agridat (version 1.16)

agridat: Datasets from agricultural experiments

Description

This package contains datasets from publications relating to agriculture, including field crops, tree crops, animal studies, and a few others.

Arguments

Details

If you use these data, please cite both the agridat package and the original source of the data.

Abbreviations in the 'other' column include: xy = coordinates, pls = partial least squares, rsm = response surface methodology, row-col = row-column design, ts = time series,

Uniformity trials with a single genotype

name dimensions other model
ansari.wheat.uniformity 96 x 8 xy
baker.barley.uniformity 3 x 19 xy, 10 years
baker.wheat.uniformity 12 x 12 xy
bancroft.peanut.uniformity 6 x 18 xy, 2 blocks
batchelor.apple.uniformity 8 x 28 xy
batchelor.lemon.uniformity 14 x 16 xy
batchelor.navel1.uniformity 20 x 50 xy
batchelor.navel2.uniformity 15 x 33 xy
batchelor.valencia.uniformity 12 x 20 xy
batchelor.walnut.uniformity 10 x 28 xy
bose.multi.uniformity 15 x 26 xy, 3 years
bradley.multi.uniformity 10 x 11 xy
christidis.cotton.uniformity 16 x 16 xy, 4 blocks
christidis.wheat.uniformity 12 x 24 xy
correa.soybean.uniformity 24 x 48 xy
davies.pasture.uniformity 19 x 40 xy
day.wheat.uniformity 31 x 100 xy
draper.safflower.uniformity 18 x 16 xy, 2 expt smith
eden.tea.uniformity 12 x 12 xy
garber.multi.uniformity 45 x 6 xy, 2 years/crops
gomez.rice.uniformity 18 x 36 xy aov,smith
goulden.barley.uniformity 20 x 20 xy
harris.multi.uniformity 2 x 23 xy, 23 crops corrgram
holtsmark.timothy.uniformity 6 x 40 xy
hutchinson.cotton.uniformity 32 x 40 xy
igue.sugarcane.uniformity 36 x 42 xy
immer.sugarbeet.uniformity 10 x 60 xy, 3 traits
iyer.wheat.uniformity 25 x 80 xy
kadam.millet.uniformity 8 x 20 xy, 2 expts
kalamkar.potato.uniformity 6 x 96 xy
kalamkar.wheat.uniformity 16 x 80 xy, 2 traits
kempton.barley.uniformity 7 x 28 xy
khin.rice.uniformity 30 x 36 xy
kiesselbach.oats.uniformity 3 x 69 xy
kristensen.barley.uniformity 22 x 11 xy
kulkarni.sorghum.uniformity 4 x 40 xy, 3 years
lander.multi.uniformity 5 x 39 xy, 4 years
lessman.sorghum.uniformity 46 x 60 xy
li.millet.uniformity 6 x 100 xy
lord.rice.uniformity 5 x 14 xy, 8 fields
love.cotton.uniformity 16 x 10 xy
lyon.potato.uniformity 34 x 6 xy
magistad.pineapple.uniformity 5 x 5 xy
masood.rice.uniformity 12 x 24 xy
mcclelland.corn.uniformity 2 x 219 xy
mercer.mangold.uniformity 10 x 20 xy
mercer.wheat.uniformity 25 x 20 xy, 2 traits spplot
montgomery.wheat.uniformity 14 x 16 xy, 2 years lm
moore.polebean.uniformity 12 x 12 xy
moore.bushbean.uniformity 24 x 24 xy
moore.sweetcorn.uniformity 24 x 12 xy
moore.carrot.uniformity 24 x 12 xy
moore.springcauliflower.uniformity 12 x 20 xy
moore.fallcauliflower.uniformity 12 x 20 xy
nagai.strawberry.uniformity 18 x 24 \ tab xy narain.sorghum.uniformity
10 x 16 xy nonnecke.peas.uniformity
15 x 18 xy, 2 traits nonnecke.sweetcorn.uniformity
32 x 18 xy, 3 loc odland.soybean.uniformity
25 x 42 xy odland.soyhay.uniformity
28 x 55 xy parker.orange.uniformity
10 x 27 xy, 6 yr polson.safflower.uniformity
52 x 33 xy smith robinson.peanut.uniformity
16 x 36 xy sawyer.multi.uniformity
8 x 6 xy, 3 year sayer.sugarcane.uniformity
8 x 136, 8 x 121 xy, 2 year smith.beans.uniformity
18 x 12, 16 x 15 xy, 2 yr, 2 crops smith.corn.uniformity
6 x 20 xy, 3 years rgl stephens.sorghum.uniformity
100 x 20 xy stickler.sorghum.uniformity
20 x 20 xy, 4 expts, 2 years strickland.apple.uniformity
11 x 18 xy strickland.grape.uniformity
5 x 31 xy strickland.peach.uniformity
8 x 18 xy strickland.tomato.uniformity
30 x 6 xy wassom.brome.uniformity
36 x 36 xy, 3 expts wiebe.wheat.uniformity
12 x 125 xy medianpolish, loess wiedemann.safflower.uniformity
54 x 33 xy smith williams.barley.uniformity
48 x 15 xy loess williams.cotton.uniformity
24 x 12 xy loess name

