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Optimizing the Allocation of Trials to Sub-regions in Multi-environment Crop Variety Testing

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  • Maryna Prus

    (Otto von Guericke University of Magdeburg)

  • Hans-Peter Piepho

    (University of Hohenheim)

Abstract

New crop varieties are extensively tested in multi-environment trials in order to obtain a solid empirical basis for recommendations to farmers. When the target population of environments is large and heterogeneous, a division into sub-regions is often advantageous. When designing such trials, the question arises how to allocate trials to the different sub-regions. We consider a solution to this problem assuming a linear mixed model. We propose an analytical approach for computation of optimal designs for best linear unbiased prediction of genotype effects and their pairwise linear contrasts and illustrate the obtained results by a real data example from Indian nation-wide maize variety trials. It is shown that, except in simple cases such as a compound symmetry model, the optimal allocation depends on the variance–covariance structure for genotypic effects nested within sub-regions.

Suggested Citation

  • Maryna Prus & Hans-Peter Piepho, 2021. "Optimizing the Allocation of Trials to Sub-regions in Multi-environment Crop Variety Testing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 267-288, June.
    • Handle: RePEc:spr:jagbes:v:26:y:2021:i:2:d:10.1007_s13253-020-00426-y
    DOI: 10.1007/s13253-020-00426-y
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    References listed on IDEAS

    as
    1. Maryna Prus, 2019. "Optimal designs for minimax-criteria in random coefficient regression models," Statistical Papers, Springer, vol. 60(2), pages 465-478, April.
    2. Maryna Prus & Rainer Schwabe, 2016. "Optimal designs for the prediction of individual parameters in hierarchical models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 175-191, January.
    3. Harman, Radoslav & Prus, Maryna, 2018. "Computing optimal experimental designs with respect to a compound Bayes risk criterion," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 135-141.
    4. 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.
    5. Nicolas Heslot & Vitaliy Feoktistov, 2020. "Optimization of Selective Phenotyping and Population Design for Genomic Prediction," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 579-600, December.
    6. Brian R. Cullis & Alison B. Smith & Nicole A. Cocks & David G. Butler, 2020. "The Design of Early-Stage Plant Breeding Trials Using Genetic Relatedness," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 553-578, December.
    7. Jiang, Jiming & Lahiri, P., 2006. "Estimation of Finite Population Domain Means: A Model-Assisted Empirical Best Prediction Approach," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 301-311, March.
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    Cited by:

    1. Prus, Maryna, 2023. "Optimal designs for prediction of random effects in two-groups models with multivariate response," Journal of Multivariate Analysis, Elsevier, vol. 198(C).

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