Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils

Ali Sakhaee (), Thomas Scholten, Ruhollah Taghizadeh-Mehrjardi, Mareike Ließ and Axel Don
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Ali Sakhaee: Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig, Germany
Thomas Scholten: Soil Science and Geomorphology, Institute of Geography, Eberhard Karls University Tübingen, 72070 Tübingen, Germany
Ruhollah Taghizadeh-Mehrjardi: Soil Science and Geomorphology, Institute of Geography, Eberhard Karls University Tübingen, 72070 Tübingen, Germany
Mareike Ließ: Department of Soil System Science, Helmholtz Centre for Environmental Research—UFZ, 06120 Halle (Saale), Germany
Axel Don: Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig, Germany

Agriculture, 2024, vol. 14, issue 8, 1-25

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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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