4.7 Article

Information Based Diagnostic for Genetic Variance Parameter Estimation in Multi-Environment Trials

Related references

Note: Only part of the references are listed.
Article Plant Sciences

Use of Contemporary Groups in the Construction of Multi-Environment Trial Datasets for Selection in Plant Breeding Programs

Alison Smith et al.

Summary: This paper presents an approach for constructing MET datasets that optimizes the information available for selection decisions by using contemporary groups and data bands. The methods are demonstrated to be superior in selecting superior lines compared to other forms, particularly those related to a single year.

FRONTIERS IN PLANT SCIENCE (2021)

Article Plant Sciences

Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects

Alison Smith et al.

Summary: This paper addresses the challenge in analyzing multi-environment datasets in plant breeding by fitting a factor analytic linear mixed model (FALMM) to define interaction classes (iClasses), allowing for predictions of overall variety performance within each iClass for selection and matching purposes.

FRONTIERS IN PLANT SCIENCE (2021)

Article Agriculture, Multidisciplinary

Rice grain quality: an Australian multi-environment study

Rachelle Ward et al.

CROP & PASTURE SCIENCE (2019)

Article Computer Science, Interdisciplinary Applications

Construction of experimental designs for estimating variance components

S. Loeza-Serrano et al.

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2014)

Article Agronomy

Joint modeling of additive and non-additive (genetic line) effects in multi-environment trials

Helena Oakey et al.

THEORETICAL AND APPLIED GENETICS (2007)

Article Statistics & Probability

The analysis of crop variety evaluation data in Australia

A Smith et al.

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS (2001)

Article Agriculture, Multidisciplinary

An examination of the efficiency of Australian crop variety evaluation programmes

BR Cullis et al.

JOURNAL OF AGRICULTURAL SCIENCE (2000)