of plants in Plant density Yield
PLOT
area (m2)
sample plot
(plants/ha)
(t/ha)
27667
A(Ripping & no irrigation)
30
83
0.67
34940
B (No irrigation & no ripping
66.4
232
0.15
C (No irrigation & no
30000
ripping)
30
90
0.75
Table 6: Maize yield in Omari plot - 04/02/05 (RECEIVED TWO ALLOCATIONS)
Sample plot Number of plants Planting density Yield
(plants/ha)
PLOT number & treatment
area (m2)
in sample plot
(t/ha)
A (Ripping & irrigation)
30
80
26667
2.50
B (Irrigation & no ripping)
30
93
31000
2.42
5
C (No irrigation & no ripping)
30
75
25000
2.83
Table 7: Maize yield in Walter’s plot (rain fed only) - WALTER MJEMA - 07/02/05
Sample plot area No. of plants in Plant
density
PLOT
(m2)
sample plot
(plants/ha)
Yield (t/ha)
30000
A (ripping)
30
90
0.50
B (no ripping)
30
100
33333
0.25
The yields in the different treatments were comparable as shown in tables 3 to 7 above. In
Elizabeth’s plot, ripping, fanya juu and one allocation of water produced the highest yield of 1.67
t/ha. Yield of plots that received supplementary irrigation coupled with ripping was higher than of
supplementary irrigation with no ripping but statistically the increase was not significant (t =
1.930 and p = 0.193). The difference in yield between the treatment with no irrigation and no
ripping and the treatment with irrigation and no ripping was not significant (p = 0.940). So basing
on the statistical analysis the hypothesis that systems innovations (ripping) improve yields can be
rejected.
Water application and water productivity
Each farmer only got one water allocation per season as supplementary irrigation except Omari
plot and Wilson’s homestead plot, which received two allocations. In the Vuli season studied, out
of 150 families that are supplied by Manoo Ndiva, only 86 farmers got an allocation. Table 8
below shows the amount of water received in each experimental plot throughout the growing
season. The rainfalls received in the experimental plots were comparable. Walter plot received
the highest rainfall and Wilson home plot received the lowest rainfall.
Table 8: Water received as rainfall and supplementary irrigation in the four experimental plots for
the period from planting to harvesting.
water
Total rainfall Water applied as Water out of the Total
system (mm)
applied (mm)
recorded
supplementary
Plot name
(mm)
irrigation (mm)
0
318.45
Elizabeth (ripping+suppl irrig)
277.14
41.31
0
270.12
Wilson Chini
270.12
0
Walter
323.07
0
0
323.07
Table 9 below shows how water productivity was changing in response to different treatments.
The water lost through evaporation and deep percolation (TDR tubes) could not be measured
because of equipment failure so the water balance equation could not be closed. Instead the water
productivity was then calculated using the total amount of water applied to the field. The field
water productivity was calculated as follows:
WP 
Yield (kgha1 )
TotalWaterApplied (m 3 ha 1 )
Table11: Yield and Water Productivity responses to different treatments
Effective rainfall + Yield
Water productivity Volume applied
Plot name & treatment suppl. Irrigation (mm) (kg/ha)
(WP) (kg/m3)
(m3)
6
Elizabeth A
263.0
1665
0.63
2630.2
Elizabeth B
263.0
799
0.30
2630.2
Elizabeth C
221.7
1000
0.45
2217.1
Wilson Chini Ripping
216.1
667
0.31
2161.0
Wilson Chini No
ripping
216.1
151
0.07
2161.0
Walter Ripping
258.5
500
0.19
2584.6
Walter No ripping
258.5
250
0.10
2584.6
NB- Elizabeth A –Fanya juu + irrigation + ripping, Elizabeth B –Funya juu + irrigation + no
ripping and Elizabeth C – Fanya juu + no irrigation + no ripping.
Crop water requirements and maize yields
The crop water requirements (CWR), reference crop evapotranspiration and effective rainfall was
calculated using the CropWat 4 Windows 4.3 version The crop water requirement for maize was
far below the total rainfall received during the growing season (figure 2). The cumulative rainfall
received (total 209 mm and effective rainfall 192.8 mm) in Elizabeth plot was far less than the
crop water requirement for maize (508 mm) in the Vuli season, as shown also in figure 2. This
shows that maize cannot produce significant yield if rain-fed only especially in the Vuli season.
