Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya

Eric Ali Ibrahim; Daisy Salifu; Samuel Musili Mwalili; Thomas Dubois; Henri Edouard Zefack Tonnang

doi:doi:10.11648/j.ijdsa.20220801.11

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Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya

Eric Ali Ibrahim, Daisy Salifu, Samuel Musili Mwalili, Thomas Dubois, Henri Edouard Zefack Tonnang

Published in International Journal of Data Science and Analysis (Volume 8, Issue 1)

Received: 10 December 2021 Accepted: 4 January 2022 Published: 16 February 2022

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Abstract

Avocado (Persea americana) farming in East Africa has expanded since recent, contributing significantly toward economic growth and livelihood for small-scale farmers. However, insects attacking avocado fruits reduce fruit quality and size, causing massive losses. Previous studies have identified key avocado insect pests, their temporal population patterns and how landscape vegetation productivity influences their population dynamics. This research analyzed insect count data collected on Bactrocera dorsalis and Ceratitis spp. in an avocado plantation in Thika, Kenya over a successive period of time, as part of pest management. These data are characterized by overdispersion due to aggregation behaviour of the insects in their habitat and serial correlations since the count data were collected over a successive period of time. Analyzing these data becomes complicated because of overdispersion and the serial correlation in the data. In this study, we explored variants of generalized linear models (GLMs) with a sinusoidal component over time; and with and without timescale decomposition of covariates (weather variables). All GLM variants were fitted assuming the negative binomial distribution to account for overdispersion. Based on the Akaike information criterion (AIC), GLMs with decomposed covariates had lower AIC values than GLMs without decomposed covariates for both B. dorsalis and Ceratitis spp., and therefore GLMs with a sinusoidal component and decomposed covariates under negative binomial distribution were the best choice for these data. The contribution of the preceding weekly insect pest counts in all models was statistically significant. The study established that both abiotic and biotic factors drive insect pest infestation.

Published in	International Journal of Data Science and Analysis (Volume 8, Issue 1)
DOI	10.11648/j.ijdsa.20220801.11
Page(s)	1-10
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

Overdispersion, Negative Binomial Distribution, Sinusoidal Component, Time Series Count Data

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APA Style

Eric Ali Ibrahim, Daisy Salifu, Samuel Musili Mwalili, Thomas Dubois, Henri Edouard Zefack Tonnang. (2022). Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya. International Journal of Data Science and Analysis, 8(1), 1-10. https://doi.org/10.11648/j.ijdsa.20220801.11

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ACS Style

Eric Ali Ibrahim; Daisy Salifu; Samuel Musili Mwalili; Thomas Dubois; Henri Edouard Zefack Tonnang. Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya. Int. J. Data Sci. Anal. 2022, 8(1), 1-10. doi: 10.11648/j.ijdsa.20220801.11

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AMA Style

Eric Ali Ibrahim, Daisy Salifu, Samuel Musili Mwalili, Thomas Dubois, Henri Edouard Zefack Tonnang. Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya. Int J Data Sci Anal. 2022;8(1):1-10. doi: 10.11648/j.ijdsa.20220801.11

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@article{10.11648/j.ijdsa.20220801.11,
  author = {Eric Ali Ibrahim and Daisy Salifu and Samuel Musili Mwalili and Thomas Dubois and Henri Edouard Zefack Tonnang},
  title = {Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya},
  journal = {International Journal of Data Science and Analysis},
  volume = {8},
  number = {1},
  pages = {1-10},
  doi = {10.11648/j.ijdsa.20220801.11},
  url = {https://doi.org/10.11648/j.ijdsa.20220801.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220801.11},
  abstract = {Avocado (Persea americana) farming in East Africa has expanded since recent, contributing significantly toward economic growth and livelihood for small-scale farmers. However, insects attacking avocado fruits reduce fruit quality and size, causing massive losses. Previous studies have identified key avocado insect pests, their temporal population patterns and how landscape vegetation productivity influences their population dynamics. This research analyzed insect count data collected on Bactrocera dorsalis and Ceratitis spp. in an avocado plantation in Thika, Kenya over a successive period of time, as part of pest management. These data are characterized by overdispersion due to aggregation behaviour of the insects in their habitat and serial correlations since the count data were collected over a successive period of time. Analyzing these data becomes complicated because of overdispersion and the serial correlation in the data. In this study, we explored variants of generalized linear models (GLMs) with a sinusoidal component over time; and with and without timescale decomposition of covariates (weather variables). All GLM variants were fitted assuming the negative binomial distribution to account for overdispersion. Based on the Akaike information criterion (AIC), GLMs with decomposed covariates had lower AIC values than GLMs without decomposed covariates for both B. dorsalis and Ceratitis spp., and therefore GLMs with a sinusoidal component and decomposed covariates under negative binomial distribution were the best choice for these data. The contribution of the preceding weekly insect pest counts in all models was statistically significant. The study established that both abiotic and biotic factors drive insect pest infestation.},
 year = {2022}
}

