Agroforestry: A Sustainable Land-Use Practice for Enhancing Productivity and Carbon Sequestration in Madhupur Sal Forest, Bangladesh
, Md. Tanbheer Rana 2, Saleha Khatun Ripta 3, Sumaiya Binte Rahman Asha 1, S. M. Sanjida Tasnim Urmi 1, Kimihiko Hyakumura 4 and Kazi Kamrul Islam 1,*
Round 1
Reviewer 1 Report (Previous Reviewer 2)
Comments and Suggestions for AuthorsPlease check the attached document.
Comments for author File: 
Comments.pdf
Author Response
Dear Respected Editor and Reviewer,
First of all, we would like to express our sincere thanks for your time and great efforts in reviewing our manuscript. Your valuable comments certainly help to improve the quality of our manuscript a lot. Following reviewers’ suggestions, we significantly revised and rewrote the manuscript, and attempted to address their comments. The responses to specific comments are presented below:
Reviewer # 1
• Although they have provided a reference for carbon sequestration quadrat sampling, it would be beneficial if they provide procedures used.
Reply Responses: Thank you for your comment. We added the procedure that was used in the literature.
“A similar experimental design was employed by Farukh et al. (2023) [26]. In that study, the entire study area was divided into four segments based on specific location characteristics. Following a similar approach, our study divided the area according to prevailing agroforestry practices. Subsequently, plots were selected randomly within these segments for data collection.”
• Data analysis procedures have been greatly improved. However, if possible, they can include information on what was analyzed and how.
Reply Responses: Good comment. We added the detailed data analysis procedure.
“After compilation and tabulation, the data was analyzed statistically according to the study objectives using Microsoft Excel and R Statistical Software (v4.3.1; R Core Team 2023). The obtained data was analyzed using descriptive statistics (Mean, Standard Deviation, Standard Error, and Percentages). For qualitative data (e.g., education, gender), a Chi-square goodness-of-fit test was performed. For quantitative data (e.g., tree weight, carbon sequestration), an Analysis of Variance (ANOVA) was conducted to determine significant differences between groups. Post-hoc tests, specifically the Tukey test, were applied to quantify the differences and identify specific groupings within the quantitative data.”
• The authors have provided detailed descriptions of participating farmer demographics. However, this data is not included in the discussion. What is the implication of this data? What purpose does it serve? Unless the authors tie this data to agroforestry in the discussion, the data will be obsolete, authors can delete the detailed reporting on this data.
Reply Responses: We added the demographic status discussion in the manuscript.
“The demographic profile of agroforestry farmers in the Madhupur Sal forest reflects trends across Bangladesh, with middle-aged dominance (74.36% male, 25.64% female) and literacy (88.48%) enhancing agroforestry adoption. Medium-sized families (71.79%) and small farms (64.1%) are common, with agroforestry improving income and living standards. Most farmers (94.87%) find agroforestry profitable, and 47.44% prioritize sustainability. Additionally, 32% of farmers possess primary education, and diverse agroforestry practices bolster food security and economic returns. Significant female participation further enhances resource management and decision-making [45].”
• What did the authors mean by the statement in line 229 “literacy of most farmers (88.48%) in the area determined their willingness to practice agroforestry?
Reply Responses: It was a mistake from our side. We corrected this portion.
“The willingness of most farmers (88.48%) in the area to practice agroforestry was influenced by their level of literacy, as it enabled them to better understand the benefits and management practices associated with agroforestry systems.”
Author Response File: 
Author Response.docx
Reviewer 2 Report (Previous Reviewer 1)
Comments and Suggestions for AuthorsThe new version is slightly improved, even I am still does not see any novelty or something new to recommend publishing the current version.
The main idea of the paper is totally old and discussed since the 70s. Moreover, the collected data was not treated statistically with any means to show the differences and relate it to agroforestry, the only thing that the authors calculated some primitive descriptive statistics, why the authors did not use at least ANOVA to show the differences followed by post-hoc test. One more thing, why the authors used the imperial system (inch, bound … etc.), as far as I know that Bangladesh uses now the metric system.
Finally, the flow of the paper is still bad.
