Corn Harvest Quality Report 2017/2018

Survey Design and Sampling

Survey Design

For this 2016/2017 Harvest Report, the target population was yellow commodity corn from the 12 key U.S. corn-producing states representing about 93.1% of U.S. corn exports. A proportionate stratified, random sampling technique was applied to ensure a sound statistical sampling of the U.S. corn crop at the first stage of the market channel. Three key characteristics define the sampling technique: the stratification of the population to be sampled, the sampling proportion per stratum, and the random sample selection procedure.

Stratification involves dividing the survey population of interest into distinct, non-overlapping subpopulations called strata. For this study, the survey population was corn produced in areas likely to export corn to foreign markets. The U.S. Department of Agriculture (USDA) divides each state into several Agricultural Statistical Districts (ASDs) and estimates corn production for each ASD. The USDA corn production data, accompanied by foreign export estimates, were used to define the survey population in 12 key corn-producing states representing 93.1% of U.S. corn exports (Source: USDA/GIPSA). The ASDs were the subpopulations or strata used for this corn quality survey. From those data, the Council calculated each ASD’s proportion of the total production and foreign exports to determine the sampling proportion (the percent of total samples per ASD) and ultimately, the number of corn samples to be collected from each ASD. The number of samples collected for the 2016/2017 Harvest Report differed among the ASDs, due to their different shares of estimated production and foreign export levels.

The number of samples collected was established so the Council could estimate the true averages of the various quality factors with a certain level of precision. The level of precision chosen for the 2016/2017 Harvest Report was a relative margin of error (Relative ME) no greater than ±10%, estimated at a 95% level of confidence. A Relative ME of ±10% is a reasonable target for biological data such as these corn quality factors.

To determine the number of samples for the targeted Relative ME, ideally the population variance (i.e., the variability of the quality factor in the corn at harvest) for each of the quality factors should be used. The more variation among the levels or values of a quality factor, the more samples needed to estimate the true mean with a given confidence limit. In addition, the variances of the quality factors typically differ from one another. As a result, different sample sizes for each of the quality factors would be needed for the same level of precision.

Since the population variances for the 17 quality factors evaluated for this year’s corn crop were not known, the variance estimates from the 2015/2016 Harvest Report were used as proxies. The variances and ultimately the estimated number of samples needed for the Relative ME of ±10% for 14 quality factors were calculated using the 2015 results of 620 samples. Broken corn, foreign material, and heat damage were not examined. Stress cracks and stress crack index (SCI), with a Relative ME of 11% and 14%, respectively, were the only quality factors for which the Relative ME exceeded ±10% for the U.S. Aggregate. Based on these data, a minimum sample size of 600 would allow the Council to estimate the true averages of the quality characteristics with the desired level of precision for the U.S. Aggregate, with the exception of stress cracks and SCI. However, the targeted number of samples became 617, due to the rounding of targeted numbers of samples per ASD, and the criterion of a minimum of two samples per ASD.

The same approach of proportionate stratified sampling was used for the mycotoxin testing of the corn samples as for the testing of the grade, moisture, chemical, and physical characteristics. In addition to using the same sampling approach, the same level of precision of a Relative ME of ±10%, estimated at a 95% level of confidence, was desired. Testing at least 25% of the minimum number of samples (600) was estimated to provide that level of precision. In other words, testing at least 150 samples would provide a 95% confidence level that the percent of tested samples with aflatoxin results below the FDA action level of 20 parts per billion (ppb) would have a Relative ME of ±10%. In addition, it was estimated that the percent of tested samples with DON results below the FDA advisory level of 5 parts per million (ppm) would also have a Relative ME of ±10%, estimated at a 95% level of confidence. The proportionate stratified sampling approach also required testing at least one sample from each ASD in the sampling area. To meet the sampling criteria of testing 25% of the minimum number of samples (600) and at least one sample from each ASD, the targeted number of samples to test for mycotoxins was 177 samples.


The random selection process was implemented by soliciting local grain elevators in the 12 states by mail, email, and phone. Postage-paid sample kits were mailed to elevators agreeing to provide the 2050- to 2250-gram corn samples requested. Samples were collected from the elevators when at least 30% of the corn in their area had been harvested. The 30% harvest threshold was established to avoid receiving old crop corn samples as farmers cleaned out their bins for the current crop or new crop harvested earlier than normal for reasons such as elevator premium incentives. The individual samples were pulled from inbound farm-originated trucks when the trucks underwent the elevators’ normal testing procedures. The number of samples each elevator provided for the survey depended on the targeted number of samples needed from the ASD along with the number of elevators willing to provide samples. A maximum of four samples from each physical location was collected. A total of 624 unblended corn samples pulled from inbound farm-originated trucks was received from local elevators from September 8 through November 28, 2016, and tested.