Water Footprint Assessment as a Decision-Support Tool for Sustainable Water Management in Cocoa (Theobroma cacao L.) Production Systems

Objective: to estimate the green and blue water footprint (WF) of Cocoa (Theobroma cacao L.) and water productivity across 25 producing municipalities in Meta, Colombia, providing operational evidence to support decision-making for sustainable water management in Cocoa production systems. Methodology: the study followed the Water Footprint Assessment Manual, integrating agroclimatic and crop-yield data to quantify green and blue WF components (m³ t-¹) and water productivity (kg m-³). A municipality-level comparison was conducted to generate territorial performance benchmarks relevant for water-management planning. Results: green WF accounted for 92% of total WF, ranging from 11,135 to 24,308 m³ t⁻¹, indicating strong reliance on rainfall. Blue water consumption was zero in 28% of the assessed areas, suggesting limited irrigation requirements under current conditions. Total WF ranged from 12,167 to 25,425 m³ t⁻¹ (mean: 18,245 m³ t⁻¹). Water productivity varied from 0.039 to 0.082 kg m⁻³, with the highest values observed in the municipalities of Acacias and Castilla la Nueva. Discussion: he observed spatial contrasts were driven by agroclimatic, edaphic, and yield variability, with direct implications for engineering-oriented water management at the territorial scale. The dominance of green WF highlights the sensitivity of Cocoa production to climatic variability and supports the use of WF and water productivity as decision-support indicators to prioritize interventions, guide adaptive management strategies, and improve water-use efficiency. Conclusions: cocoa water sustainability in Meta is primarily conditioned by effective rainfall water management. The estimated WF and water productivity values provide an applied baseline for sector planning and for designing water-management and policy instruments aligned with regional water availability and agroclimatic conditions, strengthening decision support for sustainable production.