Additive Allometric Equations to Improve Aboveground Biomass Estimation for Mongolian Pine Plantations in Mu Us Sandy Land, Inner Mongolia, China

Abstract

Afforestation is conducive to improving ecosystem service functions and ecosystem diversity in the Mu Us Sandy Land, however, the important attribute of biomass for Mongolian pine (Pinus sylvestris var. mongolica Litv.) plantations has yet to be accurately evaluated. This study aimed to develop additive allometric biomass equations for the species and evaluate biomass partitioning patterns within tree components. A total of 131 trees were measured for stem, branch, and leaf biomass by destructively sampling and tree climbing, with the latter as a supplement. For each biomass component, we tested three equations with the diameter at breast (D) alone, height (H) as additional, and diameter in combination with height (D2H) as predictors using the weighted least squared method. Weighted nonlinear seemingly unrelated regression was adopted to fit a system of additive allometric biomass equations utilizing the selected equations. A leave-one-out cross-validation method (the jackknife procedure) was used to assess the predictive ability. The biomass partitioning pattern was evaluated by calculating the ratios. The results revealed that the diameter alone is a good predictor for branches and foliage biomass estimates, while the stem requires H included to improve estimation accuracy. Mongolian pine allocates relatively more biomass to the crown (51.4%) compared to the stem (48.6%). Branch biomass fraction increased monotonously with increasing tree size while a reverse trend was observed for foliage. In conclusion, the additive models developed in this study provide a robust biomass estimation and can be extensively used to estimate Mongolian pine forests biomass in Mu Us Sandy Land.