One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to:(1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 9 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a nonparametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approach.

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