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factors: relative surface area, forestry value and 
cover percentage of vegetation. For each PSU three 
random numbers were generated, in the same manner as 
at the PSU level, identifying particular SSU’s. 
For the identification of the precise ground 
position of the ground data collection sites a 
systematic grid, composed of nine points, numbered 
from 1 to 9, is overlaid on the aerial photography 
within the limits of each selected SSU. A number 
between 1 and 9 is selected at random, identifying 
one of the points. The position directly under the 
selected point is marked and represents where the 
site must be placed in the field. 
3.2.2 Ground data collection 
The total of 349 field sites selected by the list 
sample procedure were located in the field using 
common methods of navigation, and the site location 
verified through photo interpretation. At each site 
quantative data, including vegetation cover 
percentages and heights, were collected along four 
100 meter transects. The four transects were laid 
out in a cross pattern, each transect in a cardinal 
direction. At every meter mark a graduated pole was 
held perpendicular and if the ascending pole touched 
any part of a ligneous plant, the species was 
identified and recorded at the number of the meter, 
and the height of the maximum touch on the pole 
noted. Where two or more species occured, all were 
noted and their maximum heights of touch recorded. 
All 400 points of the four transects were surveyed 
in this manner, and the appropriate data sheets 
completed. In addition, general soils, land form and 
vegetation surveys were also done, and the position 
permanently marked on the aerial photos. 
3.2.3 Data analysis and prediction of individual 
site fuelwood volumes 
For each of the sites in each urban zone the 
observations from the 400 points were reduced, 
to give cover percentage, average height and 
variability of heights, for all species together 
for the five individual species considered primary 
fuelwood species, and, two groups of species 
considered to have an affect on fuelwood volume. The 
primary fuelwood species were: Combretum micranthum, 
C. nigricans, C. glutinosum, Guiera senegalensis, and 
Balanites aegyptiaca. The two groups included 
species in the genus Boscia; B. senegalensis and B. 
anguistifolia, and those in the genus Acacia; A. 
macrostachya, A. erythrocaylx, and A. ataxancantha. 
These data were then used in a multiple regression 
expression which calculated weight of fuelwood for 
the area represented by the equation.This equation 
had been previously developed using data from 92 
transects, comparing the cover percentage and height 
data along the 100 meter transect with that of the 
fuelwood weight cut from a 2 X 100 meter corridor 
associated with the transect. A curve fitting 
operation was used to define the variables 
significant with respect to fuelwood weight, and 
define the expression. The final equation was based 
on 52 observations, used 19 variables, including 
squared and cubed terms of some of the orginal 
variables, and had an associated standard error 
the regression model of 8.2% of the calculated mean. 
This equation calculated, for each of the sites 
placed in a zone, fuelwood weights for a 200 square 
meter area. These figures were then multiplied by an 
appropriate expansion factor to bring the weight to 
a per square kilometer figure, and then divided by a 
conversion factor of 250 kilograms per stere of 
stacked fuelwood, to yield a volume figure in steres 
of stacked fuelwood per square kilometer. 
3.2.4 Prediction of fuelwood volumes for the 
individual urban zones 
3.2.4.1 Prediction of fuelwood volume using a global 
mean approach 
The development of the fuelwood volume figures for 
each urban zone was guided by the need for general 
information regarding approximate volumes in each 
zone, and the capability to compare the relative 
results for each of the zones. With these objectives 
in mind a simple global mean and standard error was 
developed to represent the fuelwood volume present 
in each urban zone. Table 1 shows the total volumes, 
their associated standard error, and average volume 
per square kilometer, for each of the five urban 
zones. It is clear that two areas, Niamey and Dosso, 
have a much higher total, and average, volume than 
the other three, and that the figures are very low 
for the Tahoua zone. Note that the total figures for 
the Maradi zone are those for only 21,000 square 
kilometers, and it is more useful to compare the 
average figures for a square kilometer. Note also 
the standard error figure for each zone. These 
figures are vey high, averaging more than 100% in 
each zone. This indicates that fuelwood volume 
varies greatly from place to place in each zone and 
evaluation, of the number of samples used, and the 
method of site selection, must be done. 
Table 1. Total fuelwood volume, associated standard 
error and average fuelwood volume per square 
kilometer for the five urban zones. 
URBAN 
ZONE 
MEAN VOLUME STANDARD ERROR 
(steres/km sq) (steres/km sq) 
TOTAL VOLUME 
(steres) 
Niamey 
860.13 
940.97 
27,008,082 
Dosso 
940.75 
1066.57 
29,539,550 
Tahoua 
185.05 
322.01 
5,810,643 
Maradi 
348.97 
995.21 
7,328,370* 
Zinder 
369.53 
347.84 
11,603,242 
* This figure represents the volume for 
only 21,000 
square kilometers 
3.2.4.2 Prediction of fuelwood volumes for each 
urban zone using a terrain unit approach 
The simplistic calculation of a global mean and 
error figure does not take into account the biasing 
factor used in the selection process. In fact, since 
the sampling is theoretically concentrated in the 
primary forestry types the figures are liable to be 
inflated, because they are calculated without taking 
appropriately into account a large surface area 
where fuelwood volumes tend to be lower, the 
marginal and agricultural lands. In an attempt to 
evaluate the validity of the global mean figures 
another method for developing fuelwood volume 
estimates was sought using the data in hand 
method selected involved developing mean volume 
figures for each terrain unit for which quantitative 
data was available, and then developing fuelwood 
volumes based on the relative surface areas of these 
TU’s mapped in the zone. Each of these sites was 
placed in it's appropriate TU and, for each, a mean 
fuelwood volume and associated error, in steres of 
stacked fuelwood per square kilometer, calculated. 
These mean volume figures were then multiplied by