Graphical Gems in the agridat PackageKevin Wright2017-11-29

Abstract

The agridat package is an extensive collection of data sets that have been previously published in booksand journals, primarily from agricultural experiments.

This vignette presents graphical views of a few of the datasets in this package.

Setup

This exhibit of agricultural data uses the following packages.

library("agridat")library("desplot")library("gge")library("HH")library("lattice")library("latticeExtra")library("mapproj")library("maps")library("reshape2")

1

Potato blight incidence over space and time

lee.potatoblight

1983−10−17 1983−10−25 1983−10−31 1983−11−07 1983−11−14 1983−11−21 1983−11−28 1983−12−05 1983−12−12 1983−12−20

1985−11−18 1985−11−25 1985−12−02 1985−12−09 1985−12−16 1985−12−23 1985−12−30

1987−11−16 1987−11−23 1987−11−30 1987−12−07 1987−12−14 1987−12−21 1987−12−28 1988−01−05

1991−11−08 1991−11−15 1991−11−27 1991−12−05 1991−12−24 1992−01−07

1993−11−19 1993−11−29 1993−12−10 1993−12−22 1993−12−31

1995−11−24 1995−12−01 1995−12−08 1995−12−14 1995−12−22

1997−11−04 1997−11−19 1997−11−29 1997−12−05 1997−12−12 1997−12−22

1999−12−07 1999−12−16 1999−12−30 2000−01−07 2000−01−15

2001−11−13 2001−11−23 2001−11−29 2001−12−06 2001−12−18

2003−12−08 2003−12−15 2003−12−23 2003−12−31 2004−01−16

2005−11−07 2005−11−15 2005−11−29 2005−12−15 2005−12−23

0

2

4

6

8

Lee (2009) analyzed a large dataset to evaluate the resistance of potato varieties to blight. This data containsevaluations of a changing set of varieties every two years, evaluated in 5 blocks, repeatedly throughout thegrowing season to track the progress of the disease. Each panel shows a field map on the given date, with aseparate row of panels for each year.

Would you include field spatial trends in a model for these data?

