Allometric models for improving aboveground biomass estimates in West African savanna ecosystems

• DOI: 10.1016/j.tfp.2021.100077
• Publication Date: 2021-02-27T16:41:39Z
• Authors: Moussa Ganamé; Philippe Bayen; Issaka Ouédraogo; Larba Hubert Balima; Adjima Thiombiano
• Keywords: Allometry; Tree Allometry

West African Sudanian savanna ecosystems greatly contribute to local peoples' livelihoods and climate change mitigation. Yet, the contribution of these carbon storage remains poorly documented due lack accurate biomass predictive tools. Therefore, allometric models developed at both species-level site-level may improve stock estimation. In this study, we for estimating aboveground (AGB) tree component-, species- site-levels in Burkina Faso. Five woody species with high socio-economic significance (Anogeissus leiocarpa, Combretum nigricans, Isoberlinia doka, Mitragyna inermis Pterocarpus erinaceus) were selected two forest sites based on their dominance ecosystem. A total 150 trees (30 per dominant species) spanning a wide range diameter breast height (DBH) destructively sampled. Models predict component independently built Ordinary Least Squares (OLS) technique. Allometric (TAGB) each all using OLS technique Seemingly Unrelated Regression (SUR) method. Biomass estimates regressed DBH as single predictor, (H) interacted variables, DBH, H wood density (ρ) three independent-input variables. The performance validated compared generalized pantropical equation model tropical forests (Chave et al., 2014). findings revealed that predictors varied between five species. goodness-of-fit statistics SUR methods provide equations, method being most accurate. more estimation AGB than model. recommend use quantification stocks ecosystems. Furthermore, established species-specific mixed-species equations constitute useful tool monitoring, reporting verification within REDD+ framework