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 <= 30% of the regional area) was recorded for Kilimanjaro, Arusha, Dodoma, Manyara, Mara, Mbeya, Ruvuma, Tanga, and Katavi, whereas negligible suitability was forecasted for most of the remaining regions by the majority of the models. These findings suggest that heavy investments in avocado production and value chain additions should be directed primarily to regions with high suitability in order to use resources efficiently and minimize investment risks. More targeted, site-specific management should be encouraged in moderately suitable regions, with a focus on helping farmers identify and manage the best avocado sites rather than promoting broad expansion across the country. The findings generated by the ensemble model could be incorporated in the Tanzania Agriculture Climate Adaptation Technology Deployment Programme (TACATDP) to enhance sustainable crop investment, lower production risks, and strengthen the resilience of the avocado sector in the country.
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