Crop management involves a series of critical, interdependent decisions or actions in a complex and highly uncertain environment, with distinct spatial and temporal variations. The ability of machine learning to efficiently interrogate complex, nonlinear, and high-dimensional datasets can revolutionize decision making in agriculture. In this paper we introduce a reinforcement learning (RL) environment/simulator that leverages the dynamics in the Texas A&M Soil and Water Assessment Tool (SWAT). We consider crop management as an optimization problem where the objective is to produce higher crop yield while minimizing the use of external farming inputs (specifically, fertilizer and irrigation amounts). This is naturally subject to environmental factors such as precipitation, solar radiation, temperature, and soil water content. Controlling for these impacts ultimately contribute to a reduction in the carbon footprint of the entire process, which is central to sustainable agriculture.