gartner.corn: Yield monitor data from a corn field in Minnesota

In agridat: Agricultural Datasets

Yield monitor data from a corn field in Minnesota

Description

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

## Not run: 

  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()
  }


## End(Not run)

agridat documentation built on Oct. 27, 2024, 5:07 p.m.