Biomass equations and data for forest stands and shrublands of the Eastern Alps
(Trentino, Italy)
P. Gasparini1, M. Nocetti1, G. Tabacchi1 and V. Tosi1
1
Forest and Range Management Research Institute, I.S.A.F.A. - C.R.A.-, Villazzano,
Trento, Italy
_______________________________________________________________________
Abstract
Studies on estimation of forest biomass have gained greater importance in the
last decades because of the rise of the general interest in the subject of climate
change, particularly regarding the increase of CO2 concentration in the
atmosphere. Biomass equations are a useful instrument to calculate the carbon
stored in the forests.
The present contribution describes the development of prediction equations for
above-ground tree phytomass and stem volume, shared out by species and
species groups, of the forest stands of the Province of Trento, Italy. Tree
phytomass was divided into four components: stem and branches; slash; dead
portion; stump. Additive equations for each component and for the total tree
phytomass were performed.
Some negative values predicting volume and phytomass in small dimensional
classes were observed especially for black pine, Swiss stone pine and beech,
but it does not limit the operative use of equations.
Furthermore, a preliminary investigation on aboveground phytomass of some
shrub stands is presented.
_______________________________________________________________________
Introduction
The topics related to the estimation of forest biomass have always had a great importance in
the scientific community because of their significance for the study of terrestrial ecosystems.
In the last decades, the attention on phytomass issues has risen as a consequence of the
need for assessing the carbon stored in the forests as required by the United Nation
Framework Convention on Climate Change (UNFCCC) and the following obligations under
the Kyoto Protocol (UNFCCC, 1997).
The participating countries to the Protocol are invited to develop practical and accurate
procedures to account carbon stocks and stock changes in forests. Therefore, many efforts
have been made to implement the techniques of monitoring and estimating this resource
(Brown, 2002). The “Good Practice Guidance for Land Use, Land-Use Change and Forestry”
by the Intergovernmental Panel on Climate Change (IPCC, 2003) supports the
methodologies which measure or estimate the biomass and then convert it to carbon.
Furthermore, according to the Ministerial Conference on the Protection of Forests in Europe
(MCPFE) the first criterion for the sustainable forest management is the “maintenance and
appropriate enhancement of forest resources and their contribution to global carbon cycles”.
Two of the Pan-European quantitative indicators defined at the last MCPFE Conference in
Vienna (MCPFE, 2002) are related to the forest biomass: the indicators 1.2 “Growing stock”
and 1.4 “Carbon stock of woody biomass and soil”. A precise estimation of forest biomass
and a continuous monitoring of this variable are therefore crucial to report on the
sustainability of the forest management.
The need for evaluating the biomass and consequently the carbon stored in forest
ecosystems as an important resource from economical and political point of view, has led to
a higher attention in monitoring and estimation issues. Consequently, many authors have
developed new biomass equations and have studied their applicability. Besides, helpful
databases containing different equations for several regions and tree species have been
developed (Ter-Mikaelian & Korzukhin, 1997; Araújo et al., 1999; Zianis & Mencuccini,
2004).
The aboveground tree phytomass is a considerable component of the total forest ecosystem
biomass and two are the main methods to evaluate it. The most accurate one is undoubtedly
the direct measurement, which consists in weighing the tree biomass in the field. This
method is however destructive and extremely time requiring, and therefore it is usually
limited to little areas and samples of small size.
The indirect method applies two major means: biomass factors and prediction equations.
The so called Biomass Factors are used to convert the volume values (usually the stem
volume or the merchantable volume) into biomass values, or to expand (Biomass Expansion
Factor) the biomass of parts to the total tree biomass (Jenkins et al., 2003; Lethonen et al.,
2004; Levy et al., 2004; Van Camp et al., 2004). Prediction equations instead link easily
measurable variables to the standing volume and the tree phytomass (Alemdag, 1980; Crow
& Laidly, 1980; Satoo & Madgwick, 1982; Snowdon, 1985; Parresol, 1999; Zianis and
Mencuccini, 2003; Fattorini et al., submitted).
Phytomass equations are particularly useful and easy to apply when the independent
variables are diameter at breast height and height, two basic attributes used in each national
forest inventory. It allows to estimate forest biomass of large areas with quite little efforts.
