WFL Publisher
Science and Technology
Food, Agriculture & Environment Vol.2 (2) : 395-399. 2004
www.world-food.net
Structure and yield models of tropical lowland rainforest ecosystem of Southwest
Nigeria
V. A. J. Adekunle*, S. O. Akindele and J. A. Fuwape
Federal University of Technology, Department of Forestry and Wood Technology, P. M. B 704, Akure, Nigeria.
*e-mail:[email protected]
Received 10 January 2004, accepted 27 April 2004.
Abstract
The structure and yield assessment of tropical rainforest ecosystem of southwest Nigeria was carried out. Three reserves (Shasha, Ala and Omo
Forest Reserves in Osun, Ondo and Ogun States respectively) were randomly selected in the ecological zone. Systematic cluster sampling
techniques was adopted to allocate eight sample plots of size 50 m x 50 m (0.25 ha) in each of the reserves. Detailed measurement of all woody
plants with diameter at breast height (dbh) of at least 20 cm was carried out in each of the sample plots. Also, five linear regression equations were
generated, assessed and validated for volume prediction in the ecosystem. For diameter distribution ha-1, majority of the trees was in the smallest
diameter class (20-20.99 cm). Presently, very few merchantable trees were available in this vegetation. The number of stems encountered per
hectare in Shasha, Ala and Omo were 150, 116 and 96. The mean dbh was 38.83 cm for Shasha, 42.56 cm for Ala and 34.70 cm for Omo and the
mean heights obtained were 14.11, 18.14 and 17.26 m respectively. For yield values, basal area of 23.41, 22.98 and 12.48 m2 were obtained at
Shasha, Ala and Omo forest reserves respectively and their volumes were 181.36, 227 and 91.71 m3. All the models were discovered to have good
fit but the best of them is given as V = 24.06Ba-0.94Ba2+0.12Ba3-30 (R= 0.91, R2 = 82%, adjusted R2 =78%). The models are recommended for
effective management of the ecosystem.
Key words: Forest reserve, diversity, species, sampling, merchantable.
Introduction
Management of tropical forests for economic production is a
key element in their conservation. Richard17 and Peters 16
reported that the species richness and distribution is one of the
outstanding features of the tropical rainforest vegetation.
Richard 17 noted further that of all other vegetation, rainforest
has an overwhelming majority of plants, extremely numerous in
species that varied in size. Trees in this forest have straight and
cylindrical boles without damage, circular and apparently sound.
Also most of the tree trunks are straight and slender and branch
only at the tops as reported by Okojie 11. Kevin et al. 13 noted
that there is a great variation in the height of the trees in rainforest
ecosystem (commonly several-layered in structure). Most of
the tree species highly desired for many purposes especially for
rural livelihood are from this ecosystem.
There is generally lack of emphasis on effective management
of this important ecosystem today. This is as a result of the long
rotation period for the indigenous species and the low
productivity per hectare when compared with plantations. This
neglect has led to the conversion of large tracts of the natural
ecosystem to monoculture (Plantations of fast growing exotic
and some indigenous species), relaxation of controls on legal
and illegal logging and encroachment.
The destruction of roughly 7.5 x 106 ha of tropical rainforest
annually has been reported by Lanly 9. World Bank 21 also
reported that between 17 million and 20 million hectares of the
world forest (mainly tropical moist forest) are being lost each
year. These have led to reduction in the natural forest and the
loss of its environmental and biological values. So, most tropical
rain forests have currently a few commercial species. A lot of
useful hardwood species have gone into extinction while many
Food, Agriculture & Environment, Vol.2 (2), April 2004
are vulnerable, rare or endangered. Schmidt 18 noted some
principal causes of deforestation as indiscriminate exploitation,
rural poverty, population growth and poor organization and
funding of forestry activities. The continuous removal of the
lowland rainforest ecosystem could bring about instability in
environmental, economic and social goals of the country. It can
also lead to structurally and genetically degraded forest, which
is extremely difficult and expensive to rehabilitate. Decisionmakers need information on the present structure and yield of
this important ecosystem for the purpose of monitoring growth
and drains. This information is valuable since it can reveal the
true status of the forest and change over time so that adequate
management strategies that can make the forest sustainable can
be put in place.
In view of this, every country requires timely and reliable data
on the status of her forest resources. This study assessed the
present physiognomy, structure and yield of the tropical
rainforest ecosystem of southwest Nigeria. This was achieved
by collecting tree growth data from three selected forest reserves
in the ecosystem. While the structure of the ecosystem was
determined from the height distribution of trees, yield was
assessed from the values obtained for basal area, volume, stand
density and diameter. Linear regression models were also
developed for volume estimation in the ecosystem. The models
were assessed and validated to select the most appropriate ones
for volume estimation.
