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\u201310 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\u2019 distribution in Germany.
", "keywords": ["2. Zero hunger", "Agriculture (General)", "04 agricultural and veterinary sciences", "15. Life on land", "carbon fraction", "01 natural sciences", "pedometrics", "S1-972", "multi-target prediction", "regressor chain", "digital soil mapping", "0401 agriculture", " forestry", " and fisheries", "agricultural soils", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2077-0472/14/8/1298/pdf"}, {"href": "https://doi.org/10.3390/agriculture14081298"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agriculture14081298", "name": "item", "description": "10.3390/agriculture14081298", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agriculture14081298"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-06T00:00:00Z"}}, {"id": "10.5281/zenodo.6574801", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:22Z", "type": "Dataset", "title": "Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe", "description": "Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale. The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1; Soil properties were predicted at fixed depths: Surface soil = s0..0cm,