The above mentioned instruments and methods are of basic importance for experts involved
in the annual reporting of carbon stocks and carbon stock changes for the Kyoto Protocol.
They also give basic information to politicians and decision makers useful to monitor
sustainability of forest management and its effects on global carbon cycles.
This paper presents volume and phytomass prediction equations for seven species and two
groups of species covering the whole species range of the Alpine region.
The study also includes a first collection of data on the aboveground phytomass of the main
shrublands of the region. Actually, shrub biomass is an important element of natural
ecosystems, nevertheless there is a lack of information about it in Europe, since reliable
quantitative data at regional or even national level are not available yet.
Material and Methods
Study area
Data were collected in the Province of Trento, North-East of Italy (46°04' N, 11°08' E). It is a
mountainous area in the Alpine region (elevation from 100 m to 3,500 m above see level).
The territory of the Province (about 622,040 ha) is mainly covered by forests, approximately
65%. Coniferous stands are predominant, being spruce the more common forest species.
Fir, larch, Scots pine and Swiss stone pine are also very widespread. Hardwood species
form usually mixed stands (spruce-fir-beech stands, hornbeam-black and Scots pine stands)
and they grow mainly in the southern part or in the valleys.
High forests prevail, but coppices are present too (about 20%), and the public ownership is
predominant (almost 80%).
The climate of the region is an alpine climate, with cold and dry winters, cool summers, rainy
springs and autumns. The annual average temperature is 10-11°C and the average
precipitation is about 1,200 mm annually. In the southern part, near Lake Garda, the climate
shows Mediterranean features.
Sampling methods
As a consequence of methodological and cost-related aspects the selection of sample trees
followed a model-based design rather than a rigid design-based sampling procedure (Cunia,
1965; 1979a,b; 1987a,b). Indeed, there were three conditions to be met: to obtain a
representative sample of the population of interest, to have a sufficient sample for the
regression analysis and to reduce the operational difficulties and the high costs of field
measurements.
The procedure adopted provided a partition of the population into several dimensional
classes, that is diameter – diameter at breast height – and height classes. A selection
proportional to the strata, would have provided a sample with many small trees compared to
large ones, because of the relative higher frequency of small trees in large area stands. On
the other hand, in a model-based sampling it is necessary to have a high number of items in
the larger dimensional classes, because of their high variability. The last option (modelbased design), however, would have been very expensive and time-consuming. Therefore, it
was decided to draw a sample with roughly the same number of items in each dimensional
class (Ouellet, 1983).
The trees measured came partly from fellings done during ordinary silvicultural practices and
partly from addressed cuttings required to fill in the lack of samples in some dimensional
classes.
For the aboveground shrub phytomass, sample plots were subjectively selected inside the
shrub stands. Data were distributed by shrub type according the classification used in the
National Forest Inventory of Italy (INFC, 2003).
Measurements
The measurement of the aboveground biomass of the trees was performed through a mixed
geometric-ponderal survey. Before felling, stem diameter, over bark at breast height, was
measured. Once cut down, stump height and tree height were recorded, then stem and
branches with a minimum diameter of 5 cm were sectioned and each section was
considered as a truncated cone for volume calculation purposes. When logs or branches had
the shape very dissimilar to a truncated cone, the fresh weight was recorded.
The green parts of the crown (twigs with a maximum diameter of 5 cm) were cut up and
divided into three groups: twigs with a diameter between 3 and 5 cm, twigs with a diameter
between 1 and 3 cm and twigs with a diameter smaller than 1 cm. Dead branches of
whatever size were gathered as a fourth group. Total fresh weight of the green parts of the
crown and the weight of the dead portion were measured in the field.
The volume of small samples of stem and branches was determined in laboratory by the
water immersion technique and samples from each part of the tree were oven-dried at 105°C
for 48 hours, until no further weight loss could be measured. Finally, the field data of volume
and fresh weight were converted to oven-dry equivalents.
Because of high costs and operational difficulties to get separate measurements of foliar and
wood components, an aggregate value for crown phytomass was determined. Therefore, the
data collection was carried out only when the deciduous species were fully leaved, whereas
for the evergreen the season was not relevant.
