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Do Spatial Designs Outperform Classic Experimental Designs?

Author

Listed:
  • Raegan Hoefler

    (University of Wisconsin–Madison)

  • Pablo González-Barrios

    (University of Wisconsin–Madison
    Univesidad de la República)

  • Madhav Bhatta

    (University of Wisconsin–Madison)

  • Jose A. R. Nunes

    (University of Wisconsin–Madison
    Federal University of Lavras)

  • Ines Berro

    (University of Wisconsin–Madison
    Univesidad de la República)

  • Rafael S. Nalin

    (Universidade de São Paulo)

  • Alejandra Borges

    (Univesidad de la República)

  • Eduardo Covarrubias

    (CGIAR Excellence in Breeding Platform (EiB)
    International Maize and Wheat Improvement Center (CIMMYT))

  • Luis Diaz-Garcia

    (Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias)

  • Martin Quincke

    (Instituto Nacional de Investigación Agropecuaria)

  • Lucia Gutierrez

    (University of Wisconsin–Madison
    Univesidad de la República)

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.

Suggested Citation

  • 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 & Lucia Gutierrez, 2020. "Do Spatial Designs Outperform Classic Experimental Designs?," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 523-552, December.
  • Handle: RePEc:spr:jagbes:v:25:y:2020:i:4:d:10.1007_s13253-020-00406-2
    DOI: 10.1007/s13253-020-00406-2
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    References listed on IDEAS

    as
    1. E. R. Williams & J. A. John, 1989. "Construction of Row and Column Designs with Contiguous Replicates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(1), pages 149-154, March.
    2. Júlio S. de S. Bueno Filho & Steven G. Gilmour, 2003. "Planning Incomplete Block Experiments When Treatments Are Genetically Related," Biometrics, The International Biometric Society, vol. 59(2), pages 375-381, June.
    3. R. A. Kempton & C. W. Howes, 1981. "The Use of Neighbouring Plot Values in the Analysis of Variety Trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(1), pages 59-70, March.
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    Cited by:

    1. Hans-Peter Piepho & Robert J. Tempelman & Emlyn R. Williams, 2020. "Guest Editors’ Introduction to the Special Issue on “Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 453-456, December.

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