IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2023i1p46-d1308093.html
   My bibliography  Save this article

Effects of Inoculation with Plant Growth-Promoting Rhizobacteria on Chemical Composition of the Substrate and Nutrient Content in Strawberry Plants Growing in Different Water Conditions

Author

Listed:
  • Dominika Paliwoda

    (Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland)

  • Grzegorz Mikiciuk

    (Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland)

  • Justyna Chudecka

    (Department of Environmental Management, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland)

  • Tomasz Tomaszewicz

    (Department of Environmental Management, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland)

  • Tymoteusz Miller

    (Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
    Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland)

  • Małgorzata Mikiciuk

    (Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland)

  • Anna Kisiel

    (Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
    Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland)

  • Lidia Sas-Paszt

    (Department of Microbiology and Rhizosphere, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

Abstract

Drought presents a critical challenge to global crop production, exacerbated by the effects of global warming. This study explores the role of rhizospheric bacteria ( Bacillus , Pantoea , and Pseudomonas ) in enhancing the drought resistance and nutrient absorption of strawberry plants. The experimental approach involved inoculating plant roots with various strains of rhizobacteria and assessing their impact under different water potential conditions in two substrates: optimal moisture and water deficit. The results showed significant changes in the nutrient content of strawberry plants, influenced by the type of bacterial strain and moisture conditions. Phosphorus and potassium content in the leaves varied considerably, with the highest levels observed in plants inoculated with specific bacterial strains under both optimal and water-deficit conditions. Similarly, calcium and magnesium content in the leaves also changed notably, depending on the bacterial strain and moisture level. The water deficit cluster, featuring the PJ1.1, DKB63, and DKB65 strains, showed PGPR’s role in maintaining nutrient availability and plant resilience. The study demonstrates that inoculation with PGPR can markedly influence the nutrient profile of strawberry plants. These findings underscore the potential of using rhizobacteria to enhance crop resilience and nutritional status, especially in the context of increasing drought conditions due to climate change.

Suggested Citation

  • Dominika Paliwoda & Grzegorz Mikiciuk & Justyna Chudecka & Tomasz Tomaszewicz & Tymoteusz Miller & Małgorzata Mikiciuk & Anna Kisiel & Lidia Sas-Paszt, 2023. "Effects of Inoculation with Plant Growth-Promoting Rhizobacteria on Chemical Composition of the Substrate and Nutrient Content in Strawberry Plants Growing in Different Water Conditions," Agriculture, MDPI, vol. 14(1), pages 1-33, December.
    • Handle: RePEc:gam:jagris:v:14:y:2023:i:1:p:46-:d:1308093
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/1/46/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/1/46/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Al-Kaisi, Mahdi & Elmore, Roger & Guzman, Jose & Hanna, Mark & Hart, Chad E. & Helmers, Matthew J. & Hodgson, Erin & Lenssen, Andrew & Mallarino, Antonio & Robertson, Alison & Sawyer, John, 2013. "Drought Impact on Crop Production and the Soil Environment: 2012 Experiences from Iowa," Staff General Research Papers Archive 35963, Iowa State University, Department of Economics.
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    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. Tan Wang & L. Jeff Hong, 2023. "Large-Scale Inventory Optimization: A Recurrent Neural Networks–Inspired Simulation Approach," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 196-215, January.
    2. Léon Faure & Bastien Mollet & Wolfram Liebermeister & Jean-Loup Faulon, 2023. "A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Claudia Quinteros-Cartaya & Guillermo Solorio-Magaña & Francisco Javier Núñez-Cornú & Felipe de Jesús Escalona-Alcázar & Diana Núñez, 2023. "Microearthquakes in the Guadalajara Metropolitan Zone, Mexico: evidence from buried active faults in Tesistán Valley, Zapopan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2797-2818, April.
    4. Hsing-Hsiang Huang & Michael R. Moore, 2018. "Farming under Weather Risk: Adaptation, Moral Hazard, and Selection on Moral Hazard," NBER Chapters, in: Agricultural Productivity and Producer Behavior, pages 77-124, National Bureau of Economic Research, Inc.
    5. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    6. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    7. Kiran Krishnamachari & Dylan Lu & Alexander Swift-Scott & Anuar Yeraliyev & Kayla Lee & Weitai Huang & Sim Ngak Leng & Anders Jacobsen Skanderup, 2022. "Accurate somatic variant detection using weakly supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    8. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    9. Negm, L.M. & Youssef, M.A. & Skaggs, R.W. & Chescheir, G.M. & Jones, J., 2014. "DRAINMOD–DSSAT model for simulating hydrology, soil carbon and nitrogen dynamics, and crop growth for drained crop land," Agricultural Water Management, Elsevier, vol. 137(C), pages 30-45.
    10. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    11. Jackie Grant & Mark Hindmarsh & Sergey E. Koposov, 2022. "The distribution of loss to future USS pensions due to the UUK cuts of April 2022," Papers 2206.06201, arXiv.org.
    12. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    13. Shukla, Mohak & Thakur, Ajay D., 2022. "An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    14. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    15. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    16. Laura Portell & Sergi Morera & Helena Ramalhinho, 2022. "Door-to-Door Transportation Services for Reduced Mobility Population: A Descriptive Analytics of the City of Barcelona," IJERPH, MDPI, vol. 19(8), pages 1-20, April.
    17. Caroline Haimerl & Douglas A. Ruff & Marlene R. Cohen & Cristina Savin & Eero P. Simoncelli, 2023. "Targeted V1 comodulation supports task-adaptive sensory decisions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    18. Jonas Bunsen & Matthias Finkbeiner, 2022. "An Introductory Review of Input-Output Analysis in Sustainability Sciences Including Potential Implications of Aggregation," Sustainability, MDPI, vol. 15(1), pages 1-24, December.
    19. Petros C. Lazaridis & Ioannis E. Kavvadias & Konstantinos Demertzis & Lazaros Iliadis & Lazaros K. Vasiliadis, 2023. "Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences," Sustainability, MDPI, vol. 15(17), pages 1-31, August.
    20. Matthias Wagener & Andriette Bekker & Mohammad Arashi, 2021. "Mastering the Body and Tail Shape of a Distribution," Mathematics, MDPI, vol. 9(21), pages 1-22, October.

    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:jagris:v:14:y:2023:i:1:p:46-:d:1308093. 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.