the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Sep 2023
Carbon footprint and greenhouse gas emissions from rice based agricultural systems calculated with a co-designed carbon footprint calculation tool
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.
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Status: closed (peer review stopped)
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CC1: 'Comment on bg-2023-165', Reiner Wassmann, 15 Nov 2023
· The manuscript addresses an important topic that is suitable for the mandate of the selected journal. The combination of (i) field measurement, (ii) software development and (iii) intercomparison of the results obtained with either approach would – in principle – also be an appropriate approach for such a study.
· The manuscript lacks clarity on the supplementary nature of these components of the study. The title does not even mention the field measurements. Although the authors do not even bother to mention the observation period in the method section, it can be assumed that this covers – at best -- only one annual cycle. By the same token, the method section does not clarify the number of cropping systems that were measured. Derived from Figure 5 and 6, it can be deduced that only 2-3 cropping systems were measured which makes the database insufficient for an international publication.
· As for the new tool, this is neither described sufficiently nor made available (see below), the statements on the software remain at the level of claims without any evidence. It is understood that such a new tool should not be made public at this point before the publication was released, but the manuscript should have at least shown the avenue of how this can be done. And how should a reviewer assess software that is not even provided? There are ways to make software available to a reviewer only, e.g. through password-protected google drive.
· Although the term “carbon footprint” is inconsistently used in the literature, it should be noted that it is clearly defined in ISO 14067 as “the greenhouse gas emissions produced during each stage in the life cycle of a product”. However, this study only addresses the field emissions without consideration of the harvest and post-harvest stages of the value chain. In turn, this term is misleading in this study. Alternatively, the terms “emission intensity” or “yield-scaled emission” provide a more accurate connotation of what is intended here.
· The text sections referring to the IPCC approach is full of errors starting with the acronym (“Interdisciplinary (sic!) Panel for (sic!) Climate Change”). The statement “Field GHG emissions were estimated through the IPCC tier 1 method, which was complemented with field measurements” is contrasted by the fact that Fig. 5 suddenly shows a 3rd approach which is not explained. The distinction between “IPCC tier 1” and “IPCC default” is neither explained nor plausible (IPCC tier 1 should inherently be based on a global default).
· Given the geographic focus, it is hard to understand why this study does not cite the official GHG calculations by the Bangladesh government in the most recent 3rd National Communications submitted to the UNFCCC (https://unfccc.int/documents/192278).
· The supplement is obsolete because it contains only one figure that can easily be integrated into the manuscript. The citations are incomplete because important publications on GHG emissions from rice in Bangladesh are missing (e.g. Sapkota et al., 2021 Gaihre et al. 2015, 2017)
· While it is true that Bangladesh has 3 rice seasons within the entire country, it is rather doubtful that all 3 seasons will be implemented on one field within a given annual cycle. The authors should provide a graphical cropping calendar as shown in https://icrea.agr.nagoya-u.ac.jp/jpn/journal/Vol14_20-29-Review-Shelley.pdf to illustrate the assumed succession of these seasons.
Citation: https://doi.org/10.5194/bg-2023-165-CC1 -
RC1: 'Comment on bg-2023-165', Anonymous Referee #1, 04 Jan 2024
This paper describes the development and application of an Excel-based tool to calculate the greenhouse gas and carbon footprint of rice-based agricultural systems in Bangladesh. It uses well-known approaches (e.g., IPCC default values and Tier 1/2 methodology) to calculate most of the major components of such footprint calculations. Most of the calculations were based either on model results (e.g., SOC dynamics), or on default values (e.g., IPCC). The authors had collected very limited data of their own at unspecified field sites (with the exception of the soil science field laboratory mentioned in the ms), i.e., GHG measurements in three of twelve crops belonging to three of five cropping systems, accompanied by soil mineral N analyses four days after the second split application of urea for soil samples taken near the GHG chamber sites, and aboveground biomass data at the time of harvest. The selection of the field sites and even more so of the measured variables and the crops and systems in which they were measured appears very arbitrary. Furthermore, it remains completely unclear whether the measured data were included in the calculations, and if so, how and on what basis the choice between measured and calculated values was made, e.g. for CH4 and N2O emissions from the few crops in which they were measured.
Although the topic is highly relevant and falls within the field of Biogeosciences, I can only recommend rejecting the paper. First of all, the paper does not present a new approach or a further development of the methodology. All the components mentioned are already known and there are already tools to perform such calculations. Furthermore, there are very many uncertainties and shortcomings in both the methodology and the presentation of the data in the paper, which make the results appear very unreliable (see detailed comments in the annotated manuscript). Finally, the manuscript is partly written in poor English, so that some sentences are incomprehensible.