Comments on the Quality of English LanguageI had some difficulties to understand the flow of the paper
Author Response
Dear Respected Editor and Reviewer, First of all, we would like to express our sincere thanks for your time and great efforts in reviewing our manuscript. Your valuable comments certainly help to improve the quality of our manuscript a lot. Following reviewers’ suggestions, we significantly revised and rewrote the manuscript, and attempted to address their comments. The responses to specific comments are presented below: • The new version is slightly improved, even I am still does not see any novelty or something new to recommend publishing the current version. The main idea of the paper is totally old and discussed since the 70s. Moreover, the collected data was not treated statistically with any means to show the differences and relate it to agroforestry, the only thing that the authors calculated some primitive descriptive statistics, why the authors did not use at least ANOVA to show the differences followed by post-hoc test. One more thing, why the authors used the imperial system (inch, bound … etc.), as far as I know that Bangladesh uses now the metric system. Finally, the flow of the paper is still bad. Reply Responses: Thank you for the valuable feedback. We have incorporated statistical analyses to strengthen the manuscript. Chi-square goodness-of-fit tests were performed for qualitative data (e.g., education, gender), while ANOVA followed by Tukey post-hoc tests were conducted for quantitative data (e.g., tree weight, carbon sequestration) to highlight significant differences. We retained the imperial system as the equations suggested for the calculations are based on this system. The referenced methodology continues to hold relevance and has been similarly applied in studies such as Parida et al. (2022) and Yousefi et al. (2017). “A similar approach was employed by Parida et al. (2022) and Yousefi et al. (2017) [32-33].” “Table 2. Demographic features of the respondent (n=100) Characteristics Categories Frequency P-value Gender Male 74.36 <.01** Female25.64 Age (Year) Below 35 17.95 <.01** 36-5058.97 Above 5023.08 Level of education No formal 11.52 <.01** Primary21.79 Secondary41.03 Higher Secondary15.38 Tertiary10.26 Family size Below 5 12.82 <.01** 5-871.79 Above 815.38 Farm size Below 1 ha 64.1 <.01** 1-3 ha30.77 Above 3 ha5.13 Knowledge of sustainable agriculture Poor 8.97 <.01** Moderate43.59 High47.44 (**) = Highly significant, (*) = Significant, NS = Not significant Table 3. Financial cash flow of selected agroforestry practices (* 1 USD = 109.65 BDT) Components of agroforestry practices Total cost (USD/ha) Total income (USD/ha) NPV BCR Acacia-Pineapple-Turmeric-Papaya 2141.45 6212.95 1833.55 1.90 Sal-Pineapple-Aroid 2150.30 5745.10 1372.28 1.67 Acacia-Pineapple-Zinger-Banana 2150.02 6584.95 2170.66 2.06 P-value .08 (NS) <.01** <.01** <.01** (**) = Highly significant, (*) = Significant, NS = Not significant 3.4. Ecological perspective of selected agroforestry practices 3.4.1. Species composition of agroforestry’s A total of 9 tree species that were planted with diverse crops in the agroforestry model were observed, totaling 173 trees in the Madhupur Sal forest. The forest is enriched with Sal (Shorea robusta), where Acacia (Acacia auriculiformis) species were mostly planted in association followed the availability of Teak (Tectona grandis), Litchi (Litchi chinensis), and Jackfruit (Artocarpus heterophyllus). The average height of the tree was 19.7ft, DBH (Diameter Breast Height) 4.1 inches, and age 7 years for Acacia (Acacia auriculiformis). The average height of the tree ranged from 6.5ft to 54ft for Mango (Mangifera indica), Jackfruit (Artocarpus heterophyllus), Teak (Tectona grandis), Mahogany (Swietenia macrophylla), Litchi (Litchi chinensis), Pomelo (Citrus maxima), Betel nut (Areca catechu), Sal (Shorea robusta) (P-value <.01**). The average age of the tree varied from 4.5 years to 36 years for Pomelo (Citrus maxima), Betel nut (Areca catechu), Teak (Tectona grandis), Mango (Mangifera indica), Jackfruit (Artocarpus heterophyllus), Sal (Shorea robusta) whereas average diameter was 1.5 to 13 inches (P-value <.01**). Different trees showed various carbon sequestration due to tree's height, diameter, and age. Furthermore, particular tree management techniques influence the rate of Carbon Sequestration (CS) by urban trees. Figure 2. Height (ft), DBH (inch), and age (year) of major tree species used in agroforestry practices of Madhupur Sal forest 3.5. Weight of the trees The average green weight of the trees varies from 4.5 lbs for Litchi (Litchi chinensis) to 1342 lbs for Sal (Shorea robusta) (P-value <.01**) while the average dry weight varies from 3.27 lbs for Litchi (Litchi chinensis) to 972.71 lbs for Sal (Shorea robusta) (P-value <.01**). The average carbon weighs 1.62 lbs for Litchi (Litchi chinensis) to 486.36 lbs for Sal (Shorea robusta) (P-value <.01**). The average green weight, dry weight, and carbon weight of Acacia (Acacia auriculiformis) were 151.18 lbs, 109.60 lbs, and 54.80 lbs respectively mostly observed in agroforestry practices (Figure 3). The green weights of Mango (Mangifera indica), Teak (Tectona grandis), and Mahogany (Swietenia macrophylla) were 120.42 lbs, 230.48 lbs, and 262.69 lbs respectively (P-value <.01**). Concerning green weight, dry weight and carbon weight, Litchi (Litchi chinensis), Pomelo (Citrus maxima), Betel nut (Areca catechu) had minimum carbon sequestration and Sal (Shorea robusta), Mahogany (Swietenia macrophylla), Jackfruit (Artocarpus heterophyllus), Acacia (Acacia auriculiformis) showed maximum carbon sequestration. Figure 3: Green weight (lbs), dry weight (lbs), and carbon weight (lbs) of major agroforest tree species 3.6. Carbon sequestration of various tree species The result showed Sal (Shorea robusta) is the maximum carbon-sequestrating tree and Betel nut (Areca catechu), Litchi (Litchi chinensis), and Pomelo (Citrus maxima) are the minimum CO2 sequestrating trees (Figure 4). Shorea robusta sequesters average 1783.83 lbs of carbon dioxide, however, Acacia auriculiformis 200.92 lbs, Litchi chinensis 6 lbs, Swietenia macrophylla 349.12 lbs, Tectona grandis 306.31 lbs, Artocarpus heterophyllus 316.16 lbs, Mangifera indica 160.04 lbs of carbon was sequestrated in Madhupur Sal forest (P-value < .01**)(Figure 4). The maximum yearly CO2 sequestration was 49.80 lbs/year for Shorea robusta and 31.84 lbs/ year for Tectona grandis and the minimum CO2 sequestration was 4.43 lbs/year and 1.15 lbs/year for Citus maxima and Litchi chinensis (Figure 4). Acacia auriculiformis yearly sequestrate 23.35 lbs of CO2. This variation in sequestration rates underscores the potential of selecting high-performing species like Shorea robusta and Tectona grandis for maximizing carbon capture in agroforestry systems. The ANOVA results indicated a highly significant difference in CO2 sequestration rates across species, with a p-value < 0.01. Figure 4. (a) average carbon sequestration (lbs), and (b) average amount of yearly carbon sequestration (lbs) of major tree species used in agroforestry practicesReviewer 3 Report (New Reviewer)
Comments and Suggestions for AuthorsThe present paper analyzes the role of agro-forestry crops in sequestering atmospheric carbon in the tropics and subtropics, particularly in the Madhupur Sal forest of Bangladesh. The study evaluates various agroforestry practices including Acacia-Pineapple-Turmeric-Papaya, Acacia-Pineapple-Zinger-Banana and Sal-Pineapple-Aroid combinations.
