Sammendrag

The fall armyworm, Spodoptera frugiperda, situation in Africa remains a priority threat despite significant efforts made since the first outbreaks in 2016 to control the pest and thereby reduce yield losses. Field surveys in Benin and Mali reported that approximately one-week post-emergence of maize plants, the presence of fall armyworm (egg/neonates) could be observed in the field. Scouting for fall armyworm eggs and neonates is, however, difficult and time consuming. In this study, we therefore hypothesized that the optimum timeframe for the fall armyworm female arriving to lay eggs in sown maize fields could be predicted. We did this by back-calculating from interval censored data of egg and neonates collected in emerging maize seedlings at young leaf developmental stage. Early time of ovipositing fall armyworm after sowing was recorded in field experiments. By using temperature-based models to predict phenological development for maize and fall armyworm, combined with analytical approaches for time-to-event data with censored status, we estimated that about 210 accumulated Degree Days (DD) is needed for early detection of neonate larvae in the field. This work is meant to provide new insights on timely pest detection and to guide for precise timing of control measures.

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The COVID-19 pandemic, surprised many through its impact on the food systems, resulting in collapses in the food production value chains and in the integrated pest disease management sector with fatal outcomes in many places. However, the impact of COVID-19 and the digital experience perspective on Integrating Pest Management (IPM) is still yet to be understood. In Africa, the impact was devastating, mostly for the vulnerable smallholder farm households, who were rendered unable to access markets to purchase inputs and sell their produce during the lockdown period. By using a holistic approach the paper reviews different Information and Communications Technologies (ICTs), digitalization, and how this enhanced the capacity of smallholder farmers resilient, and inform their smart-IPM practices in order to improve food systems' amidst climate change during and in the post-COVID-19 period. Different digital modalities were adopted to ensure continuous food production, access to inputs and finances, and selling surplus production among others. This was largely possible by using ICTs to deliver these needed services digitally. The study shares contributions and capacity perspectives of ICTs for empowering smallholder farmers to boost the resilience of their food systems based on COVID-19 successful experiences. Thus digital solutions must be embraced in the delivery of extension service on pest management and good agronomic practices, money transfers for purchasing inputs, receiving payment for sold farm produce, and markets information exchange. These are key avenues through which digital solutions strategically supported smallholder-based food systems through the pandemic.

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Sammendrag

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).