Plots Suck. Part II.

Part I of this series showed that having confidence in the results of a plot matters. It’s asking an awful lot of a 5 or 10-acre strip plot to represent hybrid selection across hundreds or thousands of acres. When seed companies use this method they rely on dozens to hundreds of them to understand how different environments impact hybrid performance. This is commonly referred to as GxE, or genetics by environment. But not every grower manages their crops the same so having different locations introduces another factor – management (M). Now we have a GxExM interaction and you can quickly see how the whole picture of product performance can get muddled (e.g. one grower may apply fungicide or late season nitrogen while another doesn’t). That’s why quality data for an individual plot are critical. The data are important to you for your farm but also think of it as a data link in a decision chain.

Are there other ways of evaluating hybrids? Sure. I personally prefer large strips of 5 or more acres for each individual hybrid being tested. But, the yield has to be checked with a weigh wagon or a calibrated yield monitoring system. Using yield maps is fine as long as the data are of good quality. So what are sources of error that can impact plot results? Here are a few:

Harvest ruts can impact next year’s yield but what happens when a plot entry is planted in this affected area while the others are not?

  • Hybrids planted in the pinch rows (see Plots Suck, Part I).
  • Plot planted last, long after most acres were planted.
  • Placing the plot in a part of the field that results in an entry being planted in an undesirable spot that clearly impacts yield (e.g. an entry happens to be planted in a low spot).
  • Supplied seed has different seed sizes and no adjustments are made resulting in different stands across the plot.
  • Sprayer runs over or “leans over” some of the rows of a single entry but not the same number across all entries in the plot.

It’s not possible to eliminate every source of error. Remember, the goal is to have each entry in the plot treated equally and to walk away at the end of harvest with a high degree of confidence that the results are correct. When we achieve this we can say, “that plot didn’t suck.”

I want y’all to do well. God bless our farmers!