Yield monitor data from a corn field in Minnesota

Usage

data("gartner.corn")

Format

A data frame with 4949 observations on the following 8 variables.

long: longitude
lat: latitude
mass: grain mass flow per second, pounds
time: GPS time, in seconds
seconds: seconds elapsed for each datum
dist: distance traveled for each datum, in inches
moist: grain moisture, percent
elev: elevation, feet

Details

The data was collected 5 Nov 2011 from a corn field south of Mankato, Minnesota, using a combine-mounted yield monitor. https://www.google.com/maps/place/43.9237575,-93.9750632

Each harvested swath was 12 rows wide = 360 inches.

Timestamp 0 = 5 Nov 2011, 12:38:03 Central Time. Timestamp 16359 = 4.54 hours later.

Yield is calculated as total dry weight (corrected to 15.5 percent moisture), divided by 56 pounds (to get bushels), divided by the harvested area:

drygrain = [massflow * seconds * (100-moisture) / (100-15.5)] / 56 harvested area = (distance * swath width) / 6272640 yield = drygrain / area

Source

University of Minnesota Precision Agriculture Center. Retrieved 27 Aug 2015 from https://web.archive.org/web/20100717003256/https://www.soils.umn.edu/academics/classes/soil4111/files/yield_a.xls

Used via license: Creative Commons BY-SA 3.0.

References

Suman Rakshit, Adrian Baddeley, Katia Stefanova, Karyn Reeves, Kefei Chen, Zhanglong Cao, Fiona Evans, Mark Gibberd (2020). Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments. Field Crops Research, 255, 15 September 2020, 107783. https://doi.org/10.1016/j.fcr.2020.107783

Examples

if (FALSE) { # \dontrun{

  library(agridat)
  data(gartner.corn)
  dat <- gartner.corn

  # Calculate yield from mass & moisture
  dat <- transform(dat,
  yield=(mass*seconds*(100-moist)/(100-15.5)/56)/(dist*360/6272640))

  # Delete low yield outliers
  dat <- subset(dat, yield >50)

  # Group yield into 20 bins for red-gray-blue colors
  medy <- median(dat$yield)
  ncols <- 20
  wwidth <- 150
  brks <- seq(from = -wwidth/2, to=wwidth/2, length=ncols-1)
  brks <- c(-250, brks, 250) # 250 is safe..we cleaned data outside ?(50,450)?
  yldbrks <- brks + medy
  dat <- transform(dat, yldbin = as.numeric(cut(yield, breaks= yldbrks)))
  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
  dat$yieldcolor = redblue(ncols)[dat$yldbin]

  # Polygons for soil map units
  # Go to: https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx
  # Click: Lat and Long. 43.924, -93.975
  # Click the little AOI rectangle icon.  Drag around the field
  # In the AOI Properties, enter the Name: Gartner
  # Click the tab Soil Map to see map unit symbols, names
  # Click: Download Soils Data.  Click: Create Download Link.
  # Download the zip file and find the soilmu_a_aoi files.

  # Read shape files
  libs(sf)
  fname <- system.file(package="agridat", "files", "gartner.corn.shp")
  shp <- sf::st_read( fname )

  # Annotate soil map units. Coordinates chosen by hand.
  mulabs = data.frame(
    name=c("110","319","319","230","105C","110","211","110","211","230","105C"),
    x = c(-93.97641, -93.97787, -93.97550, -93.97693, -93.97654, -93.97480,
          -93.97375, -93.978284, -93.977617, -93.976715, -93.975929),
    y = c(43.92185, 43.92290, 43.92358, 43.92445, 43.92532, 43.92553,
          43.92568, 43.922163, 43.926427, 43.926993, 43.926631) )
  mulabs = st_as_sf( mulabs, coords=c("x","y"), crs=4326)
  mulabs = st_transform(mulabs, 2264)

  # Trim top and bottom ends of the field
  dat <- subset(dat, lat < 43.925850 & lat > 43.921178)
  # Colored points for yield
  dat <- st_as_sf(dat, coords=c("long","lat"), crs=4326)

  libs(ggplot2)
  
  ggplot() +
    geom_sf(data=dat, aes(col=yieldcolor) ) +
    scale_color_identity() +
    geom_sf_label(data=mulabs, aes(label=name), cex=2) +
    geom_sf(data=shp["MUSYM"], fill="transparent") +
    ggtitle("gartner.corn") +
    theme_classic()
  
  if(0){
    # Draw a 3D surface.  Clearly shows the low drainage area
    # Re-run the steps above up, stop before the "Colored points" line.
    libs(rgl)
    dat <- transform(dat, x=long-min(long), y=lat-min(lat), z=elev-min(elev))
    clear3d()
    points3d(dat$x, dat$y, dat$z/50000,
             col=redblue(ncols)[dat$yldbin])
    axes3d()
    title3d(xlab='x',ylab='y',zlab='elev')
    close3d()
  }

} # }