IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-54940-5.html
   My bibliography  Save this article

Increased diversity of beneficial rhizobia enhances faba bean growth

Author

Listed:
  • Marcela Mendoza-Suárez

    (Aarhus University)

  • Turgut Yigit Akyol

    (Aarhus University)

  • Marcin Nadzieja

    (Aarhus University)

  • Stig U. Andersen

    (Aarhus University)

Abstract

Legume-rhizobium symbiosis provides a sustainable nitrogen source for agriculture. Nitrogen fixation efficiency depends on both legume and rhizobium genotypes, but the implications of their interactions for plant performance in environments with many competing rhizobium strains remain unclear. Here, we let 399 Rhizobium leguminosarum complex sv. viciae strains compete for nodulation of 212 faba bean genotypes. We find that the strains can be categorised by their nodule occupancy profiles into groups that show distinct competitive interactions and plant growth-promoting effects. Further, we show that the diversity of strains occupying root nodules affects plant growth and is under plant genetic control. These insights provide a basis for re-designing rhizobium inoculation and plant breeding strategies to enhance symbiotic nitrogen fixation in agriculture.

Suggested Citation

  • Marcela Mendoza-Suárez & Turgut Yigit Akyol & Marcin Nadzieja & Stig U. Andersen, 2024. "Increased diversity of beneficial rhizobia enhances faba bean growth," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54940-5
    DOI: 10.1038/s41467-024-54940-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-54940-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-54940-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Amir Bashan & Travis E. Gibson & Jonathan Friedman & Vincent J. Carey & Scott T. Weiss & Elizabeth L. Hohmann & Yang-Yu Liu, 2016. "Universality of human microbial dynamics," Nature, Nature, vol. 534(7606), pages 259-262, June.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Murukarthick Jayakodi & Agnieszka A. Golicz & Jonathan Kreplak & Lavinia I. Fechete & Deepti Angra & Petr Bednář & Elesandro Bornhofen & Hailin Zhang & Raphaël Boussageon & Sukhjiwan Kaur & Kwok Cheun, 2023. "The giant diploid faba genome unlocks variation in a global protein crop," Nature, Nature, vol. 615(7953), pages 652-659, March.
    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. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    2. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    3. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    4. Tania L. Maxwell & Mark D. Spalding & Daniel A. Friess & Nicholas J. Murray & Kerrylee Rogers & Andre S. Rovai & Lindsey S. Smart & Lukas Weilguny & Maria Fernanda Adame & Janine B. Adams & William E., 2024. "Soil carbon in the world’s tidal marshes," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    5. Migle Janulaitiene & Vilmantas Gegzna & Lina Baranauskiene & Aistė Bulavaitė & Martynas Simanavicius & Milda Pleckaityte, 2018. "Phenotypic characterization of Gardnerella vaginalis subgroups suggests differences in their virulence potential," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-20, July.
    6. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    7. László Pásztor & Katalin Takács & János Mészáros & Gábor Szatmári & Mátyás Árvai & Tibor Tóth & Gyöngyi Barna & Sándor Koós & Zsófia Adrienn Kovács & Péter László & Kitti Balog, 2023. "Indirect Prediction of Salt Affected Soil Indicator Properties through Habitat Types of a Natural Saline Grassland Using Unmanned Aerial Vehicle Imagery," Land, MDPI, vol. 12(8), pages 1-23, July.
    8. Hirche, Martin & Farris, Paul W. & Greenacre, Luke & Quan, Yiran & Wei, Susan, 2021. "Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship," Journal of Retailing, Elsevier, vol. 97(4), pages 697-714.
    9. Vincenzo Cribari & Michael P. Strager & Aaron E. Maxwell & Charles Yuill, 2021. "Landscape Changes in the Southern Coalfields of West Virginia: Multi-Level Intensity Analysis and Surface Mining Transitions in the Headwaters of the Coal River from 1976 to 2016," Land, MDPI, vol. 10(7), pages 1-32, July.
    10. Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.
    11. Backer, David & Billing, Trey, 2024. "Forecasting the prevalence of child acute malnutrition using environmental and conflict conditions as leading indicators," World Development, Elsevier, vol. 176(C).
    12. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    13. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    14. Piaopiao Chen & Agnès H. Michel & Jianzhi Zhang, 2022. "Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    15. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    16. Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
    17. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    18. Banks, Jonathan & Rabbani, Arif & Nadkarni, Kabir & Renaud, Evan, 2020. "Estimating parasitic loads related to brine production from a hot sedimentary aquifer geothermal project: A case study from the Clarke Lake gas field, British Columbia," Renewable Energy, Elsevier, vol. 153(C), pages 539-552.
    19. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    20. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.

    More about this item

    Statistics

    Access and download statistics

    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:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54940-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.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.