Reinforcement Learning-Based Agricultural Fertilization and Irrigation Considering N2O Emissions and Uncertain Climate Variability

Zhaoan Wang; Shaoping Xiao; Jun Wang; Ashwin Parab; Shivam Patel

doi:10.3390/agriengineering7080252

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Reinforcement Learning-Based Agricultural Fertilization and Irrigation Considering N2O Emissions and Uncertain Climate Variability

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Reinforcement Learning-Based Agricultural Fertilization and Irrigation Considering N2O Emissions and Uncertain Climate Variability

Zhaoan Wang, Shaoping Xiao, Jun Wang, Ashwin Parab and Shivam Patel

AgriEngineering, Vol.7(8), 252

08/07/2025

DOI: 10.3390/agriengineering7080252

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Abstract

Nitrous oxide (N2O) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly N2O emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate N2O emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce N2O emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings.

agricultural management

reinforcement learning

partially observable environments

soil N2O emission

climate variability

uncertainty

Details

Title: Subtitle: Reinforcement Learning-Based Agricultural Fertilization and Irrigation Considering N2O Emissions and Uncertain Climate Variability
Creators: Zhaoan Wang - University of Iowa
Shaoping Xiao - University of Iowa
Jun Wang - University of Iowa
Ashwin Parab - Purdue University West Lafayette
Shivam Patel - University of Illinois Urbana-Champaign
Resource Type: Journal article
Publication Details: AgriEngineering, Vol.7(8), 252
DOI: 10.3390/agriengineering7080252
ISSN: 2624-7402
eISSN: 2624-7402
Publisher: MDPI
Grant note: U.S. Department of EducationNational Science Foundation
This material is based upon work supported by the U.S. Department of Education under Grant Number ED#P116S210005 and the National Science Foundation under Grant Numbers 2226936 and 2420405. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Department of Education and the National Science Foundation.
Language: English
Date published: 08/07/2025
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering; Mechanical Engineering
Record Identifier: 9984946610202771

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