Ritter Atoundem Guimapi
Research Scientist
Attachments
CV 2023Biography
Ritter A. Guimapi works as research scientist in the Division of Biotechnology and plant health, under the department of Pests and Weeds in Forestry, Agriculture and Horticulture. His main area of responsibility includes: Mathematical modeling for plant protection; computer-based simulation of insect pest dynamic and their interaction with their beneficial organisms, for use among other things, in warning services.
Ritter’s academic background consists of a PhD in ecological modelling and computer science, a master's in computer science and a bachelor's in mathematics and computer science. He has extensive experience developing models to understand and predict the risk of the dynamics and invasion of various pests across the African and Asian continents. Furthermore, he has many years of international experience using model to explore the effect of both environmental and climatic factors on the dynamics of agroecological processes in relation to plant protection, and to optimize the timing of field implementation of environmentally friendly pest control solutions. Many of these models are integrated into Decision Support System like VIPS (Varsling Innen PlanteSkadegøjøre: https://www.vips-landbruk.no/ ) and are in use to help address pest (insects and plants) management challenges for the wellbeing of smallholder farmers.
Authors
Frank T. Ndjomatchoua Ritter Atoundem Guimapi Luca Rossini Byliole S. Djouda Sansao A. PedroAbstract
Life history traits have been studied under various environmental factors, but the ability to combine them into a simple function to assess pest response to climate is still lacking complete understanding. This study proposed a risk index derived by combining development, mortality, and fertility rates from a stage-structured dynamic mathematical model. The first part presents the theoretical framework behind the risk index. The second part of the study is concerned with the application of the index in two case studies of major economic pest: the brown planthopper (Nilaparvata lugens) and the spotted wing drosophila (Drosophila suzukii), pests of rice crops and soft fruits, respectively. The mathematical calculations provided a single function composed of the main thermal biodemographic rates. This function has a threshold value that determines the possibility of population increase as a function of temperature. The tests carried out on the two pest species showed the capability of the index to describe the range of favourable conditions. With this approach, we were able to identify areas where pests are tolerant to climatic conditions and to project them on a geospatial risk map. The theoretical background developed here provided a tool for understanding the biogeography of Nilaparvata lugens and Drosophila suzukii. It is flexible enough to deal with mathematically simple (N. lugens) and complex (D. Suzukii) case studies of crop insect pests. It produces biologically sound indices that behave like thermal performance curves. These theoretical results also provide a reasonable basis for addressing the challenge of pest management in the context of seasonal weather variations and climate change. This may help to improve monitoring and design management strategies to limit the spread of pests in invaded areas, as some non-invaded areas may be suitable for the species to develop.
Authors
Ritter Atoundem Guimapi Berit Nordskog Anne-Grete Roer Hjelkrem Ingeborg Klingen Ghislain Tchoromi Tepa-Yotto Manuele Tamò Karl ThunesAbstract
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.
Authors
Ritter Atoundem GuimapiAbstract
No abstract has been registered
Sustainable management of Fall armyworm
Sustainable management of the Fall Armyworm (FAW) in Africa coordinated by FAO. NIBIO is one of the implementing partners in Malawi.
Division of Biotechnology and Plant Health
Malawi Digital Plant Health Service (MaDiPHS)
This project will establish a digital agricultural plant health service at the national level in Malawi, based on coordination of internationally developed digital systems.