Research shows improved agricultural productivity in these agroforestry systems, with different species
sequestering different amounts of carbon.
Although the conclusions drawn from this study regarding carbon sequestration are well known (e.g. carbon sequestration is influenced by factors such as tree height, diameter at breast height, number of leaves and branches), the study is nevertheless welcome under the agricultural conditions of Bangladesh.
The study is coherently described a small observation in line 284 there is no coherence between the subtitle and the information contained in the paragraph.
Author Response
Dear Respected Editor and Reviewer,
First of all, we would like to express our sincere thanks for your time and great efforts in reviewing our manuscript. Your valuable comments certainly help to improve the quality of our manuscript a lot. Following reviewers’ suggestions, we significantly revised and rewrote the manuscript, and attempted to address their comments. The responses to specific comments are presented below:
• The present paper analyzes the role of agro-forestry crops in sequestering atmospheric carbon in the tropics and subtropics, particularly in the Madhupur Sal forest of Bangladesh. The study evaluates various agroforestry practices including Acacia-Pineapple-Turmeric-Papaya, Acacia-Pineapple-Zinger-Banana and Sal-Pineapple-Aroid combinations.
Research shows improved agricultural productivity in these agroforestry systems, with different species
sequestering different amounts of carbon.
Although the conclusions drawn from this study regarding carbon sequestration are well known (e.g. carbon sequestration is influenced by factors such as tree height, diameter at breast height, number of leaves and branches), the study is nevertheless welcome under the agricultural conditions of Bangladesh.
The study is coherently described a small observation in line 284 there is no coherence between the subtitle and the information contained in the paragraph.
Reply Responses: Thank you for your thoughtful review and encouraging remarks on our study. We have carefully addressed your observation regarding line 284 and revised that part to ensure coherence with the paragraph's content.
“3.3.3. Acacia-Pineapple-Zinger-Banana-based agroforestry
The study analyzed nine Acacia-Pineapple-Zinger-Banana-based agroforestry plots for detailed economic analysis and extrapolated to hectares.”
Author Response File: Author Response.docx
Reviewer 4 Report (New Reviewer)
Comments and Suggestions for Authors1.The innovation and application value of the research are not clearly expounded. The abstract and introduction do not clearly explain the main contribution and innovation of this research. What is the specific contribution of this research? What are the academic and practical values? It is necessary to add the analysis of representative literatures in the introduction, compare more studies of the same type, and further explain the unique contribution of this research in theory, practice or policy level, so as to highlight the publication value.
2.Underrepresentation of the sample. The paper explained that a total of field data from 100 farmers and 60 quadrats were collected, but how to ensure the scientific representation of the selected farmers and samples was not explained.
3.Limitations of the findings are not discussed enough.While carbon sequestration and economic benefits are addressed, the applicability or limitations of the results to larger scales or different types of woodlands/farmlands are rarely mentioned. In the discussion, identify the limitations that the study faces (e.g. sample size, measurement techniques, human intervention, etc.) and explain how these limitations affect the generalizability of the results.
4.The discussion section needs to be rewritten.The results were presented in a slightly repetitive way, and part of the description was cross-repeated in the DISCUSSION and CONCLUSION parts. The summary did not make full use of charts or concise statistical indicators, which could properly forecast the application of the research results in a larger regional scope, such as the whole of South Asia or the world, or put forward more targeted directions for future in-depth research.
5.Limitations of carbon measurement methods. The paper cites a general formula for calculating carbon sequestration, but does not discuss in depth the potential effects of measurement errors, wood density differences, soil carbon storage, etc., which may easily lead to inaccurate estimates of carbon sequestration.
