Research Article Open Access

Validation of the AquaCrop Model for Full and Deficit Irrigated Potato Production in Environmental Condition of Kor�a Zone, South-eastern Albania

Abstract

Simulation models that quantify the effects of water on yield at the farm level are valuable tools in water and irrigation management. To address this need, FAO has developed a yield-response to water model, named AquaCrop model, which simulates attainable yields of the major field and vegetable crops cultivated worldwide. Although the model is simple, it gives particular attention to the fundamental processes involved in crop productivity and in the responses to water, from a physiological and agronomic background perspective. The objectives of this study were to evaluate the AquaCrop model for its ability to simulate potato performance under full and deficit water conditions in a semi-arid environment in southeaster Albania. The ease of use of the model, the low requirement of input parameters, and its sufficient degree of simulation accuracy make it a valuable tool for estimating crop productivity under rainfed conditions, supplementary and deficit irrigation, and on-farm water management strategies for improving the efficiency of water use in agriculture. A set of conservative parameters calibrated and validated for potato in a prior study and considered applicable to a wide range of conditions and not specific to a given potato cultivar, are used to further evaluate the performance of AquaCrop model for potato using data from environmental conditions of Korça zone. The verification test shows that the model slightly overestimates canopy cover, and biomass. WP values of 31,6 g m−2 was considered to evaluate the model performance. While linear function between observed tuber yields and estimated by AquaCrop had always a correlation coefficient greater than 0.85 (p < 0.001). The AquaCrop model was able to accurately simulate soil water content of root zone, crop biomass and grain yield, with normalized root mean square error (RMSE) less than 10%..

MSc. M. Bitri, Prof. S. Grazhdani, A. Ahmeti

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