Assessing land suitability for climate-smart pond aquaculture in Tanzania’s Mara Region using AHP–GIS

Abstract Climate-smart aquaculture (CSAq) is increasingly recognized as a pathway to enhance food security and climate resilience in sub-Saharan Africa. However, identifying environmentally suitable areas remains essential for guiding sustainable aquaculture expansion. This study assessed land suitability for pond-based CSAq in the Mara Region, Tanzania, using a multi-criteria evaluation framework integrating the Analytic Hierarchy Process (AHP) with Geographic Information Systems (GIS). Six biophysical criteria slope, elevation, soil clay content, rainfall, temperature, and land use/land cover were weighted through expert pairwise comparison and integrated using a weighted overlay approach to generate a composite suitability surface. The resulting map classified the region into Unsuitable, Suitable, and Most Suitable zones, with most of the landscape falling within the Suitable and Most Suitable classes. Most Suitable clusters were concentrated in western and central districts, particularly Bunda and Musoma District Councils, where low slopes, adequate rainfall, clay-rich soils, and compatible land cover co-occur. Unsuitable areas were spatially limited and primarily associated with steeper eastern and southern highlands and localized sandy–gravelly soils. Among the evaluated criteria, slope emerged as the dominant determinant of suitability (weight = 0.25), while rainfall and soil characteristics reinforced hydrological reliability and water-retention capacity. After excluding Serengeti National Park to ensure regulatory compliance, Most Suitable zones remained spatially coherent across administratively relevant districts. Model robustness was confirmed through sensitivity analysis (CR = 0.06), and validation using 50 existing pond locations showed that all ponds were located within the two highest suitability classes, indicating strong concordance between modeled suitability and observed site selection patterns. By integrating environmental screening, conservation constraints, sensitivity testing, and empirical validation, the study provides a transparent and replicable decision-support framework for district-level planning of sustainable pond aquaculture under climate variability.