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4.2. Linear Multi-Regression
Based on the work from the previous section (Section 2), literature research and a cursory
glance at the PCA results, texture, temperature, water filled porosity, organic carbon, pH
and nitrate concentration are expected to be the main controlling factors for the
denitrification rate. A step-wise multi-regression analysis is conducted on the entire
dataset in ‘R”.
Considering the data available from Table 4.2 the following equation can be developed.
R
dn
= - 2.439 ∗ Bulk Density (g cm - 3) + 0.01 ∗ NO3 - Conc ( µg N g - 1 soil) - 0.22 ∗ OC (%)
+ 1.44
pH - 0.01 Soil Depth (cm) + 0.12 Temp (º
119
C) + 0.04 WFP (%) - 8.71. (R
Table 4.2 Stepwise multi-regression analysis for complete dataset.
2
= 0.
10)
------- Equation 4.1
Coefficients: Estimate Std.Error t-value Significance
Intercept -8.71 2.37 -3.68 ***
Bulk Density (g cm -3 ) -2.39 1.13 -2.12 *
NO3 - Conc ( µg N g -1 soil) 0.01 0.00 4.06 ***
OC (%) -0.22 0.09 -2.31 *
pH 1.44 0.33 4.32 ***
Soil Depth (cm) -0.01 0.01 -0.78
Temp (ºC) 0.12 0.04 3.52 ***
WFP (%) 0.04 0.01 4.00 ***
Signif.Codes Signif.Codes Signif.Codes
0 '***' 0.001 '**' 0.01
0.01 '*' 0.05 '.' 0.1
0.1 1
This is certainly by no means a valuable regression and the factors considered do not
reflect any significance in terms of prediction capability. The multi-regression analysis
from Table 4.2 suggests that, temperature, nitrate concentration, WFP and pH are the for
the most part the significant controlling factors of the denitrification rate. This is