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林草资源研究 ›› 2024›› Issue (2): 1-7.doi: 10.13466/j.cnki.lczyyj.2024.02.001

• 科学研究 • 上一篇    下一篇

祁连山典型植被土壤有机碳分布特征及其影响因素

姜生秀1(), 赵鹏1, 张俊年2, 李得禄1, 刘子玺2   

  1. 1.甘肃省治沙研究所 荒漠化与风沙灾害防治国家重点实验室,兰州 730070
    2.甘肃祁连山国家级自然保护区管护中心,甘肃 张掖 734000
  • 收稿日期:2024-03-06 修回日期:2024-04-15 出版日期:2024-04-28 发布日期:2024-09-02
  • 作者简介:姜生秀,副研究员,主要从事植物生理研究。Email:yanyunjiang1987@163.com
  • 基金资助:
    中华环保基金青山守护行动“祁连山国家公园典型生态系统碳储量评估研究”(CEPFQS202169-21)

Characteristics and Influencing Factors of Soil Organic Carbon in Typical Vegetation in the Qilian Mountains

JIANG Shengxiu1(), ZHAO Peng1, ZHANG Junnian2, LI Delu1, LIU Zixi2   

  1. 1. Gansu Desert Control Research Institute,State Key Laboratory of Desertification and Aeolian Sand Disaster Combating,Lanzhou 730070,China
    2. Gansu Qilian Mountain National Nature Reserve Management and Protection Center,Zhangye 734000,Gansu,China
  • Received:2024-03-06 Revised:2024-04-15 Online:2024-04-28 Published:2024-09-02

摘要:

探讨祁连山典型植被土壤有机碳空间分布特征及其影响因素,可为评估该地区森林土壤固碳效应提供科学依据。以退耕地为对照,以草地、天然乔木(青海云杉、祁连圆柏)、人工乔木(落叶松)和灌木5种典型植被为研究对象,测定0~100 cm深度土壤有机碳含量、有机碳密度、土壤粒度、pH、电导率、全氮、全钾和全磷含量,比较分析不同植被土壤有机碳组分的分布差异及其主要影响因素。结果表明:1)不同植被类型平均土壤有机碳含量排序为青海云杉(5.99 g/kg)>祁连圆柏(5.59 g/kg)>落叶松(2.91 g/kg)>灌木(1.83 g/kg)>草地(1.66 g/kg)>退耕地(1.16 g/kg);平均土壤有机碳密度排序为青海云杉(3.43 kg/m2)>祁连圆柏(2.76 kg/m2)>落叶松(2.16 kg/m2)>灌木(2.08 kg/m2)>草地(2.00 kg/m2)>退耕地(1.33 kg/m2)。2)随着土层的加深,不同植被土壤有机碳含量整体上呈减小的趋势,土壤有机碳密度呈先增后减的趋势。3)不同植被类型土壤C/N为9.30~15.73,均值表现为青海云杉(14.96)>草地(11.66)>退耕地(11.54)>灌木(10.83)>落叶松(10.69)>祁连圆柏(10.63)。4)不同植被类型土壤有机碳和全氮含量均呈极显著正相关(P<0.01),有机碳与全磷、黏粒、粉粒呈极显著或显著正相关(P<0.01,P<0.05),有机碳与砂粒含量呈极显著或显著负相关(P<0.01,P<0.05),有机碳与pH均呈显著负相关(P<0.05)。祁连山自然生长的乔木土壤有机碳固持能力较好,在建设碳汇林时应充分考虑土壤有机碳的影响,优先选择乔木植被。

关键词: 祁连山, 典型植被, 有机碳含量, 有机碳密度, 影响因素

Abstract:

By exploring the spatial distribution characteristics and influencing factors of soil organic carbon in the typical vegetation of the Qilian Mountains,we aimed to provide a scientific basis for evaluating the carbon sequestration effects of forest soil in the region.Using converted cultivated land as a control,five typical vegetation types were studied:grassland,natural trees(Picea crassifolia,Sabina przewalskii),artificial trees(Larix gmelinii)and shrub.Soil organic carbon content,organic carbon density,soil grades,pH,electrical conductivity,total nitrogen,total potassium,and total phosphorus were measured at depths of 0-100 cm compare and analyze the distribution differences and main influencing factors of organic carbon components in different vegetation soils.Results showed:1)The average soil organic carbon content and organic carbon density under different vegetation types were as follows:P.crassifolia(5.99 g/kg,3.43 kg/m2,respectively)>S.przewalskii(5.59 g/kg,2.76 kg/m2,respectively)>L.gmelinii(2.91 g/kg,2.16 kg/m2,respectively)>shrub(1.83 g/kg,2.08 kg/m2,respectively)>grassland(1.66 g/kg,2.00 kg/m2,respectively)>abandoned lands(1.16 g/kg,1.33 kg/m2,respectively).2)With soil layer deepening,the overall organic carbon content of different vegetation soils tended to decrease,while the density of soil organic carbon showed a trend of first increasing and then decreasing.3)In the 0-100 cm soil layer,the soil C/N content of different vegetation types ranged from 9.30 to 15.73,with mean values as follows:P.crassifolia(14.96)>grassland(11.66)>abandoned land(11.54)>shrub(10.83)>L.gmelinii(10.69)>S.przewalskii(10.63).4)Organic carbon and total nitrogen content in different vegetation types showed a highly significant positive correlation(P<0.01),and a highly significant or significant positive correlation with total phosphorus,clay,and silt(P<0.01,P<0.05),a highly significant or significant negative correlation with sand content(P<0.01,P<0.05),and a significant negative correlation with pH(P<0.05).Thus the soil organic carbon retention capacity of natural trees in the Qilian Mountains is superior.Future carbon sequestration forestry construction should consider factors affecting soil organic carbon and prioritize dominant vegetation.

Key words: Qilian Mountain, typical vegetation, organic carbon content, organic carbon density, influence factor

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