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The primary natural source of water for the Hass avocado crop in the tropics is precipitation. However, this is insufficient to provide most crops' water requirements due to the spatial and temporal variability. This study aims to demonstrate that Hass avocado requires irrigation in Colombia, and this is done by analyzing the dynamics of local precipitation regimes and the influence of Intertropical Convergence Zone phenomena (ITCZ) on the irrigation requirement (IR). This study was carried out in Colombia's current and potential Hass avocado production zones (PPA) by computing and mapping the monthly IR, and classifying months found to be in deficit and excess. The influence of ITCZ on IR by performing a metric relevance analysis on weights of optimized Artificial Neural Networks was computed. The water deficit map illustrates a 99.8% of PPA requires water irrigation at least one month a year. The movement of ITCZ toward latitudes far to those where PPA is located between May to September decreases precipitation and consequently increases the IR area of Hass avocado. Water deficit visualization maps could become a novel and powerful tool for Colombian farmers when scheduling irrigation in those months and periods identified in these maps. [ABSTRACT FROM AUTHOR]

This study aims to determine whether Hass avocado irrigation can be triggered based on the surface soil water content (SSWC). To address this question, the soil water dynamics from three Hass avocado orchard plots located in Valle del Cauca (Colombia) was simulated using a model provided by Hydrus-1D software, which was calibrated through the genetic algorithm NSGA-II and validated using the soil matric potential measured at several depths at nine monitoring stations installed in the three plots. The influence of each superficial (0–5, 5–10, and 10–15 cm) and deeper (15–30 and 30–60 cm) available water (AW) computed from the simulated moisture on the SWB at 0–60 cm was estimated from the artificial neural network (ANN) trained weights. The most influential depth range was used to predict the soil water balance at 0–60 cm using ANN. For validation, the RMSE slightly increased regarding calibration, varying from 1.73 to 8.20, while the R2 value varied from 0.61 to 0.89 (P < 0.001 for all cases). The AW at 5–10 cm depth had a significant influence on SWB with an average relevance index of 2.87 (Wilcoxon signed-rank test P ≤ 0.05) for Laurentina farm. The AW at 0–5 cm depth had not significant influence on SWB with an average relevance index of 1.34 (independent group) and 0.97 (P < 0.05) for Laurentina and Poncena, respectively. The ANN model predicted the SWB with a RMSE no bigger than 13.76 mm. In conclusion, the SSWC at a depth of 5–10 cm can be used as an indicator for scheduling Hass avocado irrigation. • Irrigation and precipitation modify differently the soil matric potential. • Water changes significantly in the top 15 cm of soils cropped with Hass avocado. • Soil water at 5–10 cm depth range influences the avocado irrigation scheduling. • Soil water in the top 5 cm does not influence the avocado irrigation scheduling. • Genetic algorithm NSGA-II improves the performance of Hydrus 1D calibration. [ABSTRACT FROM AUTHOR]