NDRL: Cotton Irrigation and Nitrogen Application with Nested Dual-Agent Reinforcement Learning

TL;DR

This paper introduces NDRL, a nested dual-agent reinforcement learning approach that optimizes cotton irrigation and nitrogen application, significantly improving yield and resource efficiency through dynamic, data-driven decision-making.

Contribution

The paper presents a novel nested dual-agent reinforcement learning framework that effectively addresses the complexity and delayed feedback issues in agricultural resource management.

Findings

Yield increased by 4.7% in 2023 and 2024

Irrigation water productivity increased by over 5%

Nitrogen productivity improved with up to 6.3% increase

Abstract

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.