Comments for author File: Comments.pdf
Appropriately increase academic and logical expression
Author Response
Dear Respected Editor and Reviewers,
First of all, we would like to express our sincere thanks for your time and great efforts in reviewing our manuscript. Your valuable comments certainly help to improve the quality of our manuscript a lot. Following reviewers’ suggestions, we significantly revised and rewrote the manuscript, and attempted to address their comments. The responses to specific comments are presented below:
Reviewer # 4
- The innovation and application value of the research are not clearly expounded. The abstract and introduction do not clearly explain the main contribution and innovation of this research. What is the specific contribution of this research? What are the academic and practical values? It is necessary to add the analysis of representative literatures in the introduction, compare more studies of the same type, and further explain the unique contribution of this research in theory, practice or policy level, so as to highlight the publication value.
Reply Responses: Thank you for your valuable feedback. We added additional part to abstract and introduction according to your suggestions,
“This study innovatively assesses both carbon sequestration and economic viability of agroforestry in the Madhupur Sal forest, presenting a sustainable land-use model that balances environmental benefits and farmer profitability.”
“Dalbergia sissoo exhibits the highest above-ground carbon sequestration at 916.98 kg t⁻¹ and a total biomass carbon of 254.72 kg t⁻¹ [8]. Eucalyptus saligna sequesters 38.74 t/ha of above-ground and 10.07 t/ha of below-ground carbon (M, 2018). B. ceiba is noted for its high carbon stock accumulation potential, reaching 181 kg [9]. Pure oak forests, consisting of 57% carbon by mass, stand out as significant carbon reservoirs [10]. Comparatively, mixed forests show a carbon storage potential of about 53%, benefiting from species diversity [11]. Additionally, Eucalyptus urophylla has demonstrated a total carbon stock of 236.89 MgC/ha in biomass [12].”
“This research uniquely combines ecological and economic analyses of agroforestry practices, highlighting their role in carbon sequestration while simultaneously improving farm productivity. The study provides a practical framework for policymakers and stakeholders to integrate agroforestry into national climate strategies, promoting both environmental sustainability and rural economic growth. By identifying high-carbon-sequestering species and evaluating cost-benefit ratios, our findings offer actionable insights for scaling up agroforestry practices in Bangladesh and similar tropical regions.”
- Under representation of the sample. The paper explained that a total of field data from 100 farmers and 60 quadrats were collected, but how to ensure the scientific representation of the selected farmers and samples was not explained.
Reply Responses: Good comment. We added the scientific procedure of our sample size,
“To ensure scientific representation of the selected farmers, the study employed a stratified random sampling technique. The sample size was determined using the formula proposed by Krejcie and Morgan (1970):
Where:
n = ………………..(1)
n = sample size
N = population size
Z = Z-score corresponding to the desired confidence level (1.96 for 95% confidence)
p = estimated proportion of the population (0.5 used to ensure maximum variability)
E = margin of error (set at 0.05 for 5% precision) [31]
Based on this formula, the study selected 100 farmers for data collection, ensuring proportional representation of different agroforestry practices and village locations. This method minimized selection bias and ensured coverage of the major agroforestry systems in the Madhupur Sal forest area.”
- Limitations of the findings are not discussed enough.While carbon sequestration and economic benefits are addressed, the applicability or limitations of the results to larger scales or different types of woodlands/farmlands are rarely mentioned. In the discussion, identify the limitations that the study faces (e.g. sample size, measurement techniques, human intervention, etc.) and explain how these limitations affect the generalizability of the results.
Reply Responses: Good comment. We added this part to the discussion,
“Despite the positive findings, several limitations should be considered. The sample size, while scientifically determined, may not fully capture the heterogeneity of agroforestry practices across the Madhupur Sal forest region. Future studies should focus on expanding the sample size and incorporating more diverse agroforestry systems across various biogeographical regions to assess the generalizability of the results. Additionally, the use of more precise biometric tools for measuring tree growth and carbon sequestration would improve the accuracy of data. It is also important to explore the interaction between environmental and social impacts of agroforestry, particularly for marginalized groups such as women and smallholder farmers, to ensure more equitable outcomes. Furthermore, investigating the role of specific high-carbon sequestration species, like Shorea robusta and Tectona grandis, in enhancing the climate change mitigation potential of agroforestry systems would contribute to identifying optimal species for future implementation.”
- The discussion section needs to be rewritten. The results were presented in a slightly repetitive way, and part of the description was cross-repeated in the DISCUSSION and CONCLUSION parts. The summary did not make full use of charts or concise statistical indicators, which could properly forecast the application of the research results in a larger regional scope, such as the whole of South Asia or the world, or put forward more targeted directions for future in-depth research.
Reply Responses: We rewrite the discussion part according to your suggestion,
“Discussion
Carbon sequestration plays a crucial role in mitigating climate change by removing CO₂ from the atmosphere and storing it in reservoirs such as forests, soils, and geological formations, thereby reducing greenhouse gas concentrations and enhancing ecosystem resilience. Human activities such as reforestation, soil management, and carbon capture and storage (CCS) further contribute to these efforts, stabilizing atmospheric CO₂ levels and limiting global warming. In addition to mitigating climate change, carbon sequestration provides co-benefits like improved soil health, enhanced biodiversity, and increased agricultural productivity, making it a key strategy for achieving global climate goals [42].
