Ritter Atoundem Guimapi
Forsker
Vedlegg
CV 2023Biografi
Jeg har erfaring i matematisk modellering innen plantevern. Min akademiske bakgrunn består av en doktorgrad i informatikk og økologisk modellering, en master i informatikk og en bachelor i matematikk og informatikk.
Jeg har en spesiell interesse for, og mange års erfaring i, bruk av matematisk modellering og datamaskinbasert simulering for å forstå og forutsi effekten av miljø- og klimatiske faktorer på dynamikken til agroøkologiske prosesser i forhold til plantevern.
I mitt arbeid har jeg utviklet ulike mekanistiske og empirisk-baserte modeller for å forutsi risikoen for dynamikken og spredningen av skadeinsekter over det afrikanske og asiatiske kontinentet; å optimalisere tidspunktet for feltimplementering av miljøvennlige løsninger for skadedyrbekjempelse. Mange av disse modellene er integrert i Desisjon Support System som VIPS og brukes til skadedyrovervåking og avlingsbeskyttelse.
Forfattere
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 GuimapiSammendrag
Det er ikke registrert sammendrag
Forfattere
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‐RahmanSammendrag
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.
Forfattere
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A. GilliganSammendrag
Empirical field data and simulation models are often used separately to monitor and analyse the dynamics of insect pest populations over time. Greater insight may be achieved when field data are used directly to parametrize population dynamic models. In this paper, we use a differential evolution algorithm to integrate mechanistic physiological-based population models and monitoring data to estimate the population density and the physiological age of the first cohort at the start of the field monitoring. We introduce an ad hoc temperature-driven life-cycle model of Bemisia tabaci in conjunction with field monitoring data. The likely date of local whitefly invasion is estimated, with a subsequent improvement of the model’s predictive accuracy. The method allows computation of the likely date of the first field incursion by the pest and demonstrates that the initial physiological age somewhat neglected in prior studies can improve the accuracy of model simulations. Given the increasing availability of monitoring data and models describing terrestrial arthropods, the integration of monitoring data and simulation models to improve model prediction and pioneer invasion date estimate will lead to better decision-making in pest management.
