RT Journal Article
T1 Predicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillo
A1 Egipto, Ricardo
A1 Aquino Martín, Arturo
A1 Costa, Joaquim Miguel
A1 Andújar Márquez, José Manuel
AB This 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.
PB MDPI
SN 2073-4395 (electrónico)
YR 2023
FD 2023-09
LK https://hdl.handle.net/10272/22525
UL https://hdl.handle.net/10272/22525
LA eng
NO Egipto, 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/agronomy13102463
NO We acknowledge FCT Research Unit “GREEN-IT-Bioresources for Sustainability”(UIDB/04551/2020 and UIDP/04551/2020) for financial support. We also thank the support ofthe research units CITES, Centro de Investigación en Tecnología, Energía y Sostenibilidad, Universidadde Huelva, and LEAF (UID/AGR/04129/2019). We also address our acknowledgements toHerdade do Esporão (Reguengos de Monsaraz, Alentejo, PT) and Rui Flores for their contribution tofield management of the experimental vineyard.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026