AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps
The global challenge of reducing Food Loss and Waste (FLW) is critical to achieving the UN’s Sustainable Development Goals (SDGs), particularly the commitment to halving FLW by 2030. Despite widespread recognition of the environmental, economic, and social impacts of FLW, including the prominent gre...
| Autores principales: | , , , |
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| Formato: | Brief |
| Lenguaje: | Inglés |
| Publicado: |
Wageningen University & Research
2024
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| Acceso en línea: | https://hdl.handle.net/10568/168357 |
| _version_ | 1855519662134001664 |
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| author | Guo, X. Axmann, H.B. Soethoudt, J.M. Kok, M.G. |
| author_browse | Axmann, H.B. Guo, X. Kok, M.G. Soethoudt, J.M. |
| author_facet | Guo, X. Axmann, H.B. Soethoudt, J.M. Kok, M.G. |
| author_sort | Guo, X. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The global challenge of reducing Food Loss and Waste (FLW) is critical to achieving the UN’s Sustainable Development Goals (SDGs), particularly the commitment to halving FLW by 2030. Despite widespread recognition of the environmental, economic, and social impacts of FLW, including the prominent greenhouse gas (GHG) emission issue related to climate change (Porter et al., 2016), quantifying it remains a persistent issue due to significant data gaps, especially at the national and sub-national levels. Many countries, particularly low- and middle-income countries (LMICs), struggle with the complexity of FLW monitoring due to limited resources, insufficient expertise in FLW data collection, and unclear data collection practices. These challenges hinder the identification of hotspot products and supply chain stages, the definition of strategic targets, and the design of effective interventions for reducing FLW (Axmann et al., 2024), therefore reducing the associated greenhouse gas (GHG) emissions. |
| format | Brief |
| id | CGSpace168357 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wageningen University & Research |
| publisherStr | Wageningen University & Research |
| record_format | dspace |
| spelling | CGSpace1683572024-12-28T09:37:58Z AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps Guo, X. Axmann, H.B. Soethoudt, J.M. Kok, M.G. food security waste management artificial intelligence The global challenge of reducing Food Loss and Waste (FLW) is critical to achieving the UN’s Sustainable Development Goals (SDGs), particularly the commitment to halving FLW by 2030. Despite widespread recognition of the environmental, economic, and social impacts of FLW, including the prominent greenhouse gas (GHG) emission issue related to climate change (Porter et al., 2016), quantifying it remains a persistent issue due to significant data gaps, especially at the national and sub-national levels. Many countries, particularly low- and middle-income countries (LMICs), struggle with the complexity of FLW monitoring due to limited resources, insufficient expertise in FLW data collection, and unclear data collection practices. These challenges hinder the identification of hotspot products and supply chain stages, the definition of strategic targets, and the design of effective interventions for reducing FLW (Axmann et al., 2024), therefore reducing the associated greenhouse gas (GHG) emissions. 2024-12 2024-12-26T13:03:36Z 2024-12-26T13:03:36Z Brief https://hdl.handle.net/10568/168357 en Open Access application/pdf Wageningen University & Research Guo, X., Axmann, H.B., Soethoudt, J.M., Kok, M.G. 2024. AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps. Wageningen, The Netherlands: Wageningen University & Research. |
| spellingShingle | food security waste management artificial intelligence Guo, X. Axmann, H.B. Soethoudt, J.M. Kok, M.G. AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps |
| title | AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps |
| title_full | AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps |
| title_fullStr | AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps |
| title_full_unstemmed | AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps |
| title_short | AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps |
| title_sort | ai monitoring approach to fill national food loss and waste data gaps |
| topic | food security waste management artificial intelligence |
| url | https://hdl.handle.net/10568/168357 |
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