TY - GEN A1 - Schiller, Josepha A1 - Jänicke, Clemens A1 - Reckling, Moritz A1 - Ryo, Masahiro T1 - Higher crop rotational diversity in more simplified agricultural landscapes in Northeastern Germany T2 - Landscape Ecology N2 - Context Both crop rotational diversity and landscape diversity are important for ensuring resilient agricultural production and supporting biodiversity and ecosystem services in agricultural landscapes. However, the relationship between crop rotational diversity and landscape diversity is largely understudied. Objectives We aim to assess how crop rotational diversity is spatially organised in relation to soil, climate, and landscape diversity at a regional scale in Brandenburg, Germany. Methods We used crop rotational richness, Shannon’s diversity and evenness indices per field per decade (i.e., crop rotational diversity) as a proxy for agricultural diversity and land use and land cover types and habitat types as proxies for landscape diversity. Soil and climate characteristics and geographical positions were used to identify potential drivers of the diversity facets. All spatial information was aggregated at 10 × 10 km resolution, and statistical associations were explored with interpretable machine learning methods. Results Crop rotational diversity was associated negatively with landscape diversity metrics and positively with soil quality and the proportion of agricultural land use area, even after accounting for the other variables. Conclusion Our study indicates a spatial trade-off between crop and landscape diversity (competition for space), and crop rotations are more diverse in more simplified landscapes that are used for agriculture with good quality of soil conditions. The respective strategies and targets should be tailored to the corresponding local and regional conditions for maintaining or enhancing both crop and landscape diversity jointly to gain their synergistic positive impacts on agricultural production and ecosystem management. KW - Cropping systems KW - Explainable artificial intelligence KW - Land use KW - Landscape heterogeneity KW - Multiple scales KW - Soil quality Y1 - 2024 U6 - https://doi.org/10.1007/s10980-024-01889-x SN - 1572-9761 VL - 39 IS - 4 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Palka, Marlene A1 - Nendel, Claas A1 - Weiß, Lucas A1 - Schiller, Josepha A1 - Jänicke, Clemens A1 - Gaviria, Juliana Arbeláez A1 - Ryo, Masahiro T1 - Cropping history, agronomic rules, and commodity prices shape crop rotations across Central Europe T2 - Agricultural systems N2 - 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. KW - Crop rotation KW - Machine learning KW - Farmer decision-making KW - Central Europe Y1 - 2026 U6 - https://doi.org/10.1016/j.agsy.2025.104522 SN - 0308-521X VL - 231 SP - 1 EP - 14 PB - Elsevier BV CY - Amsterdam ER -