Person: Sevgan, S.
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Sevgan
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Sevgan, S.
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0000-0003-4447-0744 5 results
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- A system dynamics model for pests and natural enemies interactions(Nature Publishing Group, 2021) Sokame, B.M.; Tonnang, H.; Sevgan, S.; Bruce, A.Y.; Dubois, T.; Ekesi, S.; Calatayud, P.A.
Publication - Economic impacts of fall armyworm and its management strategies: evidence from southern Ethiopia(Oxford University Press, 2020) Kassie, M.; Wossen,T.; De Groote, H.; Tadele Tefera; Sevgan, S.; Balew, S.
Publication - Chapter 6: low-cost agronomic practices and landscape management approaches to control FAW(CIMMYT, 2018) Thierfelder, C.; Sailou Niassy; Midega, C.; Sevgan, S.; Van den Berg, J.; Prasanna, B.M.; Baudron, F.; Harrison, R.In addition to host plant resistance, biological control, and judicious application of chemical pesticides, a number of low-cost cultural practices and landscape management options can be implemented as part of an effective Integrated Pest Management (IPM) strategy against Fall Armyworm (FAW). Such approaches can be particularly relevant to smallholders who lack financial resources to purchase improved seed, pesticides, or other relatively expensive agricultural inputs. While there is a range of experience applying cultural and landscape management practices to control other pests in Africa, there is still considerable uncertainty about how effective such approaches will be against FAW, and these knowledge gaps require additional research. Many of the measures recommended in this chapter therefore represent general agroecological best practices for pest control – though where indicated, emerging evidence suggests efficacy against FAW in Africa, particularly for the “Push-Pull” intercropping approaches. This chapter will focus on cultural and landscape management practices suitable for maize-based farming systems common in most parts of sub-Saharan Africa, with additional reference to agroforestry interventions.
Publication - Temperature-dependent phenology of Plutella xylostella (Lepidoptera: Plutellidae): simulation and visualization of current and future distributions along the Eastern Afromontane(Public Library of Science, 2017) Ngowi, B.V.; Tonnang, H.; Mwangi, E.M.; Johansson, T.; Ambale, J.; Ndegwa, P.N.; Sevgan, S.There is a scarcity of laboratory and field-based results showing the movement of the diamondback moth (DBM) Plutella xylostella (L.) across a spatial scale. We studied the population growth of the diamondback moth (DBM) Plutella xylostella (L.) under six constant temperatures, to understand and predict population changes along altitudinal gradients and under climate change scenarios. Non-linear functions were fitted to continuously model DBM development, mortality, longevity and oviposition. We compiled the best-fitted functions for each life stage to yield a phenology model, which we stochastically simulated to estimate the life table parameters. Three temperature-dependent indices (establishment, generation and activity) were derived from a logistic population growth model and then coupled to collected current (2013) and downscaled temperature data from AFRICLIM (2055) for geospatial mapping. To measure and predict the impacts of temperature change on the pest’s biology, we mapped the indices along the altitudinal gradients of Mt. Kilimanjaro (Tanzania) and Taita Hills (Kenya) and assessed the differences between 2013 and 2055 climate scenarios. The optimal temperatures for development of DBM were 32.5, 33.5 and 33°C for eggs, larvae and pupae, respectively. Mortality rates increased due to extreme temperatures to 53.3, 70.0 and 52.4% for egg, larvae and pupae, respectively. The net reproduction rate reached a peak of 87.4 female offspring/female/generation at 20°C. Spatial simulations indicated that survival and establishment of DBM increased with a decrease in temperature, from low to high altitude. However, we observed a higher number of DBM generations at low altitude. The model predicted DBM population growth reduction in the low and medium altitudes by 2055. At higher altitude, it predicted an increase in the level of suitability for establishment with a decrease in the number of generations per year. If climate change occurs as per the selected scenario, DBM infestation may reduce in the selected region. The study highlights the need to validate these predictions with other interacting factors such as cropping practices, host plants and natural enemies.
Publication - Advances in crop insect modelling methods—Towards a whole system approach(Elsevier, 2017) Tonnang, H.; Bisseleua, D.H.B.; Biber-Freudenberger, L.; Salifu, D.; Sevgan, S.; Ngowi, B.V.; Guimapi, R.Y.A.; Bruce, A.Y.; Kakmeni, F.M.M.; Affognon, H.D.; Niassy, S.; Landmann, T.; Ndjomatchoua, F.; Pedro, S.A.; Johansson, T.; Tanga, C.M.; Nana, P.; Fiaboe, K.M.; Mohamed, S.F.; Maniania, N.; Nedorezov, L.V.; Ekesi, S.; Borgemeister, C.A wide range of insects affect crop production and cause considerable yield losses. Difficulties reside on the development and adaptation of adequate strategies to predict insect pests for their timely management to ensure enhanced agricultural production. Several conceptual modelling frameworks have been proposed, and the choice of an approach depends largely on the objective of the model and the availability of data. This paper presents a summary of decades of advances in insect population dynamics, phenology models, distribution and risk mapping. Existing challenges on the modelling of insects are listed; followed by innovations in the field. New approaches include artificial neural networks, cellular automata (CA) coupled with fuzzy logic (FL), fractal, multi-fractal, percolation, synchronization and individual/agent-based approaches. A concept for assessing climate change impacts and providing adaptation options for agricultural pest management independently of the United Nations Intergovernmental Panel on Climate Change (IPCC) emission scenarios is suggested. A framework for estimating losses and optimizing yields within crop production system is proposed and a summary on modelling the economic impact of pests control is presented. The assessment shows that the majority of known insect modelling approaches are not holistic; they only concentrate on a single component of the system, i.e. the pest, rather than the whole crop production system. We suggest system thinking as a possible approach for linking crop, pest, and environmental conditions to provide a more comprehensive assessment of agricultural crop production.
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