Subsetting reduces the error of MIR spectroscopy models for soil organic carbon prediction in the U.S. Great Plains

Abstract

High demand for soil organic carbon data to support soil health and climate change mitigation efforts must be met with rapid, accurate, and inexpensive measurement methods. Mid-infrared spectroscopy shows promise as an alternative to conventional soil carbon analysis; however, its practicality depends on the construction and efficient use of soil spectral libraries. Subsetting, a calibration optimization technique, has potential to reduce model prediction errors. Nevertheless, the effectiveness of different subsetting criteria has yet to be well explored. This study assessed whether several subsetting criteria would result in calibration models with reduced error in the prediction of soil organic carbon content compared to models constructed from a full spectral dataset. A mid-infrared spectral library composed of Nebraska and Kansas soil samples was subset by (i) a nested wetland criterion, (ii) soilscapes, (iii) presence or absence of carbonates, and (iv) a combination of soilscape and carbonates. Partial least squares regression was used to construct all calibration models. Predictive performance of the subset models was compared to that of their corresponding full set model using several statistical metrics. Subsetting by wetlands reduced model error by 22 and 56%. Subsetting by soilscape yielded a 13 to 55% reduction in model error, while presence or absence of carbonates reduced model error by 21 and 46%. Five of the eight combination soilscape and carbonate subset models reduced model error by 14 to 51%. Overall, subsetting by soilscape or carbonate presence proved effective in improving model performance, with combination subsets proving beneficial under specific calibration set conditions.