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

dc.contributor.authorGuimapi, R.A.
dc.contributor.authorNiassy, S.
dc.contributor.authorMudereri, B.T.
dc.contributor.authorAbdel-Rahman, E.M.
dc.contributor.authorTepa-Yotto, G.
dc.contributor.authorSubramanian, S.
dc.contributor.authorMohamed, S.A.
dc.contributor.authorThunes, K.H.
dc.contributor.authorKimathi, E.K.
dc.contributor.authorAgboka, K.
dc.contributor.authorTamo, M.
dc.contributor.authorRwaburindi, J.C.
dc.contributor.authorHadi, B.
dc.contributor.authorElkahky, M.
dc.contributor.authorSaethre, M.G.
dc.contributor.authorBelayneh, Y.T.
dc.contributor.authorEkesi, S.
dc.contributor.authorKelemu, S.
dc.contributor.authorTonnang, H.
dc.date.accessioned2022-12-02T13:22:01Z
dc.date.available2022-12-02T13:22:01Z
dc.date.issued2022-06
dc.identifier.citationGuimapi, R.A., Niassy, S., Mudereri, B.T., Abdel-Rahman, E.M., Tepa-Yotto, G., Subramanian, S., ... & Tonnang, H. (2022). Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda)(JE Smith)(Lepidoptera: Noctuidae). Global Ecology and Conservation, 35: e02056, 1-22.
dc.identifier.issn2351-9894
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7955
dc.description.abstractAfter 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).
dc.description.sponsorshipUnited States Agency for International Development
dc.description.sponsorshipEuropean Union
dc.description.sponsorshipUKs Foreign, Commonwealth & Development Office
dc.description.sponsorshipSwiss Agency for Development and Cooperation
dc.description.sponsorshipFederal Democratic Republic of Ethiopia
dc.description.sponsorshipGovernment of the Republic of Kenya
dc.format.extent1-22
dc.language.isoen
dc.subjectDynamics
dc.subjectInsects
dc.subjectMonitoring
dc.subjectSub-Saharan Africa
dc.titleHarnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae)
dc.typeJournal Article
cg.contributor.crpGrain Legumes
cg.contributor.affiliationInternational Centre of Insect Physiology and Ecology
cg.contributor.affiliationNorwegian Institute of Bioeconomy Research
cg.contributor.affiliationMidlands State University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationUniversite Nationale d’Agriculture, Benin
cg.contributor.affiliationFood and Agriculture Organization of the United Nations
cg.contributor.affiliationNorwegian Agency for Development and Cooperation
cg.contributor.affiliationRRB 8.7.100, 1300 Pennsylvania Avenue, USA
cg.coverage.regionAfrica
cg.coverage.regionCentral Africa
cg.coverage.regionEast Africa
cg.coverage.regionSouthern Africa
cg.coverage.regionWest Africa
cg.coverage.countryBenin (Dahomey)
cg.coverage.countryBurkina Faso (Upper Volta)
cg.coverage.countryBurundi
cg.coverage.countryEthiopia
cg.coverage.countryGhana
cg.coverage.countryGuinea
cg.coverage.countryKenya
cg.coverage.countryLiberia
cg.coverage.countryMadagascar
cg.coverage.countryMalawi
cg.coverage.countryMozambique
cg.coverage.countryRwanda
cg.coverage.countrySouth Africa
cg.coverage.countryTanzania
cg.coverage.countryTogo
cg.coverage.countryUganda
cg.coverage.countryZambia
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidGUIMAPI:2022
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectFood Security
cg.iitasubjectPests of Plants
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Health
cg.iitasubjectPlant Production
cg.journalGlobal Ecology and Conservation
cg.notesOpen Access Article; Published online: 11 Feb 2022
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1016/j.gecco.2022.e02056
cg.iitaauthor.identifierManuele Tamò: 0000-0002-5863-7421
cg.futureupdate.requiredNo
cg.identifier.volume35


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