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Hybrid breeding for biomass yield in winter triticale: II. Combining ability and hybrid prediction
Author(s) -
Trini Johannes,
Maurer Hans Peter,
Weissmann Sigrid,
Würschum Tobias
Publication year - 2020
Publication title -
plant breeding
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.583
H-Index - 71
eISSN - 1439-0523
pISSN - 0179-9541
DOI - 10.1111/pbr.12816
Subject(s) - triticale , hybrid , biology , biomass (ecology) , selection (genetic algorithm) , microbiology and biotechnology , agronomy , hybrid seed , computer science , machine learning
Accurate hybrid prediction and knowledge about the relative contribution of general (GCA) and specific combining ability (SCA) are of utmost importance for efficient hybrid breeding. We therefore evaluated 91 triticale single‐cross hybrids in field trials at seven environments for plant height, heading time, fresh biomass, dry matter content and dry biomass. Fresh and dry biomass showed the highest proportion (23%) of variance due to SCA. Prediction accuracies based on GCA were slightly higher than based on mid‐parent values. Utilizing parental kinship information yielded the highest prediction accuracies when both parental lines have been tested in other hybrid combinations, but still moderate‐to‐low prediction accuracies for two untested parents. Thus, hybrid prediction for biomass traits in triticale is currently promising based on mid‐parent values as emphasized by our simulation study, but can be expected to shift to GCA‐based prediction with an increasing importance of GCA due to selection in hybrid breeding. Moreover, the performance of potential hybrids between newly developed lines can be predicted with moderate accuracy using genomic relationship information.