Remote sensing and machine learning for food crop production data in Africa post-COVID-19
The world is experiencing an unprecedented health crisis during the spread of COVID-19 (SARS-CoV-2, or Severe Acute Respiratory Syndrome Coronavirus 2). While the pandemic appears to be less severe on the African continent than in other geographic regions (Global Change Data Lab 2021), its economic...
| Main Authors: | , , |
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| Format: | Book Chapter |
| Language: | Inglés |
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AKADEMIYA2063
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/142056 |
| _version_ | 1855515593899245568 |
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| author | Ly, Racine Dia, Khadim Diallo, Mariam A. |
| author_browse | Dia, Khadim Diallo, Mariam A. Ly, Racine |
| author_facet | Ly, Racine Dia, Khadim Diallo, Mariam A. |
| author_sort | Ly, Racine |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The world is experiencing an unprecedented health crisis during the spread of COVID-19 (SARS-CoV-2, or Severe Acute Respiratory Syndrome Coronavirus 2). While the pandemic appears to be less severe on the African continent than in other geographic regions (Global Change Data Lab 2021), its economic impact is significantly more pronounced. COVID-19 is upending livelihoods, damaging business and government balance sheets, and threatening to reverse development gains and growth prospects for years to come in Africa south of the Sahara (IFC 2020). The World Bank forecasts that Africa south of the Sahara will go into recession in 2020 and that COVID-19 will cost the region between $37 billion and $79 billion in output losses in 2020 alone. The informal sector, a significant source of income and employment, will be the hardest hit. |
| format | Book Chapter |
| id | CGSpace142056 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | AKADEMIYA2063 |
| publisherStr | AKADEMIYA2063 |
| record_format | dspace |
| spelling | CGSpace1420562025-11-06T03:52:53Z Remote sensing and machine learning for food crop production data in Africa post-COVID-19 Ly, Racine Dia, Khadim Diallo, Mariam A. data food crops remote sensing covid-19 machine learning crop production resilience food systems The world is experiencing an unprecedented health crisis during the spread of COVID-19 (SARS-CoV-2, or Severe Acute Respiratory Syndrome Coronavirus 2). While the pandemic appears to be less severe on the African continent than in other geographic regions (Global Change Data Lab 2021), its economic impact is significantly more pronounced. COVID-19 is upending livelihoods, damaging business and government balance sheets, and threatening to reverse development gains and growth prospects for years to come in Africa south of the Sahara (IFC 2020). The World Bank forecasts that Africa south of the Sahara will go into recession in 2020 and that COVID-19 will cost the region between $37 billion and $79 billion in output losses in 2020 alone. The informal sector, a significant source of income and employment, will be the hardest hit. 2021-11-16 2024-05-22T12:09:53Z 2024-05-22T12:09:53Z Book Chapter https://hdl.handle.net/10568/142056 en https://doi.org/10.54067/9781737916413 Open Access application/pdf AKADEMIYA2063 International Food Policy Research Institute Ly, Racine; Dia, Khadim; and Diallo, Mariam A. 2021. Remote sensing and machine learning for food crop production data in Africa post-COVID-19. In 2021 Annual Trends and Outlook Report: Building Resilient African Food Systems After COVID-19, eds. John M. Ulimwengu, Mark A. Constas, and Éliane Ubalijoro. Chapter 9, Pp. 128-154. Kigali, Rwanda; and Washington, DC: AKADEMIYA2063; and International Food Policy Research Institute (IFPRI). https://hdl.handle.net/10568/142056 |
| spellingShingle | data food crops remote sensing covid-19 machine learning crop production resilience food systems Ly, Racine Dia, Khadim Diallo, Mariam A. Remote sensing and machine learning for food crop production data in Africa post-COVID-19 |
| title | Remote sensing and machine learning for food crop production data in Africa post-COVID-19 |
| title_full | Remote sensing and machine learning for food crop production data in Africa post-COVID-19 |
| title_fullStr | Remote sensing and machine learning for food crop production data in Africa post-COVID-19 |
| title_full_unstemmed | Remote sensing and machine learning for food crop production data in Africa post-COVID-19 |
| title_short | Remote sensing and machine learning for food crop production data in Africa post-COVID-19 |
| title_sort | remote sensing and machine learning for food crop production data in africa post covid 19 |
| topic | data food crops remote sensing covid-19 machine learning crop production resilience food systems |
| url | https://hdl.handle.net/10568/142056 |
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