Concerning the shrub phytomass, mean height and crown cover (by means of a regular grid
of point) were measured in 9 m2 square plots, then all shrubs of the plot were cut and
weighed. In laboratory, samples of some kilograms were oven dried to find out the dry weight
to fresh weight ratio. The total fresh weight was finally converted to dry weight.
Analysis
The measured trees were distinctively considered by species and species groups, obtaining
9 categories. By the regression analysis, prediction equations for stem and branch volume
and aboveground tree phytomass were developed for each category. When the tree
phytomass was considered, the model provided a prediction of four distinct components:
stem and branches with a minimum diameter of 5 cm, slash with a maximum diameter of 5
cm, dead parts and stump.
The independent variable adopted was the product of the square diameter and the total
height, adding, if necessary, another independent variable, which consisted again of tree
diameter and height, raised to the power of 1 or 2 and combined in all possible ways. The
addition of the second variable and its characterization were determined by the standard
error values of the regression coefficients and the residual distribution, as well as by
dendrometric indications, such as impossible predicted values and trends of dependent
variables not fitting the expectations.
The most suitable regression equation for each species or species group was applied to both
phytomass and volume variables prediction.
To calibrate the phytomass equations, the technique of multivariate multiple linear regression
was used. One equation for each component and for the whole tree phytomass has been
developed, maintaining the additivity of the single equations in relation to the comprehensive
one (Cunia & Briggs, 1984; Fattorini et al., submitted). Whereas, for the volume equation the
technique of univariate multiple linear regression was applied.
To solve the problem of heterogeneity of the error variance in linear regression, the
weighting function νi = (di2hi)k was employed and the coefficient k was always taken to be 2,
according to a widely accepted simplification (Cunia, 1964; Meng & Tasai, 1986; Gregoire &
Dyer, 1989; Williams & Gregoire, 1993).
The regression analysis was performed by means of the weighted least squares method.
The analysis of shrub data was performed by means of correlation between aboveground
phytomass and height and crown cover. This preliminary examination was aimed at
investigating the further possibility of developing biomass equations for shrub phytomass.
Results
On the whole, 343 trees were selected and measured for this study. As already mentioned,
the items were nearly homogeneously distributed into the different dimensional classes, as
shown in table 1. The number of sampled trees measured for each species or group of
species sorted by diameter classes are given in table 2, while some statistics are given in
table 3.
Table 1
Diameter
classes (cm)
d<12.5
12.5≤d<22.5
22.5≤d<32.5
32.5≤d<42.5
d≥42.5
Total
Number of trees sampled in the several dimensional classes.
h<10
51
28
2
81
10≤h<15
25
45
17
4
91
Height classes (m)
15≤h<20 20≤h<25 25≤h<30
1
31
6
21
16
3
15
14
5
10
17
11
78
53
19
h≥30
1
7
13
21
Total
77
110
60
45
51
343
d: diameter; h: height.
Table 2
Number of sample trees measured for each species or group of species
sorted by diameter classes.
Diameter classes (cm)
d<12.5
Picea abies
Abies alba
Larix decidua
Pinus sylvestris
Pinus nigra
Pinus cembra
Fagus sylvatica
Ostrya carpinifolia
Quercus ilex
Quercus pubescens
Fraxinus sp.
Castanea sativa
Acer sp.
Salix sp.
Other hardwoods
Total
d: diameter
11
8
4
6
5
4
4
1
9
4
8
3
3
2
5
77
12.5≤d<22.5 22.5≤d<32.5 32.5≤d<42.5
16
13
12
13
11
9
10
5
6
2
2
7
4
110
16
3
6
5
11
6
8
3
1
1
60
15
7
5
6
3
3
4
2
45
d≥42.5
24
9
6
8
4
51
Total
82
40
33
30
30
30
30
9
15
6
10
13
3
6
6
343
Table 3
Minimum, mean and maximum values of diameter and height of trees
measured for the equation development.
Diameter (cm)
Picea abies
Abies alba
Larix decidua
Pinus sylvestris
Pinus nigra
Pinus cembra
Fagus sylvatica
Ostrya et al.
Castanea et al.