2

lee.potatoblight 1983

Date

Blig

ht r

esis

tanc

e sc

ore

2468 1 1

11

11

1 1 1 1

2 22

22

2 2 2 2 2

3 33

33

3 3 3 3 3

4 44

44

4 4 4 4 4

55 5

5

5 5 55 5 5

060.11 1 1 1 1 1 1 1

1 12 2 2 2 2 2 2 2 2

23 3 3 3 3 3 3

33

3

4 4 4 4 4 4 44

44

5 5 5 5 5 5 55 5

5

064.11 1 1 1 1 1

11

11

2 2 2 2 2 22 2

22

3 3 3 3 3 33

33

3

4 4 4 4 4 44

44

4

5 5 5 5 5 55 5

5

5

064.181 1

11

11 1 1 1

1

2 22

2

2 22

2 2 2

33

33

33

3 3 3 3

4 44

4

44 4 4 4 4

5 55

5

55

5 5 5 5

064.211 1 1 1 1 1 1 1

11

2 2 2 2 2 22 2

2

2

3 3 3 3 33

33

33

4 4 4 44

44 4

4

4

5 5 5 5 5 5 55

5

5

064.24

2468 1 1 1 1 1 1 1 1

1 12 2 2 2 2 2 2 2 2

23 3 3 3 3

3 33

3

3

4 4 4 4 44 4 4

44

5 5 5 5 5 5 5 5 55

064.371 1 1 1

1 11

11

1

2 2 22 2

2

2 22

2

3 3 3 33 3

33

33

4 4 4 44 4

44

44

5 5 5 55

55

55

5

065.271

1 11

11

1 11 1

2 22

22

22 2 2 2

3 33

33

33 3 3 3

4 44

4

44 4 4

4 4

5 55

5

55

5 5 5 5

1015.471 1

1

11

11 1 1 1

2 22

22

22 2 2 2

3 33

33 3 3

33

3

4 44

4

4 4 4 4 4 4

5 55

55 5 5 5 5 5

1053.571 1 1 1 1 1 1 1 1

12 2 2 2 2 2 2

2 22

3 3 3 3 3 3 33

33

4 4 4 4 4 4 44

44

5 5 5 5 5 5 55

55

1060.9

2468 1 1

11 1

11

1 11

2 22

22 2

22 2

2

3 33

33 3 3

3 3 3

44 4

4

44 4 4 4

4

5 55

55

55 5 5

5

1067.161 1

11

11

1 1 1 1

2 22

22

2 2 2 2 2

33

33

33 3 3 3

3

4 44

4

44 4

4 4 4

5 55

5

5 55 5

5 5

826.41 1

11

11

11

1 1

2 22

22

22 2

22

3 33

33

3 33

33

4 44 4

4 4 4 44

4

5 55

55

5 5 55

5

887.891 1

1

11 1 1 1 1 1

2 22

22 2 2 2 2 2

3 33

3

3 3 3 3 3 3

44

4

44 4 4 4 4 4

5 55

5

5 5 5 5 5 5

978.31 1

11

11 1

1 1 1

2 22

2

22

2 2 2 2

3 33

33 3

3 3 3 3

4 44

4

44 4 4 4 4

5 55

5

55 5

5 5 5

993.60

2468

Oct

15

Nov

01

Nov

15

Dec

01

Dec

15

1 11

11

1 1 1 1 1

2 22

2

22 2 2 2 2

3 33

33 3 3 3 3 3

44

4

44 4 4 4 4 4

5 55

5

5 5 5 5 5 5

I.HARDY

Oct

15

Nov

01

Nov

15

Dec

01

Dec

15

1 11

11

1 11

1 1

2 22

22 2

2 22 2

33 3

33

33 3

3 3

4 4 44

44

4 44 4

5 55

55

55

5 5 5

IWAO

ct 1

5

Nov

01

Nov

15

Dec

01

Dec

15

1 1 11 1

1 11

11

2 22

22

2 22

2 2

3 3 33

33

3 3 33

4 44

44 4

4 4 44

5 5 55

55 5 5

55

RUA

Oct

15

Nov

01

Nov

15

Dec

01

Dec

15

1 11

1 11

1 1 11

2 22

22

2 2 22

2

3 33

33

3 3 3 33

4 44

44 4 4 4

44

5 55

55 5 5 5

55

TEKAU

Oct

15

Nov

01

Nov

15

Dec

01

Dec

15

1 1 11

11

1 11

1

2 2 2 22

2 22

22

3 3 3 33 3

33

3

3

4 4 4 44

4 44

44

5 5 5 55

5 55

55

WHA

In 1983, 20 varieties were evaluated in 5 blocks (shown by colored numbers) throughout the growing seasonfor disease resistance. Resistance scores start at 9 for all varieties (shown in panels). As the growing seasonprogresses, the ‘I.HARDY’ variety succumbs quickly to blight, while ‘IWA’ succumbs steadily, and ‘064.1’resists blight until near the end of the season.

Does this view show differences between blocks?

3

An informative prior

harrison.priors

Daidzein level

Berman−ABerman−U

DeviHutabaratLundry−ALundry−UMorrisonPrimomo

SeguinZhou−1Zhou−2Zhou−3Zhou−4Zhou−5Zhou−6Zhou−7Zhou−8Zhou−9

priorConstructed

500 1000 1500

Harrison, Culp, and Harrigan (2012) used a Bayesian approach to model daidzein levels in soybean samples.From 18 previous publications, they extracted the published minimum and maximum daidzein levels, and thenumber of samples tested. Each line in the dotplot shows large, dark dots for one published minimum andmaximum. The small dots are imputed using a lognormal distribution.

All observed/imputed data were then used to fit a common lognormal distribution that can be used as aninformative prior. The common prior is shown by the density at the top of the dotplot.

Do you think it is better to use a non-informative prior, or this informative prior?

4

Data densities for a binomial GLM

mead.germination

Log concentration

See

ds g

erm

inat

ing

(out

of 5

0).

