文献类型：期刊文献

英文题名：Robust Crop Planning under Uncertainty: Aligning Economic Optimality with Agronomic Sustainability

作者：Liu, Runhao[1]; Chen, Ziming[2]; Li, You[3]; Zhang, Peng[4]

机构：[1] Polytechnic Institute, Zhejiang University, Hangzhou, 310015, China; [2] School of Social and Public Administration, East China University of Science and Technology, Shanghai, 200237, China; [3] School of Data Science and Engineering, Guangdong Polytechnic Normal University, Guangdong, Guangzhou, 510665, China; [4] School of Mathematical Sciences, Zhejiang University, Hangzhou, 310058, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20260017099)

语种：英文

外文关键词：Agronomy - Crops - Decision making - Economic and social effects - Optimization - Smart agriculture - Sustainable development

摘要：Long-horizon agricultural planning requires optimizing crop allocation under complex spatial heterogeneity, temporal agronomic dependencies, and multi-source environmental uncertainty. Existing approaches often treat crop interactions, such as legume-cereal complementarity, which implicitly or rely on static deterministic formulations that fail to guarantee resilience against market and climate volatility. To address these challenges, we propose a Multi-Layer Robust Crop Planning Framework (MLRCPF) that integrates spatial reasoning, temporal dynamics, and robust optimization. Specifically, we formalize crop-to-crop relationships through a structured interaction matrix embedded within the state-transition logic, and employ a distributionally robust optimization layer to mitigate worst-case risks defined by a data-driven ambiguity set. Evaluations on a real-world high-mix farming dataset from North China demonstrate the effectiveness of the proposed approach. The framework autonomously generates sustainable checkerboard rotation patterns that restore soil fertility, significantly increasing the legume planting ratio compared to deterministic baselines. Economically, it successfully resolves the trade-off between optimality and stability. These results highlight the importance of explicitly encoding domain-specific structural priors into optimization models for resilient decision-making in complex agricultural systems. Copyright ? 2025, The Authors. All rights reserved.