Min
7.9
8.9
7.7
8.4
8.9
7.7
9.5
5.1
6.7
Mean
31.2
27.3
26.1
21.3
22.4
28.8
26.3
12.4
15.6
Height (m)
Max
61
55.5
53.9
40.6
35.9
56.3
56.5
31.6
38.4
Min
2.8
7.5
5.6
6.4
5.6
4.5
9.3
5.5
5.8
Mean
22.7
16.9
16.6
12.6
12.2
14.2
16.3
10.2
11.3
Max
35.8
26.8
24.9
20.8
20.9
22.2
22.3
17.7
21.7
The main tree species of the study area, the first seven ones listed in the tables 2 and 3, had
a sufficient number of items to develop single species regression equations. The remaining
species were grouped to develop two different prediction functions, one for hornbeam, oaks,
ash and false acacia, called Ostrya et al. group, and the other one for chestnut, maple and
other hardwood, called Castanea et al. group.
In table 4, six equations for each species and species group are given, one for each
dependent variable: stem and branch volume (V); stem and branch dry weight (w1); slash
dry weight (w2); dead portion dry weight (w3); stump dry weight (w4); total aboveground dry
weight (wtot). The equations, the corresponding sample size (N), R2 adjusted values and
standard errors (SE) are listed in the above mentioned table.
Table 4
Volume and biomass equations for each species and species groups.
N
R2 adj
SE
V = 4.37664 + 2.848·10-2 d2h + 1.165·10-2 dh2
82
0.991
162.64
w1 = 2.5338 + 9.5351·10-3 d2h + 6.2893·10-3 dh2
82
0.972
71.32
82
Equation
2
Picea abies
y = b1 + b2d h + b3dh
2
-3
2
-3
w2 = 5.4653 + 8.1739·10 d h - 5.8838·10 dh
2
0.702
82.10
-1
-4
2
-4
2
82
0.692
13.34
-1
-4
2
-4
2
82
0.794
7.25
82
0.965
128.63
40
0.987
125.38
40
0.982
53.25
40
0.883
46.17
40
0.521
11.08
40
0.627
11.64
40
0.978
52.97
33
0.991
136.95
33
0.965
90.92
33
0.816
29.58
33
0.564
9.77
33
0.807
2.55
33
0.964
86.32
30
0.972
43.15
30
0.961
27.52
w3 = 6.4730·10 + 4.2878·10 d h - 1.0435·10 dh
w4 = 1.8324·10 + 6.2237·10 d h - 3.8640·10 dh
-2
2
-5
wtot = 8.8297 + 1.8760·10 d h - 8.5316·10 dh
2
y = b1 + b2d2h + b3d2
Abies alba
V = -2.7916 + 3.4492·10-2 d2h + 8.3540·10-2 d2
-1
-2
2
-2
w1 = 9.8961·10 + 1.3980·10 d h + 1.4895·10 d
-3
2
-2
w2 = 1.6305 + 1.7321·10 d h + 6.8361·10 d
-1
-4
2
2
-3
w3 = 8.4530·10 + 4.6052·10 d h - 3.1032·10 d
-1
-2
2
2
2
-2
w4 = -1.2302·10 + 3.1463·10 d h + 1.2020·10 d
-2
2
-2
wtot = 3.3424 + 1.6487·10 d h + 8.1355·10 d
2
2
y = b1 + b2d2h +b3d
Larix decidua
V = 8.8267 + 0.3426·10-1 d2h +2.7518·10-1 d
-2
2
w1 = 1.7603·10 + 1.9161·10 d h - 1.8211 d
-4
2
