Species-specific allometric models for aboveground biomass estimation in two cinchona species in the peruvian andes

• Autores: Fernández Zarate, Franklin Hitler, Mejía, Marly, Neyra, Fiorella, Juárez Alarcón, Luis Mariano, Núñez García, Elio Rossel, Ocupa Campos, Lindeley, Espiritu Natividad, Jimmy Edward, Taboada Mitma, Víctor Hugo, Tantalean Martínez, Jerson, Sanchez Santillan, Tito, Seminario Cunya, Alejandro, Cruz Luis , Juancarlos Alejandro, Huaccha Castillo, Annick Estefany
• Formato: artículo
• Fecha de Publicación: 2026
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/3152
• Enlace del recurso: http://hdl.handle.net/20.500.12955/3152; https://doi.org/10.18280/ijdne.210205
• Nivel de acceso: acceso abierto
• Materia: Aboveground biomass; Biomasa aérea; Allometric equations; Ecuaciones alométricas; Cinchona micrantha; Cinchona pubescens; https://purl.org/pe-repo/ocde/ford#4.01.00; Árbol forestal; Forest trees; Biomasa; Biomass; Alometría; Allometry; Ordenación forestal; Forest management; Secuestro de carbono; Carbon sequestration; Bosque tropical; Tropical forests; Densidad de la madera; Wood density; Planta medicinal; Medicinal plants

Accurate estimation of aboveground biomass is an essential component for assessing carbon sequestration and ecological dynamics of forest ecosystems. This study aims to determine the aboveground biomass content using specific allometric models in two species of the genus Cinchona (C. micrantha and C. pubescens) in the Peruvian Andes. A total of 51 individuals of C. micrantha and 60 individuals of C. pubescens (diameter at breast height (DBH) > 5 cm) were sampled non-destructively. For each species, 25 combinations resulting from applying five mathematical forms (linear, exponential, logarithmic, polynomial, and power) to five independent variables (DBH, H, DBH × H, DBH² × H, DBH × H²) were evaluated. Second-order polynomial models with the composite variable DBH² × H presented the best predictive performance with an R² = 0.95 for C. micrantha and 0.97 for C. pubescens, along with low errors (RMSE < 4.35 for C. micrantha and < 9.02 for C. pubescens) and reduced Akaike information criterion (AIC) values. The results reveal morpho-functional differences between species, highlighting the importance of fitting specific models to optimize the precision of the estimates. Furthermore, the effectiveness of non-destructive sampling in conservation contexts is confirmed. This study provides robust quantitative tools for forest monitoring and ecological restoration in areas of high ecological vulnerability.