Infrared spectroscopy prediction of organic carbon and total nitrogen in soil and particulate organic matter from diverse Canadian agricultural regions

Abstract

Infrared spectroscopy has the capacity to predict soil organic carbon (SOC) and total nitrogen (TN) at local/regional scales, but no studies have been conducted to evaluate this technique at a large (cross-regional) scale in Canada. In this paper, mid-infrared (MIR) and near-infrared (NIR) spectroscopies in combination with partial least-squares regression (PLSr) were used to predict SOC and TN in whole soil and in particulate organic matter (POM) fractions on cross-regional, regional, and local scales. Both MIR- and NIR-PLSr models have well estimated SOC [coefficient of determination (R2) = 0.79–0.92, residual prediction deviation/ratio of prediction to deviation (RPD) = 2.19–3.47], TN (R2 = 0.70–0.92; RPD = 1.83–3.50), POM-C (R2 = 0.76–0.96; RPD = 2.04–5.25), and POM-N (R2 = 0.70–0.97; RPD = 1.83–5.78). The prediction efficiency of cross-regional models (R2 = 0.90–0.96; RPD = 3.13– 5.49) was similar to or better than the prediction of regional (R2 = 0.70–0.97; RPD = 1.83–5.78) and local models (R2 = 0.70–0.96; RPD = 1.83–5.33) and overall MIR-PLSr models (R2 = 0.90–0.96; RPD = 1.98–3.47) yielded similar predictions for SOC and TN relative to NIR-PLSr models (R2 = 0.70–0.92; RPD = 1.83–3.50) at cross-regional scale. Hence, it may be possible to develop MIR and (or) NIR spectral models to estimate and monitor SOC, TN, POM-C, and POM-N, and therefore, soil quality, in a rapid and cost-efficient manner across regions with diverse soil types, climate, and cropping history.