Mapping global cropping system: Challenges, opportunities and future perspectives
Spatially explicit global cropping system data products, which provide critical information on harvested areas, crop yields, other management variables, are imperative to tackle current grand challenges such as global food security and climate change. These cropping system datasets are also very use...
| Autores principales: | , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/141200 |
| _version_ | 1855537497960873984 |
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| author | You, Liangzhi Sun, Zhanli |
| author_browse | Sun, Zhanli You, Liangzhi |
| author_facet | You, Liangzhi Sun, Zhanli |
| author_sort | You, Liangzhi |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Spatially explicit global cropping system data products, which provide critical information on harvested areas, crop yields, other management variables, are imperative to tackle current grand challenges such as global food security and climate change. These cropping system datasets are also very useful for researchers as they can support various scientific analyses in research projects. Yet, effectively searching, navigating, and fully understanding various global datasets can be a daunting task for researchers and policy analysts. In this review, we first compare a few selected global data products, which use crop census and statistical data as the main data source, and identify key problems and challenges of the global crop mapping such as data accuracy and consistency. We then pointed out the future perspectives and directions in further improving the global cropping data products. Collective mechanisms and efforts with the support of open-access data hosting platforms, standard protocols, and consistent financial support are necessary to produce high-quality datasets for researchers, practitioners, and policymakers. Moreover, machine learning and data fusion approaches can also be further explored in future mapping exercises. |
| format | Journal Article |
| id | CGSpace141200 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1412002025-10-26T13:01:17Z Mapping global cropping system: Challenges, opportunities and future perspectives You, Liangzhi Sun, Zhanli models maps cropping patterns cropping systems remote sensing food security crop modelling Spatially explicit global cropping system data products, which provide critical information on harvested areas, crop yields, other management variables, are imperative to tackle current grand challenges such as global food security and climate change. These cropping system datasets are also very useful for researchers as they can support various scientific analyses in research projects. Yet, effectively searching, navigating, and fully understanding various global datasets can be a daunting task for researchers and policy analysts. In this review, we first compare a few selected global data products, which use crop census and statistical data as the main data source, and identify key problems and challenges of the global crop mapping such as data accuracy and consistency. We then pointed out the future perspectives and directions in further improving the global cropping data products. Collective mechanisms and efforts with the support of open-access data hosting platforms, standard protocols, and consistent financial support are necessary to produce high-quality datasets for researchers, practitioners, and policymakers. Moreover, machine learning and data fusion approaches can also be further explored in future mapping exercises. 2022-03 2024-04-12T13:37:27Z 2024-04-12T13:37:27Z Journal Article https://hdl.handle.net/10568/141200 en Open Access Elsevier You, Liangzhi; and Sun, Zhanli. 2022. Mapping global cropping system: Challenges, opportunities and future perspectives. Crop and Environment 1(1): 68-73. https://doi.org/10.1016/j.crope.2022.03.006 |
| spellingShingle | models maps cropping patterns cropping systems remote sensing food security crop modelling You, Liangzhi Sun, Zhanli Mapping global cropping system: Challenges, opportunities and future perspectives |
| title | Mapping global cropping system: Challenges, opportunities and future perspectives |
| title_full | Mapping global cropping system: Challenges, opportunities and future perspectives |
| title_fullStr | Mapping global cropping system: Challenges, opportunities and future perspectives |
| title_full_unstemmed | Mapping global cropping system: Challenges, opportunities and future perspectives |
| title_short | Mapping global cropping system: Challenges, opportunities and future perspectives |
| title_sort | mapping global cropping system challenges opportunities and future perspectives |
| topic | models maps cropping patterns cropping systems remote sensing food security crop modelling |
| url | https://hdl.handle.net/10568/141200 |
| work_keys_str_mv | AT youliangzhi mappingglobalcroppingsystemchallengesopportunitiesandfutureperspectives AT sunzhanli mappingglobalcroppingsystemchallengesopportunitiesandfutureperspectives |