Abstract
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 m2 in size and over 75% when using a plot size >111.5 m2 compared to a 13.9 m2 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.
Highlights
Designing field experiments with adequate precision for detecting meaningful differences among treatments is challenging when the magnitudes of economic or biologically significant differences are small [1]
Variance per unit area was reduced over 50% with plots 41.8 m2 in size and over 75% when using a plot size >111.5 m2 compared to a 13.9 m2 plot
Smith [6] developed an empirical law for describing heterogeneity among field plots based on this nonlinear relationship: Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Summary
Designing field experiments with adequate precision for detecting meaningful differences among treatments is challenging when the magnitudes of economic or biologically significant differences are small [1]. The power to test such differences depends on the magnitude of mean differences, and on the chosen alpha level, the number of samples represented in each treatment mean, and the underlying variance among field plots for whatever measurement is being made [2]. The reciprocal relationship between experimental variance and field-plot size has been noted and studied by several researchers [3,4,5]. Smith [6] developed an empirical law for describing heterogeneity among field plots based on this nonlinear relationship: Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Vs =.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
