crossa.wheat {agridat}R Documentation

Multi-environment trial of wheat for 18 genotypes at 25 locations

Description

Wheat yields for 18 genotypes at 25 locations

Format

A data frame with 450 observations on the following 3 variables.

loc

location

locgroup

location group: Grp1-Grp2

gen

genotype

gengroup

genotype group: W1, W2, W3

yield

grain yield, tons/ha

Details

Grain yield from the 8th Elite Selection Wheat Yield Trial to evaluate 18 bread wheat genotypes at 25 locations in 15 countries.

Cross et al. used this data to cluster loctions into 2 mega-environments and clustered genotypes into 3 wheat clusters.

Locations

Code Country Location Latitude (N) Elevation (m)
AK Algeria El Khroub 36 640
AL Algeria Setif 36 1,023
BJ Bangladesh Joydebpur 24 8
CA Cyprus Athalassa 35 142
EG Egypt E1 Gemmeiza 31 8
ES Egypt Sakha 31 6
EB Egypt Beni-Suef 29 28
IL India Ludhiana 31 247
ID India Delhi 29 228
JM Jordan Madaba 36 785
KN Kenya Njoro 0 2,165
MG Mexico Guanajuato 21 1,765
MS Mexico Sonora 27 38
MM Mexico Michoacfin 20 1,517
NB Nepal Bhairahwa 27 105
PI Pakistan Islamabad 34 683
PA Pakistan Ayub 32 213
SR Saudi Arabia Riyadh 24 600
SG Sudan Gezira 14 411
SE Spain Encinar 38 20
SJ Spain Jerez 37 180
SC Spain Cordoba 38 110
SS Spain Sevilla 38 20
TB Tunisia Beja 37 150
TC Thailand Chiang Mai 18 820

Used with permission of Jose' Crossa.

Source

Crossa, J and Fox, PN and Pfeiffer, WH and Rajaram, S and Gauch Jr, HG. (1991). AMMI adjustment for statistical analysis of an international wheat yield trial. Theoretical and Applied Genetics, 81, 27–37. https://doi.org/10.1007/BF00226108

References

Jean-Louis Laffont, Kevin Wright and Mohamed Hanafi (2013). Genotype + Genotype x Block of Environments (GGB) Biplots. Crop Science, 53, 2332-2341. https://doi.org/10.2135/cropsci2013.03.0178

Examples

## Not run: 

  library(agridat)
  data(crossa.wheat)
  dat <- crossa.wheat
  
  # AMMI biplot.  Fig 3 of Crossa et al.
  libs(agricolae)
  m1 <- with(dat, AMMI(E=loc, G=gen, R=1, Y=yield))
  b1 <- m1$biplot[,1:4]
  b1$PC1 <- -1 * b1$PC1 # Flip vertical
  plot(b1$yield, b1$PC1, cex=0.0,
       text(b1$yield, b1$PC1, cex=.5, labels=row.names(b1),col="brown"),
       main="crossa.wheat AMMI biplot",
       xlab="Average yield", ylab="PC1", frame=TRUE)
  mn <- mean(b1$yield)
  abline(h=0, v=mn, col='wheat')

  g1 <- subset(b1,type=="GEN")
  text(g1$yield, g1$PC1, rownames(g1), col="darkgreen", cex=.5)
  
  e1 <- subset(b1,type=="ENV")
  arrows(mn, 0,
         0.95*(e1$yield - mn) + mn, 0.95*e1$PC1,
         col= "brown", lwd=1.8,length=0.1)
  
  # GGB example
  library(agridat)
  data(crossa.wheat)
  dat2 <- crossa.wheat
  libs(gge)
  # Specify env.group as column in data frame
  m2 <- gge(dat2, yield~gen*loc,
            env.group=locgroup, gen.group=gengroup,
            scale=FALSE)
  biplot(m2, main="crossa.wheat - GGB biplot")
  

## End(Not run)

[Package agridat version 1.18 Index]