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巴基斯坦兴都库什地区主要用材树种的物种组成、生长状况和生物量碳动态。

Tree species composition, growing stock and biomass carbon dynamics of the major timber species in Hindu Kush regions of Pakistan.

机构信息

Northwest A&F University, College of Natural Resources and Environment, Yangling, Shaanxi, China.

Beijing Forestry University, Department of Forest Sciences, Beijing, China.

出版信息

Braz J Biol. 2022 Mar 14;84:e256425. doi: 10.1590/1519-6984.256425. eCollection 2022.

DOI:10.1590/1519-6984.256425
PMID:35293534
Abstract

Using inventory data, this study evaluates the species composition, growing stock volume (GSV), and biomass carbon (BMC) of the five major timber species in the sub-tropical, and temperate/sub-alpine regions of Pakistan. It was found that the stem density varies between 50 and 221 trees ha -1, with a mean of 142 trees ha-1 (13.68 million trees for entire forest area). Among the species, Pinus wallichiana showed a high species composition (27.80%) followed by Picea smithiana (24.64%). The GSV was found in the range of 67.81 to 425.94 m3 ha-1, with a total GSV value of 20.68 million m3 for the entire region. Similarly, The BMC ranged from 27.04 to 169.86 Mg ha-1, with a mean BMC value of 86.80 Mg ha-1. The total amount of stored carbon was found at 8.69 million tons for a total of 95842 ha of commercially managed forest. Furthermore, the correlation analysis between the basal area (BA) and GSV and BMC showed that BA is the best predictor of GSV and BMC. The findings provide insights to the policy makers and forest managers regarding the sustainable commercial forest management as well as forest carbon management in the recent global carbon management for climate change mitigation.

摘要

本研究利用库存数据,评估了巴基斯坦亚热带和温带/亚高山地区的五个主要木材物种的物种组成、生长量(GSV)和生物量碳(BMC)。结果发现,树干密度在 50 到 221 株/公顷之间,平均值为 142 株/公顷(整个森林面积为 1368 万株)。在这些物种中,华山松的物种组成最高(27.80%),其次是白云杉(24.64%)。GSV 的范围在 67.81 到 425.94 立方米/公顷之间,整个地区的总 GSV 值为 2068 万立方米。同样,BMC 的范围在 27.04 到 169.86 Mg/公顷之间,平均 BMC 值为 86.80 Mg/公顷。总碳储量为 869 万吨,共计 95842 公顷商业管理森林。此外,基面积(BA)与 GSV 和 BMC 之间的相关性分析表明,BA 是 GSV 和 BMC 的最佳预测因子。这些发现为决策者和森林管理者提供了有关可持续商业森林管理以及在最近的全球气候变化缓解碳管理中森林碳管理的见解。

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