Cropping history, agronomic rules, and commodity prices shape crop rotations across Central Europe

Marlene Palka, Claas Nendel, Lucas Weiß, Josepha Schiller, Clemens Jänicke, Juliana Arbeláez Gaviria and Masahiro Ryo

EconStor Open Access Articles and Book Chapters, 2026, vol. 231, No 104522, 14 pages

Abstract: Context: Crop rotations provide agronomic benefits over monocropping, such as enhanced nitrogen supply, improved weed and pest control, and higher yields. Although the theoretical understanding of optimal rotations has advanced, little is known about their real-world implementation and the factors influencing rotation decisions on large scales. Objective: Understanding these factors is key for projecting future cropping patterns, refining agricultural policy, and improving crop models that often oversimplify rotation practices. This study identifies the drivers influencing operational crop rotations across Central Europe and projects future cropping patterns in the region. Methods: We analyse over 16 million field-year combinations from Germany, Austria, and the Czech Republic. Using a random forest algorithm, we determine feature importance and apply a novel machine learning approach that incorporates uncertainty in farmers' decision-making to provide a potential outlook on cropping patterns until 2070. Results and Conclusions: Historical cropping patterns, agronomic practices, and legume commodity prices significantly shaped crop rotations across the region. Projections indicate a substantial increase in legume cultivation over the coming decades, with implications for nitrogen budgets, dietary transitions, and in-silico upscaling. Significance: Rather than optimizing rotations, this study identifies key drivers of operational crop rotations in Central Europe. The findings provide the basis for large-scale simulations that represent cropping patterns more realistically. To the best of our knowledge, the data set compiled here is the most extensive yet analysed in the context of operational crop rotation management.

Keywords: crop rotation; machine learning; farmer decision-making; Central Europe (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:espost:337501

DOI: 10.1016/j.agsy.2025.104522

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