agridat: Datasets from agricultural experiments

Description

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

Arguments

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 llll{ 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 llllllll{ 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 llllllll{ 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 llllllll{ 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 lllll{ 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 ll{ 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.