3.4.5 Soil N prediction
For N, excellent calibration prediction accuracy of
R2c=0.87; MBEc=0.10;
RMSEc=35.16; RPDc=2.49 was achieved by
using the MARS model. The validation prediction accuracy was
non-reliable with R2p=0.58;
MBEp=-8.80; RMSEp=62.08;
RPDp=1.34. Among all the tested models, the overall
ranking of MARS (2.38) and PCR (2.38) was found the best. However, the
validation prediction rank of PCR (1.25) was best to predict the N.
Similar to our findings Xu et al. (2018) reported better
prediction accuracy for total nitrogen (TN) using PCR as one of the
calibration models with R2p=0.78-0.86
and RPDp=2.13-2.69. Yet in other studies, support vector
machine regression (SVMR) and back propagation neural network (BPNN)
showed better performance with
R2p=0.69-0.88 predicting TN (Aliah
Baharom et al. , 2015; Cozzolino et al. , 2013; Kodaira &
Shibusawa, 2013; Kusumo et al. , 2008; Wenjun et al. ,
2014). Bands around 1100, 1600, 1700-1800, 2000 and 2000-2400 nm have
been identified as being important for SOC and TN (Stenberg et
al. , 2010). Martin et al. (2002) found a high correlation
(r=0.96) for the NIR predicted soil C and N. According to Williams &
Norris (2001) prediction of N could be due to the known nitrogen
specific absorption bands such as covalent bonds with H, C or O.