  This study examines how artificial intelligence (AI), especially
Reinforcement Learning (RL), can be used in farming to boost crop yields,
fine-tune nitrogen use and watering, and reduce nitrate runoff and greenhouse
gases, focusing on Nitrous Oxide (N$_2$O) emissions from soil. Facing climate
change and limited agricultural knowledge, we use Partially Observable Markov
Decision Processes (POMDPs) with a crop simulator to model AI agents'
interactions with farming environments. We apply deep Q-learning with Recurrent
Neural Network (RNN)-based Q networks for training agents on optimal actions.
Also, we develop Machine Learning (ML) models to predict N$_2$O emissions,
integrating these predictions into the simulator. Our research tackles
uncertainties in N$_2$O emission estimates with a probabilistic ML approach and
climate variability through a stochastic weather model, offering a range of
emission outcomes to improve forecast reliability and decision-making. By
incorporating climate change effects, we enhance agents' climate adaptability,
aiming for resilient agricultural practices. Results show these agents can
align crop productivity with environmental concerns by penalizing N$_2$O
emissions, adapting effectively to climate shifts like warmer temperatures and
less rain. This strategy improves farm management under climate change,
highlighting AI's role in sustainable agriculture.