Materials and Methods
Study area: The study area is the tropical rainforest ecosystem.
395
This ecological zone forms a continuous belt around the world
between latitude 24°S and 24°N and longitude 10°E and 20°W. In
southwest Nigeria, it is located a few kilometers inland along
the coast and forms a continuous strip of green belt separating
the coastal vegetation from the derived and Guinea savanna
vegetation. Three forest reserves were randomly selected for
data collection. The reserves are Shasha, Omo and Ala forest
reserves, in Osun, Ogun and Ondo States of Nigeria respectively.
Even though these forest reserves fall within three States, yet
they are within the same ecological zone; the State boundaries
being merely administrative/political boundaries. It is common
knowledge that vegetation zones (being natural phenomenon)
transcend political boundaries.
Shasha forest reserve is located in Ife South Local Government
Area of Osun State on latitude 9°4’N and 9°50’N and longitude
3°54’E and 4°6’E. It is 215.25 km2 in size and the vegetation is
mainly high forest type. Omo forest reserve is located in area J4,
Ijebu East Local Government Area of Ogun State, Nigeria on
latitude 6°50’N and longitude 4°22’ E. It covers an area of 460
km 2. Ala forest reserve is located in Akure North Local
Government Area of Ondo State, Nigeria. It lies between latitude
7°N and 6°45"N and longitude 5°E and 5°10"E. It is 166 km2 in
size.
Method of data collection: The sampling technique adopted
for plot location in each of the three sites was systematic cluster
sampling. 1000 m x 200 m (20 ha)-land area referred to as cluster
was centrally located and divided into two tracts of 600 m apart.
This was further divided into plots of equal size (50 m x 50 m or
0.25 ha). Four plots were therefore selected at the end of
southwest and southeast corners of each tract 4. So, the tree
growth data were collected from eight plots in each of the
locations, making a total of 24 plots.
The following tree data were collected in each sample plot:
dbh (stem diameter at a position of 1.3 m above the ground
level), diameters over bark at the base, middle and merchantable
top, total height using Spiegel relaskop and dominant height
(i.e. height of four largest trees in a plot representing 100 largest
trees per hectare).
Data analysis: The basal area of each tree in the enumerated
plots was calculated using the formula:
Ba =π D2
(1)
4
where, Ba = basal area (m2), D = diameter at breast height (cm)
and π = pie (3.142).
The Ba for each plot was obtained by adding all trees Ba in the
plot. Total BA was calculated by adding plot basal areas and
mean Ba for the sampling plots was estimated by dividing the
total Ba with 8 (number of plots). Basal area per hectare was
therefore obtained by multiplying mean basal area with number
of 0.25ha plot in one hectare (4 plots).
Volume calculation: The volume of each tree was calculated
using the Newton’s formula of Husch et al 6:
V = h ( Ab + 4 Am + At )
(2)
6
where V = tree volume (in m3), Ab, Am and At = tree cross-sectional
area at the base, middle and top of merchantable height,
396
respectively (in m2) and h = total height (in meters).
Plot volume was obtained by adding the volumes of all the
trees in the plot and total volume by adding the eight values for
each of the sampling plot. Mean plot volume was obtained by
dividing the total plot volume by eight and volume of trees per
hectare was estimated by multiplying the mean plot volume by
the number of 0.025 ha plots in an hectare.
Diameter measurements in the plots were grouped into diameter
frequency classes of 10 cm interval beginning from the lowest
diameter (20 cm). Kio’s 7 method was adopted in grouping tree
heights into height frequency classes. This was used to assess
the structure of the ecosystem. The height classes and their
strata are as follow: 10–20.99 m lower stratum, 21–30.99 m middle
stratum, 31–40 m upper stratum and trees with height greater
than 40 m are in the topmost storey called emergent.
Correlation coefficient calculation: Pairing of the growth
parameters to examine the type linear relationship between them
was carried out.
Yield model generation: For the purpose of modeling, individual
tree growth variables across all sample plots for the three sites
were pooled together. Linear, logarithm transformed, quadratic
and polynomial forms of regression yield models were adopted.
The linear regression models follow the general Schumacher 19
yield models of the form:
Y= f (A, SQ, SD)
(3)
where Y = function of yield e.g. volume, A = age, SQ = function
of site quality e.g. site index, height, SD = function of stand
density e.g. diameter at breast height, basal area.
