Abstract: Recent research in precision agriculture has focused on use of management zone as a method to more efficiently apply crop inputs and precise soil management. In this paper, the variables of NDVI data，soil salinity and some nutrient data were selected as clustering variables, fuzzy c-means clustering algorithm was used to define management zone in an about 10.5km2 field in coastal saline region. The results revealed that the optimal number of management zones for the study area was 3. To assess whether the defined management zones can be used to characterize spatial variability in soil chemical properties and crop productivity, 139 georeferenced soil sampling points and yield data that in the was examined by using One-way ANOVA. It was found that the there exist significantly statistical differences between the chemical properties of soil samples and yield data in each defined management, and management zone 3 presented highest nutrient level and potential crop productivity, whereas management zone 1 lowest. The results revealed that fuzzy c-means clustering algorithm can be used to delineate management zones by using the given variables. The defined management zones not only can direct the sampling design, but can be used for fertilizer recommendation to manage soil nutrient and salinity more efficiently in coastal saline field.

Key words: Fuzzy clustering, Management zones, Saline land, Precision agriculture