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\begin{document}

\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)}



\author[1]{Ritter Guimapi}%
\author[2]{Saliou Niassy}%
\author[1]{Bester Mudereri}%
\author[1]{Elfatih Abdel-Rahman}%
\author[3]{Ghislain  Tepa-Yotto}%
\author[1]{Sevgan  Subramanian}%
\author[1]{Samira  Mohamed}%
\author[4]{Karl  Thunes}%
\author[4]{Berit  Nordskog}%
\author[1]{Emily  Kimathi}%
\author[1]{Komi  Agboka}%
\author[3]{Manuele  Tamò}%
\author[5]{Jean  Rwaburindi}%
\author[5]{Buyung  Hadi }%
\author[5]{Maged Elkahky}%
\author[6]{May-Guri  Sæthre}%
\author[7]{Yeneneh  Belayneh}%
\author[1]{Sunday  Ekesi}%
\author[1]{Segenet  Kelemu}%
\author[1]{Henri Tonnang}%
\affil[1]{International Centre for Insect Physiology and Ecology}%
\affil[2]{International Centre of Insect Physiology and Ecology (icipe)}%
\affil[3]{International Institute for Tropical Agriculture Benin}%
\affil[4]{Norwegian Institute of Bioeconomy Research}%
\affil[5]{Food and Agriculture Organization of the United Nations}%
\affil[6]{Norwegian Agency for Development Cooperation}%
\affil[7]{RRB}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) threatens maize,
sorghum, and millet production in Africa. Despite rigorous work done to
reduce FAW prevalence, the dynamics and invasion mechanisms are still
poorly understood. This study applied interdisciplinary tools,
analytics, and algorithms on a FAW dataset to provide insights and
projections on the intensity of FAW infestation across Africa. The data
collected between January 2018 and December 2020 were matched with the
monthly average data of the climatic and environmental variables. The
multilevel analytics identified 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), moderate (11--30), and high (\textgreater{}30).
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 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%
\end{abstract}%



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\textbf{Hosted file}

\verb`Manuscript.docx`  available at \url{https://authorea.com/users/736866/articles/712240-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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