Mean Performance and Stability in Multi‐Environment Trials I: Combining Features of AMMI and BLUP Techniques
By Tiago Olivoto, A. D. Lúcio, J. P. D. Silva, V. Marchioro, V. Souza, E. Jost. Agronomy Journal, 2019
402 citations recorded by the source corpus.
3 Supplemental R codes 14 3.1 Predictive accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1 Printing the means of RMSPD estimates . . . . . . . . . . . . . . . . 15 3.1.2 Plotting the RMSPD estimates . . . . . . . . . . . . . . . . . . . . . 16 3.2 Estimating the response variable using the AMMI model . . . . . . . . . . . 17 3.3 Estimating the WAAS index . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3.1 Number of axes based on F-test . . . . . . . . . . . . . . . . . . . . . 19 3.3.2 Number of axes declared manually . . . . . . . . . . . . . . . . . . . 22 3.4 Other AMMI-based stability indexes . . . . . . . . . . . . . . . . . . . . . . 23 3.5 Estimating the WAASB index . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.5.1 Diagnostic plot for residuals . . . . . . . . . . . . . . . . . . . . . . . 24 3.5.2 Printing the model outputs . . . . . . . . . . . . . . . . . . . . . . . 25 3.5.2.1 Likelihood Ratio Tests . . . . . . . . . . . . . . . . . . . . . 25 3.5.2.2 Variance components and genetic parameters . . . . . . . . 25 3.5.2.3 Some useful information . . . . . . . . . . . . . . . . . . . . 26 3.5.2.4 The WAASB object . . . . . . . . . . . . . . . . . . . . . . 27 3.5.2.5 BLUP for genotypes . . . . . . . . . . . . . . . . . . . . . . 28 3.5.2.6 Plotting the BLUP for genotypes . . . . . . . . . . . . . . . 29 3.5.2.7 BLUP for genotype-environment interaction . . . . . . . . . 29 3.5.3 Eigenvalues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.5.4 Phenotypic means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.5 Biplots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5.5.1 biplot type 1: PC1 x PC2 . . . . . . . . . . . . . . . . . . . 32 3.5.5.2 biplot type 2: GY x PC1 . . . . . . . . . . . . . . . . . . . 33 3.5.5.3 biplot type 3: GY x WAASB . . . . . . . . . . . . . . . . . 33 3.5.5.4 biplot type 4 : nominal yield and environment PCA1 . . . . 34 3.6 The WAASBY index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.6.1 Different scenarios of WAASBY estimation . . . . . . . . . . . . . . . 36 3.6.2 Ranks of genotypes depending on the number of IPCA . . . . . . . . 37 3.6.2.1 Ranks of genotypes depending on the WAASB/GY ratio . . 37
Subfields: Genetics, Plant Science
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Citations in the corpus
- metan: an R package for multi-environment trial analysis (2020, methodology): “…workflow, and incorporates the most used stability statistics, as well as recent introduced stability methods (T. Olivoto, Lúcio, et al., 2019; T. Olivoto et al., 2019) in addition to options for computing cross-validation (Piepho, 1994) and BLUP-based stability statistics (Colombari Filho et…”
- A Framework for Identification of Stable Genotypes Basedon MTSI and MGDII Indexes: An Example in Guar (Cymopsis tetragonoloba L.) (2021, methodology): “The WAASBY Index To account for the mean performance and stability of individual traits, the WAASBY index [31] was then computed.”