Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications
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...
| Main Authors: | , , |
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| Format: | Book Chapter |
| Language: | Inglés |
| Published: |
Springer
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/128378 |
| _version_ | 1855530725208489984 |
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| author | Amjath-Babu, T.S. López Ridaura, Santiago Krupnik, Timothy J. |
| author_browse | Amjath-Babu, T.S. Krupnik, Timothy J. López Ridaura, Santiago |
| author_facet | Amjath-Babu, T.S. López Ridaura, Santiago Krupnik, Timothy J. |
| author_sort | Amjath-Babu, T.S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Book Chapter |
| id | CGSpace128378 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1283782025-11-06T13:00:45Z Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications Amjath-Babu, T.S. López Ridaura, Santiago Krupnik, Timothy J. social sciences machine learning policies data analysis 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. 2023 2023-02-01T08:13:08Z 2023-02-01T08:13:08Z Book Chapter https://hdl.handle.net/10568/128378 en Open Access application/pdf Springer 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. |
| spellingShingle | social sciences machine learning policies data analysis Amjath-Babu, T.S. López Ridaura, Santiago Krupnik, Timothy J. Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications |
| title | Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications |
| title_full | Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications |
| title_fullStr | Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications |
| title_full_unstemmed | Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications |
| title_short | Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications |
| title_sort | agriculture food and nutrition security concept datasets and opportunities for computational social science applications |
| topic | social sciences machine learning policies data analysis |
| url | https://hdl.handle.net/10568/128378 |
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