Status: closed (peer review stopped)
-
CC1: 'Comment on bg-2023-165', Reiner Wassmann, 15 Nov 2023
· The manuscript addresses an important topic that is suitable for the mandate of the selected journal. The combination of (i) field measurement, (ii) software development and (iii) intercomparison of the results obtained with either approach would – in principle – also be an appropriate approach for such a study.
· The manuscript lacks clarity on the supplementary nature of these components of the study. The title does not even mention the field measurements. Although the authors do not even bother to mention the observation period in the method section, it can be assumed that this covers – at best -- only one annual cycle. By the same token, the method section does not clarify the number of cropping systems that were measured. Derived from Figure 5 and 6, it can be deduced that only 2-3 cropping systems were measured which makes the database insufficient for an international publication.
· As for the new tool, this is neither described sufficiently nor made available (see below), the statements on the software remain at the level of claims without any evidence. It is understood that such a new tool should not be made public at this point before the publication was released, but the manuscript should have at least shown the avenue of how this can be done. And how should a reviewer assess software that is not even provided? There are ways to make software available to a reviewer only, e.g. through password-protected google drive.
· Although the term “carbon footprint” is inconsistently used in the literature, it should be noted that it is clearly defined in ISO 14067 as “the greenhouse gas emissions produced during each stage in the life cycle of a product”. However, this study only addresses the field emissions without consideration of the harvest and post-harvest stages of the value chain. In turn, this term is misleading in this study. Alternatively, the terms “emission intensity” or “yield-scaled emission” provide a more accurate connotation of what is intended here.
· The text sections referring to the IPCC approach is full of errors starting with the acronym (“Interdisciplinary (sic!) Panel for (sic!) Climate Change”). The statement “Field GHG emissions were estimated through the IPCC tier 1 method, which was complemented with field measurements” is contrasted by the fact that Fig. 5 suddenly shows a 3rd approach which is not explained. The distinction between “IPCC tier 1” and “IPCC default” is neither explained nor plausible (IPCC tier 1 should inherently be based on a global default).
· Given the geographic focus, it is hard to understand why this study does not cite the official GHG calculations by the Bangladesh government in the most recent 3rd National Communications submitted to the UNFCCC (https://unfccc.int/documents/192278).
· The supplement is obsolete because it contains only one figure that can easily be integrated into the manuscript. The citations are incomplete because important publications on GHG emissions from rice in Bangladesh are missing (e.g. Sapkota et al., 2021 Gaihre et al. 2015, 2017)
· While it is true that Bangladesh has 3 rice seasons within the entire country, it is rather doubtful that all 3 seasons will be implemented on one field within a given annual cycle. The authors should provide a graphical cropping calendar as shown in https://icrea.agr.nagoya-u.ac.jp/jpn/journal/Vol14_20-29-Review-Shelley.pdf to illustrate the assumed succession of these seasons.
Citation: https://doi.org/10.5194/bg-2023-165-CC1 -
RC1: 'Comment on bg-2023-165', Anonymous Referee #1, 04 Jan 2024
This paper describes the development and application of an Excel-based tool to calculate the greenhouse gas and carbon footprint of rice-based agricultural systems in Bangladesh. It uses well-known approaches (e.g., IPCC default values and Tier 1/2 methodology) to calculate most of the major components of such footprint calculations. Most of the calculations were based either on model results (e.g., SOC dynamics), or on default values (e.g., IPCC). The authors had collected very limited data of their own at unspecified field sites (with the exception of the soil science field laboratory mentioned in the ms), i.e., GHG measurements in three of twelve crops belonging to three of five cropping systems, accompanied by soil mineral N analyses four days after the second split application of urea for soil samples taken near the GHG chamber sites, and aboveground biomass data at the time of harvest. The selection of the field sites and even more so of the measured variables and the crops and systems in which they were measured appears very arbitrary. Furthermore, it remains completely unclear whether the measured data were included in the calculations, and if so, how and on what basis the choice between measured and calculated values was made, e.g. for CH4 and N2O emissions from the few crops in which they were measured.
Although the topic is highly relevant and falls within the field of Biogeosciences, I can only recommend rejecting the paper. First of all, the paper does not present a new approach or a further development of the methodology. All the components mentioned are already known and there are already tools to perform such calculations. Furthermore, there are very many uncertainties and shortcomings in both the methodology and the presentation of the data in the paper, which make the results appear very unreliable (see detailed comments in the annotated manuscript). Finally, the manuscript is partly written in poor English, so that some sentences are incomprehensible.
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