Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh
Although very preliminary, to our knowledge, this is the first attempt at ensemble machine learning applied to agricultural research and the analysis of 'big data' from farms. In addition, methods have been developed in R to graphically explore the relationships between drivers and predicted outcome...
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
2019
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| Acceso en línea: | https://hdl.handle.net/10568/123019 |
| _version_ | 1855519179263705088 |
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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 | Although very preliminary, to our knowledge, this is the first attempt at ensemble machine learning applied to agricultural research and the analysis of 'big data' from farms. In addition, methods have been developed in R to graphically explore the relationships between drivers and predicted outcomes using partial dependency plots. |
| format | Informe técnico |
| id | CGSpace123019 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| record_format | dspace |
| spelling | CGSpace1230192023-03-14T11:48:21Z Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh CGIAR Research Program on Climate Change, Agriculture and Food Security research rice agricultural research development rural development methods data wheat analysis learning greenhouse gas emissions farms systems knowledge agrifood systems machine learning gas emissions prediction plots Although very preliminary, to our knowledge, this is the first attempt at ensemble machine learning applied to agricultural research and the analysis of 'big data' from farms. In addition, methods have been developed in R to graphically explore the relationships between drivers and predicted outcomes using partial dependency plots. 2019-12-31 2022-10-06T14:17:25Z 2022-10-06T14:17:25Z Report https://hdl.handle.net/10568/123019 en Open Access application/pdf CGIAR Research Program on Climate Change, Agriculture and Food Security. 2019. Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh. Reported in Climate Change, Agriculture and Food Security Annual Report 2019. Innovations. |
| spellingShingle | research rice agricultural research development rural development methods data wheat analysis learning greenhouse gas emissions farms systems knowledge agrifood systems machine learning gas emissions prediction plots CGIAR Research Program on Climate Change, Agriculture and Food Security Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh |
| title | Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh |
| title_full | Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh |
| title_fullStr | Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh |
| title_full_unstemmed | Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh |
| title_short | Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh |
| title_sort | ensemble machine learning prediction of drivers affecting rice and wheat yield greenhouse gas emissions and yield scaled emissions in bangladesh |
| topic | research rice agricultural research development rural development methods data wheat analysis learning greenhouse gas emissions farms systems knowledge agrifood systems machine learning gas emissions prediction plots |
| url | https://hdl.handle.net/10568/123019 |
| work_keys_str_mv | AT cgiarresearchprogramonclimatechangeagricultureandfoodsecurity ensemblemachinelearningpredictionofdriversaffectingriceandwheatyieldgreenhousegasemissionsandyieldscaledemissionsinbangladesh |