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

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

if (FALSE) { # \dontrun{

  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")
  
} # }