Vis/NIR spectroscopic measurement of selected soil fertility parameters of Cuban agricultural Cambisols

The conventional methods frequently used in Cuba to determine some fertility parameters important for sugarcane production, such as organic matter (OM), available phosphorus (P) and potassium (K2O), are difficult, costly, and time-consuming procedures. This study was undertaken to build and validate Visible/Near Infrared Reflectance (Vis/NIR) calibration models of these parameters at landscape level and within a field, by taking into consideration their correlation coefficients with the OM. The parameters P and K2O, which are not spectrally active in the Vis/NIR range should be better predicted when are highly correlated with OM. Also, the wavelength intervals to simplify this methodology were selected. Samples were air-dried before scanning using a diode array spectrophotometer covering the wavelength range from 399 to 1697 nm. The regression models were built by using the linear multivariate regression method Partial Least Squares (PLS), and the nonlinear multivariate regression methods Support Vector Machines (SVM) and Locally Weighted Regression (LWR). At landscape level the best correlations between soil spectra and OM (0.90 ≤ R2 ≤ 0.93; 0.12 ≤ RMSEP≤0.14) were obtained with LWR, followed by K2O with LWR (0.77 ≤ R2 ≤ 0.79; 3.47 ≤ RMSEP≤3.62), Olsen P (0.69 ≤ R2 ≤ 0.81; 0.27 ≤ RMSEP≤0.35) and Oniani P (0.64 ≤ R2 ≤ 0.65; 3.31 ≤ RMSEP≤3.61) both with SVM. Also, the nonlinear regression models gave the best results within a field. The higher values for OM (R2 = 0.92; RMSEP = 0.14) and Olsen P (0.68 ≤ R2 ≤ 0.83; 0.27 ≤ RMSEP≤0.34) were observed with SVM, while for K2O (0.16 ≤ R2 ≤ 0.63; 5.13 ≤ RMSEP≤5.88), and Oniani P (0.70 ≤ R2 ≤ 0.72; 2.32 ≤ RMSEP≤2.52) were obtained with LWR. The soil fertility parameters studied at landscape level and within a field were best estimated by using nonlinear regression models.