MODELING DEFICIT IRRIGATION STRATEGIES FOR ALFALFA IN NORTHERN NEVADA
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Authors
Khushi, Fnu
Issue Date
2025
Type
Thesis
Language
en_US
Keywords
Alfalfa , Calibration , Crop growth model , Decision Support System for Agrotechnology Transfer , Deficit irrigation , Perennial forage model
Alternative Title
Abstract
Water scarcity in the western United States has intensified the need for irrigation strategies that sustain alfalfa yield while conserving limited water resources. This study aimed to calibrate and evaluate the Cropping System Model (CROPGRO) Perennial Forage Model to simulate alfalfa growth and yield under full and deficit irrigation management in northern Nevada. A comprehensive, multi-year (2021-2023) and multi-harvest dataset was developed from a field experiment conducted at the University of Nevada, Reno’s Valley Road Field Laboratory, using two alfalfa cultivars and three irrigation treatments. The dataset integrates detailed observations of weather, soil, irrigation, crop growth, and yield. A sensitivity analysis using a one-at-a-time approach identified three parameters as the most influential in driving the variability of simulated yield. These parameters were optimized using a two-stage genetic algorithm approach implemented in the Python programming language. The calibrated model achieved good performance with a Root Mean Square Error (RMSE) of 715 kg/ha, a coefficient of determination (R²) of 0.88, and a normalized Root Mean Square Error (nRMSE) of 16.5 % for the first two growing seasons (2021-2022) that were used for calibration, and acceptable accuracy for the third growing season (2023) (RMSE = 1695 kg/ha, R² = 0.92, nRMSE = 39.5 %) that was used to evaluate the calibrated model. The model successfully reproduced multi-harvest yield patterns and irrigation responses, demonstrating its capability to simulate alfalfa performance under arid and semi-arid conditions. This work lays the foundation for developing a regional Alfalfa yield forecasting tool that can be used to optimize irrigation scheduling and enhance water-use efficiency.