crossa.wheat.Rd
Wheat yields for 18 genotypes at 25 locations
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
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.
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
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
# \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$PC1, cex=.5, labels=row.names(b1),col="brown"), main="crossa.wheat AMMI biplot", xlab="Average yield", ylab="PC1", frame=TRUE)e1 <- subset(b1,type=="ENV") arrows(mn, 0, 0.95*(e1$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")# }