agridat (version 1.21)

gartner.corn: 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.

Time 0 is 5 Nov 2011, 12:38:03 Central Time. Time 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

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) {

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

# Calculate yield
dat <- transform(dat, yield=(mass*seconds*(100-moist)/(100-15.5)/56)/(dist*360/6272640))
# Delete yield outliers
dat <- subset(dat, yield >50)

# Colors for yield
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)))

# Add 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(rgdal)
# shp <- readOGR("C:/x/rpack/agridat/inst/files","gartner.corn")
shp <- readOGR(system.file(package="agridat", "files"), "gartner.corn")

# Plot the shapefiles first to set up the coordinate system
plot(shp, xlim=range(dat$long), ylim=range(dat$lat))
box() # Add the yield points
redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
with(dat, points(long,lat, main="yield heat map",
                 col=redblue(ncols)[yldbin], cex=.75, pch=16))
plot(shp, add=TRUE, lwd=2) # Overlay soil polygons on top
title("gartner.corn - yield heatmap with soil map unit symbols")
# Manual annotation of soil map units
text(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),
     lab=c("110","319","319","230","105C","110","211","110","211","230","105C"))

# Trim off the ends of the field & re-do image above
dat <- subset(dat, lat < 43.925850 & lat > 43.921178)

# Identify the soil type for each yield point
dat$ix <- over(SpatialPoints(dat[ , c('long','lat')]),
               SpatialPolygons(shp@polygons))
dat$mu <- shp@data[, "MUSYM"][dat$ix]
# Check the points are properly identified
# with(dat, points(long,lat, col=redblue(ncols)[ix], cex=.75, pch=16))

# Aggregate points by soil type and analyze
tapply(dat$yield, dat$mu, mean)
tapply(dat$yield, dat$mu, sd)
libs(lattice)
densityplot(~yield|mu, dat, layout=c(1,5),
            main="gartner.corn - yield density plot")


if(0){
  # Draw a 3D surface.  Clearly shows the low drainage area
  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')
  rgl.close()
}

}