Automating Uniformity Trials to Optimize Precision of Agronomic Field Trials

Plot size has an important impact on variation among plots in agronomic field trials, but is rarely considered during the design process. Uniformity trials can inform a researcher about underlying variance, but are seldom used due to their laborious nature. The objective of this research was to describe variation in maize field trials among field plots of varying size and develop a tool to optimize field-trial design using uniformity-trial statistics. Six uniformity trials were conducted in 2015-2016 in conjunction with Iowa State University and WinField United. All six uniformity trials exhibited a negative asymptotic relationship between variance and plot size. Variance per unit area was reduced over 50% with plots 41.8 m(2) in size and over 75% when using a plot size >111.5 m(2) compared to a 13.9 m(2) plot. Plot shape within a fixed plot size did not influence variance. The data illustrated fewer replicates were needed as plot size increased, since larger plots reduced variability. Use of a Shiny web application is demonstrated that allows a researcher to upload a yield map and consider uniformity-trial statistics to inform plot size and replicate decisions.

Automated summary

Plot size significantly impacts variation in agronomic field trials, with larger plots reducing variability. Optimizing trial design using uniformity-trial statistics can decrease the number of replicates needed and enhance efficiency in field trials.