Combining seasonal forecasts and artificial intelligence to improve agronomy at scale
We have used the approach developed by Dorado et al. (2019) (see link provided) combined with seasonal forecasts (Esquivel et al., 2018) to produce a proof of concept where we combine seasonal forecasts and artificial intelligence prediction models for optimizing agronomic practices.
| Autor principal: | |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
2019
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| Acceso en línea: | https://hdl.handle.net/10568/123034 |
| _version_ | 1855518531413606400 |
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| author | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_browse | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_facet | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_sort | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | We have used the approach developed by Dorado et al. (2019) (see link provided) combined with seasonal forecasts (Esquivel et al., 2018) to produce a proof of concept where we combine seasonal forecasts and artificial intelligence prediction models for optimizing agronomic practices. |
| format | Informe técnico |
| id | CGSpace123034 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| record_format | dspace |
| spelling | CGSpace1230342023-03-14T11:47:38Z Combining seasonal forecasts and artificial intelligence to improve agronomy at scale CGIAR Research Program on Climate Change, Agriculture and Food Security models development rural development agronomy systems agronomic practices agrifood systems artificial intelligence scale prediction We have used the approach developed by Dorado et al. (2019) (see link provided) combined with seasonal forecasts (Esquivel et al., 2018) to produce a proof of concept where we combine seasonal forecasts and artificial intelligence prediction models for optimizing agronomic practices. 2019-12-31 2022-10-06T14:18:07Z 2022-10-06T14:18:07Z Report https://hdl.handle.net/10568/123034 en Open Access application/pdf CGIAR Research Program on Climate Change, Agriculture and Food Security. 2019. Combining seasonal forecasts and artificial intelligence to improve agronomy at scale. Reported in Climate Change, Agriculture and Food Security Annual Report 2019. Innovations. |
| spellingShingle | models development rural development agronomy systems agronomic practices agrifood systems artificial intelligence scale prediction CGIAR Research Program on Climate Change, Agriculture and Food Security Combining seasonal forecasts and artificial intelligence to improve agronomy at scale |
| title | Combining seasonal forecasts and artificial intelligence to improve agronomy at scale |
| title_full | Combining seasonal forecasts and artificial intelligence to improve agronomy at scale |
| title_fullStr | Combining seasonal forecasts and artificial intelligence to improve agronomy at scale |
| title_full_unstemmed | Combining seasonal forecasts and artificial intelligence to improve agronomy at scale |
| title_short | Combining seasonal forecasts and artificial intelligence to improve agronomy at scale |
| title_sort | combining seasonal forecasts and artificial intelligence to improve agronomy at scale |
| topic | models development rural development agronomy systems agronomic practices agrifood systems artificial intelligence scale prediction |
| url | https://hdl.handle.net/10568/123034 |
| work_keys_str_mv | AT cgiarresearchprogramonclimatechangeagricultureandfoodsecurity combiningseasonalforecastsandartificialintelligencetoimproveagronomyatscale |