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Corn test plot results can be used to compare products in similar growing environments and can be used to help growers make selection decisions for the next season. When comparing yield data, always evaluate multiple locations, scenarios, and head-to-head comparisons. Yield data can also be presented differently depending on the source, so be sure to consider significant differences when statistics is provided.
To access corn test plot and yield results, begin by looking online by brand or contacting your local seed representative. You may also consider looking at independent and university performance trials to see how certain products performed in a range of different locations and scenarios. These tests are generally conducted by maturity range with seed from a broad array of sources and tested across a variety of growing conditions within a state or region.
When evaluating test plot results, it is important to understand that no corn product, even if it is truly superior, will win every yield plot. Over many tests, industry-leading products have typical head-to-head winning percentages of only 60 to 65%. Environmental factors, genetic potential, and test variability constitute the variables that contribute to yield differences across test plot sites. You should look for the products that are consistently among the top tier in several locations in different test plots. The more data and comparisons evaluated, the better confidence level in selecting corn products. Following are suggestions to help evaluate and understand test plot results:
Figure 1. Examples of Least Significant Difference and Coefficient of Variation.
Least Significant Difference (LSD): This example is a subset of data pulled from a university field trial whose LSD was 9 at a significance level of 0.1. The difference in yield between products A and B is 12 bu/acre. Since this is greater than the LSD of 9, we are 90% certain that the yield level is indeed different and not likely due to experimental variation in the field, but to genetic differences. Difference in yield between products B and C is 7 bu/acre. Since this is less than the LSD of 9, we cannot conclude that the yield levels are significantly different and the difference is likely due to experimental variation in the field and not genetic differences.
Coefficient of Variation (CV): The CV can be determined by dividing the standard deviation (STD DEV) by the average (AVG) and multiplying by 100 to express as a percentage. The CV calculated from this example is 2.21%. A CV of less than 15% is desired and the closer it is to zero, the lower the amount of variability in the data.