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Four Considerations for Assessing Field Trial Validity

Reviewing and interpreting field trial data can help producers to determine whether to adopt a given seed hybrid or variety, yield-enhancing treatment or other crop production input. All data aren’t equal, however. The methods and procedures used to design a trial, execute it and report its results influence the quality of those data. The following four questions are factors that you can consider when assessing data shared from a field trial.

Replacement Image 51. Are the trial procedures and data collection methods consistent? Consistently implementing field trial procedures will ensure that each data point had the same treatment. Field trial reports that share about these procedures provide the insight necessary for understanding the trial’s execution and reported results.

2. Did the trial use enough replications in different environments? Trials that use multiple replications reduce the likelihood that the trial data are one-off occurrences. When a trial includes data points from multiple environments, its results have more predictive power, according to a guide from Iowa State University. Because environmental and other factors vary by year, a treatment that yields good performance in varying environments may represent a treatment that has the greatest likelihood to perform well in the future given uncertain circumstances.

3. Was a third party involved? In field trials, a third party’s involvement can promote credibility for the data being reported. A third party has no stake invested in the trial’s outcome. As a result, that entity can independently provide results. Companies that conduct their own trials may use good methodology and produce defendable results. However, a third party may impart added confidence in the data.

4. Are results from different treatments significant? Good trial data will report a measure of significance that producers can use to evaluate whether a treatment itself caused a difference in yield or whether the differences being reported could be attributed to error or random chance. Reviewing the data used to develop a trial’s summary can provide necessary insight into data significance and other important statistics. For more information about significance measures, refer to this guide from Iowa State University.

Using its BigSmartPlotsTM system, the BigYield.usTM team prioritizes data quality. Through its trials, BigYield.usTM seeks to identify products and practices that growers can use to achieve specific yield goals and produce big yields in today’s low commodity price environment. For more information about the BigSmartPlotsTM program, contact a BigYield.usTM professional at 844-242-4367.

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