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.

翻译：长期农业规划需在复杂空间异质性、时间尺度农学依赖及多源环境不确定性下优化作物配置。现有方法要么仅隐式处理作物间相互作用（如豆科与禾谷类作物的互补性），要么依赖静态确定性模型，难以保障对市场与气候波动的鲁棒性。为此，我们提出多层鲁棒作物规划框架（MLRCPF），该框架融合空间推理、时间动态分析与鲁棒优化技术。具体而言，我们通过嵌入状态转移逻辑的结构化交互矩阵形式化描述作物间关系，并采用基于数据驱动模糊集的分布鲁棒优化层来化解最坏情景风险。基于华北地区高混合种植真实数据集的评估表明：该框架能自主生成恢复土壤肥力的可持续棋盘式轮作模式，相较于确定性基准模型显著提升豆科作物种植比例；经济层面成功实现了最优性与稳定性的平衡。结果凸显了在复杂农业系统的韧性决策中，将领域结构化先验知识显式编码至优化模型的关键价值。