Cummulative rainfall & CWR for maize in Elizabeth
plot
CWR and rainfall distributionover the growing period
80.00
cum m ulative CW R & r ainfall ( m m )
600
70.00
water (mm)
60.00
50.00
40.00
30.00
20.00
10.00
10
/6
/
10 200
4
/1
6/
10 200
4
/2
6/
2
11 004
/5
/2
0
11
/1 04
5/
2
0
11
/2 04
5/
20
0
12
/5 4
/
12 200
4
/1
5/
12 200
4
/2
5/
20
04
1/
4/
20
05
1/
14
/2
00
1/
24 5
/2
00
5
0.00
400
300
200
100
0
0
20
40
60
80
100
120
140
-100
rainfall
CWR
time (10 days sum)
500
CW R
Time (days after planting)
rainfall
(a)
(b)
Figure 2: Comparison of crop water requirement (CWR) and rainfall (a) and cumulative CWR
and rainfall received in Elizabeth plot (b).
The rainfall recorded by rain gauges in the experimental plots and that, which was recorded by
the automatic met station installed in Bangalala village during the Vuli season was compared.
There was almost the same trend in rainfall events and total rainfall for the period under
observation (see Figure 3).
Automated met
station data
Bangalala sec
school
W alter plot
Rainfall distribution
200.00
180.00
W ilson home plot
160.00
W ilson shamba
chini plot
120.00
100.00
80.00
60.00
40.00
20.00
Ap
ril
(F
irs
t
2w
ks
)
Ma
rch
y
Fe
br
ua
ry
Ja
nu
ar
er
er
em
b
De
c
em
b
No
v
Oc
tob
er
0.00
Se
p te
mb
er
rainfall (mm)
140.00
month
7
Figure 3: Graphs showing rainfall recorded in the experimental plots (Walter, Bangalala
secondary, Wilson home and Wilson shamba chini).
Sap flow4 measurements
The graphs below (figure 4) shows how the daily sap flow measured were behaving in
Elizabeth’s plot. The sap flow rate, which equates to transpiration rate showed a great variation
on different days and time of the day. The transpiration flux was varying from as low as 18 g/hr/
plant to as high as 139 g/hr/plant (1876.5 ml per day per plant) in Elizabeth’s plot and in Wilson’s
plot it was varying from as low as 15 to 139 g/hr/plant. The sap content constitutes about 99%
water so to find the volume of water transpired it was assumed that 100% of the sap content was
water. This means 1 gram/hour sap flow equates to 1 ml/hour.
Daily sap flow measurements in Elizabeth plot
daily sap flow measurements in w ilson plot
120
160
sap flow (grams/hr)
sap flow rate (grams/hr)
140
100
80
60
40
120
100
80
60
40
20
20
0
9:00
0
9:00
10:12
11:24
time of the day (hours)
12:36
13:48
sap flow
sap flow
sap flow
sap flow
10:12
11:24
12:36
13:48
time of the day (hours)
15:00
on 21/12/04
on 26/12/04
on 27/12/04
on 12/1/05
sap flow on 22/12/04
sap flow on 23/12/05
sap flow on 1/1/05
sap flow on 31/12/04
sap flow on 9/1/05
(a)
(b)
Figure 4: Daily sap flow measurements in Wilson’s plot (a) and in Elizabeth’s plot (b).
The graphs below (Figure 5) show the maize sap flow measurements done in Wilson Home
experimental plot and in Elizabeth plot.
sap flow and rainfall for Elizabeth plot
1400
70.00
12.00
1200
60.00
10.00
1000
8.00
800
6.00
600
4.00
400
2.00
200
0.00
0
1
6
11
16
21
Time (days)
26
31
900.00
800.00
rainfall (mm)
700.00
50.00
600.00
40.00
500.00
30.00
400.00
300.00
20.00
200.00
10.00
100.00
0.00
36
0.00
1
6
11
16
21
26
31
days
rainfall
sap flow
sap flow (grams/day)
14.00
sap flow (grams/day)
rainfall (mm)
sap flow and rainfall data for Wilson plot
36
41
46
51
56
rainfall
sap flow
(a)
(b)
Figure 5: The average sap flow and rainfall recorded from day 62 to day 93 after planting in
Wilson’s plot (a) and in Elizabeth’s plot (b).
A total of 31.808 litres of water was transpired by each plant in Elizabeth’s plot from day 64 to
day 123 after planting. In Wilson’s plot 32.797 litres of water was transpired by each maize plant
8
from day 62 to day 93 after planting (31 days). In Wilson’s plot there was a mechanical fault on
the sap flow recorder on 12/01/05 such that it had to be dismantled before the maize crop was
fully mature. In Elizabeth’s plot the plant population was 116 plants in a 30 m2 plot (Table 3)
which gives a plant population of 38 667 plants per hectare. This means that the total water
transpired per hectare (from 07/12/04 to 28/01/05) was 1229.93 m3.
