Learning-based agricultural management in partially observable
  environments subject to climate variability
Zhaoan Wang Shaoping Xiao Junchao Li Jun Wang
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.