Bin

omia

l den

sity

.

0

10

20

30

40

50T1

−4 −2 0 2 4

T2

−4 −2 0 2 4

T3

0

10

20

30

40

50T4

Mead, Curnow, and Hasted (2002) present data for germination of seeds under four temperatures (T1-T4)and four chemical concentrations. For each of the 4*4=16 treatments, 50 seeds were tested in each of fourreps. In the graphic, each point is one rep. The blue line is a fitted curve from a GLM with Temperature asa factor and log concentration as a covariate. The gray lines show the central 95 percent of the binomialdensity at that position.

Does this display help you understand the logit link and changing shape of the binomial density?

5

Verification of experiment layout

gomez.stripsplitplot

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

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P1

P1

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P1

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P1

P1

P1

P1

P1

P1

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P1

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P1

P1

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P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P1

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

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P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

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P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

P2

rep

genG1G2G3G4G5G6

nitro0

60120

plantingP1 P1P2 P2

K. Gomez and Gomez (1984) provide data for an experiment with 3 reps, 6 genotypes, 3 levels of nitrogenand 2 planting dates. The experiment layout is putatively a ‘’split strip-plot”. To verify the design, thedesplot package is used for plotting the design of field experiments.

How is the design different from a ‘’split-split-plot” design?

6

Visualizing main effects, two-way interactions

gomez.splitsplit

R1

R2

R3

yield ~ rep | rep

0 50 80 110

140

yield ~ nitrogen | rep

Inte

nsiv

eM

inim

umO

ptim

um

yield ~ management | rep

V1

V2

V3

46810

yieldyield

yield ~ gen | rep

yield ~ rep | nitrogenyield ~ nitrogen | nitrogenyield ~ management | nitrogen

46810

yieldyield

yield ~ gen | nitrogen

yield ~ rep | managementyield ~ nitrogen | managementyield ~ management | management

46810

yieldyield

yield ~ gen | management

yield ~ rep | genyield ~ nitrogen | genyield ~ management | gen

46810

yieldyield

yield ~ gen | gengen

V1V2V3

managementIntensiveMinimumOptimum

nitrogen05080110140

repR1R2R3

Heiberger and Holland (2004) provide an interesting way to use lattice graphics to visualize the main effects(using boxplots) and interactions (using interaction plots) in data. Rice yield is plotted versus replication,nitrogen, management type, and genotype variety. Box plots show minor differences between reps, increaingyield due to nitrogen, high yield from intensive management, and large differences between varieties.

Do you think interaction plots show interaction (lack of parallelism)?

7

3D yield response to fertilizers

Sinclair et al. (1994) examined clover yields as a function of sulfur and phosphorous fertilizer in a factorial-treatment experiment. Dodds, Sinclair, and Morrison (1996) modeled the yield response using a Mitzerlisch-likeequation that allows interacting curvature in two dimensions x and y:

yield = α ∗(

1 + β ∗(σ + τ ∗ xx+ 1

)y)∗

(1 + δ ∗

(θ + ρ ∗ yy + 1

)x)The blue dots are observed data, and the tan surface is the fitted surface drawn by the rgl package).

How would you decide the optimal fertilizer levels?

8

Mosaic plot of potato damage from harvesting

keen.potatodamage

Energy / Genotype

Rod

/ W

eigh

t

E1 E2

R1

R2

R3

R4

R5

R6

R7

R8

G1 G2 G3 G4 G5 G6

W1

W2

W3

W1

W2

W3

W1

W2

W3

W1

W2

W3

W1

W2

W3

W1

W2

W3

W1

W2

W3

W1

W2

W3

G1 G2 G3 G4 G5 G6

Keen and Engel (1997) looked at damage to potatoes caused by lifting rods during harvest. In this experiment,eight types of lifting rods were compared. Two energy levels, six genotypes and three weight classes wereused. For each combinations of treatments, about 20 potato tubers were rated as undamaged (D1, yellow) toseverely damaged (D4, red). Counts per treatment are shown in a mosaic plot.

