Territorial zoning as a strategy for sustainable natural resource management in Cajamarca, Northwestern Peru

• Autores: Gómez Fernández, Darwin, Atalaya Marin, Nilton, Arce Inga, Marielita, Tineo Flores, Daniel, Fernandez Jibaja, Jorge Antonio, Taboada Mitma, Víctor Hugo, Cabrera Hoyos, Héctor Antonio, Cruz Luis, Juancarlos Alejandro, Goñas Goñas, Malluri
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/2965
• Enlace del recurso: http://hdl.handle.net/20.500.12955/2965; https://doi.org/10.1016/j.ecoinf.2025.103440
• Nivel de acceso: acceso abierto
• Materia: Territorial zoning; Land suitability; Analytical hierarchy; Remote sensing; Google earth engine; Cajamarca; Zonificación territorial; Aptitud del suelo; Jerarquía analítica; Teledetección; Motor de Google Earth; https://purl.org/pe-repo/ocde/ford#4.01.06; Café; Coffee; Theobroma cacao; Perú; Peru; Agricultura sostenible; Sustainable agricultura; Sistema de información geográfica; GIS

Generating agricultural suitability analyses that are objective, consistent, and accessible through digital platforms remains a technical and methodological challenge, creating an information gap for certain stakeholders. To address this issue, we assessed the territorial suitability of the Cajamarca region for coffee and cocoa cultivation using 18 subcriteria grouped into climatic, edaphological, topographic, and socioeconomic categories. To reduce subjectivity and improve consistency in variable comparisons, we applied multicriteria evaluation techniques, including the analytical hierarchy process (AHP) and Shannon entropy method. On the basis of the resulting weights, suitability models were generated using two approaches: one based on threshold reclassification and another using continuous suitability functions. Both approaches were validated using 3886 presence points for coffee and 671 for cocoa. The continuous approach demonstrated a greater ability to capture internal variability and spatial transitions, with greater dispersion and significant differences between classes. The most influential subcriteria for coffee were annual mean temperature, soil texture, elevation, and land use/land cover (LULC); for cocoa, they were annual mean temperature, soil pH, elevation, and LULC. In key districts, up to 59.8 % of the territory was classified as highly suitable, highlighting localized production potential. Finally, the results were integrated into the Suitability Watch Cajamarca application, developed in the Google Earth Engine, enabling interactive inspection of spatial suitability. This tool aims to support evidence-based agricultural planning and is intended for national scaling to other strategic crops.