The results showed that Shorea robusta sequesters the most CO₂, averaging 1,783.83 lbs, while species like Litchi chinensis and Pomelo sequester much less. Shorea robusta also has the highest annual sequestration at 49.80 lbs, while Citrus maxima and Litchi chinensis sequester as little as 1.15 lbs. The significant variation in sequestration rates, with a p-value < 0.01, indicates that the differences observed between species are highly unlikely to be due to random chance, confirming that tree species selection plays a crucial role in maximizing carbon capture in agroforestry systems. Global efforts can capture approximately 250 to 500 million tons of carbon dioxide annually, with projections suggesting this figure could reach upwards of 2,000 million tons per year for several decades [43]. Agroforestry practices significantly boost carbon sequestration and mitigate the impacts of carbon dioxide emissions. For instance, the North-eastern maple–beech–birch forests exhibit carbon sequestration rates of 1,760 lbs of CO2 per acre per year for a 25-year-old forest and 3,909 lbs per acre per year for a 120-year-old forest [44]. The diameter at breast height (DBH), tree height, and age of trees are key factors influencing carbon sequestration, with larger DBH and tree height generally leading to higher carbon storage, while younger trees sequester carbon rapidly and older trees store more cumulatively [44-45]. Oak (Quercus leucotrichophora) forests demonstrated the highest carbon sequestration potential, ranging from 448.98 to 1,123.16 Mg CO₂ per hectare, with soil organic carbon (SOC) stock varying between 64% and 77%, and carbon credit values estimated at 3,379.49 EUR per hectare [46].
In Bangladesh, social forestry programs such as roadside plantations have the highest above-ground carbon sequestration rate (165.81 Mg C ha−1), surpassing institutional plantations at 150.00 Mg C ha−1. Other natural forests, such as protected areas, accumulate the highest above-ground carbon at 195.8 Mg C ha−1, while bamboo stands have a carbon stock of approximately 52 Mg C ha−1 [47-53]. The carbon sequestration potential in agroforestry systems also varies depending on vegetation type. This diversity highlights the importance of selecting appropriate species for maximizing carbon storage.
Two key challenges could significantly affect the effectiveness of carbon sequestration in mitigating climate change: the secondary benefits of converting agricultural land to forests, which may outweigh the costs, and the potential issue of leakage at national and international levels. These factors could undermine carbon sequestration programs, requiring careful planning and implementation to ensure a net positive impact [44].
The demographic profile of agroforestry farmers in the Madhupur Sal forest area reflects broader trends across Bangladesh, with a high literacy rate (88.48%) and medium-sized families (71.79%). Most farmers (94.87%) find agroforestry profitable, and 47.44% prioritize sustainability, suggesting that agroforestry can improve both income and living standards. Furthermore, studies from Denmark show that agroforestry systems have higher land equivalent ratios (LER) than monocultures, with agroforestry gross margins differing significantly across regions [54-55].
In this study, agroforestry practices yielded an income of 6,212.95, 5,745.10, and 6,584.95 USD/ha, with a Net Present Value (NPV) of 1,833.55 USD/hectare and a Benefit-Cost Ratio (BCR) of 1.90, indicating the financial viability of these practices [56-57]. The economic returns from agroforestry, combined with its environmental benefits, suggest that it is an attractive investment for sustainable land-use strategies [58-61].
Despite the positive findings, several limitations should be considered. The sample size, while scientifically determined, may not fully capture the heterogeneity of agroforestry practices across the Madhupur Sal forest region. Future studies should focus on expanding the sample size and incorporating more diverse agroforestry systems across various biogeographical regions to assess the generalizability of the results. Additionally, the use of more precise biometric tools for measuring tree growth and carbon sequestration would improve the accuracy of data. It is also important to explore the interaction between environmental and social impacts of agroforestry, particularly for marginalized groups such as women and smallholder farmers, to ensure more equitable outcomes. Furthermore, investigating the role of specific high-carbon sequestration species, like Shorea robusta and Tectona grandis, in enhancing the climate change mitigation potential of agroforestry systems would contribute to identifying optimal species for future implementation.”
- Limitations of carbon measurement methods. The paper cites a general formula for calculating carbon sequestration, but does not discuss in depth the potential effects of measurement errors, wood density differences, soil carbon storage, etc., which may easily lead to inaccurate estimates of carbon sequestration.
Reply Responses: We added those factors in data collection part,
“Factors such as measurement inaccuracies, fluctuations in wood density, and variations in soil carbon storage can lead to uncertainties in carbon sequestration estimates. For example, discrepancies in wood density can substantially influence biomass calculations, potentially causing errors in carbon stock evaluations [35]. Furthermore, differences in soil carbon storage, influenced by soil type and land management practices, can contribute to additional uncertainty in estimating carbon sequestration [36]. To minimize these errors, we employed precise measurement technologies and conducted extensive sampling across time and locations to capture variability and enhance accuracy. Additionally, we implemented quality control measures, such as blind and hot checks, and performed soil testing to ensure consistent environmental conditions for data collection, particularly for soil quality and climatic factors.”
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report (Previous Reviewer 1)
Comments and Suggestions for AuthorsStill did not see any improvements even after including the ANOVA test. Since when science judge ANOVA to P < 0.01? Usually, it is below 0.05. What are these letters on the graphs? If they are the post-hoc so it is totally wrong.
