IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v26y2021i2d10.1007_s13253-020-00426-y.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-020-00426-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-020-00426-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Maryna Prus, 2019. "Optimal designs for minimax-criteria in random coefficient regression models," Statistical Papers, Springer, vol. 60(2), pages 465-478, April.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin P. Boer & Hans-Peter Piepho & Emlyn R. Williams, 2020. "Linear Variance, P-splines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related?," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 676-698, December.
    2. Lei He & Rong-Xian Yue, 2021. "D-optimal designs for hierarchical linear models with intraclass covariance structure," Statistical Papers, Springer, vol. 62(3), pages 1349-1361, June.
    3. Liu, Xin & Ye, Min & Yue, Rong-Xian, 2021. "Optimal designs for comparing population curves in hierarchical models," Statistics & Probability Letters, Elsevier, vol. 178(C).
    4. 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.
    5. He, Lei & He, Daojiang, 2020. "R-optimal designs for individual prediction in random coefficient regression models," Statistics & Probability Letters, Elsevier, vol. 159(C).
    6. Maryna Prus, 2020. "Optimal designs in multiple group random coefficient regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 233-254, March.
    7. 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).
    8. Xin Liu & Rong‐Xian Yue & Weng Kee Wong, 2022. "Equivalence theorems for c and DA‐optimality for linear mixed effects models with applications to multitreatment group assignments in health care," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1842-1859, December.
    9. Maryna Prus, 2019. "Optimal designs for minimax-criteria in random coefficient regression models," Statistical Papers, Springer, vol. 60(2), pages 465-478, April.
    10. Nha Vo‐Thanh & Hans‐Peter Piepho, 2023. "Generating designs for comparative experiments with two blocking factors," Biometrics, The International Biometric Society, vol. 79(4), pages 3574-3585, December.
    11. Marisa Bottiroli Civardi & Renata Targetti Lenti, 2008. "Multiplier Decomposition, Inequality and Poverty in a SAM Framework," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 10(1), pages 31-57, October.
    12. Renata Eirini Tsirpitzi & Frank Miller & Carl-Fredrik Burman, 2023. "Robust optimal designs using a model misspecification term," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(7), pages 781-804, October.
    13. Karlberg Forough, 2015. "Small Area Estimation for Skewed Data in the Presence of Zeroes," Statistics in Transition New Series, Statistics Poland, vol. 16(4), pages 541-562, December.
    14. Xin Liu & Rong-Xian Yue & Weng Kee Wong, 2019. "D-optimal designs for multi-response linear mixed models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 87-98, January.
    15. Wang, Jianqiang C., 2012. "Sample distribution function based goodness-of-fit test for complex surveys," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 664-679.
    16. Guadarrama, María & Molina, Isabel & Rao, J.N.K., 2018. "Small area estimation of general parameters under complex sampling designs," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 20-40.
    17. Forough Karlberg, 2015. "Small Area Estimation For Skewed Data In The Presence Of Zeroes," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 541-562, December.
    18. Maryna Prus, 2023. "Optimal designs for prediction in random coefficient regression with one observation per individual," Statistical Papers, Springer, vol. 64(4), pages 1057-1068, August.
    19. Jerry J. Maples, 2017. "Improving small area estimates of disability: combining the American Community Survey with the Survey of Income and Program Participation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1211-1227, October.
    20. Shin-Fu Tsai & Chih-Chien Shen & Chen-Tuo Liao, 2021. "Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 519-537, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jagbes:v:26:y:2021:i:2:d:10.1007_s13253-020-00426-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.