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Prediction of variety distinctness decisions under yearly heterogeneity

Published online by Cambridge University Press:  26 January 2016

A. M. I. ROBERTS*
Affiliation:
Biomathematics and Statistics Scotland (BioSS), James Clerk Maxwell Building, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK
I. M. NEVISON
Affiliation:
Biomathematics and Statistics Scotland (BioSS), James Clerk Maxwell Building, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK
T. CHRISTIE
Affiliation:
Science and Advice for Scottish Agriculture (SASA), Roddinglaw Road, Edinburgh EH12 9FJ, UK
*
* To whom all correspondence should be addressed. Email: [email protected]

Summary

To gain protection under the International Convention for the Protection of New Varieties of Plant, new plant varieties must be distinguishable from existing varieties in at least one important characteristic. Assessment of quantitative characteristics often uses a procedure based on analysis of variance of variety-by-year means for 2 years of trials. In the current paper, a new method is described that can identify those reference varieties that are so different from a candidate that there would be no reason to compare them in the subsequent year, resulting in potential cost savings. It is more objective and transparent than existing practice for quantitative characteristics based on expert opinion. The method calculates thresholds for quantitative characteristics. The thresholds are defined so that if in the first year the difference between two varieties in a characteristic is larger than the characteristic's threshold then it is highly likely that the varieties would be distinct after 2 years. Thresholds were derived based on statistical predictions of the full decision after 2 years using the first year results combined with historical data. It is shown that these thresholds are sensitive to yearly heterogeneity in the variety-by-year variation. The method accommodates this heterogeneity by modelling yearly residual variances with the inverse gamma distribution. This extension meant that exact analytical formulae were not available so an approximation was suggested. Using simulation it was found that the approximation was reasonable; for thresholds corresponding to a high probability of distinctness, the approximate thresholds were a little higher than required. The method was evaluated on a 19-year data set for field pea, comparing decisions based on first year thresholds with those based on the full 2 years. It was found that with the probability of distinctness set at 0·99, the calculated thresholds were generally lower than the existing expert-set thresholds but had acceptable levels of false positives and false negatives.

Type
Crops and Soils Review
Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

Anon (2002). General Introduction to the Examination of Distinctness, Uniformity and Stability and Development of Harmonized Descriptions of New varieties of Plants. UPOV document TG/3/1. Geneva, Switzerland: UPOV.Google Scholar
Anon (2013). Guidance on the Use of Biochemical and Molecular Markers in the Examination of Distinctness, Uniformity and Stability (DUS). UPOV document TGP/15/1 adopted by the Council at its forty-seventh ordinary session on October 24, 2013. Geneva, Switzerland: UPOV.Google Scholar
Anon (2014 a). Trial Design and Techniques used in the Examination of Distinctness, Uniformity and Stability. UPOV document TGP/8/2 adopted by the Council at its forty-eighth ordinary session on October 16, 2014. Geneva, Switzerland: UPOV.Google Scholar
Anon (2014 b). Pea: Guidelines for the Conduct of Tests for Distinctness, Uniformity and Stability. UPOV document TG/7/10 Rev. Geneva, Switzerland: UPOV.Google Scholar
Arens, P., Mansilla, C., Deinum, D., Cavellini, L., Moretti, A., Rolland, S., van der Schoot, H., Calvache, D., Ponz, F., Collonnier, C., Mathis, R., Smilde, D., Caranta, C. & Vosman, B. (2010). Development and evaluation of robust molecular markers linked to disease resistance in tomato for distinctness, uniformity and stability testing. Theoretical and Applied Genetics 120, 655664.CrossRefGoogle ScholarPubMed
Bernardo, J. M. & Smith, A. F. M. (2000). Bayesian Theory. Chichester, UK: Wiley.Google Scholar
Camlin, M. S., Watson, S., Waters, B. G. & Weatherup, S. T. C. (2001). The potential for management of reference collections in herbage variety registration trials using a cyclic planting system for reference varieties. Plant Varieties and Seeds 14, 114.Google Scholar
Choi, S. C. & Wette, R. (1969). Maximum likelihood estimation of the parameters of the gamma distribution and their bias. Technometrics 11, 683690.Google Scholar
Ibanez, J., Dolores Velez, M., Teresa de Andres, M. & Borrego, J. (2009). Molecular markers for establishing distinctness in vegetatively propagated crops: a case study in grapevine. Theoretical and Applied Genetics 119, 12131222.CrossRefGoogle ScholarPubMed
Jones, H., Norris, C., Smith, D., Cockram, J., Lee, D., O'Sullivan, D. M. & Mackay, I. (2013). Evaluation of the use of high-density SNP genotyping to implement UPOV Model 2 for DUS testing in barley. Theoretical and Applied Genetics 126, 901911.Google Scholar
Minka, T. P. (2002). Estimating a Gamma Distribution. Technical Report. Cambridge, UK: Microsoft Research. Available from http://research.microsoft.com/en-us/um/people/minka/papers/minka-gamma.pdf (verified 20 November 2015).Google Scholar
Nevison, I. M. & Roberts, A. M. I. (2011). Predicting distinctness decisions after one growing cycle. Biuletyn Oceny Odmian 33, 3547.Google Scholar
R Core Team (2014). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.Google Scholar
VSN International (2012). GenStat for Windows, 15th edn, Hemel Hempstead, UK: VSN International.Google Scholar