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.

The Environmental Interceptor Canal supplies irrigation water to approximately 2,951 ha of agricultural land, primarily using pumped river water and effluents from the municipalities of Roldanillo, La Unión, and Toro's domestic wastewater treatment systems. In response to declining crop productivity, increased operational costs of irrigation systems, and constraints on export activities, this study aimed to evaluate the potential hazards associated with the use of water from this source for agricultural irrigation. Three composite sampling campaigns were conducted at seven points along the canal, and physicochemical and microbiological parameters were analyzed following standardized methodologies. The results revealed four main issues: a high microbiological hazard, evidenced by median fecal coliform (FC) concentrations exceeding 10,000 MPN 100 mL-1; a moderate to high hazard of chemical soil degradation due to the presence of salts and sodium (Na⁺); a high corrosion hazard; and a high hazard of emitter clogging associated with iron (Fe²⁺) concentrations above 1.5 mg L-1 and elevated median concentrations of total suspended solids (TSS). Overall, the results indicate that the water quality of the canal presents significant limitations for agricultural irrigation, highlighting the need to implement management, treatment, and control measures to reduce operational risks, comply with regulatory criteria, and improve the sustainability of irrigation systems and agricultural productivity in the study area.

Soil sealing remains an under-addressed threat to agricultural sustainability, particularly in rapidly urbanizing rural-urban interfaces. To address this challenge, a spatially explicit Soil Sealing Vulnerability Index (SSVI) was developed for 126,734.87 hectares of agricultural land in the Guachal and Amaime watersheds (GAWs), Valle del Cauca, Colombia. The SSVI integrates seven spatially referenced biophysical and institutional parameters—terrain slope, parcel size, road proximity, proximity to surface water bodies, agrological soil class, urban growth trends (2000–2024), and municipal land-use designations—using a multi-criteria analysis structured by expert consensus through the Analytic Hierarchy Process. With strong internal consistency, demonstrated by a Consistency Ratio (CR) of 2.51% that confirms the logical stability of expert judgments, the SSVI provides spatial support for decision-making in municipal land-use planning. Independent validation is deferred until sealing datasets become available, using a replicable concordance workflow. Results indicate that 42.54% of the GAWs area presents moderate vulnerability, 19.01% low vulnerability, 1.00% (1,270.1 ha) high vulnerability, and 37.44% corresponds to exclusion zones (e.g., urban cores and protected areas). Importantly, 938.7 ha of environmentally restricted soils and 228.8 ha of Mollisols fall within high-vulnerability zones, highlighting the model’s ability to identify policy-relevant risks. This study introduces the first spatially resolved SSVI tailored to Colombia’s regulatory landscape, demonstrating that vulnerability is more strongly influenced by institutional planning than by natural land constraints. Although technically replicable, effective application requires high-resolution spatial datasets and local expert participation. Integration into municipal planning instruments is essential to translate technical findings into policy action.

Soil organic matter (SOM) supports fertility, aggregation, water retention, and carbon storage. Therefore, mapping its vulnerability is crucial in tropical watersheds under intensive agriculture. In Colombia, few spatially explicit and operational frameworks integrate intrinsic soil properties and climatic-topographic factors into a single expert-weighted index. This study aimed to operationalize and apply a multi-criteria index based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP): the Soil Organic Matter Loss Vulnerability Index (SOM-LVI) to assess spatial vulnerability to SOM loss in the Tuluá and La Paila river watersheds, Valle del Cauca, Colombia. The methodology employed consisted of integrating six parameters weighted through AHP: clay content, slope, Holdridge life zones, temperature, precipitation, and soil pH, using 1:25000 cartography and raster layers of climate and relief. SOM-LVI values ranged from 0.19 to 0.79 across 135426 ha. The results showed that the High (49.6%) and Moderate (41.0%) vulnerability classes jointly represented 90.6% of the study area, while low vulnerability covered 9.4%. Spatially, hotspots were concentrated in middle and lower sectors on dissected reliefs, whereas forested headwaters showed lower vulnerability. High vulnerability predominated within Alfisols and Mollisols, whereas Andisols, Inceptisols, and Histosols were predominantly classified as Moderate or Low. Finally, it is concluded that the SOM-LVI provides a replicable methodology for prioritizing management practices that protect SOM where vulnerability is greatest, although its application depends on available cartography, expert weights, and the underlying climatic and topographic variables.