Two types of GGE biplots for analyzing multi-environment trial data

被引:228
|
作者
Yan, WK [1 ]
Cornelius, PL
Crossa, J
Hunt, LA
机构
[1] Univ Guelph, Dept Plant Agr, Guelph, ON N1G 2W1, Canada
[2] Univ Kentucky, Dept Agron, Lexington, KY 40546 USA
[3] Univ Kentucky, Dept Stat, Lexington, KY 40546 USA
[4] CIMMYT, Biometr & Stat Unit, Int Maize & Wheat Improvement Ctr, Mexico City 06600, DF, Mexico
关键词
D O I
10.2135/cropsci2001.413656x
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
SA genotype main effect plus genotype X environment interaction (GGE) biplot graphically displays the genotypic main effect (G) and the genotype X environment interaction (GE) of the multienvironment trial (MET) data and facilitates visual evaluation of both the genotypes and the environments. This paper compares the merits of two types of GGE biplots in MET data analysis. The first type is constructed by the least squares solution of the sites regression model (SREG(2)), with the first two principal components as the primary and secondary effects, respectively. The second type is constructed by Mandel's solution for sites regression as the primary effect and the first principal component extracted from the regression residual as the secondary effect (SREG(M+1)). Results indicate that both the SREG2 biplot and the SREG(M+1) biplot are equally effective in displaying the "which-won-where" pattern of the MET data, although the SREG2 biplot explains slightly more GGE variation. The SREG(M+1) biplot is more desirable, however, in that it always explicitly indicates the average yield and stability of the genotypes and the discriminating ability and representativeness of the test environments.
引用
收藏
页码:656 / 663
页数:8
相关论文
共 50 条
  • [41] Testing components of two-way interaction in multi-environment trials
    Forkman, Johannes
    Malik, Waqas Ahmed
    Hadasch, Steffen
    Piepho, Hans-Peter
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2024, 53 (05) : 1716 - 1735
  • [42] learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data
    Westhues, Cathy C.
    Simianer, Henner
    Beissinger, Timothy M.
    G3-GENES GENOMES GENETICS, 2022, 12 (11):
  • [43] Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop
    Saludares, Rica Amor
    Atanda, Sikiru Adeniyi
    Piche, Lisa
    Worral, Hannah
    Dariva, Francoise
    McPhee, Kevin
    Bandillo, Nonoy
    PLANT GENOME, 2024, 17 (03):
  • [44] Environmental stratification and optimization of a multi-environment trial net for soybean genotypes in Cerrado
    Branquinho, Rodrigo Gomes
    Duarte, Joao Batista
    Mello de Souza, Plinio Itamar
    da Silva Neto, Sebastiao Pedro
    Pacheco, Roberto Miranda
    PESQUISA AGROPECUARIA BRASILEIRA, 2014, 49 (10) : 783 - 795
  • [45] One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis
    Weihua Zhang
    Jianlin Hu
    Yuanmu Yang
    Yuanzhen Lin
    Journal of Forestry Research, 2020, 31 : 123 - 130
  • [46] Incorporating the pedigree information in multi-environment trial analyses for improving common vetch
    Santa, Isabel Munoz
    Nagel, Stuart
    Taylor, Julian Daniel
    FRONTIERS IN PLANT SCIENCE, 2023, 14
  • [47] Identification of drought and heat tolerant tepary beans in a multi-environment trial study
    Barrera, Santos
    Teran, Jorge C.
    Aparicio, Johan
    Diaz, Jairo
    Leon, Rommel
    Beebe, Steve
    Urrea, Carlos A.
    Gepts, Paul
    CROP SCIENCE, 2024, 64 (06) : 3399 - 3416
  • [48] MULTI-ENVIRONMENT RESPONSE IN SEED YIELD OF SOYBEAN [GLYCINE MAX (L.) MERRILL], GENOTYPES THROUGH GGE BIPLOT TECHNIQUE
    Ashraf, Muhammad
    Iqbal, Zafar
    Arshad, Muhammad
    Waheed, Abdul
    Glufran, Muhammad Asad
    Chaudhry, Zubeda
    Baig, Doulat
    PAKISTAN JOURNAL OF BOTANY, 2010, 42 (06) : 3899 - 3905
  • [49] Analysis of Multi-Environment Yield Trails of Clusterbean [Cyamopsis tetragonoloba (L.) Taub.] Genotypes using GGE Biplot
    Manivannan, A.
    LEGUME RESEARCH, 2020, 43 (05) : 698 - 701
  • [50] One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis
    Zhang, Weihua
    Hu, Jianlin
    Yang, Yuanmu
    Lin, Yuanzhen
    JOURNAL OF FORESTRY RESEARCH, 2020, 31 (01) : 123 - 130