Yield monitor

name reps years trt other model
gartner.corn xy,ym
lasrosas.corn 3 2 6 xy,ym lm

Animals

name gen years trt other model becker.chicken 5,12
heritability lmer crampton.pig 5
2 cov lm brandt.switchback 10 2 aov
depalluel.sheep 4 4 latin diggle.cow
4 ts foulley.calving
ordinal polr goulden.eggs controlchart
harvey.lsmeans 3,3 lm harville.lamb 5
lmer henderson.milkfat
nls,lm,glm,gam holland.arthropods 5
ilri.sheep 4 6 diallel lmer, asreml kenward.cattle
2 asreml lucas.switchback 12 3
aov mead.lamb 3 3 glm
patterson.switchback 12 4 aov urquhart.feedlot 11
3 lm zuidhof.broiler
ts name gen years trt other model

Trees

name gen loc reps years trt other model
box.cork repeated radial, asreml
harris.wateruse 2 2 repeated asreml,lme
hanover.whitepine 7*4 4 heritability lmer
lavoranti.eucalyptus 70 7 svd
pearce.apple 4 6 cov lm,lmer
williams.trees 37 6 2

Field and horticulture crops

name gen loc reps years trt other model

acorsi.grayleafspot

36 9 2 5 nonnormal gnm,ammi
adugna.sorghum 28 13 5
aastveit.barley 15 9 yr*gen~yr*trt pls
allcroft.lodging 32 7 percent tobit
archbold.apple 2 5 24 split-split lmer
ars.earlywhitecorn96 60 9 6 traits dotplot
australia.soybean 58 4 2 4-way, 6 traits biplot
battese.survey 12 1-5 2 lmer
beall.webworms 15 2,2 xy glm poisson
beaven.barley 8 20 xy
besag.bayesian 75 3 xy asreml
besag.beans 6 4*6 xy lm,competition
besag.elbatan 50 3 xy lm, gam
besag.endive xy,binary autologistic
besag.met 64 6 3 xy, incblock asreml, lme
besag.triticale 3 2,2,3 xy lm, asreml
bliss.borers 4 glm
blackman.wheat 12 7 2 biplot
bond.diallel 6*6 9 diallel
bridges.cucumber 4 2 4 xy, latin, hetero asreml
brandle.rape 5 9 4 3 lmer
burgueno.alpha 15 3 xy, alpha asreml,lmer
burgueno.rowcol 64 2 xy, row-col asreml,lmer
burgueno.unreplicated 280 xy asreml
butron.maize 49 3 2 diallel,pedigree biplot,asreml
caribbean.maize 17 4 3
carmer.density 8 4 nls,nlme
carlson.germination 15 8 glm
chinloy.fractionalfactorial 9 1/3 3^5 xy aov
christidis.competition 9 5 xy
cochran.beets 6 7
cochran.bib 13 13 bib aov, lme
cochran.crd 7 xy, crd aov
cochran.factorial 2 4^2 factorial aov
cochran.latin 6 6 xy, latin aov
cochran.lattice 5 16 xy, latin lmer
cochran.wireworms 5 5 xy, latin glm
cochran.eelworms 4 5 xy aov
connolly.potato 20 4 xy, competition lm
cornelius.maize 9 20 svd
corsten.interaction 20 7
cramer.cucumber 8 pathcoef
crossa.wheat 18 25 ammi
crowder.seeds 2 21 2 glm,jags
cox.stripsplit 4 3,4,2 split-block aov
cullis.earlygen 532 xy asreml
dasilva.maize 55 9 3
darwin.maize 12 2 t.test
denis.missing 5 26 lme
denis.ryegrass 21 7 aov
digby.jointregression 10 17 4 lm
durban.competition 36 3 xy, competition lm
durban.rowcol 272 2 xy lm, gam, asreml
durban.splitplot 70 4 2 xy lm, gam, asreml
eden.potato 4 3 4-12 xy, rcb, latin aov
eden.nonnormal 4 4 aov
engelstad.nitro 2 5 6 rsm1 nls quadratic plateau
fan.stability 13 10 2 3-way stability
federer.diagcheck 122 xy lm, lmer, asreml
federer.tobacco 8 7 xy lm
fisher.barley 5 6 2
fisher.latin 5 5 xy,latin lm
fox.wheat 22 14 lm
gathmann.bt 2 8 tost
gauch.soy 7 7 4 12 ammi
giles.wheat 19 13 2 traits gnm
gilmour.serpentine 108 3 xy, serpentine asreml
gilmour.slatehall 25 6 xy asreml
gomez.fractionalfactorial 2 1/2 2^6 xy lm
gomez.groupsplit 45 3 2 xy, 3 gen groups aov
gomez.heteroskedastic 35 3 hetero
gomez.multilocsplitplot 2 3 3 rsm1,nitro aov, lmer
gomez.nitrogen 4 8 aov, contrasts
gomez.nonnormal1 4 9 log10 lm
gomez.nonnormal2 14 3 sqrt lm
gomez.nonnormal3 12 3 arcsin lm
gomez.seedrate 4 6 rate lm
