  Nitrogen (N) management is critical to sustain soil fertility and crop
production while minimizing the negative environmental impact, but is
challenging to optimize. This paper proposes an intelligent N management system
using deep reinforcement learning (RL) and crop simulations with Decision
Support System for Agrotechnology Transfer (DSSAT). We first formulate the N
management problem as an RL problem. We then train management policies with
deep Q-network and soft actor-critic algorithms, and the Gym-DSSAT interface
that allows for daily interactions between the simulated crop environment and
RL agents. According to the experiments on the maize crop in both Iowa and
Florida in the US, our RL-trained policies outperform previous empirical
methods by achieving higher or similar yield while using less fertilizers
