Predicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillo

Egipto, RicardoAquino Martín, ArturoCosta, Joaquim MiguelAndújar Márquez, José Manuel2023-10-062023-10-062023-09Egipto, R., Aquino, A., Costa, J. M., & Andújar, J. M. (2023). Predicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillo. In Agronomy (Vol. 13, Issue 10, p. 2463). MDPI AG. https://doi.org/10.3390/agronomy131024632073-4395 (electrónico)https://hdl.handle.net/10272/22525This study focuses on assessing the accuracy of supervised machine learning regression algorithms (MLAs) in predicting actual crop evapotranspiration (ETc act) for a deficit irrigated vineyard of Vitis vinifera cv. Tempranillo, influenced by a typical Mediterranean climate. The standard approach of using the Food and Agriculture Organization (FAO) crop evapotranspiration under standard conditions (FAO-56 Kc-ET0) to estimate ETc act for irrigation purposes faces limitations in row-based, sparse, and drip irrigated crops with large, exposed soil areas, due to data requirements and potential shortcomings. One significant challenge is the accurate estimation of the basal crop coefficient (Kcb), which can be influenced by incorrect estimations of the effective transpiring leaf area and surface resistance. The research results demonstrate that the tested MLAs can accurately estimate ETc act for the vineyard with minimal errors. The Root-Mean-Square Error (RMSE) values were found to be in the range of 0.019 to 0.030 mm·h⁻¹. Additionally, the obtained MLAs reduced data requirements, which suggests their feasibility to be used to optimize sustainable irrigation management in vineyards and other row crops. The positive outcomes of the study highlight the potential advantages of employing MLAs for precise and efficient estimation of crop evapotranspiration, leading to improved water management practices in vineyards. This could promote the adoption of more sustainable and resource-efficient irrigation strategies, particularly in regions with Mediterranean climates.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/GrapevineVineyard irrigationActual crop evapotranspirationMachine learning algorithmsDecision support systemsPredicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillojournal article10.3390/agronomy13102463open access31 Ciencias Agrarias3309 Tecnología de Los Alimentos