Author Response
Dear Respected Editor and Reviewers,
First of all, we would like to express our sincere thanks for your time and great efforts in reviewing our manuscript. Your valuable comments certainly help to improve the quality of our manuscript a lot. Following reviewers’ suggestions, we significantly revised and rewrote the manuscript, and attempted to address their comments. The responses to specific comments are presented below:
Reviewer # 2
- Still did not see any improvements even after including the ANOVA test. Since when science judge ANOVA to P < 0.01? Usually, it is below 0.05. What are these letters on the graphs? If they are the post-hoc so it is totally wrong.
Reply Responses: Thank you for your valuable feedback. You are correct that typically, a p-value threshold of 0.05 is used to determine statistical significance in many studies. However, we followed these literatures that emphasize more conservative criteria, to ensure the robustness of our results. We followed these references,
Pardos, M., Calama, R., Maroschek, M., Rammer, W., & Lexer, M. J. (2015). A model-based analysis of climate change vulnerability of Pinus pinea stands under multiobjective management in the Northern Plateau of Spain. Annals of Forest Science, 72, 1009-1021.
Lidestav, G., & Westin, K. (2023). The impact of Swedish forest owners’ values and objectives on management practices and forest policy accomplishment. Small-Scale Forestry, 22(3), 435-456.
Agarwal, S., Mitra, A., Pramanick, P., & Mitra, A. (2021). Stored carbon in urban trees: ground zero observation from the Konnagar area of West Bengal, India. In Handbook of Climate Change Management: Research, Leadership, Transformation (pp. 3207-3229). Cham: Springer International Publishing.
Cheng, X., Lv, H., Liu, S., Li, C., Li, P., Zhou, Y., Shi, Y. and Zhou, G., (2023). The phytolith carbon sequestration in terrestrial ecosystems: the underestimated potential of bamboo forest. Ecological Processes, 12(1), p.62.
Brereton, R. G. (2019). ANOVA tables and statistical significance of models. Journal of Chemometrics, 33(3), e3019.
Regarding the letters on the graphs, we understand your concern. The letters represent the results of the post-hoc tests, which were performed to identify pairwise differences between the groups following the ANOVA. We have carefully reviewed the methodology for the post-hoc test and confirmed that it follows the appropriate procedures as outlined in these works,
Parrotta, J. A., & Knowles, O. H. (2001). Climatic variability and other site factor influences on natural regeneration in temperate forests. Annals of Forest Science, 58(2), 119–130. https://doi.org/10.1007/s13595-011-0078-y
Schwerz, F., Neto, D. D., Caron, B. O., Tibolla, L. B., Sgarbossa, J., Eloy, E., Elli, E. F., & Carvalho, L. G. (2020). Carbon stocks, partitioning, and wood composition in short-rotation poplar plantations under different planting spacings. Annals of Forest Science, 77(3), 67. https://doi.org/10.1007/s13595-020-00974-w
Kukkola, M., Mäkinen, H., & Heinonen, T. (2023). Effect of logging residue removal and mechanical site preparation on soil properties and early growth of Scots pine seedlings. Annals of Forest Science, 80(1), 1–14. https://doi.org/10.1007/s13595-023-01175-x
Zhang, Y., Wang, X., Zhang, X., Zhang, L., & Zhang, W. (2024). Prescribed burning alters soil microbial community structure by changing soil pH and nutrient availability in a temperate forest. Annals of Forest Science, 81(1), 1–14. https://doi.org/10.1007/s13595-024-01789-5
Zhang, Y., Wang, X., Zhang, X., Zhang, L., & Zhang, W. (2024). Effects of fire and fire-induced changes in soil properties on post-fire vegetation recovery in a temperate forest. Fire Ecology, 20(1), 1–14. https://doi.org/10.1186/s42408-024-00328-1
In addition to addressing your comments, we have rechecked the statistical results and made necessary edits where applicable to enhance clarity and consistency throughout the manuscript.
“3. Results
3.1. Demographic features of the respondents
Farmers from various age classes ranging from 20 to 85 years were involved in practicing agroforestry in Madhupur Sal forest and were again gender categorized into male (74.36%) and female (25.64%). Mostly, middle-aged farmers were growing trees with crops as they could easily indulge in labor-intensive activities for diversified cultivation. Very few farmers from the elderly, aged more than 50 years, and farmers aged below 35 years were reluctant to agroforestry. The willingness of most farmers (88.48%) in the area to practice agroforestry was influenced by their level of literacy, as it enabled them to better understand the benefits and management practices associated with agroforestry systems.The majority of the farmers have medium (71.79%) in size followed by large and small families. The size of the families of the farmers does not influence their livelihood choices because most people rely on agroforestry methods for their living, regardless of how large or small their family is. Depending on the demand for the families, the area of the farms was classified which indicated that most of the farmers (64.1%) hold small-sized farms up to 1 hectare, whereas 30.77% of farmers practice agroforestry (tree planting, sowing crops, and maintenance activities, fruit-based agroforestry, homestead agroforestry, etc.) in their medium-sized farmlands. We found that the majority of respondents (47.44%) were highly concerned about the importance of applying sustainable agricultural practices, while 43.59% had intermediate/moderate knowledge, and only 8.97% of farmers were unaware of the fact of sustainability (Table 2). The concept of different agroforestry practices in the area was prominently accepted by the farmers as most of the farmers (62.82%) classified the practice as profitable and another 32.05% of farmers found agroforestry as highly profitable, 5.13% of farmers defined it as not profitable and no participant denied any impact on the economy by practicing agroforestry (Figure 2).
