Abstracts of presentations to the working session on improving predictive modeling of Mycotoxin risk for Africa Held at the 3rd ASM2022 on 7 September 2022, in Stellenbosch, South Africa

Journal article

Rembold, Felix, Mvumi, Brighton, Miller, David, Omari, R., Battilani, P., Galani, Y., Louw, Wiana, Falade, T., Schweiger, Wolfgang and Ermolli, M. 2023. Abstracts of presentations to the working session on improving predictive modeling of Mycotoxin risk for Africa Held at the 3rd ASM2022 on 7 September 2022, in Stellenbosch, South Africa. Toxins. 15 (3), p. 174. https://doi.org/10.3390/toxins15030174
AuthorsRembold, Felix, Mvumi, Brighton, Miller, David, Omari, R., Battilani, P., Galani, Y., Louw, Wiana, Falade, T., Schweiger, Wolfgang and Ermolli, M.
AbstractIn 2008, the African Postharvest Losses Information Systems project (APHLIS, accessed on 6 September 2022) developed an algorithm for estimating the scale of cereal postharvest losses (PHLs). The relevant scientific literature and contextual information was used to build profiles of the PHLs occurring along the value chains of nine cereal crops by country and province for 37 sub-Saharan African countries. The APHLIS provides estimates of PHL figures where direct measurements are not available. A pilot project was subsequently initiated to explore the possibility of supplementing these loss estimates with information on the aflatoxin risk. Using satellite data on drought and rainfall, a time series of agro-climatic aflatoxin risk warning maps for maize was developed covering the countries and provinces of sub-Saharan Africa. The agro-climatic risk warning maps for specific countries were shared with mycotoxin experts from those countries for review and comparison with their aflatoxin incidence datasets. The present Work Session was a unique opportunity for African food safety mycotoxins experts, as well as other international experts, to meet and deepen the discussion about prospects for using their experience and their data to validate and improve agro-climatic risk modeling approaches.
KeywordsToxicology; Toxins
Year2023
JournalToxins
Journal citation15 (3), p. 174
PublisherMDPI
ISSN2072-6651
Digital Object Identifier (DOI)https://doi.org/10.3390/toxins15030174
Official URLhttps://www.mdpi.com/2072-6651/15/3/174/notes
Publication dates
Online24 Feb 2023
Publication process dates
Deposited09 Mar 2023
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Output statusPublished
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