Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras
The report aims to address the needs of those involved in the environmental management of coffee production across Ocotepeque. We quantify the impact a series of drivers had on deforestation trends in the department, thus isolating coffee driven deforestation. Based on these key results, we identify...
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| Formato: | Informe técnico |
| Lenguaje: | Inglés Español |
| Publicado: |
CGIAR Research Program on Climate Change, Agriculture and Food Security
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/111378 |
| _version_ | 1855536824054710272 |
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| author | Alliance of Bioversity International and CIAT |
| author_browse | Alliance of Bioversity International and CIAT |
| author_facet | Alliance of Bioversity International and CIAT |
| author_sort | Alliance of Bioversity International and CIAT |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The report aims to address the needs of those involved in the environmental management of coffee production across Ocotepeque. We quantify the impact a series of drivers had on deforestation trends in the department, thus isolating coffee driven deforestation. Based on these key results, we identify forests that are under current and future risk of being replaced by coffee. Finally, we identify areas of concern for the coffee sector as well as opportunities arising from climate change. |
| format | Informe técnico |
| id | CGSpace111378 |
| institution | CGIAR Consortium |
| language | Inglés Español |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| publisherStr | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| record_format | dspace |
| spelling | CGSpace1113782024-09-09T10:04:48Z Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras Terra-i+ Aprendizaje automatizado para gestionar los impactos de la producción de café en Ocotepeque, Honduras Alliance of Bioversity International and CIAT food security climate change agriculture machine learning The report aims to address the needs of those involved in the environmental management of coffee production across Ocotepeque. We quantify the impact a series of drivers had on deforestation trends in the department, thus isolating coffee driven deforestation. Based on these key results, we identify forests that are under current and future risk of being replaced by coffee. Finally, we identify areas of concern for the coffee sector as well as opportunities arising from climate change. 2020-09-01 2021-02-17T14:06:23Z 2021-02-17T14:06:23Z Report https://hdl.handle.net/10568/111378 en es Open Access application/pdf application/pdf CGIAR Research Program on Climate Change, Agriculture and Food Security Alliance of Bioversity International and CIAT. 2020. Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). |
| spellingShingle | food security climate change agriculture machine learning Alliance of Bioversity International and CIAT Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras |
| title | Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras |
| title_full | Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras |
| title_fullStr | Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras |
| title_full_unstemmed | Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras |
| title_short | Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras |
| title_sort | terra i using machine learning to manage impacts of coffee production in ocotepeque honduras |
| topic | food security climate change agriculture machine learning |
| url | https://hdl.handle.net/10568/111378 |
| work_keys_str_mv | AT allianceofbioversityinternationalandciat terraiusingmachinelearningtomanageimpactsofcoffeeproductioninocotepequehonduras AT allianceofbioversityinternationalandciat terraiaprendizajeautomatizadoparagestionarlosimpactosdelaproducciondecafeenocotepequehonduras |