Yield monitor data from a corn field in Minnesota

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

Source

Originally from University of Minnesota Precision Agriculture Center. https://www.soils.umn.edu/academics/classes/soil4111/hw/

Retrieved 27 Aug 2015 from https://web.archive.org/web/20100717003256/https://www.soils.umn.edu/academics/classes/soil4111/files/yield_a.xls

Used under Creative Commons BY-SA 3.0 license.

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

# \dontrun{

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)
#> Loading required package: sp
#> rgdal: version: 1.5-18, (SVN revision 1082)
#> Geospatial Data Abstraction Library extensions to R successfully loaded
#> Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
#> Path to GDAL shared files: C:/kw/R/win-library/4.0/rgdal/gdal
#> GDAL binary built with GEOS: TRUE 
#> Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
#> Path to PROJ shared files: C:/kw/R/win-library/4.0/rgdal/proj
#> Linking to sp version:1.4-4
#> To mute warnings of possible GDAL/OSR exportToProj4() degradation,
#> use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
# shp <- readOGR("C:/x/rpack/agridat/inst/files","gartner.corn")
shp <- readOGR(system.file(package="agridat", "files"), "gartner.corn")
#> OGR data source with driver: ESRI Shapefile 
#> Source: "C:\kw\R\win-library\4.0\agridat\files", layer: "gartner.corn"
#> with 14 features
#> It has 4 fields
#> Integer64 fields read as strings:  SPATIALVER 

# 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)
#>     105C      110      211      230      319 
#> 137.0062 134.9430 143.2656 135.3349 135.0155 
tapply(dat$yield, dat$mu, sd)
#>      105C       110       211       230       319 
#> 17.521462 11.166786  8.513848 11.958948 10.668256 
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()
}

# }