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

作　　者：Yan LI Zhou SHI Ci-fang WU Hong-yi LI Feng LI 

机构地区：[1]College of Southeast Land Management, Zhejiang University, Hangzhou 310029, China [2]Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310029, China [3]Department of Environmental Engineering, Zhejiang University, Hangzhou 310029, China

出　　处：《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》2008年第1期68-76,共9页浙江大学学报（英文版）B辑（生物医学与生物技术）

基　　金：Project supported by the National Natural Science Foundation of China (Nos. 40701007 and 40571066);the Postdoctoral Science Foundation of China (No. 20060401048)

摘　　要：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 differ-ence 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 georefer-enced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and produc-tivity 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.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 abetter 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.

关 键 词：Management zones Fuzzy clustering Spatial variability Saline land Precision agriculture 

分 类 号：S156.4[农业科学—土壤学] S127[农业科学—农业基础科学]