{ "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.", "abstract_shown": true, "affiliations": [ "Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Peru", "Grupo de Investigación en Ciencia de la Información Geoespacial (CIGEO), Instituto de Investigación para el Desarrollo del Perú, Universidad Nacional de Moquegua, Pacocha, Peru", "Área 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", "Centro Experimental Yanayacu, Dirección de Servicios Estratégicos Agrarios (DSEA), Instituto Nacional de Innovación Agraria (INIA), Cajamarca, Peru", "Facultad de Ingeniería Ambiental, Biosistemas y de la Energía (FIABE), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Peru" ], "article_type": "Research Article", "arxiv": null, "author_total": 16, "authors": [ { "affs": [ 0 ], "name": "Jhon A. Zabaleta-Santisteban" }, { "affs": [ 1 ], "name": "Angel J. Medina-Medina" }, { "affs": [ 2 ], "name": "Katerin M. Tuesta-Trauco" }, { "affs": [ 3 ], "name": "Abner S. Rivera-Fernandez" }, { "affs": [ 4 ], "name": "Teodoro B. Silva-Melendez" }, { "name": "Marlen A. Grandez-Alberca" }, { "name": "Julio Puscan-Rojas" }, { "name": "Rolando Salas López" }, { "name": "Manuel Oliva-Cruz" }, { "name": "Alexander Cotrina-Sanchez" }, { "name": "Nilton B. Rojas-Briceño" }, { "name": "Jhonsy O. Silva-López" }, { "name": "Darwin Gómez-Fernández" }, { "name": "Santos Triunfo Leiva-Espinoza" }, { "name": "Angel F. Huaman-Pilco" }, { "name": "Elgar Barboza" } ], "bibtex": "https://research.draconitic.com/paper/4eszz9f7mv9mg.bib", "cited_by": 0, "doi": "10.1371/journal.pone.0360376", "fulltext_url": null, "id": "4eszz9f7mv9mg", "issns": [ "1932-6203" ], "landing_url": "https://doi.org/10.1371/journal.pone.0360376", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1108|Cocoa and Sweet Potato Agronomy" ], [ "plos", "/Biology and life sciences/Agriculture/Agroecology/Agroforests" ], [ "plos", "/Biology and life sciences/Agriculture/Forestry/Agroforests" ], [ "plos", "/Biology and life sciences/Biophysics" ], [ "plos", "/Biology and life sciences/Computational biology/Computational neuroscience/Artificial neural networks" ], [ "plos", "/Biology and life sciences/Ecology/Agroecology/Agroforests" ], [ "plos", "/Biology and life sciences/Ecology/Ecosystems/Forests" ], [ "plos", "/Biology and life sciences/Neuroscience/Computational neuroscience/Artificial neural networks" ], [ "plos", "/Computer and information sciences/Artificial intelligence/Artificial neural networks" ], [ "plos", "/Ecology and environmental sciences/Ecology/Agroecology/Agroforests" ], [ "plos", "/Ecology and environmental sciences/Ecology/Ecosystems/Forests" ], [ "plos", "/Ecology and environmental sciences/Sustainability science" ], [ "plos", "/Ecology and environmental sciences/Terrestrial environments/Forests" ], [ "plos", "/Medicine and health sciences/Epidemiology" ], [ "plos", "/People and places/Geographical locations/South America/Peru" ], [ "plos", "/Physical sciences/Mathematics/Applied mathematics/Algorithms" ], [ "plos", "/Physical sciences/Physics/Biophysics" ], [ "plos", "/Research and analysis methods/Simulation and modeling/Algorithms" ] ], "openalex": "W7221111955", "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0360376&type=printable", "pmcid": null, "pmid": null, "published": "2026-10-09", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/4eszz9f7mv9mg.ris", "sources": [ { "first_seen": "2026-10-30T12:17:30Z", "native_id": "10.1371/journal.pone.0360376", "source": "plos", "url": "https://doi.org/10.1371/journal.pone.0360376" } ], "subjects": [ { "field": "biochemistry", "origin": "plos:biophysics", "subfield": "biophysics" }, { "field": "biology", "origin": "plos:ecology", "subfield": "ecology" } ], "title": "Geospatial suitability of cacao in Amazonas (Peru): An ensemble predictive modeling approach (BIOMOD2) integrating multisource data, territorial management, and plant health", "title_text": "Geospatial suitability of cacao in Amazonas (Peru): An ensemble predictive modeling approach (BIOMOD2) integrating multisource data, territorial management, and plant health", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:30Z", "url": "https://research.draconitic.com/paper/4eszz9f7mv9mg", "venue": "PLOS One", "versions": [] }