Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/5150
Type: Journal article
Title: Accounting for natural and extraneous variation in the analysis of field experiments
Author: Gilmour, A.
Cullis, B.
Verbyla, A.
Citation: Journal of Agricultural, Biological, and Environmental Statistics, 1997; 2(3):269-293
Publisher: Springer Verlag
Issue Date: 1997
ISSN: 1537-2693
Statement of
Responsibility: 
Arthur R. Gilmour, Brian R. Cullis and Arūnas P. Verbyla
Abstract: We 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.
Keywords: Spatial analysis; REML; Field experiments; Variogram
Rights: © 1997 American Statistical Association and the International Biometric Society
RMID: 0030006358
Published version: http://www.jstor.org/stable/1400446
Appears in Collections:Statistics publications

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