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Above-ground biomass models for dominant trees species in cacao agroforestry systems in Talamanca, Costa Rica

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Abstract

Tree biomass allometric models are essential to estimate biomass, carbon sequestration and nutrient cycling in cacao agroforestry systems and other land uses with woody perennial species. A total of 34 trees of Cordia alliodora, 74 trees of Theobroma cacao and 38 trees of eight fruit species (Inga spp., Citrus aurantifolia, C. sinensis, Spondias mombin, Nephelium lappaceum, Persea americana, Mammea americana, Mangifera indica and Syzigium malaccensis) were harvested to gravimetrically estimate aboveground biomass (total, stem, branches, and foliage). Additionally, a database with total stem volume of 208 trees of C. alliodora was used to estimate above-ground biomass using a biomass expansion factor (BEF) and wood specific gravity estimations from this study. The well-known generic allometric models were fitted to the data using ordinary least squares, and the best ones were selected based on determination coefficient (R2), adjusted R2, root of mean square error, Akaike Information Criterion, Bayesian information criterion, and residual analyses. Selected models were compared with published models for the same species or group of species. Tree BEF were estimated for C. alliodora and fruit trees. The best fit models explained 93–96% of total above-ground biomass, and 54–95% of biomass by components. BEF differed significantly between timber and fruit trees. These models represent an advance in monitoring of carbon projects.

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Acknowledgements

This study was funded by CATIE, the CGIAR Research Consortium on Forest Trees and Agroforestry (FTA), and the Universidad del Tolima. Authors also thank to indigenous promotors of Talamanca (Costa Rica) and to Ing. Harold Víquez by his support in field work.

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Correspondence to Hernán J. Andrade or Milena Segura.

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Andrade, H.J., Segura, M. & Somarriba, E. Above-ground biomass models for dominant trees species in cacao agroforestry systems in Talamanca, Costa Rica. Agroforest Syst 96, 787–797 (2022). https://doi.org/10.1007/s10457-022-00741-y

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  • DOI: https://doi.org/10.1007/s10457-022-00741-y

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