Effective irrigation and nitrogen fertilization have a significant impact on crop yield. However, existing research faces two limitations: (1) the high complexity of optimizing water-nitrogen combinations during crop growth and poor yield optimization results; and (2) the difficulty in quantifying mild stress signals and the delayed feedback, which results in less precise dynamic regulation of water and nitrogen and lower resource utilization efficiency. To address these issues, we propose a Nested Dual-Agent Reinforcement Learning (NDRL) method. The parent agent in NDRL identifies promising macroscopic irrigation and fertilization actions based on projected cumulative yield benefits, reducing ineffective explorationwhile maintaining alignment between objectives and yield. The child agent's reward function incorporates quantified Water Stress Factor (WSF) and Nitrogen Stress Factor (NSF), and uses a mixed probability distribution to dynamically optimize daily strategies, thereby enhancing both yield and resource efficiency. We used field experiment data from 2023 and 2024 to calibrate and validate the Decision Support System for Agrotechnology Transfer (DSSAT) to simulate real-world conditions and interact with NDRL. Experimental results demonstrate that, compared to the best baseline, the simulated yield increased by 4.7% in both 2023 and 2024, the irrigation water productivity increased by 5.6% and 5.1% respectively, and the nitrogen partial factor productivity increased by 6.3% and 1.0% respectively. Our method advances the development of cotton irrigation and nitrogen fertilization, providing new ideas for addressing the complexity and precision issues in agricultural resource management and for sustainable agricultural development.

翻译：有效的灌溉与氮肥施用对作物产量具有显著影响。然而，现有研究面临两个局限：（1）作物生长过程中水氮组合优化复杂度高，产量优化效果不佳；（2）轻度胁迫信号难以量化且反馈延迟，导致水氮动态调控精度不足，资源利用效率较低。为解决这些问题，我们提出了一种嵌套双智能体强化学习（NDRL）方法。NDRL中的父智能体基于预估的累积产量效益识别有前景的宏观灌溉与施肥操作，在保持目标与产量一致性的同时减少无效探索。子智能体的奖励函数融合了量化的水分胁迫因子（WSF）与氮素胁迫因子（NSF），并采用混合概率分布动态优化每日策略，从而同步提升产量与资源效率。我们利用2023年与2024年的田间试验数据校准并验证了农业技术转移决策支持系统（DSSAT），以模拟真实环境并与NDRL交互。实验结果表明，与最佳基线相比，模拟产量在2023年与2024年均提升了4.7%，灌溉水生产率分别提高了5.6%与5.1%，氮肥偏生产力分别增长了6.3%与1.0%。我们的方法推动了棉花灌溉与施氮管理的发展，为应对农业资源管理的复杂性与精准性问题以及促进农业可持续发展提供了新思路。