Crop yield maps reflect stable yield patterns and annual random yield variation. Procedures for classifying a sequence of yield maps to delineate yield zones were evaluated in two irrigated maize (Zea mays L.) fields. Yield classes were created using empirically defined yield categories or through hierarchical or nonhierarchical cluster analysis techniques. Cluster analysis was conducted using average yield (MY), average yield and its standard deviation (MS), or all individual years (AY) as input variables. All methods were compared based on the average yield variability accounted for (RVc). Methods in which yield was empirically classified into three or four classes accounted for less than 54% of the yield variability observed and failed to delineate high-yielding areas. Six to seven yield classes established by cluster analysis of MY accounted for 60 to 66% of the yield variability. Differences among cluster analysis methods were small for MY as data source. However, fuzzy-k-means clustering had lower RVc than other methods if used with the MS or AY data. The spatial fragmentation of yield class maps increased in the order MY < MS < AY. Univariate cluster analysis of mean relative yield measured for at least 5 yr should be used for yield classification in irrigated fields where six to seven classes appear to provide sufficient resolution of the yield variability observed. More research should be conducted to develop methods that result in spatially coherent yield zones and to understand differences between rainfed and irrigated environments in the importance of mapping yield goals for crop management.
