TY  - JA
AU  - Alvarado Beltrán,G.
AU  - Rodríguez,F.
AU  - Pacheco Gil,R.A.
AU  - Burgueño,J.
AU  - Crossa,J.
AU  - Vargas-Hernández,M.
AU  - Perez-Rodriguez,P.
AU  - Lopez-Cruz,M.
TI  - META-R : : a software to analyze data from multi-environment plant breeding trials
SN  - 2095-5421
PY  - 2020///
CY  - Netherlands
PB  - Elsevier
KW  - Linear models
KW  - AGROVOC
KW  - Genetic Correlation
KW  - Computer software
N1  - Peer review; Open Access
N2  - META-R (multi-environment trial analysis in R) is a suite of R scripts linked by a graphical user interface (GUI) designed in Java language. The objective of META-R is to accurately analyze multi-environment plant breeding trials (METs) by fitting mixed and fixed linear models from experimental designs such as the randomized complete block design (RCBD) and the alpha-lattice/lattice designs. META-R simultaneously estimates the best linear and unbiased estimators (BLUEs) and the best linear and unbiased predictors (BLUPs). Additionally, it computes the variance-covariance parameters, as well as some statistical and genetic parameters such as the least significant difference (LSD) at 5% significance, the coefficient of variation in percentage (CV), the genetic variance, and the broad-sense heritability. These parameters are very important in the selection of top performing genotypes in plant breeding. META-R also computes the phenotypic and genetic correlations among environments and between traits, as well as their statistical significance. The genetic correlations between environments or traits can be visualized in a biplot graph or a tree diagram (dendrogram). Genetic correlations are very important for identifying environments with similar behavior or making indirect selection and identifying the most highly associated traits. META-R performs multi-environment analyses by using the residual maximum likelihood (REML) method; these analyses can be done by environment, across environments by grouping factors (stress conditions, nitrogen content, etc.) and across environments; the analyses across environments can be done with a pre-defined degree of heritability
UR  - https://hdl.handle.net/10883/20997
DO  - https://doi.org/10.1016/j.cj.2020.03.010
T2  - The Crop Journal
ER  -