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agridat {agridat}

Datasets from agricultural experiments
Package: 
agridat
Version: 
1.8

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
kempton.barley.uniformity 7 x 28 xy
li.millet.uniformity 6 x 100 xy
lyon.potato.uniformity 34 x 6 xy
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
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
diggle.cow 4 ts
henderson.milkfat nls,lm,glm,gam
ilri.sheep 4 6 diallel lmer, asreml
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
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*Trait pls
allcroft.lodging 32 7 percent tobit
ars.earlywhitecorn96 60 9 6 traits dotplot
australia.soybean 58 4 2 4-way, 6 traits biplot
besag.bayesian 75 3 xy asreml
besag.elbatan 50 3 xy lm, gam
besag.met 64 6 3 xy, incblock asreml, lme
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
caribbean.maize 17 4 3
carmer.density 8 4 nls
cochran.bib 13 13 BIB aov, lme
cochran.crd 7 xy, crd aov
cochran.latin 6 6 xy, latin aov
cochran.wireworms 5 5 xy, latin glm
cochran.eelworms 4 5 xy aov
corsten.interaction 20 7
crossa.wheat 18 25 AMMI
crowder.seeds 2 21 2 glm,jags
cox.stripsplit 4 3,4,2 aov
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 nls quadratic plateau
fan.stability 13 10 2 3-way stability
federer.diagcheck 122 xy lm, lmer, asreml
federer.tobacco 8 7 xy 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 nitro aov, lmer
gomez.nitrogen 4 8 aov, contrasts
gomez.seedrate 4 6 rate lm
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
graybill.heteroskedastic 4 13 hetero
hanks.sprinkler 3 3 xy asreml
hayman.tobacco 8 2 diallel
hernandez.nitrogen 5 4 lm, nls
hildebrand.systems 14 4 asreml
holshouser.splitstrip 4 4 2*4 lmer
hughes.grapes 3 6 binomial lmer, aod, glmm
ivins.herbs 13 6 2 traits lm, friedman
jenkyn.mildew 9 4 lm
john.alpha 24 3 alpha lm, lmer
kempton.competition 36 3 xy, competition lme AR1
kempton.rowcol 35 2 xy, row-col lmer
kempton.slatehall 25 6 xy asreml, lmer
lyons.wheat 12 4
mcconway.turnip 2 4 2,4 hetero aov, lme
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
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
snedecor.asparagus 4 4 4 split-plot, antedependence
steel.soybeanmet 12 3 3
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*Trait pls
theobald.covariate 10 7 5 cov jags
thompson.cornsoy 5 33 repeated measures aov
vargas.wheat1 7 6 G*Y~G*Trait, Y*G~Y*Cov pls
vargas.wheat2 8 7 Env*Gen~Env*Cov pls
verbyla.lupin 9 8 2 xy, density
vsn.lupin3 336 3 xy asreml
wedderburn.barley 10 9 percent glm
yan.winterwheat 18 9 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

Other

name model
beall.borers glm
cate.potassium cate-nelson
cleveland.soil loess 2D
johnson.blight logistic regression
nebraska.farmincome choropleth
pearl.kernels chisq
waynick.soil

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 simplicity and consistency, these datasets mostly use gen (genotype). Also for consistency row and col are often used for the coordinates.

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 the use of R.

Some of the examples use the asreml package since it is the only option for fitting mixed models with complex variance structures to large datasets, and also the only option (even for small datasets) for modelling AR1xAR1 structures. The Discovery version of ASREML is free for people in academia (excluding commercial use) and for people in developing nations. This applies to both the stand-alone ASREML and the R package ASREML-R. Learn more at http://www.vsni.co.uk/software/asreml-discovery/. Commercial use requires a license: http://www.vsni.co.uk/downloads/asreml/.

References

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

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 this package and the original source of the data.

Documentation reproduced from package agridat, version 1.8. License: GPL-2