Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh
Climate change is and will continue to have significant implications for agricultural systems. While adaptation to climate change should be the priority for smallholder production systems, adoption of cost-effective mitigation options in agriculture not only contributes to food security but also red...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/171351 |
| _version_ | 1855527247103918080 |
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| author | Sapkota, Tek B. Khanam, Fahmida Mathivanan, Gokul Prasad Vetter, Sylvia Hussain, Sk. Ghulam |
| author_browse | Hussain, Sk. Ghulam Khanam, Fahmida Mathivanan, Gokul Prasad Sapkota, Tek B. Vetter, Sylvia |
| author_facet | Sapkota, Tek B. Khanam, Fahmida Mathivanan, Gokul Prasad Vetter, Sylvia Hussain, Sk. Ghulam |
| author_sort | Sapkota, Tek B. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Climate change is and will continue to have significant implications for agricultural systems. While adaptation to climate change should be the priority for smallholder production systems, adoption of cost-effective mitigation options in agriculture not only contributes to food security but also reduces the extent of climate change and future adaptation needs. Utilizing management data from 16,413 and 12,548 crop and livestock farmers and associated soil and climatic data, we estimated GHG emissions generated from crop and livestock production using crop and livestock models, respectively. Mitigation measures in crop and livestock production, their mitigation potential and cost/benefit of adoption were then obtained from literature review, stakeholder consultations and expert opinion. We applied the identified mitigation measures to a realistic scale of adoption scenario in the short- (2030) and long-term (2050). Our results were then validated through stakeholders consultations. Here, we present identified mitigation options, their mitigation potentials and cost or benefit of adoption in the form of Marginal Abatement Cost Curves (MACC). Based on our analysis, total GHG emissions from agricultural sector in Bangladesh for the year 2014–15 is 76.79 million tonne (Mt) carbon-dioxide equivalent (CO2e). Business-as-usual GHG emissions from the agricultural sector in Bangladesh are approximately 86.87 and 100.44 Mt CO2e year−1 by 2030 and 2050, respectively. Adoption of climate-smart crop and livestock management options to reduce emissions considering a realistic adoption scenario would offer GHG mitigation opportunities of 9.51 and 14.21 Mt CO2e year−1 by 2030 and 2050, respectively. Of this mitigation potential, 70–75% can be achieved through cost-saving options that could benefit smallholder farmers. Realization of this potential mitigation benefit, however, largely depends on the degree to which supportive policies and measures can encourage farmers' adoption of the identified climate smart agricultural techniques. Therefore, government should focus on facilitating uptake of these options through appropriate policy interventions, incentive mechanisms and strengthening agricultural extension programs. |
| format | Journal Article |
| id | CGSpace171351 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1713512025-02-19T14:36:19Z Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh Sapkota, Tek B. Khanam, Fahmida Mathivanan, Gokul Prasad Vetter, Sylvia Hussain, Sk. Ghulam greenhouse gas emissions climate change mitigation crops smallholders livestock farmers climate-smart agriculture data big data Climate change is and will continue to have significant implications for agricultural systems. While adaptation to climate change should be the priority for smallholder production systems, adoption of cost-effective mitigation options in agriculture not only contributes to food security but also reduces the extent of climate change and future adaptation needs. Utilizing management data from 16,413 and 12,548 crop and livestock farmers and associated soil and climatic data, we estimated GHG emissions generated from crop and livestock production using crop and livestock models, respectively. Mitigation measures in crop and livestock production, their mitigation potential and cost/benefit of adoption were then obtained from literature review, stakeholder consultations and expert opinion. We applied the identified mitigation measures to a realistic scale of adoption scenario in the short- (2030) and long-term (2050). Our results were then validated through stakeholders consultations. Here, we present identified mitigation options, their mitigation potentials and cost or benefit of adoption in the form of Marginal Abatement Cost Curves (MACC). Based on our analysis, total GHG emissions from agricultural sector in Bangladesh for the year 2014–15 is 76.79 million tonne (Mt) carbon-dioxide equivalent (CO2e). Business-as-usual GHG emissions from the agricultural sector in Bangladesh are approximately 86.87 and 100.44 Mt CO2e year−1 by 2030 and 2050, respectively. Adoption of climate-smart crop and livestock management options to reduce emissions considering a realistic adoption scenario would offer GHG mitigation opportunities of 9.51 and 14.21 Mt CO2e year−1 by 2030 and 2050, respectively. Of this mitigation potential, 70–75% can be achieved through cost-saving options that could benefit smallholder farmers. Realization of this potential mitigation benefit, however, largely depends on the degree to which supportive policies and measures can encourage farmers' adoption of the identified climate smart agricultural techniques. Therefore, government should focus on facilitating uptake of these options through appropriate policy interventions, incentive mechanisms and strengthening agricultural extension programs. 2021-09 2025-01-29T12:58:03Z 2025-01-29T12:58:03Z Journal Article https://hdl.handle.net/10568/171351 en Open Access Elsevier Sapkota, Tek B.; Khanam, Fahmida; Mathivanan, Gokul Prasad; Vetter, Sylvia; Hussain, Sk. Ghulam; et al. 2021. Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh. Science of The Total Environment 786(September 2021): 147344. https://doi.org/10.1016/j.scitotenv.2021.147344 |
| spellingShingle | greenhouse gas emissions climate change mitigation crops smallholders livestock farmers climate-smart agriculture data big data Sapkota, Tek B. Khanam, Fahmida Mathivanan, Gokul Prasad Vetter, Sylvia Hussain, Sk. Ghulam Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh |
| title | Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh |
| title_full | Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh |
| title_fullStr | Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh |
| title_full_unstemmed | Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh |
| title_short | Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh |
| title_sort | quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across bangladesh |
| topic | greenhouse gas emissions climate change mitigation crops smallholders livestock farmers climate-smart agriculture data big data |
| url | https://hdl.handle.net/10568/171351 |
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