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]; Li, You[2]; Chen, Ziming[3]; Zhang, Peng[4]

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

年份：2026

卷号：36

期号：1

起止页码：509

外文期刊名：Proceedings International Conference on Automated Planning and Scheduling, ICAPS

收录：EI(收录号：20262520960337)

语种：英文

外文关键词：Agribusiness - Agronomy - Crop rotation - Decision making - Optimization - Smart agriculture - Sustainable agriculture

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 either address crop interactions, such as legume-cereal complementarity, only implicitly or rely on static deterministic formulations that fail to ensure 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. ? 2026, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.