Krishna Panthi

Crop modelling is essential for agricultural water management but often relies on simplified water balance routines that limit representation of soil moisture dynamics. To address this limitation, we developed a coupled model that integrates the 1‐D Richards equation, solved using a finite difference method into the FAO AquaCrop. The coupled model was calibrated and validated using soil moisture, canopy cover, above‐ground biomass and seed cotton yield data from field experiments in the southeastern United States. Compared with hourly field measurements of soil moisture, AquaCrop-Richards achieved an average root mean square error (RMSE) of 0.023 m³ m⁻³ across three soil depths over the growing season. Model performance for canopy cover, biomass and yield resulted in RMSE values of 12.18%, 1.77 t ha⁻¹ and 0.96 t ha⁻¹, respectively, against observations. Under fully irrigated conditions, both models produced statistically indistinguishable yield estimates. However, under rainfed conditions, AquaCrop simulated 15.5% higher yields than AquaCrop-Richards. Analysis showed that AquaCrop produced rapid stepwise drainage, resulting in root‐zone water content 33%-37% lower than the coupled model. This reduced soil moisture triggered earlier water stress which led to yield overestimation. These results indicate that AquaCrop‐Richards improves soil moisture representation and is robust under water‐limited conditions.

Cotton is a one of the major crops in the southeastern United States. It significantly impacts regional water resources since it consumes a large amount of freshwater for irrigation. Current irrigation practices fail to optimize water use accurately since they are largely dependent on soil moisture sensors and grower experience. They do not consider dynamic factors such as soil texture, prevailing weather conditions, and the crop's phenological stage. In this paper we propose an innovative approach to enhance the irrigation efficiency through the use of Deep Reinforcement Learning (DRL) model. It takes into consideration the dynamic variables and optimizes irrigation. We utilize a crop growth simulation model as a learning environment to devise an optimal irrigation strategy. By continuously learning from crop feedback and environmental inputs, the DRL system dynamically modifies irrigation amount to optimize production while consuming the least amount of water. Our approach presents a viable alternative for sustainable irrigation decisions in water-intensive crops, since preliminary findings indicate that it can greatly conserve water without sacrificing crop health or productivity. The goal of this research is to aid in the advancement of precision irrigation technologies that guarantee cotton production's sustainability and resource efficiency.