Creating spatially contiguous yield classes for site-specific management

Annual yield maps are spatially fragmented because of random variation caused by crop management as well as measurement errors. Two approaches for creating maps of spatially contiguous yield classes were evaluated at two irrigated sites. In the first approach, prior-classification interpolation (PCI), grid size was increased from 4, 8, 16, and 32 to 64 m by kriging interpolation before cluster analysis used for mapping yield classes. Choosing a coarse resolution (>16 m) for yield interpolation before spatial classification resulted in maps that did not accurately depict yield patterns, significant decline of the yield variance accounted for, and loss of resolution in areas of sharp yield transitions caused by irrigation or near the field borders. In the second approach, postclassification filtering (PCF), cluster analysis of mean relative yield was conducted on the smallest grid size (4 m), and the classification results were postprocessed using a spatial filtering algorithm with window sizes that were equivalent to the 8-, 16-, 32-, and 64-m grid sizes used in PCI. This procedure removed erroneous map fragmentation and created maps of contiguous yield classes while preserving the class means and general yield patterns at high spatial resolution. Window sizes for spatial filtering of yield maps should be in the 30- to 60-m range. Landscape pattern metrics may offer new potential for assessing mapping techniques as well as comparing agricultural production fields with regard to ranking their relative opportunities for site-specific crop management.