Cortes, Andres
- Department of Plant Breeding, Swedish University of Agricultural Sciences
Abstract
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
Keywords
species distribution modelling (SDM); ecological niche modelling (ENM); agricultural planning; ensemble model; environmental predictors
Published in
Horticulturae
2026, volume: 12, number: 1, article number: 24
Publisher: MDPI
SLU Authors
UKÄ Subject classification
Horticulture
Publication identifier
- DOI: https://doi.org/10.3390/horticulturae12010024
Permanent link to this page (URI)
https://res.slu.se/id/publ/146086