Examinando por Materia "Sistemas cafetaleros"

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LiDAR-based assessment of above-ground biomass and carbon stocks in coffee agroforestry systems: Implications for climate mitigation and smallholder livelihoods at different elevations
(Elsevier B.V., 2026-09-21) Goñas Goñas, Malluri; García, Ligia; Oliva, Manuel; Silva López, Jhonsy O.; Gómez Fernández, Darwin; Atalaya Marin, Nilton; Tarrillo Julca, Ever; Torres Herrera, Pedro Antonio; Tineo Flores, Daniel; Rojas Briceño, Nilton B.; Cotrina Sanchez, Alexander; Veneros, Jaris
Coffee agroforestry systems (CAS) mitigate climate change, but their structural complexity hinders accurate carbon quantification. We combined aerial LiDAR and field data to assess vegetation height, aboveground biomass (AGB), and carbon stocks across three altitudinal strata (1277–1710 m a.s.l.) in six CAS plots (comprising 1559,858 LiDAR-derived observation cells) in northern Peru. Mean canopy height (2.47–8.79 m), AGB (1.55–40.59 Mg ha⁻¹), and carbon (0.73–19.08 Mg C ha⁻¹) varied widely; coefficients of variation often exceeded 100%, reflecting high heterogeneity. A mixed-effects logarithmic model (LiDAR-derived height as a fixed effect; farm as a random effect) revealed a significant positive height-biomass relationship (β₁ = 2.400; p < 0.001). The model's estimated parameters provide quantitative evidence of the importance of farm-level random effects. While the global fixed-effect slope for the height-biomass relationship was 2.400, the mixed-effects framework allowed this scaling rule to deviate by farm. For instance, the model quantified a distinct random slope of 3.065 for one specific farm. This represents a 27.7% increase in the rate of biomass accumulation per unit of canopy height compared to the global average. This quantitative divergence in allometric scaling across farms provides direct evidence that local conditions significantly alter biomass dynamics, justifying a random-effects structure over traditional fixed-effect models. Random effects captured local variations driven by management and species composition, with one farm showing distinct accumulation dynamics (β₁ = 3.065). Because of partial dependence between LiDAR metrics and field-calibrated biomass estimates, model performance reflects internal structural consistency rather than independent predictive validation. Consequently, LiDAR height should be interpreted as a robust structural indicator rather than an entirely independent indicator. Nevertheless, LiDAR-derived height reliably indicates AGB in CAS, though model transfer requires farm-scale calibration. This non-destructive, spatially explicit approach strengthens carbon monitoring in tropical agroforestry. By reducing the costs and uncertainties of Measurement, Reporting, and Verification (MRV), this workflow lowers the barriers for smallholder integration into carbon markets and REDD+ programs, directly linking accurate carbon accounting to improved livelihood incentives.