Near infrared reflectance spectroscopy prediction of soil nitrogen supply in humid temperate regions of Canada

Near infrared reflectance spectroscopy (NIRS) holds promise for rapid assessment of soil total N and organic C contents, but its ability to predict soil N supply in soils with contrasting physio-chemical properties needs to be evaluated. The objectives of this study were to: (i) evaluate NIRS predictions of measured soil parameters (total N, organic C, and C/N ratio) and as an indirect indicator of soil N supply, namely corn (Zea mays L.) N uptake and (ii) assess the effect of sample set heterogeneity on NIRS predictions. Soil samples (n = 282 in the total set) were collected between 2000 and 2009 from 52 sites across four Canadian provinces and were allocated to fine- (≥350 g clay kg -1, n = 101) and coarse-textured subsets (<350 g clay kg -1, n = 181). Prediction models were developed using modified partial least squares regression. Prediction precision was assessed using the coefficient of determination (R 2) and ratio of prediction to deviation (RPD = ratio of standard error of prediction corrected for bias to standard deviation of the reference data used in the validation). For the total set, predictions were reliable for total N, organic C and C/N ratio (0.7 ≤ R 2 ≤ 0.9, 1.75 ≤ RPD ≤ 3), and less reliable for soil N supply (R 2 < 0.7, RPD < 1.75). Prediction precision for total N, organic C, C/N ratio, and soil N supply increased with set homogeneity (i.e., better in the texture subsets than the total set), resulting in reliable predictions for soil N supply (RPD ≥ 2.00) in the texture subsets. This study demonstrated the possibility of developing reliable NIRS predictive models for total N, organic C, and C/N ratio for soils with contrasting physiochemical properties, as well as reliable NIRS predictive models for soil N supply within homogeneous texture sets. © Soil Science Society of America.