Stability analysis. SHUKLA'S STABILITY VARIANCE AND KANG'S

Description

This procedure calculates the stability variations as well as the statistics of selection for the yield and the stability. The averages of the genotype through the different environment repetitions are required for the calculations. The mean square error must be calculated from the joint variance analysis.

Usage

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stability.par(data,rep,MSerror,alpha=0.1,main=NULL,cova = FALSE,name.cov=NULL,
file.cov=0,console=FALSE)

Arguments

data

matrix of averages, by rows the genotypes and columns the environment

rep

Number of repetitions

MSerror

Mean Square Error

alpha

Label significant

main

Title

cova

Covariable

name.cov

Name covariable

file.cov

Data covariable

console

logical, print output

Details

Stable (i) determines the contribution of each genotype to GE interaction by calculating var(i); (ii) assigns ranks to genotypes from highest to lowest yield receiving the rank of 1; (iii) calculates protected LSD for mean yield comparisons; (iv) adjusts yield rank according to LSD (the adjusted rank labeled Y); (v) determines significance of var(i) usign an aproximate F-test; (vi) assigns stability rating (S) as follows: -8, -4 and -2 for var(i) significant at the 0.01, 0.05 and 0.10 probability levels, and 0 for nonsignificant var(i) ( the higher the var(i), the less stable the genotype); (vii) sums adjusted yield rank, Y, and stability rating, S, for each genotype to determine YS(i) statistic; and (viii) calculates mean YS(i) and identifies genotypes (selection) with YS(i) > mean YS(i).

Value

analysis

data.frame

statistics

data.frame

stability

data.frame

Author(s)

Felipe de Mendiburu

References

Kang, M. S. 1993. Simultaneous selection for yield and stability: Consequences for growers. Agron. J. 85:754-757. Manjit S. Kang and Robert Mangari. 1995. Stable: A basic program for calculating stability and yield-stability statistics. Agron. J. 87:276-277

See Also

stability.nonpar

Examples

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library(agricolae)
# example 1
# Experimental data,
# replication rep= 4
# Mean square error, MSerror = 1.8
# 12 environment
# 17 genotype  = 1,2,3,.., 17
# yield averages of 13 genotypes in localities
V1 <- c(10.2,8.8,8.8,9.3,9.6,7.2,8.4,9.6,7.9,10,9.3,8.0,10.1,9.4,10.8,6.3,7.4)
V2 <- c(7,7.8,7.0,6.9,7,8.3,7.4,6.5,6.8,7.9,7.3,6.8,8.1,7.1,7.1,6.4,4.1)
V3 <- c(5.3, 4.4, 5.3, 4.4, 5.5, 4.6, 6.2, 6.0, 6.5, 5.3, 5.7, 4.4, 4.2,5.6,5.8,3.9,3.8)
V4 <- c(7.8, 5.9, 7.3, 5.9, 7.8, 6.3, 7.9, 7.5, 7.6, 5.4, 5.6, 7.8, 6.5,8.1,7.5,5.0,5.4)
V5 <- c(9, 9.2, 8.8, 10.6, 8.3, 9.3, 9.6, 8.8, 7.9, 9.1, 7.7, 9.5, 9.4,9.4,10.3,8.8,8.7)
V6 <- c(6.9, 7.7, 7.9, 7.9, 7, 8.9,	9.4, 7.9, 6.5, 7.2, 5.4, 6.2, 7.2,8.8,7.3,7.1,6.4)
V7 <- c(4.9, 2.5, 3.4, 2.5, 3,2.5, 3.6, 5.6,3.8, 3.9, 3.0, 3.0, 2.5,2.6,3.8,2.8,1.6)
V8 <- c(6.4, 6.4, 8.1, 7.2, 7.5, 6.6, 7.7, 7.6, 7.8, 7.5, 6.0, 7.2, 6.8,7.6,6.9,7.2,7.3)
V9 <- c(8.4, 6.1, 6.8, 6.1, 8.2, 6.9, 6.9, 9.1, 9.2, 7.7, 6.7, 7.8, 6.5,5.2,8.3,6.8,7.1)
V10 <-c(8.7, 9.4, 8.8, 7.9, 7.8, 7.8, 11.4, 9.9, 8.6, 8.5, 8.0, 8.3, 9.1,11.0,8.1,7.8,8.0)
V11 <-c(5.4, 5.2, 5.6, 4.6, 4.8, 5.7, 6.6, 6.8, 5.2, 4.8, 4.9, 5.4, 4.5,5.6,7.0,6.0,5.6)
V12 <-c(8.6, 8.0, 9.2, 8.1, 8.3, 8.9, 8.6, 9.6, 9.5, 7.7, 7.6, 8.3, 6.6,9.5,9.0,9.0,8.5)
data<-data.frame(V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12)
rownames(data)<-LETTERS[1:17]
stability.par(data, rep=4, MSerror=1.8, alpha=0.1, main="Genotype",console=TRUE)

#example 2 covariable. precipitation
precipitation<- c(1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000,2100)
stability.par(data, rep=4, MSerror=1.8, alpha=0.1, main="Genotype",
 cova=TRUE, name.cov="Precipitation", file.cov=precipitation,console=TRUE)

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