Multi-environment trial of maize — dasilva.maize • agridat

Multi-environment trial of maize with 3 reps.

Usage

data("dasilva.maize")

Format

A data frame with 1485 observations on the following 4 variables.

env: environment
rep: replicate block, 3 per env
gen: genotype
yield: yield (tons/hectare)

Details

Each location had 3 blocks. Block numbers are unique across environments.

NOTE! The environment codes in the supplemental data file of da Silva 2015 do not quite match the environment codes of the paper, but are mostly off by 1.

DaSilva Table 1 has a footnote "Machado et al 2007". This reference appears to be:

Machado et al. Estabilidade de producao de hibridos simples e duplos de milhooriundos de um mesmo conjunto genico. Bragantia, 67, no 3. www.scielo.br/pdf/brag/v67n3/a10v67n3.pdf

In DaSilva Table 1, the mean of E1 is 10.803. This appears to be a copy of the mean from row 1 of Table 1 in Machado. Using the supplemental data from this paper, the correct mean is 8.685448.

Source

A Bayesian Shrinkage Approach for AMMI Models. Carlos Pereira da Silva, Luciano Antonio de Oliveira, Joel Jorge Nuvunga, Andrezza Kellen Alves Pamplona, Marcio Balestre. Plos One. Supplemental material. https://doi.org/10.1371/journal.pone.0131414

Used via license: Creative Commons BY-SA.

References

J.J. Nuvunga, L.A. Oliveira, A.K.A. Pamplona, C.P. Silva, R.R. Lima and M. Balestre. Factor analysis using mixed models of multi-environment trials with different levels of unbalancing. Genet. Mol. Res. 14.

Examples


library(agridat)
data(dasilva.maize)
dat <- dasilva.maize

# Try to match Table 1 of da Silva 2015.
# aggregate(yield ~ env, data=dat, FUN=mean)
##   env     yield
## 1  E1  6.211817  # match E2 in Table 1
## 2  E2  4.549104  # E3
## 3  E3  5.152254  # E4
## 4  E4  6.245904  # E5
## 5  E5  8.084609  # E6
## 6  E6 13.191890  # E7
## 7  E7  8.895721  # E8
## 8  E8  8.685448  
## 9  E9  8.737089  # E9

# Unable to match CVs in Table 2, but who knows what they used
# for residual variance.
# aggregate(yield ~ env, data=dat, FUN=function(x) 100*sd(x)/mean(x))

# Match DaSilva supplement 2, ANOVA
# m1 <- aov(yield ~ env + gen + rep:env + gen:env, dat)
# anova(m1)
## Response: yield
##            Df Sum Sq Mean Sq  F value    Pr(>F)    
## env         8 8994.2 1124.28 964.1083 < 2.2e-16 ***
## gen        54  593.5   10.99   9.4247 < 2.2e-16 ***
## env:rep    18   57.5    3.19   2.7390 0.0001274 ***
## env:gen   432  938.1    2.17   1.8622 1.825e-15 ***
## Residuals 972 1133.5    1.17