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Forests 2019, 10(2), 150; https://doi.org/10.3390/f10020150

Aboveground Biomass Allocation and Additive Allometric Models for Natural Larix gmelinii in the Western Daxing’anling Mountains, Northeastern China

1
College of Forestry, Beijing Forestry University, Beijing 100083, China
2
Qianyanzhou Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Institute of Forest Medicinal Herb and Food, Jiangxi Academy of Forestry, Nanchang 330032, China
*
Author to whom correspondence should be addressed.
Received: 3 December 2018 / Revised: 1 February 2019 / Accepted: 4 February 2019 / Published: 11 February 2019
(This article belongs to the Section Forest Ecology and Management)
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Abstract

Accurate estimates of tree component and aboveground biomass strongly depend on robust and precise allometric equations. However, site-specific and suitable biomass equations are currently scarce for natural Larix gmelinii forests in the western Daxing’anling Mountains, northeastern China. This study aimed to evaluate the biomass allocation patterns within tree components and develop additive allometric biomass equations for species of L. gmelinii. A total of 58 trees were destructively sampled and measured for wood (inside bark), bark, branch and leaf biomass. For each component, we assessed the share of biomass allocated to different components by computing its ratio; we also tested two allometric equations based on diameter at breast height (dbh) alone, and dbh fitted with height (h) as independent variables. Seemingly unrelated regression methodology was used to fit an additive system of biomass allometric equations. We performed an independent dataset to evaluate the predictive ability of the best model system. The results revealed that wood biomass accounted for approximately 60% of the aboveground biomass. Wood and branch biomass ratios increased with increasing dbh, while a reverse trend was observed for bark and leaf biomass ratios. All models showed good fitting results with Adj.R2 = 0.958–0.995. Tree dbh provided the lowest estimation errors in the regressions associated with branches and leaves, while dbh2 × h generated the most precise models for stems (wood and bark). We conclude that these allometric equations will accurately predict biomass for Larix trees in the western Daxing’anling Mountains. View Full-Text
Keywords: additive allometric equations; aboveground biomass; biomass allocation; Larix gmelinii additive allometric equations; aboveground biomass; biomass allocation; Larix gmelinii
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Meng, S.; Jia, Q.; Liu, Q.; Zhou, G.; Wang, H.; Yu, J. Aboveground Biomass Allocation and Additive Allometric Models for Natural Larix gmelinii in the Western Daxing’anling Mountains, Northeastern China. Forests 2019, 10, 150.

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