barrero.maize: Multi-environment trial of maize in Texas.
In kwstat/agridat: Agricultural Datasets

barrero.maize

R Documentation

Multi-environment trial of maize in Texas.

Description

Multi-environment trial of maize in Texas.

Usage

data("barrero.maize")

Format

A data frame with 14568 observations on the following 15 variables.

year: year of testing, 2000-2010
yor: year of release, 2000-2010
loc: location, 16 places in Texas
env: environment (year+loc), 107 levels
rep: replicate, 1-4
gen: genotype, 847 levels
daystoflower: numeric
plantheight: plant height, cm
earheight: ear height, cm
population: plants per hectare
lodged: percent of plants lodged
moisture: moisture percent
testweight: test weight kg/ha
yield: yield, Mt/ha

Details

This is a large (14500 records), multi-year, multi-location, 10-trait dataset from the Texas AgriLife Corn Performance Trials.

These data are from 2-row plots approximately 36in wide by 25 feet long.

Barrero et al. used this data to estimate the genetic gain in maize hybrids over a 10-year period of time.

Used with permission of Seth Murray.

Source

Barrero, Ivan D. et al. (2013). A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize. Field Crops Research, 149, Pages 167-176. https://doi.org/10.1016/j.fcr.2013.04.017

References

None.

Examples

## Not run: 
  library(agridat)
  data(barrero.maize)
  dat <- barrero.maize

  library(lattice)
  bwplot(yield ~ factor(year)|loc, dat,
         main="barrero.maize - Yield trends by loc",
         scales=list(x=list(rot=90)))

  #libs(connected)
  #con_view(dat, yield ~ gen * env, cex.x=0.5, cex.y=0.5)
  #con_view(dat, yield ~ gen * year, cex.x=0.5, cex.y=0.5)
  
  # Table 6 of Barrero. Model equation 1.
  if(require("asreml", quietly=TRUE)){
    libs(dplyr,lucid)
    dat <- arrange(dat, env)
    dat <- mutate(dat,
                  yearf=factor(year), env=factor(env),
                  loc=factor(loc), gen=factor(gen), rep=factor(rep))
  
    m1 <- asreml(yield ~ loc + yearf + loc:yearf, data=dat,
                 random = ~ gen + rep:loc:yearf +
                   gen:yearf + gen:loc +
                   gen:loc:yearf,
                 residual = ~ dsum( ~ units|env),
                 workspace="500mb")
  
    # Variance components for yield match Barrero table 6.
    lucid::vc(m1)[1:5,]
    ##        effect component std.error z.ratio bound 
    ## rep:loc:yearf   0.111     0.01092    10       P 0  
    ##           gen   0.505     0.03988    13       P 0  
    ##     gen:yearf   0.05157   0.01472     3.5     P 0  
    ##       gen:loc   0.02283   0.0152      1.5     P 0.2
    ## gen:loc:yearf   0.2068    0.01806    11       P 0  
    
    summary(vc(m1)[6:112,"component"]) # Means match last row of table 6
    ##   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    ## 0.1286  0.3577  0.5571  0.8330  1.0322  2.9867 
  }

## End(Not run)

kwstat/agridat documentation built on Dec. 17, 2024, 3:56 p.m.