Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Insecta: Lepidoptera: Noctuidae)

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Main Authors: Ritter A. Guimapi, Saliou Niassy, Bester Tawona Mudereri, Elfatih M. Abdel-Rahman, Ghislain T. Tepa-Yotto, Sevgan Subramanian, Samira A. Mohamed, Karl H. Thunes, Emily Kimathi, Komi Mensah Agboka, Manuele Tamò, Jean Claude Rwaburindi, Buyung Hadi, Maged Elkahky, May-Guri Sæthre, Yeneneh Belayneh, Sunday Ekesi, Segenet Kelemu, Henri E.Z. Tonnang
Other Authors: International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya Norwegian Institute of Bioeconomy Research (NIBIO), Box 115, NO-1431 Ås, Norway
Format: research article
Language:English
Published: Elsevier BV 2023
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Online Access:https://cris.library.msu.ac.zw//handle/11408/5498
https://doi.org/10.1016/j.gecco.2022.e02056
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author Ritter A. Guimapi
Saliou Niassy
Bester Tawona Mudereri
Elfatih M. Abdel-Rahman
Ghislain T. Tepa-Yotto
Sevgan Subramanian
Samira A. Mohamed
Karl H. Thunes
Emily Kimathi
Komi Mensah Agboka
Manuele Tamò
Jean Claude Rwaburindi
Buyung Hadi
Maged Elkahky
May-Guri Sæthre
Yeneneh Belayneh
Sunday Ekesi
Segenet Kelemu
Henri E.Z. Tonnang
author2 International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya Norwegian Institute of Bioeconomy Research (NIBIO), Box 115, NO-1431 Ås, Norway
author_facet International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya Norwegian Institute of Bioeconomy Research (NIBIO), Box 115, NO-1431 Ås, Norway
Ritter A. Guimapi
Saliou Niassy
Bester Tawona Mudereri
Elfatih M. Abdel-Rahman
Ghislain T. Tepa-Yotto
Sevgan Subramanian
Samira A. Mohamed
Karl H. Thunes
Emily Kimathi
Komi Mensah Agboka
Manuele Tamò
Jean Claude Rwaburindi
Buyung Hadi
Maged Elkahky
May-Guri Sæthre
Yeneneh Belayneh
Sunday Ekesi
Segenet Kelemu
Henri E.Z. Tonnang
author_sort Ritter A. Guimapi
collection DSpace
description Abstract
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publishDate 2023
publisher Elsevier BV
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spelling ir-11408-54982023-03-29T06:57:21Z Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Insecta: Lepidoptera: Noctuidae) Ritter A. Guimapi Saliou Niassy Bester Tawona Mudereri Elfatih M. Abdel-Rahman Ghislain T. Tepa-Yotto Sevgan Subramanian Samira A. Mohamed Karl H. Thunes Emily Kimathi Komi Mensah Agboka Manuele Tamò Jean Claude Rwaburindi Buyung Hadi Maged Elkahky May-Guri Sæthre Yeneneh Belayneh Sunday Ekesi Segenet Kelemu Henri E.Z. Tonnang International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya Norwegian Institute of Bioeconomy Research (NIBIO), Box 115, NO-1431 Ås, Norway International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya Department of Animal and Wildlife Sciences, Midlands State University (MSU), P. Bag 9055, Gweru, ZimbabweInternational Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya Biorisk Management Facility (BIMAF), International Institute of Tropical Agriculture (IITA-Benin), 08-01000 Cotonou, Benin International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya Biorisk Management Facility (BIMAF), International Institute of Tropical Agriculture (IITA-Benin), 08-01000 Cotonou, Benin Plant Production and Protection Division (NSP), Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy Plant Production and Protection Division (NSP), Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy Plant Production and Protection Division (NSP), Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy Department for Climate, Energy and Environment, Norwegian Agency for Development and Cooperation (NORAD), NO-1303 Oslo, Norway RRB 8.7.100, 1300 Pennsylvania Avenue, NW, Washington, DC 20523, USA International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya Analytics Dynamics Insect Monitoring Spatial Temporal Abstract After five years of its first report on the African continent, Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) is considered a major threat to maize, sorghum, and millet production in sub-Saharan Africa. Despite the rigorous work already conducted to reduce FAW prevalence, the dynamics and invasion mechanisms of FAW in Africa are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset with a spatial lens to provide insights and project the intensity of FAW infestation across Africa. The data collected between January 2018 and December 2020 in selected locations were matched with the monthly average data of the climatic and environmental variables. The multilevel analytics aimed to identify the key factors that influence the dynamics of spatial and temporal pest density and occurrence at a 2 km x 2 km grid resolution. The seasonal variations of the identified factors and dynamics were used to calibrate rule-based analytics employed to simulate the monthly densities and occurrence of the FAW for the years 2018, 2019, and 2020. Three FAW density level classes were inferred, i.e., low (0–10 FAW moth per trap), moderate (11–30 FAW moth per trap), and high (>30 FAW moth per trap). Results show that monthly density projections were sensitive to the type of FAW host vegetation and the seasonal variability of climatic factors. Moreover, the diversity in the climate patterns and cropping systems across the African sub-regions are considered the main drivers of FAW abundance and variation. An optimum overall accuracy of 53% was obtained across the three years and at a continental scale, however, a gradual increase in prediction accuracy was observed among the years, with 2020 predictions providing accuracies greater than 70%. Apart from the low amount of data in 2018 and 2019, the average level of accuracy obtained could also be explained by the non-inclusion of data related to certain key factors such as the influence of natural enemies (predators, parasitoids, and pathogens) into the analysis. Further detailed data on the occurrence and efficiency of FAW natural enemies in the region may help to complete the tri-trophic interactions between the host plants, pests, and beneficial organisms. Nevertheless, the tool developed in this study provides a framework for field monitoring of FAW in Africa that may be a basis for a future decision support system (DSS). 35 1 22 2023-03-29T06:57:19Z 2023-03-29T06:57:19Z 2022-02-10 research article https://cris.library.msu.ac.zw//handle/11408/5498 https://doi.org/10.1016/j.gecco.2022.e02056 en Global Ecology and Conservation 2351-9894 open Elsevier BV
spellingShingle Analytics
Dynamics
Insect
Monitoring
Spatial
Temporal
Ritter A. Guimapi
Saliou Niassy
Bester Tawona Mudereri
Elfatih M. Abdel-Rahman
Ghislain T. Tepa-Yotto
Sevgan Subramanian
Samira A. Mohamed
Karl H. Thunes
Emily Kimathi
Komi Mensah Agboka
Manuele Tamò
Jean Claude Rwaburindi
Buyung Hadi
Maged Elkahky
May-Guri Sæthre
Yeneneh Belayneh
Sunday Ekesi
Segenet Kelemu
Henri E.Z. Tonnang
Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Insecta: Lepidoptera: Noctuidae)
title Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Insecta: Lepidoptera: Noctuidae)
title_full Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Insecta: Lepidoptera: Noctuidae)
title_fullStr Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Insecta: Lepidoptera: Noctuidae)
title_full_unstemmed Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Insecta: Lepidoptera: Noctuidae)
title_short Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Insecta: Lepidoptera: Noctuidae)
title_sort harnessing data science to improve integrated management of invasive pest species across africa: an application to fall armyworm (spodoptera frugiperda) (j.e. smith) (insecta: lepidoptera: noctuidae)
topic Analytics
Dynamics
Insect
Monitoring
Spatial
Temporal
url https://cris.library.msu.ac.zw//handle/11408/5498
https://doi.org/10.1016/j.gecco.2022.e02056
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