Nutrient Cycling in Agroecosystems

, Volume 90, Issue 2, pp 243–252 | Cite as

GIS-model based estimation of nitrogen leaching from croplands of China

  • Jianjun Qiu
  • Hu Li
  • Ligang Wang
  • Huajun Tang
  • Changsheng Li
  • Eric Van Ranst
Original Article

Abstract

Nitrogen (N) is the most widely used fertilizer nutrient, and its application has increased substantially in recent decades in China. N loss through leaching has been recognized as one of the most common agricultural sources of groundwater contamination. Thus, prediction of N leaching from cropland is crucial for preventing groundwater pollution. This paper quantifies nitrogen leaching from China’s croplands, identifies its spatial distribution under current cropping systems at national scale, and finally puts forward some policies or strategies to reduce rates of N leaching. A computer process simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems (DNDC) was applied to predict nitrogen leaching in the soil layer of agricultural ecosystems at national scale. Data on climate, soil properties, cropping systems, acreage, and management practices at county scale were collected from various sources and integrated into a spatial GIS database to run the model. The total amount of N-leaching was predicted at 4.57 million t N/year, which is equivalent to 48 kg N per ha cropland in 1998. The spatial distribution of N leaching in China showed a sharp discrepancy between the northern and southern counties due to the differences in climatic conditions, soil properties, as well as farm management practices. The study also suggests that applying management alternatives, such as proper fertilizer, crop, water and soil management, could be efficient means for decreasing N leaching rates.

Keywords

Nitrogen leaching Croplands of China DNDC model Spatial distribution 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Jianjun Qiu
    • 1
  • Hu Li
    • 1
  • Ligang Wang
    • 1
  • Huajun Tang
    • 1
  • Changsheng Li
    • 2
  • Eric Van Ranst
    • 3
  1. 1.Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences (CAAS)/Key Laboratory of Resources Remote Sensing and Digital Agriculture, Ministry of AgricultureBeijingChina
  2. 2.Institute for the Study of Earth, Oceans and SpaceUniversity of New HampshireDurhamUSA
  3. 3.Department of Geology and Soil Science (WE13), Laboratory of Soil ScienceGhent UniversityGhentBelgium

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