It was assumed that the transpiration period was from 0530hrs to 1900hrs because transpiration
only occurs during the day when there is light and during the night plants hardly transpire. It was
also assumed that the water consumed by the plant in the process of photosynthesis and
accumulation in the plant cells to maintain the osmotic gradients was negligible because about
99% of the water taken up by plants is lost as water vapour (transpiration).
The rainfall received in Elizabeth’s plot during sap flow measurement was 106 mm. The effective
rainfall calculated using the Cropwat was 97.88 mm. The crop water requirement during the
same period was 342.36 mm. Contribution of water from rainfall only was 978.8 m3/ha and the
crop water requirement over the same period was 3423.6 m3/ha (for the maximum potential yield
to be achieved). Water applied as supplementary irrigation amounted to 413.1 m3. In total, the
water applied to the experimental plot (Elizabeth plot) was 1391.9 m3.
Infiltration
The infiltration results are shown in the graphs (Figure 6) below. The basic infiltration rates on
the rip lines were greater than the infiltration rates between the rip lines that are on the ridge. For
example in Elizabeth plot on the ridge it was 6 cm/hr whereas the rate on the rip line was around
26 cm/hr (figure 6 (a) and (b)). From the statistical analysis ripping significantly increased
infiltration rate (p=0.007).
Elizabeth plot - infiltration rates
Infiltration rate on hand hoe plot Elizabeth plot
infiltration-ridge
140
90
infiltration-rip
infiltration rate (cm/hr)
infiltration rate (cm/hr)
160
120
100
80
60
40
20
80
70
60
50
40
30
20
10
0
0
0
50
100
0
150
10
20
30
40
50
time (mins)
time (mins)
(a)
(b)
Figure 6: Infiltration rates for three different sites in Elizabeth plot on the ridge (between two rip
lines) and on the rip line (a) and in hand-hoe ploughed plot (b).
infiltration rate (cm/hr)
Walter plot - infiltration rates
120
infiltrationridge
100
infiltration - rip
80
60
40
20
0
0
50
100
150
200
time (mins)
Figure 7: Infiltration rates for Walter plot on the ridge (between two rip lines) and on rip line.
9
DISCUSSION
Soil physical and chemical analysis
Soils in the Makanya watershed are alkaline in nature with pH ranging from 7.9 to 8.8 because
the mean annual rainfall is low. Also the use of manure with a high pH of around 8.5 contributes
to the increase in the overall soil pH. High evaporation in the area is also contributing to
accumulation of salts in the soils. High soil pH affects the availability of micronutrients needed
by maize such as boron and zinc in the soil. In saline soils more water is applied to the field to
balance the osmotic pressure between the soil and plant roots and to drain excess salts.
Maize is relatively sensitive to salinity and does well in well-drained soils with a pH range of 5.57 (Landon, 1991) but can tolerate pH up to 8. Beans do well in pH ranges of 5.5-6.5 such that the
above pHs recorded might be affecting bean crop yields. It might not be beneficial to the farmers
to switch to the bean crop that is regarded as a high value crop because of the pH levels of most
of the soils as its yield is likely to be very low.
Soils in the study area are rich in the major nutrients required for crop growth such as N, P and K
as well as the percentage organic matter. This can help explain that the addition of fertilizers
might not significantly increase crop yield and water productivity in the area. The porosity of the
soils in the study area is on the average to slightly above average side and from that it shows that
the soils are medium textured (sandy loam) soils (Hillel, 1982). Porosity generally ranges from
30% to 60% with coarse-textured soils being less porous than fine textured soils. On bulk density,
usually sandy soils have high bulk density of around 1.5-1.6 g/cc whereas in aggregated loams
and in clay soils it can be as low as 1.1 g/cc. The bulk density is affected by soil structure and
degree of compaction. Poor structure and high degree of compaction results in low infiltration
and poor root penetration. Ripping destroys the plough pan that normally reduces infiltration rate.
Low infiltration rate affect water productivity as it limits root growth and infiltration of water into
the soil. Even though ripping increased infiltration, high concentration of salts in the soils makes
it difficult for plant roots to absorb as much water as possible.
Crop yields
Deep ripping only and deep ripping coupled with supplementary irrigation increased yield.
