Literature DB >> 24166112

The analysis of the NSW wheat variety database. I. Modelling trial error variance.

B R Cullis1, F M Thomson, J A Fisher, A R Gilmour, R Thompson.   

Abstract

The retrospective analysis of a large database on wheat variety testing in New South Wales (NSW) is considered. This analysis involved three key steps. Initially error variance heterogeneity is modelled, indicating significant differences in error variance due to trial location, year of trialling, sowing date and trial mean yield. The implication of this modelling for the estimaion of variance components is discussed.

Entities:  

Year:  1996        PMID： 24166112     DOI： 10.1007/BF00222947

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN： 0040-5752            Impact factor:   5.699


  2 in total

1.  Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat.

Authors:  M M Nachit; G Nachit; H Ketata; H G Gauch; R W Zobel
Journal:  Theor Appl Genet       Date:  1992-03       Impact factor: 5.699

2.  A statistical model which combines features of factor analytic and analysis of variance techniques.

Authors:  H F Gollob
Journal:  Psychometrika       Date:  1968-03       Impact factor: 2.500
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1.  Advantage of single-trial models for response to selection in wheat breeding multi-environment trials.

Authors:  C G Qiao; K E Basford; I H DeLacy; M Cooper
Journal:  Theor Appl Genet       Date:  2003-12-20       Impact factor: 5.699

2.  The analysis of the NSW wheat variety database. II. Variance component estimation.

Authors:  B R Cullis; F M Thomson; J A Fisher; A R Gilmour; R Thompson
Journal:  Theor Appl Genet       Date:  1996-01       Impact factor: 5.699

3.  Comparisons of single-stage and two-stage approaches to genomic selection.

Authors:  Torben Schulz-Streeck; Joseph O Ogutu; Hans-Peter Piepho
Journal:  Theor Appl Genet       Date:  2012-08-19       Impact factor: 5.699

4.  Cost and accuracy of advanced breeding trial designs in apple.

Authors:  Julia M Harshman; Kate M Evans; Craig M Hardner
Journal:  Hortic Res       Date:  2016-03-23       Impact factor: 6.793

5.  The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis.

Authors:  Marcos Malosetti; Jean-Marcel Ribaut; Fred A van Eeuwijk
Journal:  Front Physiol       Date:  2013-03-12       Impact factor: 4.566

6.  Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding.

Authors:  A P Wasson; G J Rebetzke; J A Kirkegaard; J Christopher; R A Richards; M Watt
Journal:  J Exp Bot       Date:  2014-06-24       Impact factor: 6.992

7.  Historical Datasets Support Genomic Selection Models for the Prediction of Cotton Fiber Quality Phenotypes Across Multiple Environments.

Authors:  Washington Gapare; Shiming Liu; Warren Conaty; Qian-Hao Zhu; Vanessa Gillespie; Danny Llewellyn; Warwick Stiller; Iain Wilson
Journal:  G3 (Bethesda)       Date:  2018-05-04       Impact factor: 3.154
  7 in total

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