Evaluating Yield Data

Corn and soybean seed product selection and placement are the most important decisions a farmer can make to attain full yield potential. Performance data and qualitative product assessments at harvest can be invaluable for making proper seed selection decisions. It’s important to remember that trial results will vary with the climate and production conditions at each location and the same product may not produce the same results unless it’s produced under similar conditions. Multi-location and multi-year data can improve the predictability of selection decisions. It is best to review data from trials that use randomization, replication, and multiple locations within a region.

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Multi-Year Data. Consider selecting products that are proven to be high-yielding for two or more consecutive years. Single year data has a lower predictive probability.

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Evaluate Multiple Locations to help achieve the most accurate look at product performance and consistency. Consider performance consistency based on geography. Some products may provide great yield in one location and yield poorly in another. Numerous variables, such as weather, fertility, and insect pressure can affect product performance across locations. Consider products that have a stable performance over a range of environmental conditions. If there is inconsistency across locations, the predictive ability of the data is compromised. The best data are those that show consistency in performance over years and across locations.

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Seek Head-to-Head Comparisons. Compare products at multiple locations. If applicable, compare each product to the nearest check product in close proximity to it.

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Look at Field History. Factors to consider in addition to geography and seasonal growing conditions include: field management history such as planting timeframe, crop rotation, tillage type, and how insects and weeds were controlled. If traits were present in the product, consider how they contributed to the performance.

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Consider Statistical Differences and Reliability. Most university and independent yield data that you access includes a measure of experimental variability, Least Significant Difference (LSD) and Coefficient of Variation (CV), which basically shows what amount of yield variation can be attributed to the product itself versus influence from outside factors. Yield differences greater than the LSD can be attributed to actual differences in genetic yield potential of products. Yield differences less than the LSD are not considered statistically different and are likely due to outside factors. A high CV indicates greater experimental variability, which results in the yield data having a lower predictive value. A low CV generally results from a more uniform plot location. A CV of 15% or less is desirable for field test results and the closer it is to zero, the higher the data quality.