Abstract: 【Objective】OM, AN, AP, AK determined in 193 topsoil (0-30cm) samples were collected from the cotton field in the 148th regiment of the 7th Agricultural Division in Xinjiang, the study of management zones was achieved by the use of fuzzy c-mean algorithm (FCM).【Method】In this paper fuzzy c-means clustering algorithm was used to delineate management zones and the cotton yield was chosen as the external variable, FPI (Fuzziness Performance Index) and the derivative of the objective function with respect to fuzziness exponent and the adjusted coefficient of determination of the external variable were used to select the most suitable number of classes and fuzziness exponent. 【Result】The optimal number of management zones for the study area was 4. Management zone 1 presented the highest nutrient level, whereas management zone 2 was the lowest. Meanwhile, the mean confusion index of study area was 0.1875.【Conclusion】It was demonstrated that FCM approach combined with geostatistical techniques could produce a desirable prediction results which has more information compared with conventional direct kriging methods. On the other hand, the external variable can help to select the most suitable number of classes and fuzziness exponent.The results revealed that the defined management zones not only direct soil-sampling design, but can be used for fertilizer recommendation to manage soil properties more efficiently in semiarid cotton system of Xinjiang.

Key words: Management zones, Fuzzy clustering, Soil properties, external variable, Oasis field