Literature DB >> 24177943

Best Linear Unbiased Prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis.

H P Piepho1.   

Abstract

Multilocation trials are often used to analyse the adaptability of genotypes in different environments and to find for each environment the genotype that is best adapted; i.e. that is highest yielding in that environment. For this purpose, it is of interest to obtain a reliable estimate of the mean yield of a cultivar in a given environment. This article compares two different statistical estimation procedures for this task: the Additive Main Effects and Multiplicative Interaction (AMMI) analysis and Best Linear Unbiased Prediction (BLUP). A modification of a cross validation procedure commonly used with AMMI is suggested for trials that are laid out as a randomized complete block design. The use of these procedure is exemplified using five faba bean datasets from German registration trails. BLUP was found to outperform AMMI in four of five faba bean datasets.

Entities:  

Year:  1994        PMID: 24177943     DOI: 10.1007/BF00222462

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


  7 in total

1.  Best linear unbiased estimation and prediction under a selection model.

Authors:  C R Henderson
Journal:  Biometrics       Date:  1975-06       Impact factor: 2.571

2.  Predictive and postdictive success of statistical analyses of yield trials.

Authors:  H G Gauch; R W Zobel
Journal:  Theor Appl Genet       Date:  1988-07       Impact factor: 5.699

3.  Imputing missing yield trial data.

Authors:  H G Gauch; R W Zobel
Journal:  Theor Appl Genet       Date:  1990-06       Impact factor: 5.699

4.  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

5.  Using the shifted multiplicative model to search for "separability" in crop cultivar trials.

Authors:  P L Cornelius; M Seyedsadr; J Crossa
Journal:  Theor Appl Genet       Date:  1992-06       Impact factor: 5.699

6.  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

7.  AMMI adjustment for statistical analysis of an international wheat yield trial.

Authors:  J Crossa; P N Fox; W H Pfeiffer; S Rajaram; H G Gauch
Journal:  Theor Appl Genet       Date:  1991-01       Impact factor: 5.699

  7 in total
  5 in total

1.  Robustness of statistical tests for multiplicative terms in the additive main effects and multiplicative interaction model for cultivar trials.

Authors:  H P Piepho
Journal:  Theor Appl Genet       Date:  1995-03       Impact factor: 5.699

2.  Testing multiplicative terms in AMMI and GGE models for multienvironment trials with replicates.

Authors:  Waqas Ahmed Malik; Johannes Forkman; Hans-Peter Piepho
Journal:  Theor Appl Genet       Date:  2019-04-15       Impact factor: 5.699

3.  Stability and genotype by environment interaction of provitamin A carotenoid and dry matter content in cassava in Uganda.

Authors:  Williams Esuma; Robert Sezi Kawuki; Liezel Herselman; Maryke Tine Labuschagne
Journal:  Breed Sci       Date:  2016-05-20       Impact factor: 2.086

4.  Genome-Wide Association Study of Topsoil Root System Architecture in Field-Grown Soybean [Glycine max (L.) Merr.].

Authors:  Arun Prabhu Dhanapal; Larry M York; Kasey A Hames; Felix B Fritschi
Journal:  Front Plant Sci       Date:  2021-02-10       Impact factor: 5.753

5.  Phenotypic Selection in Ornamental Breeding: It's Better to Have the BLUPs Than to Have the BLUEs.

Authors:  Heike Molenaar; Robert Boehm; Hans-Peter Piepho
Journal:  Front Plant Sci       Date:  2018-11-05       Impact factor: 5.753

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.