Chapter 11. 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 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.
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| Format: | Book Chapter biblioteca |
| Language: | English |
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Springer Cham
2023
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| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, FOOD SECURITY, NUTRITION SECURITY, POLICIES, DATA, MACHINE LEARNING, SOCIAL SCIENCES, Sustainable Agrifood Systems, |
| Online Access: | https://hdl.handle.net/10883/22486 |
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dig-cimmyt-10883-224862023-10-18T15:23:47Z Chapter 11. Agriculture, food and nutrition security: Concept, datasets and opportunities for computational social science applications Amjath Babu, T.S. Lopez-Ridaura, S. Krupnik, T.J. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY FOOD SECURITY NUTRITION SECURITY POLICIES DATA MACHINE LEARNING SOCIAL SCIENCES Sustainable Agrifood Systems 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. 15 pages 2023-02-01T01:30:13Z 2023-02-01T01:30:13Z 2023 Book Chapter Published Version 978-3-031-16623-5 978-3-031-16624-2 (Online) https://hdl.handle.net/10883/22486 10.1007/978-3-031-16624-2_11 English Nutrition, health & food security Transforming Agrifood Systems in South Asia Resilient Agrifood Systems CGIAR Trust Fund https://hdl.handle.net/10568/128378 CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose Open Access Switzerland Springer Cham 978-3-031-16623-5 |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY FOOD SECURITY NUTRITION SECURITY POLICIES DATA MACHINE LEARNING SOCIAL SCIENCES Sustainable Agrifood Systems AGRICULTURAL SCIENCES AND BIOTECHNOLOGY FOOD SECURITY NUTRITION SECURITY POLICIES DATA MACHINE LEARNING SOCIAL SCIENCES Sustainable Agrifood Systems |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY FOOD SECURITY NUTRITION SECURITY POLICIES DATA MACHINE LEARNING SOCIAL SCIENCES Sustainable Agrifood Systems AGRICULTURAL SCIENCES AND BIOTECHNOLOGY FOOD SECURITY NUTRITION SECURITY POLICIES DATA MACHINE LEARNING SOCIAL SCIENCES Sustainable Agrifood Systems Amjath Babu, T.S. Lopez-Ridaura, S. Krupnik, T.J. Chapter 11. Agriculture, food and nutrition security: Concept, datasets and opportunities for computational social science applications |
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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 |
| topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY FOOD SECURITY NUTRITION SECURITY POLICIES DATA MACHINE LEARNING SOCIAL SCIENCES Sustainable Agrifood Systems |
| author |
Amjath Babu, T.S. Lopez-Ridaura, S. Krupnik, T.J. |
| author_facet |
Amjath Babu, T.S. Lopez-Ridaura, S. Krupnik, T.J. |
| author_sort |
Amjath Babu, T.S. |
| title |
Chapter 11. Agriculture, food and nutrition security: Concept, datasets and opportunities for computational social science applications |
| title_short |
Chapter 11. Agriculture, food and nutrition security: Concept, datasets and opportunities for computational social science applications |
| title_full |
Chapter 11. Agriculture, food and nutrition security: Concept, datasets and opportunities for computational social science applications |
| title_fullStr |
Chapter 11. Agriculture, food and nutrition security: Concept, datasets and opportunities for computational social science applications |
| title_full_unstemmed |
Chapter 11. Agriculture, food and nutrition security: Concept, datasets and opportunities for computational social science applications |
| title_sort |
chapter 11. agriculture, food and nutrition security: concept, datasets and opportunities for computational social science applications |
| publisher |
Springer Cham |
| publishDate |
2023 |
| url |
https://hdl.handle.net/10883/22486 |
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