However, statistically, the increase was not significant because some parameters like rate of
manure application to the field and the residual effect of manure from the previous seasons were
not monitored in the experimental plots. Therefore though ripping increased infiltration capacity,
this did not result in the increase in yield, which means that it is not only infiltration rates, which
affect yields. Again presence of salts creates an osmotic gradient that make water absorption by
roots difficult. Due to limited time and experimental plots there were very few replicates for the
different treatments so the real effect of ripping on yield was not significant. Rate of manure
application and other management practices such as weeding also have an effect on yield. In the
experimental plots the farmers did not timely weed because of the labour problems.
Infiltration rate increased as a result of ripping because ripping improves the soil structure and
increase the rate of basic infiltration thereby reducing run-off. This will then increase soil water
available to the crops. Ripping is considered an in-situ water harvesting technique. The manure
being applied to the fields are rich in nutrients as shown in the analysis results. Therefore the
combined use of deep ripping and manure contributed much to the increased yields though the
increase was not significant. Ripping increased infiltration rate significantly. The rate of
infiltration relative to the rate of water supply determines how much water enter the root zone and
how much will run off (Hillel, 1982). The knowledge of infiltration process helps in the efficient
10
management of soil and water. Supplementary irrigation did not coincide with the period when
the maize plant required a lot of water resulting in low yields being achieved. The period from
flowering up to grain filling requires a lot of water. When the maize plant is stressed at this stage
yields are greatly reduced as this period is very sensitive to water stress. However in Makanya
especially during the dry spell the water is usually unavailable in the storage structures (Ndivas)
to irrigate. This results in yield reduction. Stewart (1988) based on research in East Africa,
indicated that severe yield reductions due to dry spells occur once or twice in five years.
The crop water requirement was far below the total rainfall received during the growing season in
Makanya catchment. That is the crop water requirement for the maize crop was 508 mm (from
CropWat4 calculations) whilst the rainfall for the corresponding period was only 209 mm and of
this, effective rainfall was 192.8 mm. This shows that in Makanya, maize cannot produce
significant yield if rain-fed only. For maximum yields to be realized an additional 315 mm needs
to be applied either as supplementary irrigation or in-situ rainwater harvesting as it makes more
water available to the plant roots.
Makanya catchment receives up to 600 mm annually but the major problem is the spatial and
temporal distribution of the rainfall. The rainfall distribution in the catchment is bimodal such
that almost half of the rainfall is received in one season whilst the other half is received in the
other season. Therefore even though maize can do well in areas with seasonal rainfall of about
600 mm the rainfall received in one of the seasons cannot sustain good yields as it falls far below
the 600 mm required by the maize crop. In Makanya catchment farmers receives supplementary
irrigation before the dry spell or well after the dry spell so instead of bridging the dry spell period,
the water is allocated too early or too late. For example Elizabeth received water on the 8 th of
December 2004 just before the dry spell commenced. So during the dry spell period from day 67
up to day 88 after planting there was no water allocation to Elizabeth’s plot.
Water productivity
Water productivities in the experimental plots responded well to the introduction of deep ripping
coupled with supplementary irrigation as ripping tends to increase infiltration rate hence making
more water available to the plant roots. For example the plots that were only rain fed with ripping
the water productivity was relatively low compared with the plots with ripping coupled with
supplementary irrigation. Conversion of more evaporation into transpiration boost water
productivity as water evaporated from the soil is considered as lost. Therefore ripping improves
water productivity though statistically there was no significant difference.
In areas that received partial supplementary irrigation, such as Elizabeth’s plot, the water
productivity recorded was 0.6 kg/m3, which was relatively low, compared to international
standards. For example water productivity for irrigated cereals (excluding rice) ranges from 1.0 to
1.7 kg/m3 in China, the United States and Brazil whilst in western European countries it ranges
from 1.7 to 2.4 kg/m3 (Rosegrant et. al., 2002). However the water productivity results from this
study compares well with reported results for sub-Saharan Africa, which ranges from 0.1 to 0.6
kg/m3 with an average of 0.3 kg/m3 for cereals (excluding rice) (Rosegrant et al., 2002).
Sap flow4 measurements
From the sap flow measurements results maize plants absorb more water after a rainfall event
resulting in a high sap flow up the plant. As the soil dries up the average sap flow decrease as it
becomes more difficult for the plant roots to absorb water. This usually results in reduction in
yield. For example from 12 up to 30 December 2004 there was no rainfall yet it’s the critical
period when the maize plant required a lot of water (that is during tussling and grain filling). This
can be identified as the two weeks dry spell and it explains why the average yields from Wilson
11
homestead plot was only 1.833 t/ha instead of the potential 5 t/ha which can be achieved by
communal farmers with TMV1 maize variety. Supplementary irrigation was supposed to bridge
dry spell.