w2 = -6.1618 - 9.4460·10 d h +2.1432 d
-4
2
-1
w3 = 2.243 + 4.5782·10 d h - 1.5684·10 d
-1
-4
2
-1
w4 = -4.3937·10 + 1.1090·10 d h +1.5787·10 d
-2
2
-1
wtot = 1.3245·10 + 1.8785·10 d h +3.2315·10 d
Pinus sylvestris
y = b1 + b2d2h
V = 2.6374 + 0.4102·10-1 d2h
-1
-2
2
w1 = -7.3626·10 + 1.8465·10 d h
-3
2
w2 = 2.5406 + 4.2895·10 d h
30
0.746
28.83
-1
-4
2
30
0.671
7.04
-2
-4
2
30
0.813
1.33
30
0.952
30.68
30
0.992
54.40
30
0.984
29.45
w3 = 1.4696·10 + 6.8895·10 d h
w4 = 9.4123·10 + 2.8144·10 d h
-2
2
wtot = 2.7081 + 2.3724·10 d h
y = b1 + b2d2h +b3d2
Pinus nigra
V = -5.6704 + 3.1896·10-2 d2h +0.1271 d2
-2
2
-2
2
-1
2
w1 = -3.5712 + 1.4429·10 d h +6.8047·10 d
-4
2
w2 = -8.7135 - 6.7203·10 d h + 1.1893·10 d
30
0.845
24.95
-1
-5
2
-2
2
30
0.758
4.44
-3
-6
2
-3
2
30
0.894
1.29
-2
2
-1
2
30
0.974
51.39
30
0.991
129.49
w3 = -6.7033·10 + 4.0558·10 d h +1.0169·10 d
w4 = -3.0325·10 + 9.5000·10 d h +4.9177·10 d
wtot = -1.2958·10 + 1.3807·10 d h +2.0206·10 d
us
ce
mb
y = b1 + b2d2h +b3d2
V = -5.5632 + 3.0080·10-2 d2h +0.1546 d2
w1 = -2.9695 + 1.0066·10-2 d2h +8.4233·10-2 d2
-1
-4
2
-2
w2 = 6.4194·10 - 1.5615·10 d h +3.9256·10 d
-6
2
-2
w3 = -1.0563 + 9.7619·10 d h +1.6480·10 d
-2
-4
2
2
2
-3
w4 = -4.2908·10 + 3.3702·10 d h +1.4672·10 d
-2
2
-1
wtot = -3.4268 + 1.0256·10 d h +1.4144·10 d
2
2
30
0.991
39.67
30
0.901
30.99
30
0.782
12.90
30
0.681
6.21
30
0.990
61.41
30
0.960
290.75
30
0.955
178.08
30
0.881
83.04
30
0.217
11.36
30
0.780
11.28
30
0.956
194.71
40
0.971
21.53
40
0.958
18.47
Fagus sylvatica
y = b1 + b2d2h +b3d2
V = -8.015 + 0.3108·10-1 d2h +1.8083·10-2 d2
-2
2
-2
w1 = -3.7197 + 1.9559·10 d h +8.8089·10 d
-3
2
-1
2
2
w2 = -5.587 - 1.9468·10 d h +1.5641·10 d
-1
-4
2
-3
w3 = -3.2310·10 + 5.0689·10 d h - 3.5765·10 d
-4
2
-2
w4 = -1.1678 - 1.0182·10 d h +1.7957·10 d
-2
2
2
2
-1
wtot = -1.0798 + 1.8017·10 d h +2.5888·10 d
2
y = b1 + b2d2h +b3d2
Ostrya et al.
V = -5.4732 + 0.2448·10-1 d2h +2.3231·10-1 d2
-2
2
-1
w1 = -4.6965 + 1.2034·10 d h + 2.1771·10 d
-1
-3
2
2
-1
w2 = 2.7434·10 - 6.8965·10 d h +1.8006·10 d
-2
-4
2
2
40
0.770
13.83
-3
2
40
0.340
0.57
-3
2
40
0.631
1.78
40
0.939
29.41
28
0.971
20.61
28
0.952
17.90
28
0.951
16.58
28
0.407
3.56
28
0.637
4.26
28
0.978
15.03
w3 = -9.2522·10 - 3.4284·10 d h + 7.2860·10 d
-2
-4
2
w4 = -7.3006·10 + 4.6921·10 d h +3.9370·10 d
-3
2
-1
wtot = -4.5877 + 5.2638·10 d h + 4.0900·10 d
2
Castanea et al.