Which style of lifting rods cause the least/most damage to potatoes?

9

Yield vs covariate for lattice::barley

minnesota.barley

Weather covariate

Bar

ley

yiel

d

103050

200 400 600 8001000

7 9 01

23

45

6Cro

okst

on

1000 1500 2000

7901

2 3

45

6

5 10 15 20 25

7901

23

45

6

7

89 0123

4

56

Dul

uth 7

8901 2

3

4

56 10

30507

890 12

3

4

56

103050

78 90

123

45

6

Gra

ndR

apid

s

78 90

123

45

67

89 01 2

3

45

6

78 9 01

2 56M

orris 7

890 12 5

61030507

89 012 5

6

103050 7

8

90 12

3

4

5

6StP

aul 7

8

90 12

3

4

5

6

78

901 2

3

4

5

6

78

9 01

23

4

56

cddW

asec

a

7890

12

3

4

56

hdd

1030507

89 0

12

3

4

56

precip

Wright (2013) investigated the lattice::barley data. The original two years of data were extended to 10years (from original source documents), and supplemented with weather covariates for the 6 locations and 10years. Each panel shows a scatterplot and regression for average location yield verses the weather covariate.Horizontal strips are for locations, vertical strips are for covariates: cdd = Cooling Degree Days, hdd =Heating Degree Days, precip = Precipitation). Higher values of heating imply cooler weather. Each plottingsymbol is the last digit of the year (1927-1936) for that location.

Does barley yield better in cooler or warmer weather?

10

GGE biplot

PC 1 (32% TSS)

PC

2 (

12%

TS

S)

crossa.wheat

method=svd, center=TRUE, scale=FALSE, missing: 0%

AKAS

BJ

CAEB

EG

ES

ID

ILJM

KN

MGMM

MS

NBPA

PI

SCSE

SG

SJSRSS

TB

TC

Grp1

Grp2

G01

G02

G03G04

G05

G06

G07

G08

G09

G10

G11

G12G13

G14

G15

G16

G17

G18

Laffont, Wright, and Hanafi (2013) developed a variation of the GGE (genotype plus genotype-by-environment)biplot to include auxiliary information about a block/group of environments. Each location is classified intoone of two mega-environments (colored). The mosaic plots partition variation simultaneously by principalcomponent axis and source (genotype, genotype-by-block, residual).

Which genotypes are best to each mega-environment?

11

Nebraska farming income choropleth

nebraska.farmincome

Inco

me

($10

00)

per

coun

ty

crop

animal

100

200

300

400

500

600

700

The Red-Blue palette in the RColorBrewer package is a divergent palette with light colors near the middleof the scale. This can cause problems when there are missing values, which appear as white (technically, thebackground). In order to increase the visibility of missing values, the agridat package uses a Red-Gray-Bluepalette, with a gray color that is dark enough to clearly distinguish missing values.

How does the outlier county (Butler) in northeast Nebraska limit interpration of spatial patterns in the data?

12

nebraska.farmincome

Inco

me

($10

00)

per

squa

re m

ile (

perc

entil

e br

eaks

)

crop.rate

animal.rate

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Because counties are different sizes, the second graphic uses an income rate per square mile. Because of theoutlier, it might be smart to use percentile break points, but doing so hides the outlier. Instead, the breakpoints are calulated using a method called Fisher-Jenks. These break points show both the outlier and thespatial patterns. It is now easy to see that northwest (Sandhills) Nebraska has low farming income, especiallyfor crops. Counties with missing data are white, which is easily distinguished from gray.

Where are farm incomes highest? Why?