gomez.splitplot.subsample 3 8,4 subsample aov
gomez.splitsplit 3 3 xy, nitro, mgmt aov, lmer
gomez.stripplot 6 3 xy, nitro aov
gomez.stripsplitplot 6 3 xy, nitro aov
gomez.wetdry 3 2 5 nitro lmer
gotway.hessianfly 16 4 xy lmer
goulden.latin 5 5 xy, latin lm
goulden.splitsplit 2 4 2*5 xy, split aov
graybill.heteroskedastic 4 13 hetero
gregory.cotton 2 4*3*2*2 polar
gumpertz.pepper xy glm
hanks.sprinkler 3 3 xy asreml
hayman.tobacco 8 2 2 diallel asreml
hazell.vegetables 4 6 linprog
heady.fertilizer 2 9*9 rsm2 lm,rgl
hernandez.nitrogen 5 4 rsm1 lm, nls
hildebrand.systems 14 4 asreml
holshouser.splitstrip 4 4 2*4 rsm1,pop lmer
huehn.wheat 20 10 huehn
hughes.grapes 3 6 binomial lmer, aod, glmm
hunter.corn 12 3 1 rsm1 xyplot
ivins.herbs 13 6 2 traits lm, friedman
jansen.apple 3 4 3 binomial glmer
jansen.carrot 16 3 2 binomial glmer
jansen.strawberry 12 4 ordinal mosaicplot
jenkyn.mildew 9 4 lm
john.alpha 24 3 alpha lm, lmer
johnson.blight 2 logistic
kang.maize 17 4 3 2,4
kang.peanut 10 15 4 gge
karcher.turfgrass 4 2,4 ordinal polr
keen.potatodamage 6 4 2,3,8 ordinal mosaicplot
kempton.competition 36 3 xy, competition lme AR1
kempton.rowcol 35 2 xy, row-col lmer
kempton.slatehall 25 6 xy asreml, lmer
lee.potatoblight 337 4 11 xy, ordinal, repeated
lehner.soybeanmold 35 4 11 metafor, lmer
lillemo.wheat 24 13 7 qq medpolish, huehn
lin.superiority 33 12 superiority
lin.unbalanced 33 18 superiority
little.splitblock 4 4,5 xy, split-block aov
lonnquist.maize 11 diallel asreml
lyons.wheat 12 4
lu.stability 5 6 huehn
mcconway.turnip 2 4 2,4 hetero aov, lme
mcleod.barley 8 6 aggregate
mead.cauliflower 2 poisson glm
mead.cowpeamaize 3,2 3 4 intercrop
mead.germination 4 4,4 binomial glm
mead.strawberry 8 4
mead.turnip 3 5,4 aov
minnesota.barley.weather 6 10
minnesota.barley.yield 22 6 10 dotplot
omer.sorghum 18 2 4 3 jags
onofri.winterwheat 8 3 7 ammi
ortiz.tomato 15 18 16 env*gen~env*cov pls
pacheco.soybean 18 11 ammi
perry.springwheat 28 5 4 gain lm,lmer,asreml
piepho.cocksfoot 25 7 lmer
ratkowsky.onions lm
reid.grasses 4 3 21 nlme SSfpl
ridout.appleshoots 30 2,4 zip zeroinfl
rothamsted.brussels 4 6
ryder.groundnut 5 4 xy, rcb lm
salmon.bunt 10 2 20 betareg
senshu.rice 40 lm,Fieller
shafii.rapeseed 6 14 3 3 biplot
silva.cotton 5 5 5 traits glm,poisson
sinclair.clover 5,5 rsm2,mitzerlich nls,rgl
snedecor.asparagus 4 4 4 split-plot, antedependence
snijders.fusarium 17 3 4 percent glm/gnm,gammi
steptoe.morex.pheno 152 16 10 traits
steptoe.morex.geno 150 223 markers, qtl
streibig.competition 2 3 glm
stroup.nin 56 4 xy asreml
stroup.splitplot 4 asreml, MCMCglmm
student.barley 2 51 6 lmer
tai.potato 8 3 2 tai
talbot.potato 9 12 gen*env~gen*trt pls
theobald.barley 3 5 2 5 rsm1
theobald.covariate 10 7 5 cov jags
thompson.cornsoy 5 33 repeated measures aov
vaneeuwijk.fusarium 20 4 7 3-way aov
vaneeuwijk.drymatter 6 4 7 3-way aov,lmer
vaneeuwijk.nematodes 11 nonnormal,poisson gnm, gammi
vargas.wheat1 7 6 gen*yr~gen*trt, yr*gen~yr*cov pls
vargas.wheat2 8 7 env*gen~env*cov pls
vargas.txe 10 24 yr*trt~yr*cov pls
verbyla.lupin 9 8 2-3 2 rsm1, xy, density asreml
vold.longterm 19 4 rsm1 nls,nlme
vsn.lupin3 336 3 xy asreml
wedderburn.barley 10 9 percent glm/gnm
weiss.incblock 31 6 xy,incblock asreml
weiss.lattice 49 4 xy,lattice lm,asreml
welch.bermudagrass 4,4,4 rsm3, factorial lm, jags
wheatley.carrot 3 11 glm-binomial
yan.winterwheat 18 9 gge,biplot
yang.barley 6 18 biplot
yates.missing 10 3^2 factorial lm, pca
yates.oats 3 6 xy,split,nitro lmer