y = b1 + b2d2h +b3d2
V = -2.4818 + 0.2788·10-1 d2h + 1.1537·10-1 d2
-1
-2
2
-2
w1 = 2.1616·10 + 1.4282·10 d h + 4.4323·10 d
-2
-3
2
-1
w2 = 7.3436·10 - 5.0700·10 d h + 1.5037·10 d
-3
-4
2
-2
-2
2
-2
2
2
w3 = -8.7737·10 + 7.8059·10 d h - 3.0046·10 d
-2
2
w4 = -9.9791·10 + 7.4754·10 d h + 1.0198·10 d
-1
-2
2
-1
2
wtot = 1.8104·10 + 1.0740·10 d h + 2.0189·10 d
2
3
V = dm ; w = kg; d = cm; h = m
V: stem and branch volume; w1: stem and branch dry weight; w2: slash dry weight; w3: dead
portion dry weight; w4: stump dry weight; wtot: total above ground dry weight; d: diameter at
breast height; h: tree height; N: number of items; R2 adj: R2 adjusted value; SE: standard
error.
In this study, the linear equations did not show notable problems to predict large tree
phytomass values. Concerning small trees, for some species the equations predict negative
values, but this happens only for very small dimensional classes (diameter of 6-7
centimetres or less) and not for all the equations. Only for beech the problem of negative
predicted values affects also larger dimensional classes. (Tab.5)
Table 5
Species and related dimensional classes for which the equations predict
negative values. (For symbol explanation, see table 4)
diameter class (cm)
height class (m)
equation
5
3-5
w4
Larix decidua
Pinus sylvestris
Pinus nigra
8
6-7
5
5
related h
related h
3-5
3
w2
w2,w3
w1,w2,w3
V
Pinus cembra
6-7
5
related h
3
w3
w1
Fagus sylvatica
<16
7-8
5-6
<5
<5
related h
related h
related h
some h
3-4
w3
w4
w2
w1
V
Ostrya et al.
Castanea et al.
8-10
3
w3
Picea abies
Abies alba
As regards the shrub phytomass, a total of 57 sample plots were examined. Table 6 shows
some statistics on shrub stands. For each shrub type, the number of items and the related
main descriptive statistics of height and crow cover are given.
Table 7 reports on the values of Pearson Correlation Coefficient calculated for the shrub
types with a sufficient number of items.
Table 6
Minimum, mean and maximum values and Coefficient of Variation (CV) of
height, crow cover and dry weight of the several shrub types (n = number of items).
Shrub type
Subalpine shrubs
- Pinus mugo stands
(n = 10)
- other needle-leaved shrubs
(n = 10)
- subalpine moor
(n = 9)
- Alnus viridis stands
(n = 12)
- Salix stands
(n = 5)
Temperate climate shrubs
- Juniperus stands
(n = 2)
- Prunus and Corilus stands
(n = 5)
- other
(n = 4)
Mean
height (m)
Crown
Dry weight
cover (%)
(Mg/ha)
min
mean
max
CV
min
mean
max
CV
min
mean
max
CV
min
mean
max
CV
min
mean
max
CV
1.5
2.1
3.5
0.29
0.3
0.5
0.7
0.23
0.5
0.6
0.9
0.23
1.6
3.4
5.0
0.33
3.2
4.0
4.6
0.16
68.8
87.5
100.0
0.12
75.0
88.8
100.0
0.12
50.0
84.7
100.0
0.20
62.5
94.3
100.0
0.11
81.3
91.3
100.0
0.09
29.1
57.3
92.8
0.30
15.0
25.1
46.1
0.42
9.3
25.0
48.8
0.44
20.4
68.1
119.8
0.42
37.6
69.2
104.5
0.41
min
mean
max
CV
min
mean
max
CV
min
mean
max
CV
1.0
1.0
1.0
0.00
4.8
5.2
5.8
0.07
3.0
3.2
3.5
0.07
50.0
53.1
56.3
0.08
87.5
96.3
100.0
0.06
62.5
75.0
87.5
0.14
8.2
8.7
9.1
0.08
62.3
85.1
111.2
0.26
8.2
24.8
60.6
0.97
Table 7
Pearson Correlation Coefficient between height and dry weight and between
crown cover and dry weight.
