Abstract

Low-cost techniques, such as mid-infrared (MIR) spectroscopy, are increasingly necessary to detect soil organic carbon (SOC) and nitrogen (N) changes in rangelands following improved grazing management. Specifically, Adaptive Multi-Paddock (AMP) grazing is being implemented to restore grassland ecosystems and sequester SOC often for commercialization in C markets. To determine how the accuracy of SOC and N predictions using MIR spectroscopy is affected by the number of calibration samples and by different predictive models, we analyzed 1000 samples from grassland soils. We tested the effect of calibration sample size from 100 to 1000 samples, as well as the predictive ability of the partial least squares (PLS), random forest (RF) and support vector machine (SVM) algorithms on SOC and N predictions. The samples were obtained from five different farm pairs corresponding to AMP and Conventional Grazing (CG), covering a 0–50 cm soil depth profile along a latitudinal gradient in the Southeast USA. Overall, the sample size had only a moderate influence on these predictions. The predictive accuracy of all three models was less affected by variation in sample size when >400 samples were used. The predictive ability of non-linear models SVM and RF was similar to classical PLS. Additionally, all three models performed better for the deeper soil samples, i.e., from below the A horizon to the –50 cm depth. For topsoil samples, the particulate organic matter (POM) content also influenced the model accuracy. The selection of representative calibration samples efficiently reduces analysis costs without affecting the quality of results. Our study is an effort to improve the efficiency of SOC and N monitoring techniques.