High sap flow mostly occurred when the plot received water allocation on the same period and
also when it rained. As expected when it rains more water is available in the root zone such that
the plants easily absorb water through the roots resulting in high sap flow being realized. The sap
flow varied with time due to variations of weather conditions. However a sudden change in one
climatic factor changes the sap flow pattern. Transpiration rate reduces to almost zero when the
plant dries up, as transpiration would have almost ceased.
Nearly all water taken up by the plant is lost by transpiration and only a tiny fraction is used
within the plant (Allen, et al, 1998). This explains why there was high sap flow especially after a
rainfall event. Also the amount of water in the soil, the growth stage and the ability of the soil to
conduct water to the roots determine the transpiration rate and the amount of nutrients taken up
by the plants. Reduction in transpiration rate greatly affects yield. Water, which is the main
component in the uptake of nutrients and in the formation of carbohydrates during
photosynthesis, determines the crop yield apart from fertilizers and other factors so if
transpiration rate is reduced then yields are greatly affected.
Amount of water lost as evaporation from the experimental plot (Elizabeth) was around 11%,
(assuming that all water added to the field as rainfall and supplementary irrigation was
evapotranspired) which compares well with what is in the literature. The period measured
coincides with the mid to late season stage when there is full crop cover. For example according
to Allen, et. al, (1998) at sowing nearly 100% of evapotranspiration comes from evaporation
while at full crop cover more than 90% of evapotranspiration comes from transpiration. The
water lost through evaporation is relatively higher than the expected value of less than 10%. This
can be explained by the low plant density of 38 667 plants per hectare. Although the plant
population is within the standard range of 36 000 to 60 000 plants per hectare its on the lower
side of the range. Low plant population reduces the canopy cover thereby allowing more water to
be lost through direct evaporation instead of transpiration.
Water Productivity and Transpiration
Water actually transpired was much less than the water applied to the field and later “lost” as
evaporation and transpiration. The transpired water always makes the benchmark of water
actually used productively. Previous research (Papendick et al., 1988) has shown that there is a
strong relationship between crop transpiration and dry matter production. Papendick et al. (1988)
showed that for every one-millimeter of water transpired by crops about 0.032 t/ha of wheat dry
matter could be produced. Also Rockstrom et al. (2003) found out that rainfall water productivity
of maize in Kenya ranged from 2.2 to 3.1 kg/ha/mm, which is typical of rain fed agriculture in
sub-Saharan Africa. In this study the rainfall water productivity ranged from 0.7 to 3.1 kg/ha/mm,
which is quite comparable with results found by Rockstrom et al. (2003).
Assessment of water received at farm level could not be adequately accomplished as the water
balance could not be closed because of the failure of the equipment used to measure deep
percolation (the TDR tube). This calls for further studies in order to close the water balance
equation. Other methods such as digging a monitoring well in the experimental plots will show
the movement of ground water through capillary action and water loss through deep percolation.
12
CONCLUSION
The conclusion from the research is that there is room to significantly improve the water use
techniques being applied for crop productivity in Makanya catchment. A clear understanding and
quantification of the water partitioning processes is required to maximise water used for
transpiration by the plant as transpiration is directly related to biomass production. However,
scientific approaches need to be complemented by efficient farm management practices for any
technologies to be successful. Maize grain yield was not significantly increased by supplementary
irrigation coupled with deep ripping. Ripping significantly increased infiltration rate of the soil in
the study area from 6cm/hr to 26cm/hr (Elizabeth’s plot). It can be concluded that supplementary
irrigation together with rainfall contribution cannot meet the overall crop water requirement for
maize in the study area. Sap flow4 recorder can be used in the separation of transpiration from
evapotranspiration.
RECOMMENDATIONS
Recommend an integrated approach in rainwater management if higher water productivity is to be
realised. Ripping coupled with supplementary irrigation should be further analysed over a long
time and more replicates in order to see if ripping does not actually increase yields. These
introduced innovations are recommended for further use especially ripping. However these
innovations should be further studied to find their response to extreme weather conditions like
prolonged dry spells and meteorological droughts. Growing of crops with lower crop water
requirement like beans is recommended. Use of the Sap flow4 recorder on the measurement of
transpiration rate have proved useful and is recommended for further use as it can better quantify
transpiration as a proportion of what comes into the field.
ACKNOWLEDGEMENTS
This study was conducted as part of a collaborative research program between the Smallholder
Systems Innovations in Watershed Management (SSI) and the Soil Water Management Research
Group (SWMRG) of Sokoine University of Agriculture (SUA) and was funded by Waternet and
SSI.
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