3.4.1 Soil pH prediction
The soil pH was most accurately predicted by MARS (R2c=0.90; MBE=-0.003; RMSEc=0.34; RPDc=2.98) and SVR (R2p=0.66; MBEp=0.04; RMSEp=0.51; RPDp=1.62) during calibration and validation, respectively (Figure 4a). The calibration accuracy of the SVR (R2c=0.89; MBEc=-0.008; RMSEc=0.35; RPDc=2.89) was comparable to that of MARS. The validation prediction accuracy of SVR was classified as acceptable. The overall rank based on the model evaluation parameters indicated SVR (1.5) as the best performing model to predict soil pH. Though the calibration ranking of MARS was 1, the rank during validation was 4.25 indicated inefficiency of the MARS model. The cross-validation accuracy to predict soil pH using the PLSR of reflectance in mid-infrared (MIR) and combined VIS-NIR-MIR was recorded by Viscarra Rossel et al. , (2006) as R2adj 0.75 and 0.33, respectively. They recorded the best prediction using MIR and observed RMSE of 0.10 unit. The prediction accuracies reported in the present study for pH were less accurate than literatures as R2of 0.74 using PLSR (Reeves & McCarty, 2001), 0.73 using PLSR (Reeveset al. , 1999), 0.70 using MARS (Shepherd & Walsh, 2002), 0.70 using PCR (Islam et al. , 2003), 0.56 using PCR (Sun et al. , 2003). In the present study, the poor predictions for the soil pH might be attributable to the lower variability in the full (CV=16.75%), calibration (CV=18.46%) and validation (CV=15.44%) dataset (Table 1). Most of the samples of the study had acidic soil reaction.