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TY - JOUR
T1 - Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya
AU - Eric Ali Ibrahim
AU - Daisy Salifu
AU - Samuel Musili Mwalili
AU - Thomas Dubois
AU - Henri Edouard Zefack Tonnang
Y1 - 2022/02/16
PY - 2022
N1 - https://doi.org/10.11648/j.ijdsa.20220801.11
DO - 10.11648/j.ijdsa.20220801.11
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 - 1
EP - 10
PB - Science Publishing Group
SN - 2575-1891
UR - https://doi.org/10.11648/j.ijdsa.20220801.11
AB - Avocado (Persea americana) farming in East Africa has expanded since recent, contributing significantly toward economic growth and livelihood for small-scale farmers. However, insects attacking avocado fruits reduce fruit quality and size, causing massive losses. Previous studies have identified key avocado insect pests, their temporal population patterns and how landscape vegetation productivity influences their population dynamics. This research analyzed insect count data collected on Bactrocera dorsalis and Ceratitis spp. in an avocado plantation in Thika, Kenya over a successive period of time, as part of pest management. These data are characterized by overdispersion due to aggregation behaviour of the insects in their habitat and serial correlations since the count data were collected over a successive period of time. Analyzing these data becomes complicated because of overdispersion and the serial correlation in the data. In this study, we explored variants of generalized linear models (GLMs) with a sinusoidal component over time; and with and without timescale decomposition of covariates (weather variables). All GLM variants were fitted assuming the negative binomial distribution to account for overdispersion. Based on the Akaike information criterion (AIC), GLMs with decomposed covariates had lower AIC values than GLMs without decomposed covariates for both B. dorsalis and Ceratitis spp., and therefore GLMs with a sinusoidal component and decomposed covariates under negative binomial distribution were the best choice for these data. The contribution of the preceding weekly insect pest counts in all models was statistically significant. The study established that both abiotic and biotic factors drive insect pest infestation.
VL - 8
IS - 1
ER -

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Author Information

Eric Ali Ibrahim

Data Management, Modelling, and Geo-Information Unit, International Centre of Insect Physiology and Ecology (Icipe), Nairobi, Kenya
Daisy Salifu

Data Management, Modelling, and Geo-Information Unit, International Centre of Insect Physiology and Ecology (Icipe), Nairobi, Kenya
Samuel Musili Mwalili

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Thomas Dubois

Plant Health Theme, International Centre of Insect Physiology and Ecology (Icipe), Nairobi, Kenya
Henri Edouard Zefack Tonnang

Data Management, Modelling, and Geo-Information Unit, International Centre of Insect Physiology and Ecology (Icipe), Nairobi, Kenya

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Eric Ali Ibrahim, Daisy Salifu, Samuel Musili Mwalili, Thomas Dubois, Henri Edouard Zefack Tonnang. (2022). Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya. International Journal of Data Science and Analysis, 8(1), 1-10. https://doi.org/10.11648/j.ijdsa.20220801.11

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ACS Style

Eric Ali Ibrahim; Daisy Salifu; Samuel Musili Mwalili; Thomas Dubois; Henri Edouard Zefack Tonnang. Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya. Int. J. Data Sci. Anal. 2022, 8(1), 1-10. doi: 10.11648/j.ijdsa.20220801.11

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AMA Style

Eric Ali Ibrahim, Daisy Salifu, Samuel Musili Mwalili, Thomas Dubois, Henri Edouard Zefack Tonnang. Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya. Int J Data Sci Anal. 2022;8(1):1-10. doi: 10.11648/j.ijdsa.20220801.11

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@article{10.11648/j.ijdsa.20220801.11,
  author = {Eric Ali Ibrahim and Daisy Salifu and Samuel Musili Mwalili and Thomas Dubois and Henri Edouard Zefack Tonnang},
  title = {Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya},
  journal = {International Journal of Data Science and Analysis},
  volume = {8},
  number = {1},
  pages = {1-10},
  doi = {10.11648/j.ijdsa.20220801.11},
  url = {https://doi.org/10.11648/j.ijdsa.20220801.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220801.11},
  abstract = {Avocado (Persea americana) farming in East Africa has expanded since recent, contributing significantly toward economic growth and livelihood for small-scale farmers. However, insects attacking avocado fruits reduce fruit quality and size, causing massive losses. Previous studies have identified key avocado insect pests, their temporal population patterns and how landscape vegetation productivity influences their population dynamics. This research analyzed insect count data collected on Bactrocera dorsalis and Ceratitis spp. in an avocado plantation in Thika, Kenya over a successive period of time, as part of pest management. These data are characterized by overdispersion due to aggregation behaviour of the insects in their habitat and serial correlations since the count data were collected over a successive period of time. Analyzing these data becomes complicated because of overdispersion and the serial correlation in the data. In this study, we explored variants of generalized linear models (GLMs) with a sinusoidal component over time; and with and without timescale decomposition of covariates (weather variables). All GLM variants were fitted assuming the negative binomial distribution to account for overdispersion. Based on the Akaike information criterion (AIC), GLMs with decomposed covariates had lower AIC values than GLMs without decomposed covariates for both B. dorsalis and Ceratitis spp., and therefore GLMs with a sinusoidal component and decomposed covariates under negative binomial distribution were the best choice for these data. The contribution of the preceding weekly insect pest counts in all models was statistically significant. The study established that both abiotic and biotic factors drive insect pest infestation.},
 year = {2022}
}

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