Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications

cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeTransforming Agrifood Systems in South Asia
cg.creator.identifierTimothy Joseph Krupnik: 0000-0001-6973-0106
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1007/978-3-031-16624-2_11en
cg.isbn9783031166235en
cg.isbn9783031166242en
cg.placeCham, Switzerlanden
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.impactAreaNutrition, health and food security
dc.contributor.authorAmjath-Babu, T.S.en
dc.contributor.authorLópez Ridaura, Santiagoen
dc.contributor.authorKrupnik, Timothy J.en
dc.date.accessioned2023-02-01T08:13:08Zen
dc.date.available2023-02-01T08:13:08Zen
dc.identifier.urihttps://hdl.handle.net/10568/128378
dc.titleAgriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applicationsen
dcterms.abstractEnsuring food and nutritional security requires effective policy actions that consider the multitude of direct and indirect drivers. The limitations of data and tools to unravel complex impact pathways to nutritional outcomes have constrained efficient policy actions in both developed and developing countries. Novel digital data sources and innovations in computational social science have resulted in new opportunities for understanding complex challenges and deriving policy outcomes. The current chapter discusses the major issues in the agriculture and nutrition data interface and provides a conceptual overview of analytical possibilities for deriving policy insights. The chapter also discusses emerging digital data sources, modelling approaches, machine learning and deep learning techniques that can potentially revolutionize the analysis and interpretation of nutritional outcomes in relation to food production, supply chains, food environment, individual behaviour and external drivers. An integrated data platform for digital diet data and nutritional information is required for realizing the presented possibilities.en
dcterms.accessRightsOpen Access
dcterms.audienceScientistsen
dcterms.available2023-01-24
dcterms.bibliographicCitationAmjath-Babu, T.S., Ridaura Lopez, S., Krupnik, T.J. (2023). Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications. In: Bertoni, E., Fontana, M., Gabrielli, L., Signorelli, S., Vespe, M. (eds) Handbook of Computational Social Science for Policy. Cham: Springer.en
dcterms.issued2023
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherSpringeren
dcterms.subjectsocial sciencesen
dcterms.subjectmachine learningen
dcterms.subjectpoliciesen
dcterms.subjectdata analysisen
dcterms.typeBook Chapter

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