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Modeling the Incidence of Maize Spotted Stem-Borer (Chilo partellus) Infestation Under Long-Term Organic and Conventional Farming Systems

Received: 7 October 2022     Accepted: 24 October 2022     Published: 4 November 2022
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Abstract

The damage levels of the maize spotted stem borers (Chilo partellus Swinhoe) are estimated at 400,000 metric tons, which is equivalent to 13.5% of farmers' annual maize harvest accounting for US$80 million. Despite the economic importance of the pest, information on the incidence under long-term organic and conventional farming systems is lacking. This study evaluated three different link functions [logit, probit, and complementary log-log – (clog-log)] to reduce prediction errors in overdispersed stem borer incidence data for 12 years in four farming systems. The clog-log link function had the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) indexes for the pest incidence model in Thika. Contrarily, probit showed the lowest AIC and BIC in the Chuka incidence data model. The residual diagnostic plots with clog-log demonstrated no patterns against the predicted values. Our findings revealed that clog-log link function provided the best fit in beta-binomial mixed models compared to others. We advocate for the use of clog-log for long-term pest incidence data modelling to obtain biologically realistic projections. Users of mixed models must incorporate explicit consideration of suitable link function discrimination, model fit and model complexity into their decision-making processes if they build biologically realistic models.

Published in International Journal of Data Science and Analysis (Volume 8, Issue 6)
DOI 10.11648/j.ijdsa.20220806.11
Page(s) 169-181
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Maize, Spotted Stem Borer, Pest Incidence, Overdispersion, Binomial Proportions, Beta-Binomial Distribution

References
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Cite This Article
  • APA Style

    Wainaina Stephen, Anthony Waititu, Daisy Salifu, Samuel Mwalili, Edward Karanja, et al. (2022). Modeling the Incidence of Maize Spotted Stem-Borer (Chilo partellus) Infestation Under Long-Term Organic and Conventional Farming Systems. International Journal of Data Science and Analysis, 8(6), 169-181. https://doi.org/10.11648/j.ijdsa.20220806.11

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    ACS Style

    Wainaina Stephen; Anthony Waititu; Daisy Salifu; Samuel Mwalili; Edward Karanja, et al. Modeling the Incidence of Maize Spotted Stem-Borer (Chilo partellus) Infestation Under Long-Term Organic and Conventional Farming Systems. Int. J. Data Sci. Anal. 2022, 8(6), 169-181. doi: 10.11648/j.ijdsa.20220806.11

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    AMA Style

    Wainaina Stephen, Anthony Waititu, Daisy Salifu, Samuel Mwalili, Edward Karanja, et al. Modeling the Incidence of Maize Spotted Stem-Borer (Chilo partellus) Infestation Under Long-Term Organic and Conventional Farming Systems. Int J Data Sci Anal. 2022;8(6):169-181. doi: 10.11648/j.ijdsa.20220806.11

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  • @article{10.11648/j.ijdsa.20220806.11,
      author = {Wainaina Stephen and Anthony Waititu and Daisy Salifu and Samuel Mwalili and Edward Karanja and Noah Adamtey and Henri Tonnang and Felix Matheri and Edwin Mwangi and David Bautze and Chrysantus Tanga},
      title = {Modeling the Incidence of Maize Spotted Stem-Borer (Chilo partellus) Infestation Under Long-Term Organic and Conventional Farming Systems},
      journal = {International Journal of Data Science and Analysis},
      volume = {8},
      number = {6},
      pages = {169-181},
      doi = {10.11648/j.ijdsa.20220806.11},
      url = {https://doi.org/10.11648/j.ijdsa.20220806.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220806.11},
      abstract = {The damage levels of the maize spotted stem borers (Chilo partellus Swinhoe) are estimated at 400,000 metric tons, which is equivalent to 13.5% of farmers' annual maize harvest accounting for US$80 million. Despite the economic importance of the pest, information on the incidence under long-term organic and conventional farming systems is lacking. This study evaluated three different link functions [logit, probit, and complementary log-log – (clog-log)] to reduce prediction errors in overdispersed stem borer incidence data for 12 years in four farming systems. The clog-log link function had the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) indexes for the pest incidence model in Thika. Contrarily, probit showed the lowest AIC and BIC in the Chuka incidence data model. The residual diagnostic plots with clog-log demonstrated no patterns against the predicted values. Our findings revealed that clog-log link function provided the best fit in beta-binomial mixed models compared to others. We advocate for the use of clog-log for long-term pest incidence data modelling to obtain biologically realistic projections. Users of mixed models must incorporate explicit consideration of suitable link function discrimination, model fit and model complexity into their decision-making processes if they build biologically realistic models.},
     year = {2022}
    }
    

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    T1  - Modeling the Incidence of Maize Spotted Stem-Borer (Chilo partellus) Infestation Under Long-Term Organic and Conventional Farming Systems
    AU  - Wainaina Stephen
    AU  - Anthony Waititu
    AU  - Daisy Salifu
    AU  - Samuel Mwalili
    AU  - Edward Karanja
    AU  - Noah Adamtey
    AU  - Henri Tonnang
    AU  - Felix Matheri
    AU  - Edwin Mwangi
    AU  - David Bautze
    AU  - Chrysantus Tanga
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    DO  - 10.11648/j.ijdsa.20220806.11
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    PB  - Science Publishing Group
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    AB  - The damage levels of the maize spotted stem borers (Chilo partellus Swinhoe) are estimated at 400,000 metric tons, which is equivalent to 13.5% of farmers' annual maize harvest accounting for US$80 million. Despite the economic importance of the pest, information on the incidence under long-term organic and conventional farming systems is lacking. This study evaluated three different link functions [logit, probit, and complementary log-log – (clog-log)] to reduce prediction errors in overdispersed stem borer incidence data for 12 years in four farming systems. The clog-log link function had the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) indexes for the pest incidence model in Thika. Contrarily, probit showed the lowest AIC and BIC in the Chuka incidence data model. The residual diagnostic plots with clog-log demonstrated no patterns against the predicted values. Our findings revealed that clog-log link function provided the best fit in beta-binomial mixed models compared to others. We advocate for the use of clog-log for long-term pest incidence data modelling to obtain biologically realistic projections. Users of mixed models must incorporate explicit consideration of suitable link function discrimination, model fit and model complexity into their decision-making processes if they build biologically realistic models.
    VL  - 8
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Author Information
  • International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

  • International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

  • International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya

  • Research Institute of Organic Agriculture (Forschungsinstitut für Biologischen Landbau (FiBL)), Frick, Switzerland

  • International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya

  • International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya

  • International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya

  • Research Institute of Organic Agriculture (Forschungsinstitut für Biologischen Landbau (FiBL)), Frick, Switzerland

  • International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya

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