Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems?
Systematic tools and approaches for measuring climate change adaptation at multiple scales of spatial resolution are lacking, limiting measurement of progress toward the adaptation goals of the Paris Agreement. In particular, there is a lack of adaptation measurement or tracking systems that are coh...
| Main Authors: | , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Frontiers Media
2022
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| Online Access: | https://hdl.handle.net/10568/126091 |
| _version_ | 1855536160039763968 |
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| author | Ferguson, Amy Murray, Catherine Tessema, Yared Mesfin McKeown, Peter C. Reymondin, Louis Loboguerrero Rodriguez, Ana María Talsma, Tiffany Allen, Brenden Jarvis, Andy Golden, Aaron Spillane, Charles |
| author_browse | Allen, Brenden Ferguson, Amy Golden, Aaron Jarvis, Andy Loboguerrero Rodriguez, Ana María McKeown, Peter C. Murray, Catherine Reymondin, Louis Spillane, Charles Talsma, Tiffany Tessema, Yared Mesfin |
| author_facet | Ferguson, Amy Murray, Catherine Tessema, Yared Mesfin McKeown, Peter C. Reymondin, Louis Loboguerrero Rodriguez, Ana María Talsma, Tiffany Allen, Brenden Jarvis, Andy Golden, Aaron Spillane, Charles |
| author_sort | Ferguson, Amy |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Systematic tools and approaches for measuring climate change adaptation at multiple scales of spatial resolution are lacking, limiting measurement of progress toward the adaptation goals of the Paris Agreement. In particular, there is a lack of adaptation measurement or tracking systems that are coherent (measuring adaptation itself), comparable (allowing comparisons across geographies and systems), and comprehensive (are supported by the necessary data). In addition, most adaptation measurement efforts lack an appropriate counterfactual baseline to assess the effectiveness of adaptation-related interventions. To address this, we are developing a “Biomass Climate Adaptation Index” (Biomass CAI) for agricultural systems, where climate adaptation progress across multiple scales can be measured by satellite remote sensing. The Biomass CAI can be used at global, national, landscape and farm-level to remotely monitor agri-biomass productivity associated with adaptation interventions, and to facilitate more tailored “precision adaptation”. The Biomass CAI places focus on decision-support for end-users to ensure that the most effective climate change adaptation investments and interventions can be made in agricultural and food systems. |
| format | Journal Article |
| id | CGSpace126091 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1260912025-12-08T10:29:22Z Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? Ferguson, Amy Murray, Catherine Tessema, Yared Mesfin McKeown, Peter C. Reymondin, Louis Loboguerrero Rodriguez, Ana María Talsma, Tiffany Allen, Brenden Jarvis, Andy Golden, Aaron Spillane, Charles climate change agriculture resilience adaptation remote sensing artificial intelligence machine learning data farming systems Systematic tools and approaches for measuring climate change adaptation at multiple scales of spatial resolution are lacking, limiting measurement of progress toward the adaptation goals of the Paris Agreement. In particular, there is a lack of adaptation measurement or tracking systems that are coherent (measuring adaptation itself), comparable (allowing comparisons across geographies and systems), and comprehensive (are supported by the necessary data). In addition, most adaptation measurement efforts lack an appropriate counterfactual baseline to assess the effectiveness of adaptation-related interventions. To address this, we are developing a “Biomass Climate Adaptation Index” (Biomass CAI) for agricultural systems, where climate adaptation progress across multiple scales can be measured by satellite remote sensing. The Biomass CAI can be used at global, national, landscape and farm-level to remotely monitor agri-biomass productivity associated with adaptation interventions, and to facilitate more tailored “precision adaptation”. The Biomass CAI places focus on decision-support for end-users to ensure that the most effective climate change adaptation investments and interventions can be made in agricultural and food systems. 2022-11-30 2022-12-19T15:28:39Z 2022-12-19T15:28:39Z Journal Article https://hdl.handle.net/10568/126091 en Open Access application/pdf Frontiers Media Ferguson, Amy; Murray, Catherine; Tessema, Yared Mesfin; McKeown, Peter C.; Reymondin, Louis; Loboguerrero, Ana Maria; Talsma, Tiffany; Allen, Brenden; Jarvis, Andy; Golden, Aaron; Spillane, Charles. 2022. Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? Frontiers in Climate 4:938975. https://doi.org/10.3389/fclim.2022.938975 |
| spellingShingle | climate change agriculture resilience adaptation remote sensing artificial intelligence machine learning data farming systems Ferguson, Amy Murray, Catherine Tessema, Yared Mesfin McKeown, Peter C. Reymondin, Louis Loboguerrero Rodriguez, Ana María Talsma, Tiffany Allen, Brenden Jarvis, Andy Golden, Aaron Spillane, Charles Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? |
| title | Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? |
| title_full | Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? |
| title_fullStr | Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? |
| title_full_unstemmed | Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? |
| title_short | Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? |
| title_sort | can remote sensing enable a biomass climate adaptation index for agricultural systems |
| topic | climate change agriculture resilience adaptation remote sensing artificial intelligence machine learning data farming systems |
| url | https://hdl.handle.net/10568/126091 |
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