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
Lumbani Benedicto Banda Frank Thomas Ndjomatchoua Ritter Atoundem Guimapi Komi Mensah Agboka Abdelmutalab G.A. Azrag Wezi G. Mhango Trust Kasambala Donga Chikondi Makwiza Karl Thunes Elfatih M. Abdel-RahmanAbstract
The cassava whitefly (Bemisia tabaci) greatly constrains cassava production across Africa due to its role as a vector of viral diseases that cause substantial yield losses. Effective management of this insect pest requires detailed knowledge of its spatio-temporal distribution, however long-term datasets are scarce. Mechanistic models circumvent these long-term data needs by modelling temperature-dependent processes that govern population dynamics. Nevertheless, their application to B. tabaci remains poorly explored. Here, we developed a mechanistic model to derive a risk index (RI) for B. tabaci across Africa, focusing on Malawi. The model integrates the effects of temperature on the life stages of B. tabaci to predict temporal risk dynamics and assess climate change impacts. Validation against historical data demonstrated strong agreement, with high cosine similarity values (0.95 in 1988 and 0.96 in 1990) and high correlation coefficients (0.73 and 0.78 in 1988 and 1990, respectively), supporting its suitability as a proxy for whitefly population dynamics. Areas with temperatures between 20.2 °C and 32.5 °C are conducive to B. tabaci population increase, with suitability peaking near 27.5 °C. Cassava-growing regions in central and western Africa experience year-round higher RI values, whereas southeastern Africa experiences peak RI values from October to March. In Malawi, the lakeshore and southern regions were most vulnerable, with RI peaking in these areas during the rainy season. At continental and national scales, climate change is projected to increase RI values. These findings underscore the importance of timing pest control interventions to align with peak risk periods and highlight the utility of mechanistic models for informing region-specific whitefly management strategies.
Authors
Beatrice T. Nganso Komi Mensah Agboka Salvador D. Atagong Sidonie Fameni Topé Tchouzeube Massing Tobias Landmann Subramanian Sevgan Willy Mwiza Fredrick Odera Emmanuel D. Piiru Z. Ngalo Otieno-Ayayo Victoria Soroker Ritter Atoundem GuimapiAbstract
No abstract has been registered
Authors
Komi Mensah Agboka Frank Thomas Ndjomatchoua Ritter Atoundem Guimapi Luca Rossini Abdelmutalab G. A. Azrag Quinto Juma Meltus Tobias Landmann Sunday Ekesi Elfatih M. Abdel‐RahmanAbstract
The multitasking lesser mealworm ( Alphitobius diaperinus ) is a special beetle known as a pest in poultry, a resource for waste degradation and an alternative for protein production. This study compares the predictive accuracy of correlative species distribution models (SDMs) with a risk index derived from a mechanistic model. The study derives the mechanistic‐based risk index from the ordinary differential equation that describes the population dynamics of A. diaperinus using the temperature‐dependent bio‐demographic rates, while the ensemble SDM is derived using well‐known algorithms such as maximum entropy, random forest and so forth. We finally propose a hybrid model combining both approaches using a weighted average approach. When overlaid on occurrence data, the predictive accuracy of the mechanistic model globally varied across temporal scales, with the highest performance observed in the October–December quarter (27% of occurrences were predicted correctly). The comparison across geographic regions model had the best performance in Asia (94.4% accuracy), outperforming the two scenario SDMs (78.3%). In contrast, the correlative ensemble SDM performed better in Europe (93%), where we have most of the data, but was very sensitive to data gaps, especially in Africa. Finally, the proposed hybrid model outperforms both individual models in the global scenario (86.5% accuracy). These findings highlight the strengths and limitations of both modelling approaches and provide critical insights to optimise pest management strategies, sustainable utilisation and ecological forecasting by refining SDM through the integration of biological realism and empirical data.
