Few crop traits accurately predict variables important to productivity of processing sweet corn

• Field research on sweet corn ‘yield’ often reports only ear number or ear mass.
• Processor decisions rely on recovery, case production, and gross profit margin.
• Effort to collect fresh kernel mass data greatly improves relevance of field research.

Recovery, case production, and gross profit margin, hereafter called ‘processor variables’, are as important metrics to processing sweet corn as grain yield is to field corn production. However, crop traits such as ear number or ear mass alone are reported in sweet corn production research rather than processor variables. The objective of this research was to determine the extent to which certain crop traits could be used to predict variables important to productivity of sweet corn grown for processing. The data used in this research reflected 22 different growing environments over an 8-year period representing 31 processing hybrids. Relations between processor variables and 17 crop traits (5 plant traits, 8 ear traits, and 4 yield traits) were characterized. None of the crop traits adequately predicted recovery, defined as the percentage of green ear mass (i.e. complete ears with husk leaves) represented by fresh kernel mass. Case production, defined as cases of kernels per unit area, was strongly associated (ρ ≥ 0.869) with ear number, green ear mass, husked ear mass, and fresh kernel mass. Similar correlations (ρ ≥ 0.854) were found between the yield traits and gross profit margin, defined as the value of case production less the contracted cost of green ear mass. However, regression analyses of relationships between processor variables and individual yield traits showed that fresh kernel mass was by far the best predictor of case production and gross profit margin. While ear number or green ear mass are commonly reported in field research of processing sweet corn, relevancy of the research would be enhanced if fresh kernel mass were measured and reported.