Shrub type
Number
of item
Subalpine shrubs
Pinus mugo stands
Other needle-leaved shrubs
Subalpine moors
Alnus viridis stands
Salix stands
Temperate climate shrubs
Juniperus stands
Prunus and Corilus stands
Other
Total
46
10
10
9
12
5
11
2
5
4
57
Pearson Correlation Coefficient
Dry weight – mean
Dry weight – crown
height
cover
0.800***
0.326*
-0.028
-0.225
0.367
0.886***
0.585*
0.775**
0.707**
0.021
0.865*
0.885*
0.852***
0.900***
0.643
0.565
0.178
0.887
0.746***
0.492***
* 95%; ** 99%; *** 99,9%
Discussion and conclusions
The form of the regression function most commonly used in biomass studies is the allometric
non-linear function (Ouellet, 1983; Pastor et al., 1983/1984; Snowdon, 1985; Tahvanainen,
1996; Ter-Mikaelian & Korzukhin, 1997; Ketterings et al., 2001; Van Camp et al., 2004;
Zianis & Mencuccini, 2003; 2004). It is very effective to solve the problem of the nonhomogeneity of the variance of tree biomass and it is a good approximation of the real
regression equation, but this approximation can be poor for very small and very large trees.
Linear models can be as good as the non-linear ones and, furthermore, they have the benefit
to be simple to understand and easy to apply (Cunia, 1979a).
Besides the above mentioned benefits, in the present work, the choice of using linear models
was determined by the need for deriving additive equations of phytomass components.
Thanks to the multivariate multiple linear regression used to calibrate them, this aim was
achieved (Fattorini et al., sumitted).
As regards the structure of the equations, it is clear that the most suitable was that with the
square diameter as second independent variable (Tab. 4). This equation was the most
suitable out for fir, Swiss stone pine, Austrian black pine and for all the hardwood species.
The equation performance is satisfactory for all species. The functions explain successfully
most of the variation in the phytomass components, as indicated by the relative high values
of coefficient of determination for all equations. The R2 adj. is lower for dead portion and
stump dry weight components and higher for the other prediction equations. The
determination coefficient of the total aboveground phytomass equations ranged from 0.939
of hornbeam to 0.990 of stone pine, whilst the values of R2 of volume prediction equations
are always the higher ones, but also because the selection of the best model was applied on
volume dataset (Tab. 4).
As previously noted, some biomass equations predict negative values for small dimensional
classes in certain species. However, concerning the application issue, the predicted values
can be set to 0 when the negative values occurs, and the total aboveground phytomass can
then be calculate as the sum of the other components instead of using the specific equation
for the total dry weight.
At this point, it is important to notice that the choice of diameter and height as independent
variables make prediction equations a useful instrument to derive the biomass stocks in
large forest areas by data of forest inventories. Inventory surveys usually provide extensive
data of diameter, height and volume of trees because they are easily measurable in the field.
Therefore, these surveys provide the basic information to calculate forest carbon stocks and
consequently to take decisions regarding the land use management and to report on data
required for forests by the UNFCCC.
This study gave us also the opportunity of developing a preliminary investigation on shrub
phytomass. The analysis carried out on shrubs shows descriptive statistics of the sample
plots (Tab. 6). The number of plots is not very high, but a general homogeneity can be
noticed referring to the quite low Coefficient of Variation. About the investigation on
correlation between dry weight and height or crown cover of shrubs (Tab. 7), it can be
noticed that in some cases the coefficient was significant, but a general correlation between
the variables couldn’t be found. Nevertheless, results shouldn’t be considered exhaustive
because they may be affected by the subjectivity of the sampling.
In conclusion we can state that biomass equations and biomass data are to be numbered
among the essential instruments to make the assessment and monitoring of the forest
resources more comprehensive and interested in revaluating the role of forests in the
mitigation of climate change, but not only. Sustainable development includes many related
issues, such as economic, social, environmental and biological values. A comprehensive
and reliable periodic monitoring of the natural resources is essential to protect them and to
assure the sustainability of their management.
The procedure illustrated in the paper is being used in a similar extended project involving
the whole country and aiming at the development of previsional equations of phytomass for
the entire national territory and covering the complete range of Italian tree species. For some
of these species, particularly broadleaves, data and models on phytomass are quite lacking
in Europe.
Acknowledgments
The whole activity is the result of the research project EFOMI funded by Research Fund of
the Province of Trento.
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