Predicting yields using crop simulation models and gridded weather data in India
We find that gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns, creating large uncertainties in estimated crop yield responses to growing season weather. This highlights the need to consider input data uncertainty in applyin...
| Autor principal: | |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
2019
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| Acceso en línea: | https://hdl.handle.net/10568/122949 |
| _version_ | 1855518335110742016 |
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| author | CGIAR Platform for Big Data in Agriculture |
| author_browse | CGIAR Platform for Big Data in Agriculture |
| author_facet | CGIAR Platform for Big Data in Agriculture |
| author_sort | CGIAR Platform for Big Data in Agriculture |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | We find that gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns, creating large uncertainties in estimated crop yield responses to growing season weather. This highlights the need to consider input data uncertainty in applying crop simulation models with gridded weather data. |
| format | Informe técnico |
| id | CGSpace122949 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| record_format | dspace |
| spelling | CGSpace1229492023-03-14T11:47:14Z Predicting yields using crop simulation models and gridded weather data in India CGIAR Platform for Big Data in Agriculture models yields crop yield development simulation models rural development data precipitation temperature simulation systems weather uncertainty agrifood systems weather data We find that gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns, creating large uncertainties in estimated crop yield responses to growing season weather. This highlights the need to consider input data uncertainty in applying crop simulation models with gridded weather data. 2019-12-31 2022-10-06T14:14:28Z 2022-10-06T14:14:28Z Report https://hdl.handle.net/10568/122949 en Open Access application/pdf CGIAR Platform for Big Data in Agriculture. 2019. Predicting yields using crop simulation models and gridded weather data in India. Reported in Platform for Big Data in Agriculture Annual Report 2019. Innovations. |
| spellingShingle | models yields crop yield development simulation models rural development data precipitation temperature simulation systems weather uncertainty agrifood systems weather data CGIAR Platform for Big Data in Agriculture Predicting yields using crop simulation models and gridded weather data in India |
| title | Predicting yields using crop simulation models and gridded weather data in India |
| title_full | Predicting yields using crop simulation models and gridded weather data in India |
| title_fullStr | Predicting yields using crop simulation models and gridded weather data in India |
| title_full_unstemmed | Predicting yields using crop simulation models and gridded weather data in India |
| title_short | Predicting yields using crop simulation models and gridded weather data in India |
| title_sort | predicting yields using crop simulation models and gridded weather data in india |
| topic | models yields crop yield development simulation models rural development data precipitation temperature simulation systems weather uncertainty agrifood systems weather data |
| url | https://hdl.handle.net/10568/122949 |
| work_keys_str_mv | AT cgiarplatformforbigdatainagriculture predictingyieldsusingcropsimulationmodelsandgriddedweatherdatainindia |