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Journal of Zhejiang University SCIENCE B

, Volume 9, Issue 1, pp 68–76 | Cite as

Determination of potential management zones from soil electrical conductivity, yield and crop data

  • Yan Li
  • Zhou Shi
  • Ci-fang Wu
  • Hong-yi Li
  • Feng Li
Article

Abstract

One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper, the variables of soil electrical conductivity (EC) data, cotton yield data and normalized difference vegetation index (NDVI) data in an about 15 ha field in a coastal saline land were selected as data resources, and their spatial variabilities were firstly analyzed and spatial distribution maps constructed with geostatistics technique. Then fuzzy c-means clustering algorithm was used to define management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Finally one-way variance analysis was performed on 224 georeferenced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and productivity level. The results reveal that the optimal number of management zones for the present study area was 3 and the defined management zones provided a better description of soil properties and yield variation. Statistical analyses indicate significant differences between the chemical properties of soil samples and crop yield in each management zone, and management zone 3 presented the highest nutrient level and potential crop productivity, whereas management zone 1 the lowest. Based on these findings, we conclude that fuzzy c-means clustering approach can be used to delineate management zones by using the given three variables in the coastal saline soils, and the defined management zones form an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies.

Key words

Management zones Fuzzy clustering Spatial variability Saline land Precision agriculture 

CLC number

S156.4 S127 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Yan Li
    • 1
    • 2
  • Zhou Shi
    • 2
  • Ci-fang Wu
    • 1
  • Hong-yi Li
    • 2
  • Feng Li
    • 3
  1. 1.College of Southeast Land ManagementZhejiang UniversityHangzhouChina
  2. 2.Institute of Agricultural Remote Sensing and Information Technology ApplicationZhejiang UniversityHangzhouChina
  3. 3.Department of Environmental EngineeringZhejiang UniversityHangzhouChina

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