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agridat {agridat}

Datasets from agricultural experiments
Package: 
agridat
Version: 
1.12

Description

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

Details

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

Uniformity trials with a single genotype

name dimensions other model
baker.barley.uniformity 3 x 19 xy, 10 years
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
garber.multi.uniformity 45 x 6 xy, 2 years/crops
gomez.rice.uniformity 18 x 36 xy aov
goulden.barley.uniformity 20 x 20 xy
harris.multi.uniformity 2 x 23 xy, 23 crops corrgram
immer.sugarbeet.uniformity 10 x 60 xy, 3 traits
kalamkar.potato.uniformity 6 x 96 xy
kempton.barley.uniformity 7 x 28 xy
li.millet.uniformity 6 x 100 xy
lyon.potato.uniformity 34 x 6 xy
mercer.mangold.uniformity 10 x 25 xy, 2 traits
mercer.wheat.uniformity 25 x 20 xy, 2 traits spplot
odland.soybean.uniformity 25 x 42 xy
odland.soyhay.uniformity 28 x 55 xy
smith.corn.uniformity 6 x 20 xy, 3 years rgl
stephens.sorghum.uniformity 100 x 20 xy
wassom.brome1.uniformity 36 x 36 xy
wassom.brome2.uniformity 36 x 36 xy
wassom.brome3.uniformity 36 x 36 xy
wiebe.wheat.uniformity 12 x 125 xy medianpolish, loess
williams.barley.uniformity 48 x 15 xy loess
williams.cotton.uniformity 24 x 12 xy loess

Animals

name gen loc years trt other model
brandt.switchback 10 2 aov
diggle.cow 4 ts
foulley.calving ordinal polr
henderson.milkfat nls,lm,glm,gam
holland.arthropods 5
ilri.sheep 4 6 diallel lmer, asreml
lucas.switchback 12 3 aov
patterson.switchback 12 4 aov
zuidhof.broiler ts

Trees

name gen loc reps years trt other model
archbold.apple 2 5 24 split-split lmer
box.cork repeated radial, asreml
harris.wateruse 2 2 repeated asreml,lme
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
adugna.sorghum 28 13 5
aastveit.barley 15 9 yr*gen~yr*trt pls
allcroft.lodging 32 7 percent tobit
ars.earlywhitecorn96 60 9 6 traits dotplot
australia.soybean 58 4 2 4-way, 6 traits biplot
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
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
crossa.wheat 18 25 AMMI
crowder.seeds 2 21 2 glm,jags
cox.stripsplit 4 3,4,2 aov
cullis.earlygen 532 xy asreml
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
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.latin 5 5 xy,lating lm
fox.wheat 22 14 lm
gathmann.bt 2 8 TOST
gauch.soy 7 7 4 12 AMMI
gilmour.serpentine 108 3 xy, serpentine asreml
gilmour.slatehall 25 6 xy asreml
gomez.fractionalfactorial 2 6 xy lm
gomez.groupsplit 45 3 2 xy, 3 gen groups aov
gomez.multilocsplitplot 2 3 3 rsm1,nitro aov, lmer
gomez.nitrogen 4 8 aov, contrasts
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
gotway.hessianfly 16 4 xy lmer
goulden.latin 5 5 xy, latin lm
graybill.heteroskedastic 4 13 hetero
gumpertz.pepper 2 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
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
lasrosas.corn 3 2 6 xy lm
lee.potatoblight 337 4 11 xy, ordinal, repeated
lonnquist.maize 11 diallel asreml
lyons.wheat 12 4
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
minnesota.barley.weather 6 10
minnesota.barley.yield 22 6 10 dotplot
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
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
sinclair.clover 5,5 rsm2,mitzerlich nls,rgl
snedecor.asparagus 4 4 4 split-plot, antedependence
snijders.fusarium 17 3 4 percent glm/gnm,AMMI
steel.soybean 12 3 3
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
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
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 rsm1, xy, density
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
yan.winterwheat 18 9 GGE,biplot
yang.barley 6 18 biplot
yates.missing 10 3^2 factorial lm, pca
yates.oats 3 6 xy, nitro lmer

Time series

name years trt other model
byers.apple lme
broadbalk.wheat 74 17
hessling.argentina 30 temp,precip
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
wallace.iowaland lm, choropleth
waynick.soil spatial, nitro/carbon

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 these data is: 1. Validating published analyses (reproducible research). 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 almost the only option (even for small datasets) 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

The original sources for these data use several different words to refer to genotypes including 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 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).

Most data are presented as data frames. In a few cases, the data are lists of matrices.

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.

Note: In the U.S., raw data are generally not subject to copyright. See http://www.lib.umich.edu/copyright-office-mpublishing/copyrightability-charts-tables-and-graphs and http://sciencecommons.org/about/towards for some discussion.

Nonetheless, substantial effort has been made to contact people to secure permission to include data (published within the past few decades) in this package.

Data produced from work of the United States government is not subject to copyright. http://en.wikipedia.org/wiki/Copyright_status_of_work_by_the_U.S._government

Other resources

The SDaA package http://cran.r-project.org/package=SDaA contains county-level data from the United States Census of Agriculture, along with a vignette to illustrate survey sampling analyses.

lmtest:ChickEgg has time series of annual chicken and egg production in the United States 1930-1983.

Payne 2013 - Design and Analysis of Long-Term Rotation Experiments. https://www.agronomy.org/publications/aj/pdfs/107/2/772 Data and R code.

References

Box G. E. P. (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.

Author(s)

Kevin Wright, kw.stat@gmail.com

The author is grateful to the many people who granted permission to include their data in this package.

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

Documentation reproduced from package agridat, version 1.12. License: GPL-2