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Patterns and abiotic drivers of soil organic carbon in perennial tea (Camellia sinensis L.) plantation system of China

文献类型： 外文期刊

作者： Yang, Xiangde 1 ; Yi, Xiaoyun 1 ; Ni, Kang 1 ; Zhang, Qunfeng 1 ; Shi, Yuanzhi 1 ; Chen, Linbo 2 ; Zhao, Yuanyan 3 ; Zhang, Yongli 1 ; Ma, Qingxu 4 ; Cai, Yanjiang 5 ; Ma, Lifeng 1 ; Ruan, Jianyun 1 ;

作者机构： 1.Chinese Acad Agr Sci, Tea Res Inst, Key Lab Biol Genet & Breeding Special Econ Anim &, Minist Agr & Rural Affairs, Hangzhou 310008, Peoples R China

2.Tea Res Inst, Yunnan Acad Agr Sci, Yunnan Prov Key Lab Tea Sci, 2 Jingnan Rd, Menghai 666201, Yunnan, Peoples R China

3.Puer Tea Sci Res Inst, Puer 665000, Peoples R China

4.Zhejiang Univ, Coll Environm & Resource Sci, Zhejiang Prov Key Lab Agr Resources & Environm, Hangzhou 310058, Peoples R China

5.Zhejiang A&F Univ, State Key Lab Subtrop Silviculture, Hangzhou 311300, Peoples R China

关键词： Soil organic carbon; Spatial patterns; Abiotic factors; Perennial crops; Acidic soil

期刊名称：ENVIRONMENTAL RESEARCH （ 影响因子：8.3； 五年影响因子：8.2 ）

ISSN： 0013-9351

年卷期： 2023 年 237 卷

页码：

收录情况： SCI

摘要： Understanding soil organic carbon (SOC), the largest carbon (C) pool of a terrestrial ecosystem, is essential for mitigating climate change. Currently, the spatial patterns and drivers of SOC in the plantations of tea, a perennial leaf crop, remain unclear. Therefore, the present study surveyed SOC across the main tea-producing areas of China, which is the largest tea producer in the world. We analyzed the soil samples from tea plantations under different scenarios, such as provinces, regions [southwest China (SW), south China (SC), south Yangtze (SY), and north Yangtze (NY)], climatic zones (temperate, subtropical, and tropical), and cultivars [large-leaf (LL) and middle or small-leaf (ML) cultivars]. Preliminary analysis revealed that most tea-producing areas (45%) had SOC content ranging from 10 to 20 g kg-1. The highest SOC was recorded for Yunnan among the various provinces, the SW tea-producing area among the four regions, the tropical region among the different climatic zones, and the areas with LL cultivars compared to those with ML cultivars. Further Pearson correlation analysis demonstrated significant associations between SOC and soil variables and random forest modeling (RF) identified that total nitrogen (TN) and available aluminum [Ava(Al)] of soil explained the maximum differences in SOC. Besides, a large indirect effect of geography (latitude and altitude) on SOC was detected through partial least squares path modeling (PLS-PM) analysis. Thus, the study revealed a high spatial heterogeneity in SOC across the major tea-producing areas of China. The findings also serve as a basis for planning fertilization strategies and C sequestration policies for tea plantations.

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