Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh

Tek B. Sapkota*, Fahmida Khanam, Gokul Prasad Mathivanan, Sylvia Vetter, Sk Ghulam Hussain, Anne Laure Pilat, Sumona Shahrin, Md Khaled Hossain, Nathu Ram Sarker, Timothy J. Krupnik

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
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Abstract

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.

Original languageEnglish
Article number147344
Number of pages16
JournalScience of the Total Environment
Volume786
Early online date29 Apr 2021
DOIs
Publication statusPublished - 10 Sept 2021

Bibliographical note

Funding Information:
The International Maize and Wheat Improvement Center (CIMMYT) carried out this work with support of the CGIAR research program on Climate Change, Agriculture and Food Security (CCAFS) and the Climate Services for Resilient Development (CSRD; https://ccafs.cgiar.org/research/projects/climate-services-resilient-development-south-asia ) for South Asia project supported by USAID . This work was also supported by the USAID and Bill and Melinda Gates Foundation (BMGF) supported Cereal Systems Initiative for South Asia (CSISA; https://csisa.org ). CCAFS' work is supported by CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors . The views expressed in this paper cannot be taken to reflect the official opinions of CCAFS, USAID, or BMGF, and shall not be used for advertising. We sincerely acknowledge the input and support provided by various stakeholders in Bangladesh during stakeholder meetings. We are thankful to Robel Takele and Sanjay Pothireddy for graphics assistance.

Keywords

  • Adaptation
  • Big data
  • Climate change
  • Climate smart agriculture
  • Greenhouse gas
  • Mitigation

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