Coordinate descent based agricultural model calibration and optimized input management

Abstract

Well-calibrated agricultural system model with many parameters is critical for optimized agriculture management and decision making. Here, Root Zone Water Quality Model (RZWQM) was calibrated automatically using Coordinate Descent (CD) algorithm against measured data from a fully irrigated corn field in terms of yield, plant height, leaf area index, evapotranspiration, and soil water content. Fifty-six soil hydraulic and three crop parameters were calibrated. The CD calibrated model was validated against data from adjacent deficit irrigated field. Average R2 (Coefficient of Determination) measure was found to be 0.77 (against 0.74 for prior works) and average ME (Model Efficiency) was 0.64 (against 0.61 for prior works). Once calibrated, fertilization and irrigation decisions were optimized so that farm profit is maximized. Three global optimization methods, namely, Differential-Evolution, Basin-Hopping, and Particle-Swarm and two local optimization methods, namely, Sequential-Least-Square and Constrained-optimization-by-linear-approximation were implemented. These methods increased the yield by 7% and profit by 10% as compared to what was applied in the field.