Preprints
https://doi.org/10.5194/bg-2023-165
https://doi.org/10.5194/bg-2023-165
26 Sep 2023
 | 26 Sep 2023
Status: this preprint was under review for the journal BG. A final paper is not foreseen.

Carbon footprint and greenhouse gas emissions from rice based agricultural systems calculated with a co-designed carbon footprint calculation tool

Mohammad Mofizur Rahman Jahangir, Eduardo Aguilera, Jannatul Ferdous, Farah Mahjabin, Abdullah Al Asif, Hassan Ahmad, Maximilian Bauer, Alberto Sanz Cobeña, Christoph Müller, and Mohammad Zaman

Abstract. There are many cropping systems followed in Floodplain soils for enhancing cropping intensity for increasing crop production, but greenhouse gas (GHG) emissions balances of agricultural systems are rarely reported. To estimate the carbon (C) footprints of agricultural products a co-designed C footprint calculation tool with a life cycle assessment approach was used in major cropping systems in Bangladesh: rice-rice-rice (R-R-R/boro-aus-aman), rice-fallow-rice (R-F-R/boro-fallow-aman), maize-fallow-rice (M-F-R), wheat-mungbean-rice (W-M-R), and potato-rice-fallow (P-R-F). GHG emissions were estimated using the tool along with the field measurements. It was found that rice-based cropping pattern with dryland crops had higher nitrous oxide (N2O) emissions (3.98 in maize, 3.89 in potato and 0.72 kg N2O-N ha−1 in mungbean) than sole rice-based (0.73 in boro, 0.57 in aus and 1.94 kg N2O-N ha−1 in aman) cropping systems but methane (CH4) emissions were higher in sole rice-based patterns than dryland crops. Methane contributed to about 50–80 % of total GHG emissions from rice cultivation due to waterlogging conditions throughout the season. In R-R-R and R-F-R cropping patterns, the only ones including boro rice, had the highest total C footprint with 26.3 and 19.5 Mg CO2e ha−1, respectively while the P-F-R and M-F-R had the lowest C footprint with 13 Mg CO2e ha−1. Changes in soil organic C generally had a minor influence on C footprints in the studied systems, and only boro and aus from R-F-R and R-R-R patterns were relatively more suitable for reducing C footprint as they sequestered C in soil. Measured CH4 and N2O emissions agreed well with IPCC tier 1 estimates, but they were only available for boro, maize and wheat so further study is required for validation and suggesting suitable GHG mitigation strategies from agricultural fields.

This preprint has been withdrawn.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Mohammad Mofizur Rahman Jahangir, Eduardo Aguilera, Jannatul Ferdous, Farah Mahjabin, Abdullah Al Asif, Hassan Ahmad, Maximilian Bauer, Alberto Sanz Cobeña, Christoph Müller, and Mohammad Zaman

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on bg-2023-165', Reiner Wassmann, 15 Nov 2023
  • RC1: 'Comment on bg-2023-165', Anonymous Referee #1, 04 Jan 2024

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on bg-2023-165', Reiner Wassmann, 15 Nov 2023
  • RC1: 'Comment on bg-2023-165', Anonymous Referee #1, 04 Jan 2024
Mohammad Mofizur Rahman Jahangir, Eduardo Aguilera, Jannatul Ferdous, Farah Mahjabin, Abdullah Al Asif, Hassan Ahmad, Maximilian Bauer, Alberto Sanz Cobeña, Christoph Müller, and Mohammad Zaman
Mohammad Mofizur Rahman Jahangir, Eduardo Aguilera, Jannatul Ferdous, Farah Mahjabin, Abdullah Al Asif, Hassan Ahmad, Maximilian Bauer, Alberto Sanz Cobeña, Christoph Müller, and Mohammad Zaman

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This preprint has been withdrawn.

Short summary
Greenhouse gas from major agricultural systems from the Indo Gangetic Plane has been estimated and compared with the measured data which will help develop regional GHG inventories and the global GHG budget. These data will reduce the uncertainty in global GHG budget which had a large uncertainty due to lack of regional data.
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