Evaluating and Understanding Corn Test Plot Results

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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.

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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.

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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:

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Figure 1. Examples of Least Significant Difference and Coefficient of Variation.

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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.

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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.

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  • Evaluate Multiple Locations. This helps to achieve the most accurate look at product performance and consistency. Data from a single plot location near one’s farm is only one snapshot of performance, and may not provide a complete picture of product potential. Products may yield well at one location and poorly at another. Weather, fertility, and insect pressure are just a few variables that can affect product performance across locations. Therefore, evaluating products across multiple locations allows the greatest opportunity to get an accurate picture of performance and consistency. If there is data available, an evaluation of product performance across years is also beneficial.
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  • Seek Head-to-Head Comparisons. Compare corn products in multiple head-to-head trials. In large strip trials with many entries, it may be tempting to compare two products in the same plot. However, if product A is entry #3 and product B is entry #15, it may not make sense to compare the two when they are located so far from each other in the plot. If periodic check products are planted in a field, it is better to compare each product to the nearest check product. The purpose of the check product is to provide a relative measure of performance in that general area of the field.
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  • Look at Field History. Field management can also affect product performance. When was the field planted? What was the crop rotation? How much tillage was involved? Was a soil insecticide used? How were weeds controlled? What traits were in the product and how did they contribute to yield? There are factors aside from the geography and growing season that can influence a product’s performance.
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  • Consider Statistical Differences and Reliability. Statistical differences signify that the results are unlikely to have occurred by chance and have a high probability of repeating themselves. University and independent plot results may include an LSD (least significant difference) and CV (coefficient of variance). Figure1 shows an example of evaluating products using LSD and CV values.