We identify three major components of spatial variation in plot errors from eld experiments and extend the two-dimensional spatial procedures of Cullis and Gleeson (1991) to account for them. The components are non-stationary large scale (global) variation across the eld, stationary variation within the trial (natural variation or local trend) and extraneous variation which 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 which 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 while there is no one model that adequately ts all eld experiments, the separable autoregressive model is dominant. However, there is often additional identi able variation present.

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After estimation of e ects from a linear mixed model, it is often useful to form predicted values for certain factor/variate combinations. This process has been well-deÿned for linear models, but the introduction of random e ects means that a decision has to be made about the inclusion or exclusion of random model terms from the predictions, including the residual error. For spatially correlated data, kriging then becomes prediction from the ÿtted model. In many cases, the size of the matrices required to calculate predictions and their covariance matrix directly can be prohibitive. An e cient computational strategy for calculating predictions and their standard errors is given, which includes the ability to detect the invariance of predictions to the parameterisation used in the model.

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The effect of high hydrostatic pressure (up to 700 MPa) at 20°C on the survival of vegetative pathogens was investigated in 10 mM phosphate buffer (pH 7.0), ultra high-temperature-treated (UHT) milk, and poultry meat. In buffer, Yersinia enterocolitica was most sensitive, with a pressure of 275 MPa for 15 min resulting in more than a 105 reduction in numbers of cells. Treatments of 350 MPa, 375 MPa, 450 MPa, 700 MPa, and 700 MPa for 15 min were needed to achieve a similar reduction in Salmonella typhimurium, Listeria monocytogenes, Salmonella enteritidis, Escherichia coli O157:H7, and Staphylococcus aureus respectively. A significant variation in pressure sensitivity was observed between different strains of both L monocytogenes and E. coli O157:H7. The most resistant strains (L. monocytogenes NCTC 11994 and E. coli O157:H7 NCTC 12079) were chosen for further studies on the effect of substrate on pressure sensitivity. In both cases the organisms were more resistant to pressure when treated in UHT milk than in poultry meat or buffer. There was evidence, assessed by differential plating using trypticase soy agar with and without additional NaCl, that sublethally injured cells were present at pressures lower than were required for death. This information may be of value if pressure is combined with preservation treatments such as mild heating. The variation in results obtained with different organisms and in different substrates should be recognized when recommendations for the pressure processing of foods are being considered.