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.
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.
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