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.

Under tropical conditions, Hass avocado irrigation is a controversial issue due to insufficient scientific evidence. The rapid progression of technological advances and its incorporation in agriculture have expanded options to improve the irrigation scheduling (IS) of Hass avocado. The concept featuring those technological advances in agriculture is digital agriculture (DA). Here, we present a mixture of well-known studies in the Hass avocado irrigation focused on proximal sensing (PS) technologies and recent studies emphasizing the potential of remote sensing (RS), and application technologies to schedule the irrigation. PS takes advantage of the soil or trees' proximity to output reliable measurements with a high temporal resolution, while RS provides a broad set of spectral data in continuous and large areas that can be transformed into crop-related biophysical variables. Applications – a term grouping mobile (smartphone) apps, desktop programs, and web-based platforms – offers portability, high precision, and graphic visualization of variables obtained or estimated by sensors. Integrating RS and PS technologies through user-friendly applications can represent a suitable option to improve Hass avocado irrigation in developing countries. Our review is presented in the following sections: general introduction, DA approach definition, use of proximal sensing, use of remote sensing, and scheduling irrigation applications.

As the Hass avocado crop expands exponentially in Colombia, concern about its increasing water use is on the rise. This research aimed to develop IS-SAR, a free-access web application to schedule irrigation for Valle del Cauca’s Hass growers. We calibrated the water cloud (WCM) and artificial neural network (ANN) models using field data measurements from Hass avocado orchard plots in Valle del Cauca (Colombia) and Sentinel 1 (S1) satellite imagery measurements and evaluated their performance computing the root-mean-square error (RMSE) and Pearson correlation coefficient ( $$r$$r ). IS-SAR estimates the surface soil water content from the most recent S1 image, becomes it in water depth, and recommends to users apply irrigation according to allowable depletion limits, computed from a spatially distributed $$\theta$$θ at field capacity and permanent wilting point obtained from a soil database of the study area. Our results indicate that the surface soil water content was retrieved with a better performance by the ANN (RMSE = 0.05 m³ m⁻³, $$r=0.74$$r = 0.74 ), compared with the WCM (RMSE = 0.06 m³ m⁻³, $$r=0.47$$r = 0.47 ). IS-SAR simulations in validation orchard plots result in irrigation events of up to 107 L tree⁻¹ for 3.4 h. The IS-SAR web application provides near-real-time irrigation information to assist Hass avocado growers in designing better irrigation routines and improving the regional understanding of water crop consumption.

Reliable estimates of volumetric water content at field capacity (θFC) are important inputs for irrigation scheduling because θFC contributes to the estimation of plant-available water, depletion thresholds, and refill targets. In irrigated systems, θFC is therefore an operational decision variable rather than a fixed soil property. However, θFC varies systematically across estimation methods, introducing uncertainty into irrigation management. This study evaluated method-dependent differences in θFC for irrigated tropical soils in the Roldanillo–Unión–Toro agricultural irrigation district (Valle del Cauca, Colombia). Field capacity was estimated at 42 sampling points (0–0.10 m depth) using four methods: Mariotte bottle (MB), filter paper (FP), a pedotransfer function (PTF), and the Richards pressure plate method (RPP). The RPP method was used as an operational reference for comparative purposes, not as an absolute representation of true FC. Agreement and bias were assessed using descriptive statistics, error metrics, regression, Bland–Altman analysis, and texture-stratified comparisons. RPP θFC averaged 39.37% (range: 29.85–46.41%), whereas MB, FP, and PTF produced higher mean values of 42.66%, 44.26%, and 46.38%, respectively. Relative to RPP, mean error and root mean square error increased from MB (3.29% and 5.21%) to FP (4.89% and 8.16%) and PTF (7.01% and 10.82%). Disagreement also varied with soil texture. These results show that low-cost θFC methods are not directly interchangeable with RPP measurements in the evaluated surface layer. Because θFC is commonly used in irrigation calculations, the observed method-dependent differences may affect the estimation of depletion thresholds and refill targets if surface-layer values are extrapolated without local validation. Overall, surface-layer θFC in the Roldanillo–Unión–Toro irrigation district was strongly method-dependent, highlighting the need to account for method-related uncertainty before using alternative θFC estimates as inputs for irrigation decision support.

La variabilidad espacial del suelo es un factor importante para entender los cambios de las variables de respuesta en experimentos agrícolas. El muestreo de suelos se ejecuta con base en un patrón espacial, el cual puede ser aleatorio o sistemático. El objetivo de este trabajo fue validar un nuevo algoritmo para generar patrones espaciales de muestreo de suelos en este contexto. Para esto se diseñaron tres funciones en el software R, las cuales fueron comparadas con cinco aplicaciones (tres programas y dos librerías de R). La validación se realizó replicando tres patrones espaciales de suelos en experimentos agrícolas reportados en investigaciones anteriores, además de comparar la localización manual de puntos de muestreo en un experimento de cosecha de caña de azúcar con la localización generada por el algoritmo. Los resultados indican que el algoritmo tiene la capacidad exclusiva de realizar muestreos sistemáticos por unidad de área y centrarlos en el polígono correspondiente. El resto de las características, tales como el cálculo de los demás patrones y la generación de puntos sobre líneas, es posible encontrarlas en las otras aplicaciones. Con respecto a la validación en campo, la distancia promedio entre puntos generados con el algoritmo y los ubicados manualmente en campo es 2,58 m. La distancia promedio entre los puntos ubicados manualmente en campo y la línea de surco más cercana es 0,46 m. En conclusión, el algoritmo permite ubicar puntos de muestreo en sitios específicos del campo, como lo son las partes altas del surco o el entresurco.