Age in the original model was surrogated with basal area as
independent variables in this study. This is because the natural
forest contained species and trees of different ages and these
ages are very difficult to determine (uneven-aged/uneven-sized).
Laiho et al. 8 noted that the age structure of an uneven-aged
stand is highly heterogeneous and so its determination in
practical forestry is not meaningful and also very rear on research
plots. So age is non-essential for yield generation in this case.
Several other researchers also replaced age with diameter during
model generation in their studies 3, 4, 12.
The models generated are:
Simple linear regression model
V = bo + b1 Ba
Multiple linear regression models
V = bo + b1Ba + b1Hm
Logarithm transformed models
LnV = bo + b1LnBa
Quadratic model
V = bo + b1Ba + b2Ba2
Polynomial models
V = bo + b1Ba + b2Ba2 + b3Ba3
(4)
(5)
(6)
(7)
(8)
where V = volume per ha (m3), Hm = mean height (m), Ba = basal
area per ha (m2), Ln = natural log, bo = regression constant
(intercept), b1,b 2, and b3, = regression coefficients to be
estimated.
Assessment of the models: The volume models were assessed
Food, Agriculture & Environment, Vol.2 (2), April 2004
with the view of recommending those with good fit for further
uses. The following criteria were used:
Significance of regression (f - ratio): This is to test the overall
significance of the regression equations. The critical value of F
(i.e. f-tabulated) at p < 0.05 level of significance was compared
with the f-ratio (f -calculated). Where the variance ratio
(f-calculated) is greater than f-tabulated such equation is
therefore significant and can be accepted for prediction.
Multiple correlation coefficient (R) : This measures the degree
of association between two variables i.e. Y - dependent variable
and X- independent variable 15. The r- value must be high
(> 0.50) for the model to have good fit.
Coefficient of determination (R2): This is the measure of the
proportion of variation in the dependent variable that is explained
by the behaviour of the independent variable 20. For the model
to be accepted, the R2 value must be high (>50%), i.e. most of
the variability in the dependent variable is accounted for by the
independent variables.
Validation of the Models: Validation was done by comparing
the models’ output with values observed on the field. The
validation process examines the usefulness or validity of the
models 14. The original data from the ecosystem were divided
into two. The first set (calibrating set), which comprised tree
data from 16 plots, was used for generating the models while the
second set (validating set) which comprised tree data from eight
plots was used for validating the models 2. The models’ outputs
were compared with observed values obtained from the field for
any significant difference with the student t-test of Goulding 5.
For valid models, it must not be significant at p<0.05.
Percentage bias estimation: The absolute percentage difference
(% bias) was determined by dividing the difference between
volumes obtained with Newton’s formula (observed volume)
and models’ output by the same observed volume and multiplied
by 100. The value must be relatively small for the model to be
acceptable for management purpose.
Results and Discussion
The distribution of trees into height classes corresponding to
each of the stratum in the ecosystem is shown in Table 1. Four
strata existed in Shasha Forest Reserve while three were present
in Ala and Omo Forest Reserves. The greatest proportion of
trees encountered per hectare in the reserves (i.e. 57% in Shasha,
69% in Ala and 60% in Omo) belonged to the lower stratum (tree
height <20m). The middle stratum comprised of trees whose
heights are between 21 and 30 m and about 21, 24 and 35% of
the trees per hectare fall into this category in Shasha, Ala and
Omo forest reserves respectively. The upper storey, with tree
height between 30 and 40 m was the third stratum in the ecosystem
and 14, 7 and 5% of trees were represented in this storey in
Shasha, Ala and Omo Forest Reserves respectively. Trees
referred to as emergent (height above 40 m), in the fourth stratum,
were encountered in Shasha forest reserves only in very small
proportion (8%). Such trees might have been exploited as timber
in the ecosystem. Nowadays, trees of smaller dimension are
generally logged immediately they are discovered, especially
Food, Agriculture & Environment, Vol.2 (2), April 2004
the most economic and desirable species like Mansonia aitisima,
Milicia excelsa, Afzelia africana and Khaya spp. Also the
increased pressure on the tropical rainforest for several uses
has affected the physiognomy and structure of the forest.
The summary of tree growth parameter obtained per hectare in
the study areas is presented in Table 2. The basal area per hectare
for Shasha forest reserve is 23.41 m2, Ala Forest Reserve is
22.98 m2 while that of Omo Forest Reserve is 12.48 m2. The basal
area per hectare obtained in Shasha and Ala forest reserves is a
little more than 15 m2 suggested for a well-stocked forest by
Alder and Abayomi 1 while what is obtained in Omo forest reserve
is less than 15 m2. So further exploitation is only possible for a
short time in Shasha and Ala forest reserves while further felling
is not ideal in Omo forest reserve. The volume per hectare is
181.36 m3, 227.00 m3 and 91.71 m3 for Shasha, Ala and Omo forest
reserves respectively and 150, 117 and 97 trees respectively,
were encountered per hectare.
