Do Spatial Designs Outperform Classic Experimental Designs?

Raegan Hoefler, Pablo González-Barrios, Madhav Bhatta, Jose A. R. Nunes, Ines Berro, Rafael S. Nalin, Alejandra Borges, Eduardo Covarrubias, Luis Diaz-Garcia, Martin Quincke and Lucia Gutierrez ()
Additional contact information
Raegan Hoefler: University of Wisconsin–Madison
Pablo González-Barrios: University of Wisconsin–Madison
Madhav Bhatta: University of Wisconsin–Madison
Jose A. R. Nunes: University of Wisconsin–Madison
Ines Berro: University of Wisconsin–Madison
Rafael S. Nalin: Universidade de São Paulo
Alejandra Borges: Univesidad de la República
Eduardo Covarrubias: CGIAR Excellence in Breeding Platform (EiB)
Luis Diaz-Garcia: Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias
Martin Quincke: Instituto Nacional de Investigación Agropecuaria
Lucia Gutierrez: University of Wisconsin–Madison

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 4, No 5, 523-552

Abstract: Abstract Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a two-dimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments. However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1 $$\times $$ × AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments. Supplementary materials accompanying this paper appear online.

Keywords: Experimental design; Autoregressive process; Prediction accuracy; Response to selection; Spatial correction; Randomization-based experimental designs (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-020-00406-2

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