Mpakairi, Kudzai ShaunDube, TimothySibanda, Mbulisi2026-01-122026-01-122025Mpakairi, K.S., Dube, T., Sibanda, M., Mutanga, O., Nhamo, L. and Mpandeli, S., 2025. Fine-scale mapping of irrigation suitability in South Africa using ensemble modelling. Scientific Reports, 15(1), p.36524.https://doi.org/10.1038/s41598-025-12820-yhttps://hdl.handle.net/10566/21643Food insecurity, exacerbated by a growing population and environmental change, poses a significant challenge in Southern Africa. Enhancing agricultural productivity through efficient irrigation practices is crucial for achieving food and water security and sustainable development goals. This study applied an ensemble modelling approach to identify and assess irrigation suitability areas across South Africa, combining the predictive power of Random Forest, Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM) algorithms. These machine learning models were applied using cropland presence/pseudo-absence data and a suite of predictor variables. The ensemble model, leveraging a weighted averaging approach based on individual model performance, outperformed the individual models, achieving a TSS of 0.66 and an AUC of 0.90. Land use, population density, and elevation were identified as key factors determining irrigation suitability. The ensemble model also revealed substantial spatial variation in irrigation potential across South Africa, with the Northern Cape and Western Cape provinces exhibiting the largest suitable areas. The results provide critical information for targeted irrigation development, enabling efficient resource allocation, and maximising agricultural productivity. This data-driven approach offers a robust framework for sustainable agrarian planning in the face of increasing food demands and climate change, contributing to enhanced food security and economic development in South Africa.enAgriculture potential areasClimate changeCroplandsFood securityMachine learningArticle