Diffuse agricultural pollution is a leading contributor to surface water degradation, particularly in regions undergoing rapid land use change and agricultural intensification. In many developing countries, conventional assessment approaches fall short of capturing the spatial complexity and cumulative nature of multiple environmental drivers that influence surface water vulnerability. This study addresses this gap by introducing the Integral Index of Vulnerability to Diffuse Contamination (IIVDC), a spatially explicit, multi-criteria framework that combines the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS). The IIVDC integrates six key indicators—slope, soil erodibility, land use, runoff potential, hydrological connectivity, and observed water quality—weighted through expert elicitation and mapped at high spatial resolution. The methodology was applied to the Guachal River watershed in Valle del Cauca, Colombia, where agricultural pressures are pronounced. Results indicate that 33.0% of the watershed exhibits high vulnerability and 4.3% very high vulnerability, with critical zones aligned with steep slopes, limited vegetation cover, and strong hydrological connectivity to cultivated areas. By accounting for both biophysical attributes and pollutant transport pathways, the IIVDC offers a replicable tool for prioritizing land management interventions. Beyond its technical application, the IIVDC contributes to sustainability by enabling evidence-based decision-making for water resource protection and land use planning. It supports integrated, spatially targeted actions that can reduce long-term contamination risks, guide sustainable agricultural practices, and improve institutional capacity for watershed governance. The approach is particularly suited for contexts where data are limited but spatial planning is essential. Future refinement should consider dynamic water quality monitoring and validation across contrasting hydro-climatic regions to enhance transferability.

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.

Water-quality monitoring in agricultural irrigation systems is challenging due to the dynamic and heterogeneous nature of mixed water sources, which complicates traditional and remote sensing-based assessment methods. Traditional water quality monitoring relies on water sampling and laboratory analysis, which can be time-consuming, labor-intensive, and spatially limited. In situ hyperspectral reflectance sensing (HRS) presents a promising alternative, offering high-resolution, non-invasive monitoring capabilities. However, applying HRS in mixed-water environments—where served-water effluent, precipitation, and natural river water converge—presents significant challenges due to variability in water composition and environmental conditions. While HRS has been widely explored in controlled or homogeneous water bodies, its application in highly dynamic agricultural mixed-water systems remains understudied. This study addresses this gap by evaluating the relationships between in situ hyperspectral data (450–900 nm) and key water-quality parameters—pH, turbidity, nitrates, and chlorophyll-a—across three campaigns in a Colombian tropical agricultural irrigation system. A Pearson’s correlation analysis revealed the strongest spectral associations for nitrates, with positive correlations at 500 nm (r ≈ 0.76) and 700 nm (r ≈ 0.85) and negative correlations in the near-infrared (850 nm, r ≈ −0.88). Conversely, the pH exhibited weak and diffuse correlations, with a maximum of r ≈ 0.51. Despite their optical activity, turbidity and chlorophyll-a showed unexpectedly weak correlations, likely due to the optical complexity of the mixed water matrix. Random Forest regression identified key spectral regions for each parameter, yet model performance was limited, with R² values ranging from 0.51 (pH) to −1.30 (chlorophyll-a), and RMSE values between 0.41 and 1.51, reflecting the challenges of predictive modeling in spatially and temporally heterogeneous wastewater systems. Despite these challenges, this study establishes a baseline for future hyperspectral applications in complex agricultural water monitoring and highlights critical spectral regions for further investigation. To improve the feasibility of HRS in mixed-water assessments, future research should focus on enhancing data-preprocessing techniques, integrating complementary sensing modalities, and refining predictive models to better account for environmental variability.

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.

Tropical agriculture requires sustainable irrigation solutions that balance water availability with quality and environmental protection. This review synthesizes current knowledge on riverbank filtration (RBF)—a nature-based technology for improving agricultural water quality—with objectives to elucidate design principles, water quality performance, and operational challenges specific to tropical contexts. Through systematic analysis of 128 peer-reviewed articles across topics including RBF hydrogeology, contaminant removal mechanisms, sediment transport, pathogen reduction, site selection criteria, and monitoring strategies, this work consolidates interdisciplinary evidence on RBF effectiveness for irrigation water supply. The Roldanillo–Unión–Toro (RUT) district in Valle del Cauca, Colombia, serves as a case study illustrating RBF application to sediment-rich, pathogen-prone rivers typical of tropical agricultural regions. While RBF is established for drinking water supply in temperate zones, its adaptation to tropical irrigation remains underexplored. This review identifies critical hydrogeological, environmental, and operational considerations for implementing RBF systems in tropical agricultural settings characterized by high water demand, seasonal variability, and challenging water quality conditions. Key findings are synthesized into a practitioner-oriented framework—covering site selection, design optimization, and adaptive management—intended to guide deployment of RBF for irrigation in tropical agricultural settings.