Interpreting Yield Data of Seed Products
BY Dairyland Seed Agronomy Team
This is an exciting time of the year with combines rolling across the Midwest. Part of that excitement is in validating management decisions or trying something new that unlocks more yield potential for your own farm. We’re talking about anything from seed to herbicide programs. You could also reference data from an outside source to help drive decisions, including Dairyland Seed’s Product and Agronomy Research (PAR) Plots. Here are some things to ponder as you digest harvest data and plan for 2023.
- Balance your analysis with local and regional results. Seek data from environments that are similar to your target environment. There might be something to learn, even if data is not within your exact geography.
- Consistency of a product or practice should be a high priority, possibly more than pure yield. It’s nice to study trial winners but look for those which are consistently in the top 1/3.
- Find benchmark or check varieties that are entered in many trials to help draw conclusions across testing programs or locations.
- If looking at strip trial results, pay attention to the number of repetitions and realize that data may not be Look beyond the average yield data. If Product X averaged 250bu and Product Y averaged 230bu, were they at the same locations the same number of times? If so, the data is balanced. If not, look at performance in relation to the average of the experiment because a measure of bushels may not be a fair comparison.
- For seed, consider maturity or moisture in your evaluations: later maturing varieties bring more yield. A plot winner may not be as impressive if its 5 points wetter than average at harvest. Compare products of similar maturity. On the flip side, its impressive when a really dry product finishes near the top!
- Small-plot statistics lingo
- Coefficient of Variance (CV): standard deviation ÷ trial mean. Standard deviation is a measure of the dispersion of data points around the mean. Higher CV=more variation.
- Least significant difference (LSD): based on the variation in a trial, this is the minimum difference between products required to call them statistically different.
Testing Type |
Pros |
Cons |
Whole Field |
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Strip Trials |
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Small Plots |
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Brian Weller Western Region 507.456.3034 |
Dan Ritter Central Region 219.863.0583 |
Branden Furseth Northern Region 608.513.4265 |
Mark Gibson Eastern Region 260.330.8968 |
Amanda Goffnett Eastern Region 989.400.3793 |