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    Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land

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    Abstract
    Recent research in precision agriculture has focused on use of management zones as a method for variable application of inputs. In this paper, five soil and landscape attributes, including a NDVI image, soil electrical conductivity, total nitrogen, organic matter and cation exchange capacity acquired for a coastal saline land were selected as data sources, and their spatial variability were analyzed and spatial distribution maps constructed with geostatistics technique. Principal component analysis and fuzzy c-means clustering algorithm were then performed to delineate the management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) was used to determine the optimal cluster number. To assess whether the defined three management zones can be used to characterize spatial variability of soil chemical properties and crop productivity, 139 georeferenced soil and yield sampling points across each management zone was examined by using variance analysis. It was found that the optimal number of management zones for the study area was three and there existed significantly statistical differences between the chemical properties of soil samples and yield data in each defined management zone, management zone 3 presented highest nutrient level and potential crop productivity, whereas management zone 1 lowest. The results revealed that the given five variables could be aggregated into management zones that characterize spatial variability in soil chemical properties and crop productivity. The defined management zones not only can direct soil sampling design, but also provide valuable information for site-specific management in precision agriculture.
    Article Outline
    1. Introduction
    2. Materials and methods
    2.1. Area description, sampling, and measurements
    2.2. Conventional statistics
    2.3. Geostatistics analysis
    2.4. Principal component analysis
    2.5. Fuzzy c-means clustering algorithm
    3. Results
    3.1. Conventional statistics of soil properties and crop yield
    3.2. Geostatistics analysis
    3.3. Principal component analysis
    3.4. Fuzzy c-means algorithm
    4. Discussion
    5. Conclusion
    Acknowledgements
    References
     

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    作者:Li, Yan, Shi, Zhou, Li, Feng, Li, Hong-Yi 来源:Elsevier 发布时间:2011年07月12日