13

Las Rosas yield monitor

lasrosas.corn grain yield (qu/ha)

Longitude

Latit

ude

−33.052

−33.051

−33.050

−33.049

−63.848 −63.846 −63.844 −63.842

1999

−63.848 −63.846 −63.844 −63.842

2001

20

40

60

80

100

120

lasrosas.corn experiment design

−33.0520

−33.0515

−33.0510

−33.0505

−33.0500

−33.0495

−33.0490

−63.848 −63.846 −63.844 −63.842

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1999

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2001

Anselin, Bongiovanni, and Lowenberg-DeBoer (2004) and Lambert, Lowenberg-Deboer, and Bongiovanni(2004) looked at yield monitor data collected from a corn field in Argentina in 1999 and 2001, to see how yieldwas affected by field topography and nitrogen fertilizer. The figures here show heatmaps for the yield eachyear, and also the experiment design (colors are reps, shades of color are nitrogen level, plotting character istopography).

14

Which year showed greater spatial variation in yield?

Time series of corn yields by state

nass.corn

Year

Mill

ion

acre

s of

cor

n

0

5

10

Alabama

1900 2000

Arkansas Colorado

1900 2000

Georgia Illinois

1900 2000

Indiana

Iowa Kansas Kentucky Louisiana Maryland

0

5

10

Michigan

0

5

10

Minnesota Mississippi Missouri Nebraska New York North Carolina

North Dakota Ohio Oklahoma Pennsylvania South Carolina

0

5

10

South Dakota

0

5

10

1900 2000

Tennessee Texas

1900 2000

Virginia Wisconsin

15

The National Agricultural Statistics Service tracks the total number of acres planted to corn (and other crops)for each state in the U.S. There are large changes over the past century in corn acreage for selected states.

Which states were in the corn belt in 1925?

Which states were in the corn belt in 2000?

References

Anselin, Luc, Rodolfo Bongiovanni, and Jess Lowenberg-DeBoer. 2004. “A Spatial Econometric Approach tothe Economics of Site-Specific Nitrogen Management in Corn Production.” American Journal of AgriculturalEconomics 86 (3): 675–87. doi:10.1111/j.0002-9092.2004.00610.x.

Dodds, KG, AG Sinclair, and JD Morrison. 1996. “A Bivariate Response Surface for Growth Data.” FertilizerResearch 45 (2): 117–22. doi:10.1007/BF00790661.

Gomez, K.A., and A.A. Gomez. 1984. Statistical Procedures for Agricultural Research. Wiley-Interscience.

Harrison, J.M., D. Culp, and G.G. Harrigan. 2012. “Bayesian MCMC Analyses for Regulatory Assessments ofFood Composition.” In Kansas State University Conference on Applied Statistics in Agriculture, Manhattan,Kansas.

Heiberger, Richard M, and Burt Holland. 2004. Statistical Analysis and Data Display: An IntermediateCourse with Examples in S-Plus, R, and SAS. Springer.

Keen, A., and B. Engel. 1997. “Analysis of a Mixed Model for Ordinal Data by Iterative Re-WeightedREML.” Statistica Neerlandica 51 (2): 129–44. doi:10.1111/1467-9574.00044.

Laffont, Jean-Louis, Kevin Wright, and Mohamed Hanafi. 2013. “Genotype Plus Genotype× Block ofEnvironments Biplots.” Crop Science 53 (6): 2332–41. doi:10.2135/cropsci2013.03.0178.

Lambert, Dayton M, James Lowenberg-Deboer, and Rodolfo Bongiovanni. 2004. “A Comparison of FourSpatial Regression Models for Yield Monitor Data: A Case Study from Argentina.” Precision Agriculture 5(6): 579–600. doi:10.1007/s11119-004-6344-3.

Lee, Arier Chi-Lun. 2009. “Random Effects Models for Ordinal Data.” PhD thesis, University of Auckland.

Mead, R., R.N. Curnow, and A.M. Hasted. 2002. Statistical Methods in Agriculture and Experimental Biology.9th ed. CRC Press.

Sinclair, AG, WH Risk, LC Smith, JD Morrison, and KG Dodds. 1994. “Sulphur and Phosphorus in BalancedPasture Nutrition.” In Proceedings of the New Zealand Grassland Association, 56:13–16.

Wright, Kevin. 2013. “Revisiting Immer’s Barley Data.” The American Statistician 67: 129–33.doi:10.1080/00031305.2013.801783.

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