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Irrigated agriculture is the human activity responsible for the highest consumption of water from the environment. The evapotranspiration represents, in practice, the consumption of water by a culture and the quantitative information of this parameter assists in a large number of water management problems. By employing spatial interpolation methods, one can determine evapotranspiration in places where there is no information of this parameter. This work evaluates algorithms for spatial interpolation of evapotranspiration data in terms of precision and performance. It compares conventional strategies as the Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), and machine learning strategies, represented by the Random Forest (RF) and a Random Forest variation for spatial predictions (RFsp). The evaluation uses data of evapotranspiration from automatic meteorological stations located in the northeast …