Temporal and spatial crop diversity are related and affected by farm and landscape configurations

Josepha Schiller, Clemens Jänicke, Moritz Reckling and Masahiro Ryo

EconStor Open Access Articles and Book Chapters, 2026, vol. 234, No 104685, 13 pages

Abstract: CONTEXT: Numerous studies underscore the importance of temporal and spatial diversification in cropping systems for enhancing agricultural resilience under growing uncertainty. OBJECTIVE: Although positive effects of crop diversification have been widely reported, the factors influencing temporal and spatial crop diversity remain largely unknown. METHODS: We address this gap by analyzing spatiotemporal crop diversity patterns across more than 5500 farms with up to 170,000 fields in Germany, and associating them with farm, edaphic, topographic, and landscape attributes. Using Pearson's correlation, principal component analysis, and interpretable machine learning, we assess links between spatial and temporal crop diversity and identify predictors. RESULTS AND CONCLUSIONS: Landscapes with high spatial diversity also tend to have higher temporal diversity, indicating moderate positive links (r = 0.41 and 0.49). Crop diversity was expressed along two gradients: overall diversity in time and space (explaining 58.9% variance) and a contrast between higher spatial or temporal diversity (explaining 21.6% variance). Farm-level diversity strongly predicted both gradients (mean variable importance R² ≈ 0.47 and ≈ 0.14). Higher spatial diversity was also linked with an increasing number of farms, while higher temporal diversity was linked with decreasing landscape configuration. SIGNIFICANCE: Our study indicates that at the landscape level, there is a co-occurrence between more diverse crop rotations and more diverse crop mosaics, explained by the number of farms, farm crop portfolio, and landscape configurations. Future studies should cover a broader geographic extent across Europe to confirm the generalizability of our findings to understand how agricultural diversity is shaped in space and time.

Keywords: diverse cropping systems; machine learning; landscape complexity; explainable artificial intelligence; crop rotations (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:espost:339396

DOI: 10.1016/j.agsy.2026.104685

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