Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.3390/agriculture14081298 |
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| Title (Primary) | Spatial prediction of organic matter quality in German agricultural topsoils |
| Author | Sakhaee, A.; Scholten, T.; Taghizadeh-Mehrjardi, R.; Ließ, M.; Don, A. |
| Source Titel | Agriculture-Basel |
| Year | 2024 |
| Department | BOSYS |
| Volume | 14 |
| Issue | 8 |
| Page From | art. 1298 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.3220/DATA20200203151139 |
| Supplements | https://www.mdpi.com/article/10.3390/agriculture14081298/s1 |
| Keywords | pedometrics; digital soil mapping; multi-target prediction; regressor chain; carbon fraction; agricultural soils |
| 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. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29518 |
| Sakhaee, A., Scholten, T., Taghizadeh-Mehrjardi, R., Ließ, M., Don, A. (2024): Spatial prediction of organic matter quality in German agricultural topsoils Agriculture-Basel 14 (8), art. 1298 10.3390/agriculture14081298 |
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