Maize multi-environment trial

Grain yield of 9 maize (Zea mays L.) genotypes in 20 sites, a randomized complete block design with 4 replicates in every site: the example of Crossa et al. (2011), also used by Burgueno et al. (2008).

Usage

Maiz

Format

A data.frame with 720 rows and 7 columns:

site

site (environment), 1 to 20.

entry

genotype, 1 to 9.

fold10, fold5, fold3

fold numbers of 10-, 5- and 3-fold cross-validation (by genotype).

rep

replicate within the site, 1 to 4.

y

grain yield (kg/ha).

Source

International Maize and Wheat Improvement Center (CIMMYT).

Details

The grand mean is 4858 kg/ha; the least-squares singular values of the interaction are 5923 and 3070 for the first two terms (Crossa et al. 2011).

Change from the earlier versions. The help page described another data set (1320 rows; columns Gen, Institute, Rep, Block, Env, Yield).

References

Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J. M., Viele, K., Liu, G. and Cornelius, P. L. (2011). Bayesian estimation of the additive main effects and multiplicative interaction model. Crop Science, 51(4), 1458–1469. doi:10.2135/cropsci2010.06.0343

Burgueno, J., Crossa, J., Cornelius, P. L. and Yang, R.-C. (2008). Using factor analytic models for joining environments and genotypes without crossover genotype x environment interaction. Crop Science, 48(4), 1291–1305.

Author

Muhammad Yaseen (myaseen208@gmail.com), Jose Crossa (j.crossa@cgiar.org)

Examples

data(Maiz)
str(Maiz)
#> 'data.frame':	720 obs. of  7 variables:
#>  $ site  : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ entry : int  1 1 1 1 2 2 2 2 3 3 ...
#>  $ fold10: int  1 1 1 1 2 2 2 2 3 3 ...
#>  $ rep   : int  1 2 3 4 1 2 3 4 1 2 ...
#>  $ y     : num  3700 2948 3745 4093 3801 ...
#>  $ fold5 : int  1 1 1 1 2 2 2 2 3 3 ...
#>  $ fold3 : int  1 1 1 1 2 2 2 2 3 3 ...
ge_mean(
    .data = Maiz
  , .y    = y
  , .gen  = entry
  , .env  = site
  )$grand_mean
#> [1] 4858.138