Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia.

Usage

data("kayad.alfalfa")

Format

A data frame with 8628 observations on the following 4 variables.

harvest: harvest number
lat: latitude
long: longitude
yield: yield, tons/ha

Details

Data was collected from a 23.5 ha field of alfalfa in Saudia Arabia. The field was harvested four consecutive times (H8 = 5 Dec 2013, H9 = 16 Feb 2014, H10 = 2 Apr 2014, H11 = 6 May 2014). Data were collected using a geo-referenced yield monitor. Supporting information contains yield monitor data for 4 hay harvests on a center-pivot field.

# TODO: Normalize the yields for each harvest, then average together # to create a productivity map. Two ways to normalize: # Normalize to 0-100: ((mapValue - min) * 100) / (max - min) # Standardize: ((mapValue - mean) / stdev) * 100

Source

Ahmed G. Kayad, et al. (2016). Assessing the Spatial Variability of Alfalfa Yield Using Satellite Imagery and Ground-Based Data. PLOS One, 11(6). https://doi.org/10.1371/journal.pone.0157166

References

None

Examples


  library(agridat)
  data(kayad.alfalfa)
  dat <- kayad.alfalfa

  # match Kayad table 1 stats
  libs(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:gridExtra’:
#> 
#>     combine
#> The following object is masked from ‘package:MASS’:
#> 
#>     select
#> The following object is masked from ‘package:nlme’:
#> 
#>     collapse
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
  dat <- group_by(dat, harvest)
  summarize(dat, min=min(yield), max=max(yield),
            mean=mean(yield), stdev=sd(yield), var=var(yield))
#> # A tibble: 4 × 6
#>   harvest   min   max  mean stdev   var
#>   <fct>   <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 H10     0      6.68  2.86  1.24  1.55
#> 2 H11     0      9.96  4.01  1.69  2.85
#> 3 H8      0      5.86  2.32  1.01  1.03
#> 4 H9      0.191  5.97  2.45  1.07  1.14

  # Figure 4 of Kayad
  libs(latticeExtra)
  catcols <- c("#cccccc","#ff0000","#ffff00","#55ff00","#0070ff","#c500ff","#73004c")
  levelplot(yield ~ long*lat |harvest, dat,
            aspect=1, at = c(0,2,3,4,5,6,7,10), col.regions=catcols,
            main="kayad.alfalfa",
            prepanel=prepanel.default.xyplot,
            panel=panel.levelplot.points)


  # Similar to Kayad fig 5.
  ## levelplot(yield ~ long*lat |harvest, dat,
  ##           prepanel=prepanel.default.xyplot,
  ##           panel=panel.levelplot.points,
  ##           col.regions=pals::brewer.reds)