Visible-to-near-infrared spectroscopy for prediction of soil nitrogen mineralization after sample stratification by textural homogeneity criteria

Abstract

On-time and accurate estimation of the soil nitrogen mineralization rate (SNMR) is critical for nitrogen (N) management and protecting the environment. This study evaluated the performance of a visible-to-near-infrared reflectance (vis-NIR) spectroscopy for predicting SNMR for four texture groups. A total of 62 topsoil samples were collected from 17 management zones distributed over four fields and incubated with seven destructive sampling events. Samples were analysed for total mineral N (NH4++NO3–) content and scanned using a vis-NIR sensor simultaneously at each of the seven-sampling times. Four partial least squares regression models were calibrated and validated for four textural groups (groups- 1– 4) identified over the United State Department of Agriculture (USDA) texture triangle. Prediction accuracies indicated that vis-NIR sensor was moderately to highly accurate for predicting SNMR, while observing variable accuracies across texture groups. The highest accuracy was obtained for group 1 (sandy-loam; coefficient of determination, R2 = 0.90; root mean square error, RMSE = 0.04 mg N kg−1 soil day−1), successively followed by group 2 (mostly loam; R2 = 0.80, RMSE = 0.05 mg N kg−1 soil day−1) group 4 (mostly silt; R2 = 0.66, RMSE = 0.08 mg N kg−1 soil day−1), and group 3 (silt-loam; R2 = 0.44, RMSE = 0.08 mg N kg−1 soil day−1). Variable importance in projection score revealed that the key spectral bands to predict SNMR were in 2150 – 2260 nm and 2470 – 2480 nm, resembling the key bands associated with soil organic compounds and clay minerals. In-advance texture information required for soil stratification is regarded a limitation of the proposed approach. In conclusion, vis-NIR holds potential for a rapid estimation of SNMR when samples are stratified into similar texture groups in advance, however, confirmatory research will be needed to validate the current findings for soils from different origin and under different management.