MultiProduct Optimization of Cedrelinga cateniformis (Ducke) Ducke in Different Plantation Systems in the Peruvian Amazon

• Autores: Baselly-Villanueva, Juan Rodrigo, Fernández-Sandoval, Andrés, Salazar-Hinostroza, Evelin Judith, Cárdenas-Rengifo, Gloria Patricia, Puerta, Ronald, Chuquizuta Trigoso, Tony Steven, Rufasto-Peralta, Yennifer Lisbeth, Vallejos-Torres, Geomar, Goycochea Casas, Gianmarco, Araújo Junior, Carlos Alberto, Quiñónez-Barraza, Gerónimo, Álvarez-Álvarez, Pedro, Garcia Leite, Helio
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2636
• Enlace del recurso: http://hdl.handle.net/20.500.12955/2636; https://doi.org/10.3390/f16010164
• Nivel de acceso: acceso abierto
• Materia: agroforestry; dynamic programming; logs; production planning; sawn lumber; taper; wood industry; https://purl.org/pe-repo/ocde/ford#4.01.02; FORESTAL

This study addressed multi-product optimization in Cedrelinga cateniformis plantations in the Peruvian Amazon, aiming to maximize volumetric yields of logs and sawn lumber. Data from seven plantations of different ages and types, established on degraded land, were analyzed by using ten stem profile models to predict taper and optimize wood use. In addition, the structure of each plantation was evaluated using diameter distributions and height–diameter ratios; log and sawn timber production was optimized using SigmaE 2.0 software. The Garay model proved most effective, providing high predictive accuracy (adjusted R2 values up to 0.963) and biological realism. Marked differences in volumetric yield were observed between plantations: older and more widely spaced plantations produced higher timber volumes. Logs of optimal length (1.83–3.05 m) and larger dimension wood (e.g., 25.40 × 5.08 cm) were identified as key contributors to maximizing volumetric yields. The highest yields were observed in mature plantations, in which the total log volume reached 508.1 m3ha−1 and the sawn lumber volume 333.6 m3ha−1 . The findings demonstrate the power of data-driven decision-making in the timber industry. By combining precise modeling and optimization techniques, we developed a framework that enables sawmill operators to maximize log and lumber yields. The insights gained from this research can be used to improve operational efficiency and reduce waste, ultimately leading to increased profitability. These practices promote support for smallholders and the forestry industry while contributing to the long-term development of the Peruvian Amazon.