Geospatial suitability of cacao in Amazonas (Peru): An ensemble predictive modeling approach (BIOMOD2) integrating multisource data, territorial management, and plant health
5 affiliations
- 1Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Peru
- 2Grupo de Investigación en Ciencia de la Información Geoespacial (CIGEO), Instituto de Investigación para el Desarrollo del Perú, Universidad Nacional de Moquegua, Pacocha, Peru
- 3Área de cartografía y teledetección, Laboratorio de Agrostología, Instituto de Investigación en Ganadería y Biotecnología, Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Peru
- 4Centro Experimental Yanayacu, Dirección de Servicios Estratégicos Agrarios (DSEA), Instituto Nacional de Innovación Agraria (INIA), Cajamarca, Peru
- 5Facultad de Ingeniería Ambiental, Biosistemas y de la Energía (FIABE), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Peru
Abstract
Identifying climatically suitable areas for cacao cultivation (Theobroma cacao L.) is essential to guide sustainable intensification, territorial planning, and compliance with emerging environmental standards in tropical regions. This study assessed the current climatic suitability of cacao in the Amazonas department (northwestern Peru) using an ensemble modeling approach implemented in biomod2, integrating bioclimatic, topographic, and edaphic predictors with spatial biophysical exclusion criteria, legal conservation constraints, regulatory eligibility associated with the European Union Deforestation-Free Products Regulation (EUDR), and local phytosanitary evidence. A total of 403 quality-controlled presence records were used to calibrate multiple machine-learning algorithms under cross-validation, retaining only those with high predictive performance (AUC > 0.9; TSS > 0.7). Decision tree–based models, particularly Random Forest and XGBoost, showed the best individual performance, while the consensus model exhibited greater spatial stability and overall accuracy. Results indicate that approximately 14.8% of the regional territory presents high climatic suitability under current conditions, concentrated in inter-Andean valleys and low- to mid-elevation zones, with thermal seasonality identified as the main environmental predictor. However, the incorporation of territorial and regulatory constraints substantially reduced the effectively available area, especially under the EUDR scenario, demonstrating that cacao expansion potential is strongly conditioned by conservation and regulatory frameworks. High-priority degraded areas emerged as focal opportunities for productive restoration through agroforestry systems. Phytosanitary validation (2021–2024) revealed high spatial concordance between areas of greatest climatic suitability and the prevalence and incidence of major cacao pathogens and pests, particularly Moniliophthora roreri and M. perniciosa, confirming that optimal climatic niches for cultivation also favor biotic pressure. Overall, this study provides an integrative framework combining climatic modeling, territorial management, and phytosanitary risk, delivering robust scientific inputs to inform public policy, land-use planning, and sustainable cacao production strategies in the Amazon.
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BibTeX
@article{zabaletasantisteban2026geospatial,
title = {{Geospatial suitability of cacao in Amazonas (Peru): An ensemble predictive modeling approach (BIOMOD2) integrating multisource data, territorial management, and plant health}},
author = {Jhon A. Zabaleta-Santisteban and Angel J. Medina-Medina and Katerin M. Tuesta-Trauco and Abner S. Rivera-Fernandez and Teodoro B. Silva-Melendez and Marlen A. Grandez-Alberca and Julio Puscan-Rojas and Rolando Salas López and Manuel Oliva-Cruz and Alexander Cotrina-Sanchez and Nilton B. Rojas-Briceño and Jhonsy O. Silva-López and Darwin Gómez-Fernández and Santos Triunfo Leiva-Espinoza and Angel F. Huaman-Pilco and Elgar Barboza},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0360376},
url = {https://doi.org/10.1371/journal.pone.0360376},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Geospatial suitability of cacao in Amazonas (Peru): An ensemble predictive modeling approach (BIOMOD2) integrating multisource data, territorial management, and plant health
AU - Jhon A. Zabaleta-Santisteban
AU - Angel J. Medina-Medina
AU - Katerin M. Tuesta-Trauco
AU - Abner S. Rivera-Fernandez
AU - Teodoro B. Silva-Melendez
AU - Marlen A. Grandez-Alberca
AU - Julio Puscan-Rojas
AU - Rolando Salas López
AU - Manuel Oliva-Cruz
AU - Alexander Cotrina-Sanchez
AU - Nilton B. Rojas-Briceño
AU - Jhonsy O. Silva-López
AU - Darwin Gómez-Fernández
AU - Santos Triunfo Leiva-Espinoza
AU - Angel F. Huaman-Pilco
AU - Elgar Barboza
PY - 2026
DA - 2026/10/09
JO - PLOS One
DO - 10.1371/journal.pone.0360376
UR - https://doi.org/10.1371/journal.pone.0360376
AB - Identifying climatically suitable areas for cacao cultivation (Theobroma cacao L.) is essential to guide sustainable intensification, territorial planning, and compliance with emerging environmental standards in tropical regions. This study assessed the current climatic suitability of cacao in the Amazonas department (northwestern Peru) using an ensemble modeling approach implemented in biomod2, integrating bioclimatic, topographic, and edaphic predictors with spatial biophysical exclusion criteria, legal conservation constraints, regulatory eligibility associated with the European Union Deforestation-Free Products Regulation (EUDR), and local phytosanitary evidence. A total of 403 quality-controlled presence records were used to calibrate multiple machine-learning algorithms under cross-validation, retaining only those with high predictive performance (AUC > 0.9; TSS > 0.7). Decision tree–based models, particularly Random Forest and XGBoost, showed the best individual performance, while the consensus model exhibited greater spatial stability and overall accuracy. Results indicate that approximately 14.8% of the regional territory presents high climatic suitability under current conditions, concentrated in inter-Andean valleys and low- to mid-elevation zones, with thermal seasonality identified as the main environmental predictor. However, the incorporation of territorial and regulatory constraints substantially reduced the effectively available area, especially under the EUDR scenario, demonstrating that cacao expansion potential is strongly conditioned by conservation and regulatory frameworks. High-priority degraded areas emerged as focal opportunities for productive restoration through agroforestry systems. Phytosanitary validation (2021–2024) revealed high spatial concordance between areas of greatest climatic suitability and the prevalence and incidence of major cacao pathogens and pests, particularly Moniliophthora roreri and M. perniciosa, confirming that optimal climatic niches for cultivation also favor biotic pressure. Overall, this study provides an integrative framework combining climatic modeling, territorial management, and phytosanitary risk, delivering robust scientific inputs to inform public policy, land-use planning, and sustainable cacao production strategies in the Amazon.
SN - 1932-6203
ER -