WBC-VIII-Vol.4 – Resources – Forestry, Plantations and ... - BambuSC

showed higher coefficient of determination (R 2 ), low root mean square error (RMSE) and low bias were
considered to be preferred. Analyses were performed using the PROC REG procedure of the SAS/STAT®
statistical programme (SAS INSTITUTE 1999).
Models for predicting culm length, basal area and culm hardness, showed the better performance. However, the
variability in the productivity and quality of guadua could not be entirely described by the variables measured
and a significant proportion of the variability remains unexplained. The best for growth variables was for culm
length which includes 11 variables and explains 54% of total variation (R 2 = 0.54, RMSE = 2.3). Most of the
variables contained in the model were related to soil properties (especially physical soil properties), however the
temperature was the most important independent variable. Residuals analyses did not show any special trend and
for all of the classes and the residual variation must be interpreted as random (Figure 3).
Residuals (m)
6
4
2
0
14 16 18 20 22 24 26 28 30
-2
-4
-6
Average h predicted (m)
Figure 3. Plot of residuals versus predicted values of average culm length (h).
For stand variables the regression model for basal area gave the more reasonable goodness of fit (R 2 = 0.51,
RMSE = 20.3). It means that 51% of total variation was explicated by the model. Five of the eight variables
included in the model are soil properties and the most relevant was apparent density. The residuals analysis did
not show any systematic bias when plotted against predicted values (Figure 4).
VIII World Bamboo Congress Proceedings Vol 4-40