4.3 Article

Effects of Field Selection Parameters and Specific Gravity on Culinary Evaluation Traits in a Potato Breeding Programme

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POTATO RESEARCH
卷 62, 期 4, 页码 361-377

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SPRINGER
DOI: 10.1007/s11540-019-9416-5

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Broad-sense heritability; Culinary traits; Field selection parameters; Linear mixed model; Principal component analysis; Solanum tuberosum; Specific gravity

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Potato culinary evaluation is an integral component of a breeding programme to determine suitability of new cultivars to specific end-uses. In most breeding programmes, quality assessment is conducted after a few generations of field selection on tubers collected from the field or from storage conditions. In order to optimize selection procedures, we analysed the relationship between specific gravity of potato tubers and quality evaluation traits as well as the effects of field selection parameters in clones that are intended for fresh market and processing uses. A total number of 237 advanced breeding clones were analysed over three consecutive years in the potato breeding programme at Agriculture and Agri-Food Canada (AAFC). Principal component analysis (PCA) and linear mixed-effects model fit by maximum likelihood indicated that parameters used in field selection significantly contributed to specific gravity and culinary components. Specific gravity also contributed significantly to culinary traits related to acceptable quality of baked, boiled and French-fried products. However, such relationship was not observed for chip quality. Significant associations were found between field selection parameters, such as maturity, early vigour, top vigour, early harvest, tuber size and tuber appearance and culinary components related to French fry and chip quality, but not for quality of baked and boiled products. Broad-sense heritability estimates for various culinary evaluation traits ranged from 57 to 92%. The results suggest that specific gravity and culinary traits should be incorporated in early or intermediate generation selection parameters in order to optimize selection gains for specific end-uses.

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