资源科学 ›› 2018, Vol. 40 ›› Issue (1): 44-52.doi: 10.18402/resci.2018.01.05

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基于水环境质量控制的高集约化农业景观格局优化研究

李洪庆1(), 刘黎明2(), 郑菲1, 赵姚阳1   

  1. 1. 河海大学公共管理学院,南京 210098
    2. 中国农业大学资源与环境学院,北京 100193
  • 收稿日期:2017-05-26 修回日期:2017-10-09 出版日期:2018-01-20 发布日期:2018-01-20
  • 作者简介:

    作者简介: 李洪庆,男,山东烟台人,博士,讲师,主要研究方向为土地利用环境风险控制与景观生态。E-mail:lihongqing163@126.com

  • 基金资助:
    中央高校基本科研业务费专项资金(2015B13614);国家自然科学基金重点项目(41130526)

Agricultural landscape pattern optimization of high intensive agricultural areas based on water quality control

Hongqing LI1(), Liming LIU2(), Fei ZHENG1, Yaoyang ZHAO1   

  1. 1. College of public administration, Hohai university, Nanjing 210098, China
    2. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
  • Received:2017-05-26 Revised:2017-10-09 Online:2018-01-20 Published:2018-01-20

摘要:

农业非点源污染治理已经成为制约高集约化农业区可持续发展的瓶颈之一。本文以洞庭湖高集约化农业区金井镇为案例,在分析景观格局和水污染特点的基础上,采用多元回归法建立景观格局指数与水环境质量季节关系模型,基于模拟结果分别从景观格局优化、生活废水和固体废弃物处理、养殖业调控出发,提出农业景观格局优化模式。评价结果表明:优化后水环境质量提升,总氮浓度控制在地表水环境质量最低标准内,农业直接经济效益和生态系统服务价值均高于基准年。本文提出的模型构建方法不仅验证了景观格局与水环境质量关系,也为农业景观格局优化提供依据,其结果可为政府制定农业可持续发展、生态环境保护等提供科学依据。

关键词: 景观格局, 水环境质量, 回归模型, 综合评价

Abstract:

Agricultural non-point source pollution control has become a bottleneck for sustainable development especially in high intensive agricultural areas in China. Therefore, maintaining economic growth along with water quality improvements is a great challenge for policymakers. Taking Jinjing Town as a case study, we first analyzed water quality dynamics at spatial and temporal scales based on pollution monitoring data. Then, three different width buffer areas along the river were drawn to analyze the correlation between landscape pattern and water quality pollutions according to Pearson's correlation coefficient analysis; catchment was chosen as the suitable scale, and four landscape types forest, tea garden, paddy land and residential were chosen as major landscape parameters. A multiple regression analysis model was adopted to build the relationship between landscape pattern characteristics and water quality pollution for NO3--N, NH4+-N and TN for each season. According to the results, we designed agricultural landscape pattern modes from three aspects, one is landscape pattern optimization by changing land use types, shape and area; another is raising sanitary wastewater and solid waste treatment rate; and the third is livestock industry adjustment by livestock amount control. The assessment results show that water quality improves effectively and nitrogen pollutant concentration meets level V of environmental quality standards for surface water in the new landscape scenario. Biodiversity benefits, agricultural economic value, ecosystem service value and household net agricultural income are higher than in 2010. The relationship model method proposed here not only verifies the relationship between landscape pattern and water quality, but supports landscape pattern design. These landscape pattern optimization results will assist agricultural sustainable development and environmental protection planning for decision-makers.

Key words: landscape pattern, water quality, Regression Model, integrated assessment