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Related Experiment Videos

Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend.

A Smith1, B Cullis, R Thompson

  • 1Wagga Wagga Agricultural Institute, NSW, Australia. alison.smith@agric.nsw.gov.au

Biometrics
|January 5, 2002
PubMed
Summary

Predicting new plant variety yield requires analyzing multi-environment trials (MET). This study introduces a new multiplicative model for variety effects in MET, improving genetic covariance analysis for better commercial recommendations.

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Area of Science:

  • Agricultural science
  • Biometrics
  • Plant breeding

Background:

  • Commercial plant variety recommendation relies on accurate yield prediction across diverse environments.
  • Multi-environment trials (MET) are crucial for assessing variety performance and genotype-by-environment interactions.
  • Previous work by Cullis et al. (1998) utilized spatial mixed models for MET data analysis.

Purpose of the Study:

  • To extend existing spatial mixed models for MET data analysis.
  • To incorporate multiplicative models for variety effects within each environment.
  • To provide a parsimonious and interpretable model for genetic covariances between environments.

Main Methods:

  • Application of multiplicative models for variety effects in MET data.

Related Experiment Videos

  • Utilizing a factor analysis approach for modeling genetic covariances.
  • Extending spatial mixed models to include these multiplicative variety effects.
  • Analysis of a large barley breeding program dataset from South Australia.

    • Main Results:

    • The proposed multiplicative model offers a parsimonious representation of genetic covariances between environments.
    • The model allows for distinct genetic variances for each environment.
    • It provides an interpretable framework for understanding genotype-by-environment interactions.
    • Demonstrated effectiveness using real-world barley MET data.

    Conclusions:

    • The new multiplicative model enhances the analysis of MET data by providing a more interpretable structure for genetic variation and covariation.
    • This approach improves predictions of average yield and interactions, aiding commercial plant variety recommendations.
    • The method is a random effects analogue to the AMMI model, offering a valuable alternative for MET analysis.