Throughout Premier Crop’s nearly 20-year history, we’ve perhaps been the most diligent at communicating that what we do – big data analysis – would be considered “observational data analysis,” which can show relationships and correlations, but stops short of providing cause and effect.
Within crop production and agronomy scientific circles, making decisions using observational data analysis has been viewed as inferior and some would argue its an informed guess.
It’s only been in the last few years, with the dynamic investments by major ag companies, that millions of yield observations have been validated as valuable in crop production decision-making. For decades, the foundation of agronomic knowledge has been the trial results from small randomized and replicated plots.
That experimental design and the statistical analysis dates back to the 1930s, analysis of variance (ANOVA) created by Sir Ronald Fisher, whom many consider the father of modern statistics. All universities and industry companies have adopted and use replicated plots as the gold standard for conducting trials and proving that change in treatment actually causes change in yield.
Premier Crop’s new Enhanced Learning Blocks (ELBs), built to enable randomization and replication of trials in the farmer’s own fields, allow us to move beyond correlation and experimentally establish causation.
ELBs represent a breakthrough in generating new agronomic knowledge cost-effectively, and on a much larger scale than ever before possible.
