Additive modeling systems to simultaneously predict aboveground biomass and carbon for Litsea glutinosa of agroforestry model in tropical highlands

Abstract

Aim of study: To develop and cross-validate simultaneous modeling systems for estimating components and total tree aboveground biomass and carbon of Litsea glutinosa in an agroforestry model with cassava. Area of study: In the Central Highlands of Vietnam, the agroforestry model widely planted on fallow land of ethnic minorities is a mixture of 65% L. glutinosa in combination with 35% cassava (Manihot esculenta). Material and methods: Twenty-two 300-m2 circular sample plots were located, representing the range of tree age, plantation density, and a 6-7 year rotation cycle. In each sample plot, one selected tree with a diameter at breast height equal to the plot quadratic mean diameter was destructively sampled. The relationships among tree aboveground biomass and carbon (AGB/AGC) and their components with dendrometric variables diameter, height, age, and crown area were examined using factor analysis. To fit systems of equations for AGB/AGC and their components, we compared two methods: weighted nonlinear least-squares (WNLS) and weighted nonlinear seemingly unrelated regression (WNSUR). Main results: The results of the leave-one-out cross-validation showed that the simultaneous WNSUR approach to modeling systems of four tree components, total biomass, and carbon provided better results than independent WNLS models. Research highlights: The simultaneous WNSUR modeling system provided improved and reliable estimates of tree components, total biomass, and carbon for L. glutinosa in an agroforestry model with cassava compared to independently fitted WNLS models.