Evaluating regression-kriging for mid-infrared spectroscopy prediction of soil properties in western Kenya

Abstract

In this study, the utility of regression-kriging was investigated in building prediction models for soil properties using mid-infrared (7498 to 600cm−1) spectral data for soil samples collected from Nyando, Nzoia and Yala catchment areas in Kenya, sampled at 0–20cm and 20–50cm depths. Using a systematic technique, 158 samples were selected for analysis of a number of soil properties of interest using wet chemistry methods. We randomly divided the dataset into two groups: 118 samples in the calibration and 40 samples in the holdout validation set. The calibration set was first used to develop partial least squares regression (PLS) models for all the soil properties. Residuals from these models were used to generate semivariograms, which revealed a strong spatial dependence as determined by the ratio of nugget to sill for nitrogen, 9%; Al, 12%; and B, 36%, but with weak spatial dependence for exchangeable Ca (ExCa), 100%; and carbon, 76%. The fitted theoretical semivariograms were used to fit regression-kriging models. Lastly, both the PLS and regression-kriging models were assessed with the validation set and their prediction performance evaluated by R2 and root mean square error (RMSE). The results showed that regression kriging method gave lower RMSE values for all the evaluated soil properties except for ExCa, B and exchangeable acidity, with the best predictions, compared with the PLS model, obtained for ExMg (R2, 0.93 vs 0.88; RMSE, 6.1 vs 8.4cmolckg−1) and total nitrogen (R2=0.92 vs R2=0.74; RMSE, 0.11%, RMSE=0.2%). In this study, regression-kriging, which takes into account spatial variation normally ignored by other methods, improved use of infrared spectroscopy for predicting soil properties.