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ARS Home » Midwest Area » Urbana, Illinois » Global Change and Photosynthesis Research » Research » Publications at this Location » Publication #300017

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

item Williams, Martin

Submitted to: Field Crops Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/3/2013
Publication Date: 1/1/2014
Citation: Williams, M. 2014. Few crop traits accurately predict variables important to productivity of processing sweet corn. Field Crops Research. 157:20-26.

Interpretive Summary: Measurement of ‘yield’ is critical in research aimed to improve crop production. For most agronomic crops, grain yield directly relates to both productivity of the plant and revenue to the grower. In contrast, important ‘yield’ metrics in processed vegetables differ between the processor, who makes several important crop production decisions, and the grower, who is contracted to produce the crop. For instance in processing sweet corn, nearly all published field studies report number or mass of freshly harvested ears; metrics of limited usefulness to the processor. This work determined how well these and other crop traits characterize yield metrics important to the sweet corn processor. Results showed that fresh kernel mass, though rarely measured, is a far superior measurement of yield in processing sweet corn. Measuring kernel mass in sweet corn production comes at an expense; however, failing to collect and report this data greatly limits the usefulness and relevancy of the research to the vegetable processing industry.

Technical Abstract: 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 (' = 0.869) with ear number, green ear mass, husked ear mass, and fresh kernel mass. imilar correlations (' = 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. owever, 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.