Modelos alométricos para biomassa fresca aérea de Moringa oleifera Lam. e Musa sp. em sistema agroflorestal no bioma Mata Atlântica

Abstract

Estimating biomass in agroforestry systems (AFSs) poses a significant methodological challenge, given that allometric equations developed for natural forests have limited applicability in systems with distinct floristic compositions. This study aimed to develop allometric equations to estimate the fresh above-ground biomass of *Moringa oleifera* Lam. and *Musa* sp. in an experimental agroforestry system established at Embrapa Dairy Cattle (Embrapa Gado de Leite) in Coronel Pacheco, Minas Gerais, within the Atlantic Forest biome. Nineteen *Moringa oleifera* and twenty *Musa* sp. individuals were sampled using a destructive method, with measurements taken for diameter at breast height (DBH) and total height. Four allometric models were tested for each species: simple regression using DBH, simple regression using height, a volumetric model using the composite variable DBH²·H, and a composite power model (DBHᵇ·Hᶜ). The models were fitted via linear regression following logarithmic data transformation and evaluated based on the coefficient of determination (R²), coefficient of variation (CV%), root mean square error (RMSE), Furnival Index (FI), and Akaike Information Criterion (AIC). For *Moringa oleifera*, the composite model performed best (R² = 0.9355; RMSE = 1.9608 kg; CV = 12.29%; FI = 1.7997; AIC = −15.64), showing a negative coefficient for height, which indicates an etiolated growth pattern under light competition. For *Musa* sp., the volumetric and composite models showed statistically equivalent performance (R² = 0.9846 and 0.9854, respectively; ΔAIC ≈ 0.98); the volumetric model is recommended based on the principle of parsimony, a choice supported by the analysis showing moderate multicollinearity between DBH and height (variance inflation factor, VIF = 4.23). It is concluded that species-specific allometric equations for these species in agroforestry systems are feasible and accurate, contributing to the monitoring of biomass and carbon stocks in agroforestry systems within the Atlantic Forest biome.