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

Wainaina Stephen; Anthony Waititu; Daisy Salifu; Samuel Mwalili; Edward Karanja; Noah Adamtey; Henri Tonnang; Felix Matheri; Edwin Mwangi; David Bautze; Chrysantus Tanga

doi:doi:10.11648/j.ijdsa.20220806.11

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

Wainaina Stephen, Anthony Waititu, Daisy Salifu, Samuel Mwalili, Edward Karanja, Noah Adamtey, Henri Tonnang, Felix Matheri, Edwin Mwangi, David Bautze, Chrysantus Tanga

Published in International Journal of Data Science and Analysis (Volume 8, Issue 6)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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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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TY  - JOUR
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
Y1  - 2022/11/04
PY  - 2022
N1  - https://doi.org/10.11648/j.ijdsa.20220806.11
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
SP  - 169
EP  - 181
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20220806.11
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
IS  - 6
ER  -

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Author Information

Wainaina Stephen

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

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

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

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

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

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

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

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

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

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

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
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
Y1  - 2022/11/04
PY  - 2022
N1  - https://doi.org/10.11648/j.ijdsa.20220806.11
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
SP  - 169
EP  - 181
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20220806.11
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
IS  - 6
ER  -

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