Table 3 shows the summary of the descriptive statistics of
diameter at breast height for all the trees enumerated in the
sample plots. The mean dbh obtained for trees in Shasha forest
reserve is 39.07± 21.65 cm. For Ala Forest Reserve, the mean
dbh is 42.56±23.06 cm and Omo has a mean dbh of 34.70±11.20
cm. The high value of the standard deviation and range is an
indication of wide variation in tree size. The skewness of the
dbh distribution is positive because there are more trees in the
lower dbh classes than in the upper classes. Table 4 shows the
proportional distribution of the trees among the dbh classes for
the Forest Reserves. The highest distribution is in the lowest
class (20–29 cm) that had 40, 41 and 44% for Shasha, Ala and
Omo Forest Reserves respectively. There was a general
reduction in this proportion with increase in dbh class. The
highest class (≤100cm) had the lowest number of trees per
hectare. The percentage of trees that have reached merchantable
size (i.e. dbh of 48 cm and above) is 20% in Shasha, 36% in Ala
and only 18% in Omo Forest Reserves. These are the trees that
should be available for exploitation in the forest only. The rest,
which are of smaller sizes, will provide an ingrowth into the
merchantable class that will be ready for harvest in the future
and they should be included in the working plan for
conservation. There is generally low correlation between the
tree growth variables paired in this study. This may be due to
the nature of the vegetation that usually contains many species
of different ages and sizes. The correlation values obtained vary
between 0.04 and 0.81. The highest R-value was between the
volume and basal area. Small R-values were recorded between
number of trees per hectare and other variables. This is may be
attributed to the fact that number of trees is not a growth data in
the real sense of it. For the volume models, all the assessment
criteria used show that they are all suitable for volume estimation
of rainforest ecosystem. The R-values of the models are very
high (between 0.68 and 0.91) and the R2 value ranges between
82% and 66%. Also, all the equations are significant at p<0.05
level.
The validation results obtained with the t-test (Table 6) and
the one-way analysis of variance (Table 7) for all the types of
linear regression models reveal that there are no significant
differences in the observed volumes (Volumes with Newton’s
formula) and models outputs for the ecosystem. The small
percentage bias values obtained that range between 1.62 and
397
Table 1. Summary of tree growth variables (per hectare) in the study area.
Yield parameters
Basal area (m2)
Volume (m3)
Average diameter at breast height (cm)
Average dominant diameter at breast height (cm)
Average total height (m)
Average dominant height (m)
Confidence limit of volume estimate -lower limit (m3)
Confidence limit of volume estimate -upper limit (m3)
No. of stems per hectare
Coefficient of variation
Table 2. Descriptive statistics of diameter at breast height (cm)
for all trees enumerated in the study area.
Parameters
Mean
Standard error
Median
Mode
Standard deviation
Sample variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Shasha
39.07
0.89
32.7
20
21.65
468.55
6.80
2.33
150
10.00
160
23363.6
598
Value obtained
Ala
Omo
42.56
34.70
1.507
1.120
35.90
30.40
55.00
25.00
23.056
15.363
531.50
236.015
10.64
3.347
2.447
1.764
190
90
10.00
10.00
200.00
100.00
9960.00
6524.20
234
188
Table 3. Proportional distribution of diameter at breast height
per hectare (cm) of trees in the study area.
Dbh class
20-29.99
30-39.99
40-49.99
50-59.99
60-69.99
70-79.99
80-89.99
90-100
<100
Total
Values
Ala
22.98
227.00
46.43
129.88
18.14
25.30
223.56
235.20
116
3.12
Shasha
23.41
181.36
38.83
110.6
14.11
24.01
176.23
186.49
150
2.96
Distribution per hectare
Shasha Forest
Ala Forest
Omo Forest
Reserve
Reserve
reserve
61 (41)
41 (35)
44 (46)
32 (21)
27 (23)
25 (26)
28 (19)
15 (13)
10 (10)
12 (8)
14 (12)
9 (9)
8 (5)
7 (6)
3 (3)
1 (1)
6 (5)
1 (1)
2 (1)
5 (4)
2 (2)
1 (1)
2 (1)
1 (1)
5 (3)
2 (1)
2 (2)
150 (100)
117 (100)
97 (100)
Conclusions
The information that is provided by the results of this research
clearly indicates the deplorable condition of the tropical
rainforest ecosystem of southwest Nigeria in terms of its
structure, appearance and yield. The small percentage of timber
available presently as industrial timber in these forest reserves
is an indication that the lowland rain forest ecosystem of
southwest Nigeria has been greatly disturbed over the years.
