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ABSTRACT
Genebanks routinely assess plant characteristics of accessions on an ordinal rating scale. A common problem with long-term data is that the rating scale is changed from time to time. This paper proposes a method for joint analysis of data from different rating scales, assuming a threshold model with a common latent scale for the different rating systems. The method may be used to derive mean scores on any one of the rating scales based on a long-term series of evaluation trials, as is illustrated using evaluation data on barley (Hordeum spp.). While the proposed method was motivated by data problems encountered in genebanks, it may be of equal interest in other areas of application, such as plant breeding, where rating scales change across time.
Abbreviations: BLUP, best linear unbiased prediction.
GENEBANKS EVALUATE a large number of accessions each year, mostly in unreplicated field trials. Many traits (e.g., resistance to diseases) are assessed on an ordinal rating scale comprising a small number of ordered categories, typically between five and nine. For example, a rating scale for a disease may have the following categories for degree of susceptibility: 1 = no disease, 2 = low, 3 = intermediate, 4 = high, 5 = very high.
Evaluation data covering many years of field trials raise the problem of how to combine different years into a single value per accession. For metric data (yield, thousand-kernel weight, etc.), the standard approach is to use an appropriate linear model for the series of trials and to estimate least squares means per accession (Piepho, 2003a). Rating data are often analyzed using the same type of model, despite the fact that ordinal data strictly do not meet the usual assumptions of homogeneity of variance, normality, and linearity/additivity. When replicate data (on a plant basis) are available per field plot, mean scores per plot often show no significant departures from the usual assumptions (Thöni, 1985; Schumacher and Thöni, 1990). Genebanks, however, assess scores on a plot basis, so results for replicate data per plot are not directly applicable.
Genebanks are frequently faced with the problem that rating scales change across years. This complicates the integration of multiyear data into a single score per accession. As of yet, sound statistical procedures...