IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0178109.html
   My bibliography  Save this article

Impact of environmental variables on Dubas bug infestation rate: A case study from the Sultanate of Oman

Author

Listed:
  • Khalifa M Al-Kindi
  • Paul Kwan
  • Nigel Andrew
  • Mitchell Welch

Abstract

Date palm cultivation is economically important in the Sultanate of Oman, with significant financial investment coming from both the government and from private individuals. However, a global infestation of Dubas bug (Ommatissus lybicus Bergevin) has impacted the Middle East region, and infestations of date palms have been widespread. In this study, spatial analysis and geostatistical techniques were used to model the spatial distribution of Dubas bug infestations to (a) identify correlations between Dubas bug densities and different environmental variables, and (b) predict the locations of future Dubas bug infestations in Oman. Firstly, we considered individual environmental variables and their correlations with infestation locations. Then, we applied more complex predictive models and regression analysis techniques to investigate the combinations of environmental factors most conducive to the survival and spread of the Dubas bug. Environmental variables including elevation, geology, and distance to drainage pathways were found to significantly affect Dubas bug infestations. In contrast, aspect and hillshade did not significantly impact on Dubas bug infestations. Understanding their distribution and therefore applying targeted controls on their spread is important for effective mapping, control and management (e.g., resource allocation) of Dubas bug infestations.

Suggested Citation

  • Khalifa M Al-Kindi & Paul Kwan & Nigel Andrew & Mitchell Welch, 2017. "Impact of environmental variables on Dubas bug infestation rate: A case study from the Sultanate of Oman," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
  • Handle: RePEc:plo:pone00:0178109
    DOI: 10.1371/journal.pone.0178109
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178109
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0178109&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0178109?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. Luc Anselin & Arthur Getis, 2010. "Spatial Statistical Analysis and Geographic Information Systems," Advances in Spatial Science, in: Luc Anselin & Sergio J. Rey (ed.), Perspectives on Spatial Data Analysis, chapter 0, pages 35-47, Springer.
    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. Rashid H. Al Shidi & Lalit Kumar & Salim A. H. Al-Khatri & Najat A. Al-Ajmi, 2019. "Ommatissus lybicus Infestation in Relation to Spatial Characteristics of Date Palm Plantations in Oman," Agriculture, MDPI, vol. 9(3), pages 1-14, March.

    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. Sunak, Yasin & Madlener, Reinhard, 2012. "The Impact of Wind Farms on Property Values: A Geographically Weighted Hedonic Pricing Model," FCN Working Papers 3/2012, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Mar 2013.
    2. Ye, Xinyue & Yue, Wenze, 2014. "Comparative analysis of regional development: Exploratory space-time data analysis and open source implementation," Economics Discussion Papers 2014-20, Kiel Institute for the World Economy (IfW Kiel).
    3. Kang Hou & Xuxiang Li & Jing Zhang, 2015. "GIS Analysis of Changes in Ecological Vulnerability Using a SPCA Model in the Loess Plateau of Northern Shaanxi, China," IJERPH, MDPI, vol. 12(4), pages 1-14, April.
    4. Sunak, Yasin & Madlener, Reinhard, 2016. "The impact of wind farm visibility on property values: A spatial difference-in-differences analysis," Energy Economics, Elsevier, vol. 55(C), pages 79-91.
    5. Sunak, Yasin & Madlener, Reinhard, 2014. "Local Impacts of Wind Farms on Property Values: A Spatial Difference-in-Differences Analysis," FCN Working Papers 1/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Oct 2014.
    6. Schmidtner, Eva & Lippert, Christian & Dabbert, Stephan, 2015. "Does Spatial Dependence Depend on Spatial Resolution? – An Empirical Analysis of Organic Farming in Southern Germany," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 64(03), September.
    7. Stefania Bertazzon & Rizwan Shahid, 2017. "Schools, Air Pollution, and Active Transportation: An Exploratory Spatial Analysis of Calgary, Canada," IJERPH, MDPI, vol. 14(8), pages 1-16, July.
    8. Scott R. Sanders & Michael R. Cope & Elizabeth R. Pulsipher, 2018. "Do Factory Audits Improve International Labor Standards? An Examination of Voluntary Corporate Labor Regulations in Global Production Networks," Social Sciences, MDPI, vol. 7(6), pages 1-12, May.
    9. Jiyong Chen & Dabo Chen & Aiping Yao, 2020. "Trade development between China and countries along the Belt and Road: A spatial econometric analysis based on trade competitiveness and complementarity," Pacific Economic Review, Wiley Blackwell, vol. 25(2), pages 205-227, May.
    10. Alice Barreca & Rocco Curto & Diana Rolando, 2020. "Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market," Sustainability, MDPI, vol. 12(1), pages 1-23, January.
    11. Tan, Ronghui & Liu, Pengcheng & Zhou, Kehao & He, Qingsong, 2022. "Evaluating the effectiveness of development-limiting boundary control policy: Spatial difference-in-difference analysis," Land Use Policy, Elsevier, vol. 120(C).
    12. Avner Bar-Hen & Servane Gey & Jean-Michel Poggi, 2021. "Spatial CART classification trees," Computational Statistics, Springer, vol. 36(4), pages 2591-2613, December.
    13. Yaqing Liu & Hongbing Ouyang & Xiaolu Wei, 2021. "A Spatial Panel Structural Vector Autoregressive Model with Interactive Effects and Its Simulation," Mathematics, MDPI, vol. 9(8), pages 1-8, April.
    14. Philip Salesses & Katja Schechtner & César A Hidalgo, 2013. "The Collaborative Image of The City: Mapping the Inequality of Urban Perception," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.

    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:plo:pone00:0178109. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.