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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

  • Ultimate representation of local environment
  • True on-farm performance
  • No specialized equipment required
  • Requires high resource level
  • Relatively low number of variables of varieties can be tested
  • Often lack replications
  • Difficult to isolate variables from one another

 

Strip Trials

  • Easy to implement on the farm
  • Balances environmental focus of whole field testing with genetic focus of small plot
  • Low resource investment: use current equipment and technology
  • Risk of making decisions with a low amount of data: still need to pool data with other locations
  • 8-15 side-by-side repetitions are needed before we can adequately predict future performance.

 

Small Plots

  • Applies a uniform environment to all variables. ie isolates the genetic differences of seed products
  • Can test a high number of varieties
  • High confidence in predicting future outcomes for a given environment
  • Not practical at the farm level: need specialized equipment
  • Need many locations to capture different environments
  • Design and analysis require statistical understanding

 

Brian Weller Dan Ritter Branden Furseth Mark Gibson Amanda Goffnett
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
Enjoying our Agronomy Updates? Suggestions for topics you'd like us to weigh in on? Drop us an email at dairylandseed@dairylandseed.com. We'd love to hear from you!
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