Accounting for natural and extraneous variation in the analysis of field experiments

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dc.contributor.authorGilmour, A.
dc.contributor.authorCullis, B.
dc.contributor.authorVerbyla, A.
dc.date.issued1997
dc.description.abstractWe identify three major components of spatial variation in plot errors from field experiments and extend the two-dimensional spatial procedures of Cullis and Gleeson (1991) to account for them. The components are nonstationary, large-scale (global) variation across the field, stationary variation within the trial (natural variation or local trend), and extraneous variation that is often induced by experimental procedures and is predominantly aligned with rows and columns. We present a strategy for identifying a model for the plot errors that uses a trellis plot of residuals, a perspective plot of the sample variogram and, where possible, likelihood ratio tests to identify which components are present. We demonstrate the strategy using two illustrative examples. We conclude that although there is no one model that adequately fits all field experiments, the separable autoregressive model is dominant. However, there is often additional identifiable variation present.
dc.description.statementofresponsibilityArthur R. Gilmour, Brian R. Cullis and Arūnas P. Verbyla
dc.identifier.citationJournal of Agricultural, Biological, and Environmental Statistics, 1997; 2(3):269-293
dc.identifier.issn1537-2693
dc.identifier.urihttp://hdl.handle.net/2440/5150
dc.language.isoen
dc.publisherSpringer Verlag
dc.rights© 1997 American Statistical Association and the International Biometric Society
dc.source.urihttp://www.jstor.org/stable/1400446
dc.subjectSpatial analysis; REML; Field experiments; Variogram
dc.titleAccounting for natural and extraneous variation in the analysis of field experiments
dc.typeJournal article
pubs.publication-statusPublished

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