Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications
| cg.contributor.affiliation | International Maize and Wheat Improvement Center | en |
| cg.contributor.donor | CGIAR Trust Fund | en |
| cg.contributor.initiative | Transforming Agrifood Systems in South Asia | |
| cg.creator.identifier | Timothy Joseph Krupnik: 0000-0001-6973-0106 | |
| cg.howPublished | Formally Published | en |
| cg.identifier.doi | https://doi.org/10.1007/978-3-031-16624-2_11 | en |
| cg.isbn | 9783031166235 | en |
| cg.isbn | 9783031166242 | en |
| cg.place | Cham, Switzerland | en |
| cg.reviewStatus | Peer Review | en |
| cg.subject.actionArea | Resilient Agrifood Systems | |
| cg.subject.impactArea | Nutrition, health and food security | |
| dc.contributor.author | Amjath-Babu, T.S. | en |
| dc.contributor.author | López Ridaura, Santiago | en |
| dc.contributor.author | Krupnik, Timothy J. | en |
| dc.date.accessioned | 2023-02-01T08:13:08Z | en |
| dc.date.available | 2023-02-01T08:13:08Z | en |
| dc.identifier.uri | https://hdl.handle.net/10568/128378 | |
| dc.title | Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications | en |
| dcterms.abstract | Ensuring 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.accessRights | Open Access | |
| dcterms.audience | Scientists | en |
| dcterms.available | 2023-01-24 | |
| dcterms.bibliographicCitation | Amjath-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.issued | 2023 | |
| dcterms.language | en | |
| dcterms.license | CC-BY-4.0 | |
| dcterms.publisher | Springer | en |
| dcterms.subject | social sciences | en |
| dcterms.subject | machine learning | en |
| dcterms.subject | policies | en |
| dcterms.subject | data analysis | en |
| dcterms.type | Book Chapter |