Evaluating the Performance of AquaCrop Model for Potato Production Under Deficit Irrigation

Crop modeling is a powerful tool for estimating yield and water use efficiency, and it plays an important role in determining water management strategies. Under the condition of scarce water supply and drought, deficit irrigation can lead to greater economic gains by maximizing yield per unit of water. Studies have shown that deficit irrigation significantly increased yield, crop evapotranspiration, and water use efficiency as compared to full irrigation requirement. However, this approach requires precise knowledge of crop response to water as drought tolerance varies considerably by growth stage, species and cultivars. This study was conducted in Lasta district, for two successive years to evaluate the effects of water shortage on potato production and water use efficiency, as well as to test the AquaCrop model for potato-producing areas. The irrigation water levels for potatoes were 100%, 75%, and 50% of crop evapotranspiration (ETc). Six treatments were arranged using a randomized complete block design. Climate, soil, and crop data were calibrated using observed weather parameters, and measured crop parameters conducted in the 2018/19 growing season. The model was validated using the observed data conducted in the 2019/20 growing season. The calibration of the model revealed a good fit for canopy cover (CC) with a coefficient of determination (R-2) = .98, Root mean square error (RMSE) = 9.6%, Nash-Sutcliffe efficiency (E) = 0.92, index of agreement (d) = 0.98, and coefficient of residual moss (CRM) = -0.07, and good prediction for biomass (R-2 = .98, RMSE = 1.8 t ha(-1), E = 0.96, d = 0.99, CRM = -0.13). Similarly, the validation result showed good fit for CC by 100% water application at development and mid growth season and a 75% water applied at the other stages (R-2 = .98, RMSE = 9.4%, E = 0.94, d = 0.98, CRM = -0.12). The AquaCrop model is simple to use, requires fewer input data, and has a high level of simulation precision, making it a useful tool for forecasting crop yield under deficit irrigation and water management to increase agricultural water efficiency in data-scarce areas.