Aboveground tree additive biomass models in Ecuadorian highland agroforestry systems

• Published in: Biomass & bioenergy Vol. 80; pp. 252 - 259
• Main Authors: Riofrío, José, Herrero, Celia, Grijalva, Jorge, Bravo, Felipe
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2015
• Subjects: Allometric models; Andean species; Multi-stemmed species; SUR; Weighted regression; Allometric models; Andean species; SUR; Weighted regression; Multi-stemmed species
• ISSN: 0961-9534; 1873-2909
• DOI: 10.1016/j.biombioe.2015.05.026

Agroforestry land-use systems in the Andean region have great socioeconomical and biophysical relevance due to the abundance of products and services they provide. Biomass estimation in these systems constitutes a priority concern as it facilitates assessment of carbon sink potential and functionality for biomass production. In this paper, a set of equations were fitted to enable easy and reliable estimation of the total aboveground biomass of four frequently used species in Andean agroforestry systems: Acacia melanoxylon L., Alnus acuminata Kunth., Buddleja coriacea Remy. and Polylepis racemosa Ruiz&Pav. The best models for each biomass component (stem, thick branches, thin branches and leaves) per species were fitted simultaneously according to SUR methodology (seemingly unrelated regressions). All models showed high goodness of fit statistics and more than 70% of the observed variation in biomass components was explained by the independent variables. The inclusion of height as a predictive variable in the models improved their predictive reliability and expanded the application range. The models developed here are useful for assessing the sustainability of agroforestry systems and could support governmental or non-governmental forest conservation incentive programs and initiatives. •Allometric equations provide an operational method for accurately estimate AGB.•Allometric models based on SUR guarantee additivity among biomass components.•Total height in models express indirectly the competitive environment of species.•Models developed are a simple and efficient method for measuring biomass production.•Models structural traits increase their predictive reliability and application range.