In spite of its short history of being an oil crop in China, the Chinese semi-winter rapeseed (Brassica napus L., 2n = 38, AACC) has been improved rapidly by intentional introgression of genomic components from Chinese B. rapa (2n = 20, AA). As a result, the Chinese semi-winter rapeseed has diversified genetically from the spring and winter rapeseed grown in the other regions such as Europe and North America. The objectives of this study were to investigate the roles of the introgression of the genomic components from the Chinese B. rapa in widening the genetic diversity of rapeseed and to verify the role of this introgression in the evolution of the Chinese rapeseed. Ten lines of the new type of rapeseed, which were produced by introgression of Chinese B. rapa to Chinese normal rapeseed, were compared for genetic diversity using amplified fragment length polymorphism (AFLP) with three groups of 35 lines of the normal rapeseed, including 9 semi-winter rapeseed lines from China, 9 winter rapeseed lines from Europe and 17 spring rapeseed lines from Northern Europe, Canada and Australia. Analysis of 799 polymorphic fragments revealed that within the groups, the new type rapeseed had the highest genetic diversity, followed by the semi-winter normal rapeseed from China. Spring and winter rapeseed had the lowest genetic diversity. Among the groups, the new type rapeseed group had the largest average genetic distance to the other three groups. Principal component analysis and cluster analysis, however, could not separate the new type rapeseed group from Chinese normal rapeseed group. Our data suggested that the introgression of Chinese B. rapa could significantly diversify the genetic basis of the rapeseed and play an important role in the evolution of Chinese rapeseed. The use of new genetic variation for the exploitation of heterosis in Brassica hybrid breeding is discussed.
Chinese semi-winter rapeseed is genetically diverse from Canadian and European spring rapeseed. This study was conducted to evaluate the potential of semi-winter rapeseed for spring rapeseed hybrid breeding, to assess the genetic effects involved, and to estimate the correlation of parental genetic distance (GD) with hybrid performance, heterosis, general combining ability (GCA) and specific combining ability (SCA) in crosses between spring and semi-winter rapeseed lines. Four spring male sterile lines from Germany and Canada as testers were crossed with 13 Chinese semi-winter rapeseed lines to develop 52 hybrids, which were evaluated together with their parents and commercial hybrids for seed yield and oil content in three sets of field trials with 8 environments in Canada and Europe. The Chinese parental lines were not adapted to local environmental conditions as demonstrated by poor seed yields per se. However, the hybrids between the Chinese parents and the adapted spring rapeseed lines exhibited high heterosis for seed yield. The average mid-parent heterosis was 15% and ca. 50% of the hybrids were superior to the respective hybrid control across three sets of field trials. Additive gene effects mainly contributed to hybrid performance since the mean squares of GCA were higher as compared to SCA. The correlation between parental GD and hybrid performance and heterosis was found to be low whereas the correlation between GCA((f + m)) and hybrid performance was high and significant in each set of field trials, with an average of r = 0.87 for seed yield and r = 0.89 for oil content, indicating that hybrid performance can be predicted by GCA((f + m)). These results demonstrate that Chinese semi-winter rapeseed germplasm has a great potential to increase seed yield in spring rapeseed hybrid breeding programs in Canada and Europe.
Faba bean (Vicia faba L.) is a cool season grain legume whose acreage has constantly declined in traditional producer countries as it has been replaced by more productive cereal crops. However, faba bean is still considered to have great potential as rainfed crop. In order to satisfy the renewed interest in faba bean cultivation yield stability should be improved by exploiting different germplasm types and sowing seasons.In order to understand of genotype by environment interactions and to compare cultivar performance over years and locations a spring faba bean network was established with twenty cultivars grown over three crop seasons at thirteen contrasting locations covering most of Europe. Analysis was performed by heritability-adjusted genotype plus genotype × environment interaction (HA-GGE) biplot analysis. HA-GGE biplot analyses identified that the effect of genotype by environment interaction was higher than the effect of genotypes, allowing identification of three mega-environments, namely Continental, Oceanic, and Mediterranean, in which cultivars performed differently. This supports the need for specific breeding for each specific geoclimatic area. Espresso was the highest yielding cultivar, being also highly stable over the Oceanic and Continental mega-environments. Cultivars Fuego, Hobbit and SR-1060 had also good yield but with a moderate unstability in both Oceanic and Continental mega-environments. Baraca and Alameda yielded poorly at all environments although were the best yielders at Mediterranean locations. Environments as Sumperk and Premesques were identified as the best core test locations for Continental and Oceanic mega-enviroments, respectively, being the locations in which best genotypes could be most easily identified.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.