Biomass regression models are presented describing total above-ground biomass, stem wood, branch wood, foliage and bark production for Tejpat (Cinnamomum tamala), a multipurpose tree which is found abundantly distributed and grown in western hill districts of Nepal. A total of 56 Tejpat trees between 6.2 and 16.5 cm diameter at breast height (DBH) from farmers’ farmland and marginal land in Arghakhanchi, Gulmi and Palpa districts were sampled and harvested. Mean fresh weight of total above-ground biomass, stem wood, branch wood, foliage and bark was 77.03, 36.39, 15.16, 17.53 and 8.2 kg tree -1, respectively. Allocation of biomass was more in stem (47.24% tree-1) than in foliage (22.75% tree-1), branch (19.69% tree-1) and bark (10.31% tree-1). Weight of tree component was estimated as a function of DBH. After removal of the outliers, data were randomly divided into two datasets: 70% for model calibration and another 30% for model validation. Correlation analysis showed positive stronger linear relationship between DBH and biomass. Five regression models (linear, logarithmic, quadratic, power and exponential) were developed. All models were statistically significant, with R2 ranging from 0.64 to 0.83. Model validation was based on root mean square error (RMSE). RMSE percentage for the best-fit equation varied between 16.64% and 44.82%. Linear model resulted in the least error and was selected as the best-fit model for prediction of biomass of bark, foliage, branch, stem and total above ground tree biomass. Biomass models developed could be applied to obtain biomass of different tree components of Tejpat grown in the study area and could even be applied to other areas which have similar conditions; but it should be validated before using them in new sites and conditions. DOI: http://dx.doi.org/10.3126/banko.v21i1.9058 Banko Janakari, Vol. 21, No. 1 2011; 3-12
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