Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 109460
Title Coupling estimated effects of QTLs for physiological traits to a crop growth model: Predicting yield variation among recombinant inbred lines in barley
Author(s) Yin, X.Y.; Chasalow, S.D.; Dourleijn, C.J.; Stam, P.; Kropff, M.J.
Source Heredity 85 (2000)6. - ISSN 0018-067X - p. 539 - 549.
Department(s) Crop and Weed Ecology
Plant Breeding
Centre for Crop Systems Analysis
Publication type Refereed Article in a scientific journal
Publication year 2000
Abstract Advances in the use of molecular markers to elucidate the inheritance of quantitative traits enable the integration of genetic information on physiological traits into crop growth models. The objective of this study was to assess the ability of a crop growth model with QTL-based estimates of physiological input parameters to predict the yield of recombinant inbred lines (RILs) of barley. The model used predicts yield as spike biomass accumulated over the post-flowering period. We describe a two-stage procedure for predicting trait values from estimated additive and epistatic effects of QTLs. Values of physiological traits estimated by that procedure or measured in the field were used as input to the crop growth model. The output values (yield and shoot biomass) from the growth model using these two types of input values were highly correlated, indicating that QTL information can successfully replace measured input parameters. With the current crop growth model, however, both types of input values often resulted in large discrepancies between observed and predicted values. Improvement of performance may be achieved by incorporating physiological processes not yet included in the model. The prospects of using QTL-based predictions of model-input traits to identify new, high yielding barley genotypes are discussed.
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