Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils

Abstract

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties’ distribution in Germany.

Keywords

• pedometrics
• digital soil mapping
• multi-target prediction
• regressor chain
• carbon fraction
• agricultural soils