Predicting Suitable Regions for Avocado (Persea americana Mill.) Tree Cultivation in Tanzania

Avocado cultivation is expanding rapidly in East Africa, driven by growing market demand, yet planning often relies on farmers' experience rather than systematic spatial analysis, raising risks of inefficient land and resource use. Therefore, this study applied four species distribution models (SDMs), Generalized Additive Models (GAM), Boosted Regression Trees (BRT), Maximum Entropy (MaxEnt), and Random Forest (RF), along with an ensemble model to map potential avocado suitability in Tanzania. The models were calibrated using 199 Variance Inflation Factor (VIF)-depurated occurrence records from which climatic, edaphic, and topographic predictor variables were extracted. BRT and RF had the best predictive abilities, with AUC values ranging from 0.77 +/- 0.20 to 0.81 +/- 0.13. The individual models identified Njombe, Iringa, Songwe, Kigoma, Rukwa, Kagera, and Morogoro as regions with high suitability, with more than 30% of each region's total area predicted to be suitable for avocado production. Moderate suitability (15% to