Individual Plant Allometric Equations for Estimating Aboveground Biomass and Its Components for a Common Bamboo Species (Bambusa procera A. Chev. and A. Camus) in Tropical Forests

Abstract

Bamboo forests play an important role in achieving the objectives of the United Nations program on Reducing Emission from Deforestation and Forest Degradation. We developed and validated a modeling system that simultaneously estimate aboveground biomass and its components for a common bamboo species (Bambusa procera A. Chev. and A. Camus) in tropical forests. Eighty-three bamboo culms were destructively sampled from seventeen 100 m2 sample plots located in different parts of the Central Highlands in Viet Nam to obtain total plant aboveground biomass (AGB) and its components. We examined the performance of weighted nonlinear models fit by maximum likelihood and weighted nonlinear seemingly unrelated regression fit by generalized least squares for predicting bamboo biomass. The simultaneous estimation of AGB and its components produced higher reliability than the models of components and total developed separately. With a large number of bamboo species, it may not be feasible to develop species- specific biomass models, hence genus-specific allometric models may be considered.

An allometric model-based approach for estimating biomass in seven Indian bamboo species in western Himalayan foothills, India

“…Monte Carlo cross-validation method was applied to select, evaluate and compare the models 48 – 51 . The method divides the dataset randomly into two parts, with about 80% for model development and 20% for cross-validation, repeated R times (about 100 times) to obtain stable statistics.…”

Species-Specific Allometric Equations for Predicting Belowground Root Biomass in Plantations: Case Study of Spotted Gums (Corymbia citriodora subspecies variegata) in Queensland

“…Allometry involves linear or nonlinear modelling approaches to describe the correlation between biomass and tree variables (such as D and H) [42][43][44]. The power-law relationships are typical in many biomass equations [14,[44][45][46][47][48]. Power models can be fitted using log-linear models or nonlinear models [44,47,49].…”

“…The power-law relationships are typical in many biomass equations [14,[44][45][46][47][48]. Power models can be fitted using log-linear models or nonlinear models [44,47,49]. We applied both methods in our study.…”

“…Models with the same response (dependent) variables (i.e., Equations (3)-( 7), fitted by transformed data or Equations ( 11)-( 15) fitted by weighted nonlinear analysis) were compared based on five fit statistics: (i) Akaike information criterion (AIC); (ii) adjusted R 2 (adj. R 2 ); (iii) average bias, used to estimate model errors and for calculating the difference between the estimate and the true value [61]; (iv) root mean square error (RMSE) [44,61]; and (v) mean absolute percentage error (MAPE) [61].…”

“…A Monte Carlo cross-validation procedure (MCCV) [65][66][67] was used to test the robustness and predictive value of each regression model. This procedure involves several steps; for each equation, the dataset was randomly split into two parts with 70% for training and 30% for testing [44,68]. The process was repeated 23 times with different random selections with the criteria for the selection of the models based on an average over 23 iterations.…”