# Learning-based agricultural management in partially observable environments subject to climate variability

arXiv: https://arxiv.org/abs/2401.01273 · HTML: https://mlsanity.com/paper/2401.01273/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [CropGym: a Reinforcement Learning Environment for Crop Management](https://mlsanity.com/paper/2104.04326/) (2021)
- [Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations](https://mlsanity.com/paper/2204.10394/) (2022)
- [gym-DSSAT: a crop model turned into a Reinforcement Learning environment](https://mlsanity.com/paper/2207.03270/) (2022)
- [Optimizing Crop Management with Reinforcement Learning and Imitation Learning](https://mlsanity.com/paper/2209.09991/) (2022)

## Built on by (most cited first, 3 of 3)

- [Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties](https://mlsanity.com/paper/2402.08832/) (2024)
- [Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management](https://mlsanity.com/paper/2505.10803/) (2025)
- [Reinforcement learning with timed constraints for robotics motion planning](https://mlsanity.com/paper/2601.00087/) (2025)

## Abstract (arXiv)

Agricultural management, with a particular focus on fertilization strategies, holds a central role in shaping crop yield, economic profitability, and environmental sustainability. While conventional guidelines offer valuable insights, their efficacy diminishes when confronted with extreme weather conditions, such as heatwaves and droughts. In this study, we introduce an innovative framework that integrates Deep Reinforcement Learning (DRL) with Recurrent Neural Networks (RNNs). Leveraging the Gym-DSSAT simulator, we train an intelligent agent to master optimal nitrogen fertilization management. Through a series of simulation experiments conducted on corn crops in Iowa, we compare Partially Observable Markov Decision Process (POMDP) models with Markov Decision Process (MDP) models. Our research underscores the advantages of utilizing sequential observations in developing more efficient nitrogen input policies. Additionally, we explore the impact of climate variability, particularly during extreme weather events, on agricultural outcomes and management. Our findings demonstrate the adaptability of fertilization policies to varying climate conditions. Notably, a fixed policy exhibits resilience in the face of minor climate fluctuations, leading to commendable corn yields, cost-effectiveness, and environmental conservation. However, our study illuminates the need for agent retraining to acquire new optimal policies under extreme weather events. This research charts a promising course toward adaptable fertilization strategies that can seamlessly align with dynamic climate scenarios, ultimately contributing to the optimization of crop management practices.