IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i24p6950-d294729.html
   My bibliography  Save this article

Diversity and Pre-Breeding Prospects for Local Adaptation in Oat Genetic Resources

Author

Listed:
  • Leona Leišová-Svobodová

    (Crop Research Institute, Drnovska 507, 161 06 Prague 6, Czech Republic)

  • Sebastian Michel

    (BOKU-University of Natural Resources and Life Sciences, Vienna, Dept. IFA-Tulln, Konrad Lorenz Str. 20, 3430 Tulln an der Donau, Austria)

  • Ilmar Tamm

    (Estonian Crop Research Institute, J. Aamisepa 1, 48309 Jõgeva, Estonia)

  • Marie Chourová

    (Selgen a.s., Šlechtitelské stanice Krukanice, 330 36 Pernarec, Czech Republic)

  • Dagmar Janovska

    (Crop Research Institute, Drnovska 507, 161 06 Prague 6, Czech Republic)

  • Heinrich Grausgruber

    (BOKU-University of Natural Resources and Life Sciences, Vienna, Dept. Crop Sciences, Konrad Lorenz Str. 24, 3430 Tulln an der Donau, Austria)

Abstract

Acreage of oat ( Avena sativa L.) in Europe was steadily declining during the last century due to less breeding progress compared to other cereals. However, oat remains a valuable crop for food and feed, as well as for sustainable crop rotations. To unravel the genetic and phenotypic diversity in oat breeders’ germplasm collections, a diversity panel including 260 accessions was investigated by molecular markers and in multi-environment field trials. Due to the large genetic variation in the present diversity panel, high heritabilities were observed for most agro-morphological traits, even for complex traits such as grain yield. Population structure analyses identified three subpopulations which were not straightforwardly related to the geographic origin of the accessions. Accessions from France, Germany, and the Czech Republic in particular were present in approximately equal proportions among all three subpopulations. Breeders’ selection after one year of field trials was mainly based on grain yield, grain weight, grading, plant height, and maturity and did not result in a loss of genetic diversity. However, the low number of polymorphic markers must be considered in this case. The present study provides basic knowledge for further oat improvement through the identification of valuable genetic resources which can be exploited in breeding programs as e.g., parental genotypes in crossings.

Suggested Citation

  • Leona Leišová-Svobodová & Sebastian Michel & Ilmar Tamm & Marie Chourová & Dagmar Janovska & Heinrich Grausgruber, 2019. "Diversity and Pre-Breeding Prospects for Local Adaptation in Oat Genetic Resources," Sustainability, MDPI, vol. 11(24), pages 1-15, December.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:6950-:d:294729
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/24/6950/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/24/6950/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Coffman, Franklin A., 1977. "Oat History: Identification and Classification," Technical Bulletins 158127, United States Department of Agriculture, Economic Research Service.
    2. Butts, Carter T., 2008. "Social Network Analysis with sna," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i06).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Juhwan Kim & Sunghae Jun & Dongsik Jang & Sangsung Park, 2018. "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models," Sustainability, MDPI, vol. 10(1), pages 1-12, January.
    2. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    3. Kanu, Edmond Augustine & Henning, Christian H. C. A., 2019. "An assessment of land reform policy processes in Sierra Leone: A network based approach," Working Papers of Agricultural Policy WP2019-04, University of Kiel, Department of Agricultural Economics, Chair of Agricultural Policy.
    4. Liberati, Caterina & Marzo, Massimiliano & Zagaglia, Paolo & Zappa, Paola, 2012. "Structural distortions in the Euro interbank market: the role of 'key players' during the recent market turmoil," MPRA Paper 40223, University Library of Munich, Germany.
    5. Maria Cristiana Martini & Elvira Pelle & Francesco Poggi & Andrea Sciandra, 2022. "The role of citation networks to explain academic promotions: an empirical analysis of the Italian national scientific qualification," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5633-5659, October.
    6. Taffi, Marianna & Paoletti, Nicola & Liò, Pietro & Pucciarelli, Sandra & Marini, Mauro, 2015. "Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea," Ecological Modelling, Elsevier, vol. 306(C), pages 205-215.
    7. Frank Rijnsoever & Leon Welle & Sjoerd Bakker, 2014. "Credibility and legitimacy in policy-driven innovation networks: resource dependencies and expectations in Dutch electric vehicle subsidies," The Journal of Technology Transfer, Springer, vol. 39(4), pages 635-661, August.
    8. Lan, Jing & Liu, Zhen, 2019. "Social network effect on income structure of SLCP participants: Evidence from Baitoutan Village, China," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    9. Caterina Liberati & Massimiliano Marzo & Paolo Zagaglia & Paola Zappa, 2015. "Drivers of demand and supply in the Euro interbank market: the role of “Key Players” during the recent turmoil," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(3), pages 207-250, August.
    10. Valente, Thomas W. & Dyal, Stephanie R. & Chu, Kar-Hai & Wipfli, Heather & Fujimoto, Kayo, 2015. "Diffusion of innovations theory applied to global tobacco control treaty ratification," Social Science & Medicine, Elsevier, vol. 145(C), pages 89-97.
    11. Nunes, Matthew, 2015. "Statistical Analysis of Network Data with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(b01).
    12. Manafi, Ioana & Huru, Dragos & Dobre, Florin & Capbun, Andreea Gabriela & Roman, Mihai Daniel, 2023. "Resilience Mechanisms of the European Trade Network During the Pandemic," Economic and Regional Studies (Studia Ekonomiczne i Regionalne), John Paul II University of Applied Sciences in Biala Podlaska, vol. 16(2), June.
    13. van Rijnsoever, Frank J. & van den Berg, Jesse & Koch, Joost & Hekkert, Marko P., 2015. "Smart innovation policy: How network position and project composition affect the diversity of an emerging technology," Research Policy, Elsevier, vol. 44(5), pages 1094-1107.
    14. Konda, Bruhan & González‐Sauri, Mario & Cowan, Robin & Yashodha, Yashodha & Chellattan Veettil, Prakashan, 2021. "Social networks and agricultural performance: A multiplex analysis of interactions among Indian rice farmers," MERIT Working Papers 2021-030, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    15. Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
    16. Rauktis, Mary E. & McCarthy, Sharon & Krackhardt, David & Cahalane, Helen, 2010. "Innovation in child welfare: The adoption and implementation of Family Group Decision Making in Pennsylvania," Children and Youth Services Review, Elsevier, vol. 32(5), pages 732-739, May.
    17. Håvard Bergesen Dalen & Ørnulf Seippel, 2021. "Friends in Sports: Social Networks in Leisure, School and Social Media," IJERPH, MDPI, vol. 18(12), pages 1-15, June.
    18. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    19. Julija N. Mell & Sujin Jang & Sen Chai, 2021. "Bridging Temporal Divides: Temporal Brokerage in Global Teams and Its Impact on Individual Performance," Organization Science, INFORMS, vol. 32(3), pages 731-751, May.
    20. Jaehyun Choi & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Predictive Model of Technology Transfer Using Patent Analysis," Sustainability, MDPI, vol. 7(12), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:6950-:d:294729. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.