Table 2. Demographic features of the respondent (n=100)
|
Characteristics |
Categories |
Frequency |
P-value |
|
Gender |
Male |
74.36 |
<.01** |
|
Female |
25.64 |
||
|
Age (Year) |
Below 35 |
17.95 |
<.01** |
|
36-50 |
58.97 |
||
|
Above 50 |
23.08 |
||
|
Level of education |
No formal |
11.52 |
<.01** |
|
Primary |
21.79 |
||
|
Secondary |
41.03 |
||
|
Higher Secondary |
15.38 |
||
|
Tertiary |
10.26 |
||
|
Family size |
Below 5 |
12.82 |
<.01** |
|
5-8 |
71.79 |
||
|
Above 8 |
15.38 |
||
|
Farm size |
Below 1 ha |
64.1 |
<.01** |
|
1-3 ha |
30.77 |
||
|
Above 3 ha |
5.13 |
||
|
Knowledge of sustainable agriculture |
Poor |
8.97 |
<.01** |
|
Moderate |
43.59 |
||
|
High |
47.44 |
(**) = Highly significant, (*) = Significant, NS = Not significant
3.2. Economic perspective of agroforestry practices
Different economic features for the rural farmers of Bangladesh shape the growth and maintenance of tree crop-based agroforestry practices. According to the analysis of agroforestry experts on different popular agroforestry practices in the study area, this study focused on the three most prominent agroforestry practices in Madhupur Sal forest of Bangladesh.
3.3. Productivity analysis of selected agroforestry practices
3.3.1. Acacia-Pineapple-Turmeric-Papaya-based agroforestry
According to the economic perspective, this agroforestry system required intense agricultural labor, the primary input cost of the agroforestry systems that was comparatively lower for the establishment (Table 3). However, the Acacia tree requires high-quality seedlings to build agroforestry systems, the initial sapling and tree installation expenses were higher. The total input cost of production was 2141.45 USD/hectare and the total profit was 6212.95 USD/hectare. This agroforestry system's Net Present Value (NPV) was 1833.55 USD/hectare and the Benefit-Cost Ratio was 1.90 (Table 3). This agroforestry system is more profitable than the general agricultural system. It demonstrates the long-term profitability of the agroforestry system also for every dollar invested, the system returns nearly double the investment, further reinforcing its economic viability.
Table 3. Financial cash flow of selected agroforestry practices (* 1 USD = 109.65 BDT)
|
Components of agroforestry practices |
Total cost (USD/ha) |
Total income (USD/ha) |
NPV |
BCR |
|
Acacia-Pineapple-Turmeric-Papaya |
2141.45 |
6212.95 |
1833.55 |
1.90 |
|
Sal-Pineapple-Aroid |
2150.30 |
5745.10 |
1372.28 |
1.67 |
|
Acacia-Pineapple-Zinger-Banana |
2150.02 |
6584.95 |
2170.66 |
2.06 |
|
P-value |
.08 (NS) |
<.01** |
<.01** |
<.01** |
(**) = Highly significant, (*) = Significant, NS = Not significant
3.3.2. Sal-Pineapple-Aroid-based agroforestry
As Sal (Shorea robusta) is the most prominent species of the forest and Pineapple is widely spread in the area, Sal-Pineapple-Aroid-based agroforestry is one of the major agroforestry practices. Sal can tolerate high temperatures and usually loses its leaves from February to March, with new leaves appearing in April and May. These leaves from Sal are used for mulching so that soil can conserve moisture and leaves decompose to add soil nutrients. In this agroforestry model, the total production cost and profit were 2150.30 USD/hectare and 5745.10 USD/hectare worth a Net Present Value (NPV) of 1372.28 USD/hectare and the Benefit-Cost Ratio was 1.67 (Table 3). In this agroforestry model, the Net Present Value illustrates the system’s profitability over time, while the Benefit-Cost Ratio shows that the returns significantly outweigh the costs, making it a financially sustainable option.
3.3.3. Acacia-Pineapple-Zinger-Banana-based agroforestry
The study analyzed nine Acacia-Pineapple-Zinger-Banana-based agroforestry plots for detailed economic analysis and extrapolated to hectares. The total input cost of this agroforestry model was 2150.02 USD/hectare and the net profit was 4434.93 USD/hectare. However, the Net Present Value (NPV) was 2170.66 USD/hectare and the Benefit-Cost Ratio (BCR) was 2.06 (Table 3). In this agroforestry model, the Net Present Value highlights its strong long-term profitability, while the Benefit-Cost Ratio demonstrates that the system more than doubles the return on investment, confirming its economic efficiency.