So insitu and exsitu conservation are recommended and forest
policy makers should intensify the management of tropical natural
forest. The conversion of the natural forest into plantation of
fast growing exotic species should be avoided, as monoculture
does not give room for biodiversity conservation. Marginal and
degraded land should be used for this purpose. However,
majority of the trees is in the smallest diameter class (20–30 cm).
Only 35% of the trees are merchantable (≥48 cm) while others
can either be available as saplings or poles. Three strata common
in natural vegetation were identified in this study area. The
physiognomy classification shows that most of the species
encountered is in the lower strata (height between 10-20 m) and
no tree can be referred to as emergent (height ≥40 m). The small
values of stand density per hectare, basal area per hectare,
volumes per hectare and mean height obtained in this study
show that Nigerian lowland rainforest has been seriously
degraded. The basal area per hectare obtained is a little greater
than 15 m2 suggested for a well-stocked forest. So further
exploitation is not recommended.
Height
classes (m)
10-20.99
21-30.99
31-40
<40
Total
Distribution per hectare
Shasha Forest
Ala Forest
Omo Forest
Reserve
Reserve
reserve
85 (57)
81(69)
58 (60)
32 (21)
28 (24)
34 (35)
21 (14)
8 (7)
5 (5)
12 (8)
0
0 (0)
150 (100)
117 (100)
97 (100)
21.21 are also an indication of how suitable these models are for
further use. Similar bias values (i.e. within 20%) were also
obtained by Latif et al. 10 for Senna siamea in Bangladesh.
Therefore the models generated in this study are very appropriate
for volume estimation in tropical lowland rainforest ecosystem
of SW Nigeria.
398
References
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1
Table 4. Height distribution of tree species per ha (m)
in the study area.
Omo
12.48
91.71
36.25
82.75
17.26
24.05
90.08
93.44
96
2.45
Food, Agriculture & Environment, Vol.2 (2), April 2004
Table 5. Correlations coefficient for tree growth variables in tropical lowland rainforest ecosystem of SW Nigeria.
Ba
1.000
.806
.333
.187
-.037
.329
.199
.948
.743
Ba
Vol
Hd
N
Hm
Dd
Dm
LnBa
LnVol
Vol
Hd
N
Hm
Dd
Dm
LnBa
LnVol
1.000
.280
.058
.201
.229
.341
.684
.933
1.000
-.070
-.058
-.104
-.099
.268
.209
1.000
-.281
.215
.041
.290
.131
1.000
-.108
.352
-.095
.191
1.000
.562
.410
.310
1.000
.272
.434
1.000
.698
1.000
Table 6. Models generated for volume estimation in lowland rainforest ecosystem of SW Nigeria and their assessment criteria.
Model
no.
1
2
3
4
5
R
R2
Adjusted R2
F-ratio
Significant level
0.80
0.68
0.87
0.91
0.82
64%
46%
76%
82%
68%
61%
42%
73%
78%
62%
24.83
11.72
20.86
18.45
13.00
0.04*
0.01*
0.00*
0.001*
0.00*
Equation
V = 26.61+4.97Ba
V = 0.61+2.19LnBa
V = 172.04-6.44Ba+0.18Ba2
V = 24.06Ba-0.94Ba2+0.12Ba3-30
V = 6.96Ba+4.34Hm-48.72
*Significant (p≤0.05)
Table 7. Validation result of mean volume per hectare (m3) predicted with the models and percentage bias from the observed volume.
Model no.
Observed
1
2
3
4
5
Mean volume/ha
103.90
107.51
105.58
125.94
123.23
108.33
% Bias
t-test critical value (0.025)
Remark
3.47
1.62
21.21
18.60
4.26
0.651
0.861
0.651
0.097
0.753
ns
ns
ns
ns
ns
ns = not significant (p≤0.05)
Table 8. Analysis of variance table for assessing the significant difference in volumes per hectare (observed volume and
volumes obtained with the models).
Sources of
variation
Volumes (m3)
Error
Total
Degree of
freedom
5
42
47
Sum of square
1953.36
41406.86
93360.22
Mean sum of
square
390.67
985.88
F-ratio
Significant level
0.396
0.849ns
ns = not significant ( ≥0.05)
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