IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13531-d947530.html
   My bibliography  Save this article

Relationship of Microbial Activity with Soil Properties in Banana Plantations in Venezuela

Author

Listed:
  • Barlin O. Olivares

    (Programa de Doctorado en Ingeniería Agraria, Alimentaria, Forestal y del Desarrollo Rural Sostenible, Campus Rabanales, Universidad de Córdoba, Carretera Nacional IV, km 396, 14014 Cordoba, Spain)

  • Juan C. Rey

    (Unidad de Recursos Agroecológicos, Instituto Nacional de Investigaciones Agrícolas (INIA-CENIAP), Av. Universidad vía El Limón, Maracay 02105, Venezuela
    Facultad de Agronomía, Universidad Central de Venezuela, Av. Universidad, Maracay 02105, Venezuela)

  • Guillermo Perichi

    (Facultad de Agronomía, Universidad Central de Venezuela, Av. Universidad, Maracay 02105, Venezuela)

  • Deyanira Lobo

    (Facultad de Agronomía, Universidad Central de Venezuela, Av. Universidad, Maracay 02105, Venezuela)

Abstract

The present work aims to analyze the relationship of microbial activity with the physicochemical properties of the soil in banana plantations in Venezuela. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. The experimental sites were differentiated with two levels of productivity (high and low) of the “Gran Nain” banana. Ten variables were selected: total free-living nematodes (FLN), bacteriophages, predators, omnivores, Phytonematodes, saturated hydraulic conductivity, total organic carbon, nitrate (NO 3 ), microbial respiration and the variable other fungi. Subsequently, machine learning algorithms were used. First, the Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to find the soil properties that could distinguish the banana productivity levels. Second, the Debiased Sparse Partial Correlation (DSPC) algorithm was applied to obtain the correlation network of the most important variables. The variable free-living nematode predators had a degree of 3 and a betweenness of 4 in the correlation network, followed by NO 3 . The network shows positive correlations between FLN predators and microbial respiration (r = 1.00; p = 0.014), and NO 3 (r = 1.00; p = 0.032). The selected variables are proposed to characterize the soil productivity in bananas and could be used for the management of soil diseases affecting bananas.

Suggested Citation

  • Barlin O. Olivares & Juan C. Rey & Guillermo Perichi & Deyanira Lobo, 2022. "Relationship of Microbial Activity with Soil Properties in Banana Plantations in Venezuela," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13531-:d:947530
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13531/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13531/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. H. Jeong & S. P. Mason & A.-L. Barabási & Z. N. Oltvai, 2001. "Lethality and centrality in protein networks," Nature, Nature, vol. 411(6833), pages 41-42, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdul Munaf Mohamed Irfeey & Mohamed M. M. Najim & Bader Alhafi Alotaibi & Abou Traore, 2023. "Groundwater Pollution Impact on Food Security," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    2. Nelino Florida Rofner & Cesar Augusto Gozme Sulca & Alex Rengifo Rojas, 2023. "Modelling of alluvial soil quality and production in permanent banana Harton plantations," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 18(3), pages 192-203.

    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. Wilhelm, Thomas & Hollunder, Jens, 2007. "Information theoretic description of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 385-396.
    2. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.
    3. Chung-Yen Yu & Yung-Ting Chuang & Hsi-Peng Kuan, 2017. "Understanding Faculty Collaboration and Productivity: A Case Study," Asian Social Science, Canadian Center of Science and Education, vol. 13(3), pages 1-1, March.
    4. Pei Wang & Shunjie Chen & Sijia Yang, 2022. "Recent Advances on Penalized Regression Models for Biological Data," Mathematics, MDPI, vol. 10(19), pages 1-24, October.
    5. Jordán, Ferenc, 2022. "The network perspective: Vertical connections linking organizational levels," Ecological Modelling, Elsevier, vol. 473(C).
    6. P.B., Divya & Lekha, Divya Sindhu & Johnson, T.P. & Balakrishnan, Kannan, 2022. "Vulnerability of link-weighted complex networks in central attacks and fallback strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    7. Nadadoor Venkat R. & Ben-Zvi Amos & Shah Sirish L., 2011. "Inferring Gene Networks using Robust Statistical Techniques," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-30, May.
    8. Mustafa C. Camur & Thomas Sharkey & Chrysafis Vogiatzis, 2022. "The Star Degree Centrality Problem: A Decomposition Approach," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 93-112, January.
    9. Ramesh Ummanni & Frederike Mundt & Heike Pospisil & Simone Venz & Christian Scharf & Christine Barett & Maria Fälth & Jens Köllermann & Reinhard Walther & Thorsten Schlomm & Guido Sauter & Carsten Bok, 2011. "Identification of Clinically Relevant Protein Targets in Prostate Cancer with 2D-DIGE Coupled Mass Spectrometry and Systems Biology Network Platform," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-14, February.
    10. Peter Langfelder & Paul S Mischel & Steve Horvath, 2013. "When Is Hub Gene Selection Better than Standard Meta-Analysis?," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
    11. Kim, Jongkwang & Wilhelm, Thomas, 2008. "What is a complex graph?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2637-2652.
    12. Erica W. Carter & Orlene Guerra Peraza & Nian Wang, 2023. "The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    13. Dong, Chen & Xu, Guiqiong & Meng, Lei & Yang, Pingle, 2022. "CPR-TOPSIS: A novel algorithm for finding influential nodes in complex networks based on communication probability and relative entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    14. Jiawei Luo & Yi Qi, 2015. "Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-27, June.
    15. Xia Cao & Chuanyun Li & Wei Chen & Jinqiu Li & Chaoran Lin, 2020. "Research on the invulnerability and optimization of the technical cooperation innovation network based on the patent perspective—A case study of new energy vehicles," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    16. Jiang, Nan, 2014. "WDEM: Weighted dynamics and evolution models for energy-constrained wireless sensor networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 323-331.
    17. Xu, Chunsui & Liu, Zengrong & Wang, Ruiqi, 2010. "How divergence mechanisms influence disassortative mixing property in biology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 643-650.
    18. Raghav, Tanu & Jalan, Sarika, 2022. "Random matrix analysis of multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    19. Tracy Chih-Ting Koubkova-Yu & Jung-Chi Chao & Jun-Yi Leu, 2018. "Heterologous Hsp90 promotes phenotypic diversity through network evolution," PLOS Biology, Public Library of Science, vol. 16(11), pages 1-29, November.
    20. Jacob D Feala & Jorge Cortes & Phillip M Duxbury & Andrew D McCulloch & Carlo Piermarocchi & Giovanni Paternostro, 2012. "Statistical Properties and Robustness of Biological Controller-Target Networks," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-11, January.

    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:14:y:2022:i:20:p:13531-:d:947530. 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.