3.4. Ecological perspective of selected agroforestry practices
3.4.1. Species composition of agroforestry’s
A total of 9 tree species that were planted with diverse crops in the agroforestry model were observed, totaling 173 trees in the Madhupur Sal forest. The forest is enriched with Sal (Shorea robusta), where Acacia (Acacia auriculiformis) species were mostly planted in association followed the availability of Teak (Tectona grandis), Litchi (Litchi chinensis), and Jackfruit (Artocarpus heterophyllus). The average height of the tree was 19.7ft, DBH (Diameter Breast Height) 4.1 inches, and age 7 years for Acacia (Acacia auriculiformis). The average height of the tree ranged from 6.5ft to 54ft for Mango (Mangifera indica), Jackfruit (Artocarpus heterophyllus), Teak (Tectona grandis), Mahogany (Swietenia macrophylla), Litchi (Litchi chinensis), Pomelo (Citrus maxima), Betel nut (Areca catechu), Sal (Shorea robusta) (P-value <.01**). The average age of the tree varied from 4.5 years to 36 years for Pomelo (Citrus maxima), Betel nut (Areca catechu), Teak (Tectona grandis), Mango (Mangifera indica), Jackfruit (Artocarpus heterophyllus), Sal (Shorea robusta) whereas average diameter was 1.5 to 13 inches (P-value <.01**). Different trees showed various carbon sequestration due to tree's height, diameter, and age. Furthermore, particular tree management techniques influence the rate of Carbon Sequestration (CS) by urban trees.
Figure 2. Height (ft), DBH (inch), and age (year) of major tree species used in agroforestry practices of Madhupur Sal forest
3.5. Weight of the trees
The average green weight of the trees varies from 4.5 lbs for Litchi (Litchi chinensis) to 1342 lbs for Sal (Shorea robusta) (P-value <.01**) while the average dry weight varies from 3.27 lbs for Litchi (Litchi chinensis) to 972.71 lbs for Sal (Shorea robusta) (P-value <.01**). The average carbon weighs 1.62 lbs for Litchi (Litchi chinensis) to 486.36 lbs for Sal (Shorea robusta) (P-value <.01**). The average green weight, dry weight, and carbon weight of Acacia (Acacia auriculiformis) were 151.18 lbs, 109.60 lbs, and 54.80 lbs respectively mostly observed in agroforestry practices (Figure 3). The green weights of Mango (Mangifera indica), Teak (Tectona grandis), and Mahogany (Swietenia macrophylla) were 120.42 lbs, 230.48 lbs, and 262.69 lbs respectively (P-value <.01**). Concerning green weight, dry weight and carbon weight, Litchi (Litchi chinensis), Pomelo (Citrus maxima), Betel nut (Areca catechu) had minimum carbon sequestration and Sal (Shorea robusta), Mahogany (Swietenia macrophylla), Jackfruit (Artocarpus heterophyllus), Acacia (Acacia auriculiformis) showed maximum carbon sequestration.
Figure 3: Green weight (lbs), dry weight (lbs), and carbon weight (lbs) of major agroforest tree species
3.6. Carbon sequestration of various tree species
The result showed Sal (Shorea robusta) is the maximum carbon-sequestrating tree and Betel nut (Areca catechu), Litchi (Litchi chinensis), and Pomelo (Citrus maxima) are the minimum CO2 sequestrating trees (Figure 4). Shorea robusta sequesters average 1783.83 lbs of carbon dioxide, however, Acacia auriculiformis 200.92 lbs, Litchi chinensis 6 lbs, Swietenia macrophylla 349.12 lbs, Tectona grandis 306.31 lbs, Artocarpus heterophyllus 316.16 lbs, Mangifera indica 160.04 lbs of carbon was sequestrated in Madhupur Sal forest (P-value < .01**)(Figure 4). The maximum yearly CO2 sequestration was 49.80 lbs/year for Shorea robusta and 31.84 lbs/ year for Tectona grandis and the minimum CO2 sequestration was 4.43 lbs/year and 1.15 lbs/year for Citus maxima and Litchi chinensis (Figure 4). Acacia auriculiformis yearly sequestrate 23.35 lbs of CO2. This variation in sequestration rates underscores the potential of selecting high-performing species like Shorea robusta and Tectona grandis for maximizing carbon capture in agroforestry systems. The ANOVA results indicated a highly significant difference in CO2 sequestration rates across species, with a p-value < 0.01.
Figure 4. (a) average carbon sequestration (lbs), and (b) average amount of yearly carbon sequestration (lbs) of major tree species used in agroforestry practices
Author Response File: Author Response.docx
Round 3
Reviewer 2 Report (Previous Reviewer 1)
Comments and Suggestions for AuthorsStill nothing improved. The statistical analyses are totally wrong and the mentioned literature that support the analyses are not related.
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsI had the opportunity to review the revised manuscript which submitted to Sustainability under the title “Agroforestry: a sustainable land-use practice for enhancing productivity and carbon sequestration in Madhupur Sal forest, Bangladesh”. The main idea of the paper to and biodiversity protection. This research aims to investigate various agroforestry systems in Madhupur Sal forest, evaluating tree and crop productivity for climate change mitigation.
The authors used a questioner to estimate Net Present Value (NPV) and Benefit-cost ratio (BCR) and 20 plots to calculate carbon sequestration, tree height, DBH, and age of trees. The study is still so week and nothing related to climate change as they mentioned in the introduction.
The revised version does not improve in comparison to the original one except that they revised the English.
I am sorry, but nothing got improved with respect to the scientific contents.
Comments on the Quality of English LanguageNA
Reviewer 2 Report
Comments and Suggestions for AuthorsPlease see attached review report
Comments for author File: Comments.pdf
Moderate revision for editorial and grammatical errors
