Simulating crop yields and water productivity for three cotton-based cropping systems in the Texas High Plains

Abstract

Implementing appropriate cropping systems suited to specific soil types and climatic conditions is crucial for improving crop yield and conserving water in semi-arid environments. The Decision Support System for Agrotechnology Transfer (DSSAT) was applied to simulate crop yields of cotton, sorghum, and winter wheat across three cropping systems, including continuous cotton, cotton–sorghum, and cotton–wheat. Simulations were conducted for 48 fields with various soil types across six counties in the Texas High Plains, spanning growing seasons from 2000 to 2022. Cotton water productivity, derived from DSSAT-simulated cotton yield and crop evapotranspiration (ET), was compared among these cropping systems. The DSSAT demonstrated good performance (R² ​≥ ​0.79, nRMSE ​≤ ​15.74%, and d-index ​≥ ​0.95) in predicting yields of cotton, sorghum, and winter wheat. The CROPGRO-Cotton model showed slightly better accuracy in predicting cotton yield under the continuous cotton system than under the cotton–sorghum and cotton–wheat systems. Model performance was similar across different soil types, with slightly higher accuracy in fine-textured soils such as clay loam (R² ​≥ ​0.84, MAPE ​= ​12.35, and d-index ​= ​0.95) than in other soils (R² ​≤ ​0.82, MAPE ​≥ ​13.76, and d-index ​≤ ​0.94). Additionally, the model performance varied by season, showing high accuracy in years with adequate precipitation but generally underpredicting cotton yields in drought seasons. Among the three cropping systems, cotton yield and water productivity were the highest for the cotton–sorghum system (6.3 ​kg ​ha⁻¹ ​mm⁻¹), followed by the cotton–wheat and continuous cotton systems. Overall, the DSSAT models effectively captured the effects of management practices, soil types, and growing seasons in predicting crop yield and crop water productivity across three cotton-based cropping systems. The findings provide valuable information for decision support in adopting cropping systems across various soil types and environmental conditions, fostering sustainable agriculture and water conservation in semi-arid regions.