Time series

name years trt other model
byers.apple lme
broadbalk.wheat 74 17
hessling.argentina 30 temp,precip
kreusler.maize 4 5 plant growth
lambert.soiltemp 1 7
nass.barley 146
nass.corn 146
nass.cotton 146
nass.hay 104
nass.sorghum 93
nass.wheat 146
nass.rice 117
nass.soybean 88
walsh.cottonprice 34 cor

Other

name model
cate.potassium cate-nelson
cleveland.soil loess 2D
harrison.priors nls, prior
nebraska.farmincome choropleth
pearl.kernels chisq
stirret.borers lm, 4 trt
turner.herbicide glm, 4 trt
usgs.herbicides non-detect
wallace.iowaland lm, choropleth
waynick.soil spatial, nitro/carbon

Comments of the package purpose

Box (1957) said, "I had hoped that we had seen the end of the obscene tribal habit practiced by statisticians of continually exhuming and massaging dead data sets after their purpose in life has long since been forgotten and there was no possibility of doing anything useful as a result of this treatment."

Massaging these dead data sets will not lead to any of the genetics being released for commercial use. The value of this package is: 1. Validating published analyses. 2. Providing data for testing new analysis methods. 3. Illustrating (and validating) the use of R.

White and van Evert (2008) present some guidelines for publication of data.

Some of the examples use the asreml package since it is the _only_ R tool for fitting mixed models with complex variance structures to large datasets, and the best option for modelling AR1xAR1 residual variance structures. Commercial use of asreml requires a license: http://www.vsni.co.uk/downloads/asreml.

Comments on the package structure

A large portion of these datasets appear in electronic form here for the first time.

A tremendous amount of effort has been given to the curating process of identifying datasets, extracting the data from source materials, checking data values, and documenting the data. In effect, to make the data somewhat 'computable' (Wolfram 2017).

The original sources for these data use several different words to refer to genotypes including accession, breed, cultivar, genotype, hybrid, line, progeny, stock, type, and variety. For consistency, these datasets mostly use gen (genotype).

Also for consistency row and col are usually used for the field coordinates.

In dataframes, 'block', 'rep', and similar terms are almost always coded like B1, B2, B3 instead of 1, 2, 3. This causes R to treat the data as a factor instead of a numeric covariate (which is a good thing).

Almost all of the data are presented as 'tidy' dataframes with 'observations' in rows and 'variables' in columns.

Although using data() is not necessary to access the data files, the example sections do include the use of data() because devtools::run_examples() needs it.

Please report any bugs to the package author or at the package github site.

References

G. E. P. Box (1957), Integration of Techniques in Process Development, Transactions of the American Society for Quality Control.

J. White and Frits van Evert. (2008). Publishing Agronomic Data. Agron J. 100, 1396-1400. http://doi.org/10.2134/agronj2008.0080F

Stephen Wolfram (2017). Launching the Wolfram Data Repository: Data Publishing that Really Works. http://blog.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/