Data science for food security and safety
Chair: Elisabetta Carfagna, Chair of the ISI Special Interest Group on Data Science, University of Bologna, Italy
Oliver Chinganya, ISI Vice-President, Director at the African Centre for Statistics of UN Economic Commission for Africa (UNECA), Ethiopia “How Africa is leveraging on new and novel techniques to address data gaps for food security”
Carola Fabi, Senior Statistician in Statistics Division, Food and Agriculture Organization of the United Nations (FAO) “Monitoring Global Food Insecurity: An Artificial Intelligence Approach”
Ross Darnell, Data61 CSIRO, Australia “Predicting the risk of mycotoxin in food crops“ Ross.Darnell@data61.csiro.au
Alison Kelly, The University of Queensland, Australia “Genetic improvement; a safe and sustainable solution for improved agricultural food systems” a.kelly1@uq.edu.au
Alessandra Garbero, Senior Econometrician, International Fund for Agricultural Development (IFAD) “Predictive analytics to optimize development interventions and policies”
The global challenge of food insecurity poses a significant obstacle to achieving Sustainable Development Goal (SDG) 2: Zero Hunger. Tracking this problem is a complex task, as food security is influenced by a multitude of interconnected factors, from climate change and agricultural productivity to geopolitical conflicts and socio-economic policies. In this context, timely and accurate monitoring has become imperative and more and more often data science and artificial intelligence are applied to new data sources for obtaining early insights into global food insecurity trends.
This session discusses efforts underway to address data gaps for food security and the advances in new data sources analysis for identifying food crisis drivers and risks. Moreover, the use of data science for food safety and of predictive analytics to optimize development policies are taken into consideration.
This IPS is proposed by the ISI Special Interest Group on Data Science.