Growth, Productivity, and Biomass–Carbon Allometry in Teak (Tectona grandis) Plantations of Western Mexico

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Overall Recommendation

This research is relevant for forest science, particularly in the context of biomass and carbon dynamics in tropical plantations. The methodology for biomass and carbon determination is well-grounded in relevant literature and appropriate for the species studied. However, some sections require clarification and additional detail to improve transparency and reproducibility. For example:

1. Clarify sampling design and scaling procedures.
2. Provide more transparent reporting of biomass measurements and subsampling.
3. Validation procedures.

Below I provide specific comments to strengthen the manuscript.

Abstract

• Line 38: Please clarify whether C concentrations in stem/roots were consistently 48–50%.
• Line 41: Define the indicator “A/B” (A/B ≈ 4.9; BEF ≈ 1.5).
• Line 42: The statement “CAI showed two pulses (20 and 11 Mg ha⁻¹ yr⁻¹)”.

2.2. Sampling Sites and Dasometric Sampling

• Line 117: Provide a summary of sample tree distribution, including DBH and age (height) ranges, across all three sites.
• Lines 119–120: Clarify how stand ages were determined (e.g., management records, plantation establishment year, or tree-ring data).

2.3. Growth and Productivity Estimation

• The text does not clearly specify whether estimates refer to individual trees, plots, or stands. Please state explicitly.
• Confirm whether basal area, volume, and increments (MAI, CAI) are calculated at the plot level and then scaled to stands.

2.4. Biomass and Carbon Determination

• Line 167: Since destructive sampling was conducted, specify how many trees were harvested per plot/stand.
• Line 171: Clarify what is meant by “representative subsamples.” Which proportion of each component (leaves, branches, stems, roots) was weighed?
• Line 169: Did you actually weigh the roots? If so, describe the excavation method and any mass losses.
• Lines 171–173: Specify how representative subsamples were chosen and whether uncertainty/error estimates were considered. Was biomass variability across subsamples tested?

2.5. Development of Allometric Equations

• Line 203: What is you intention in “(7) y (8)” seems incorrect. Present both equations in different lines.
• Lines 207–209: Clarify: carbon was calculated as biomass × %C (from CHNS-O analysis), so stand-alone C models were unnecessary.
• Line 210: “using” instead of Using?
• Line 213: Verify notation for “DBHp.”
• Lines 215–216: Did you test the models with an independent validation set beyond the cross-validation procedure? If not, note this limitation.
• Line 220: Provide details of sampling intensity: how many 1000 m² plots, how many trees per plot, and per stand. The total sample size (n=35) seems small relative to the number of plots/stands/sites; clarify if estimates are stand-level or population-level.

Results

• Lines 259-281 is very dense for reading.
• Line 305: Clarify whether means/SDs are based on individual tree data or plot/stand-level estimates.
• Line 326–328: Explain why stem biomass allometries are less precise than total biomass. Did you detect and address potential outliers?
• Line 352: State clearly whether the same data were used for model fitting and validation (Figure 5). If cross-validation, specify.
• Figure 5. This is more diagnostic rather “Validation”. In panel (a), the observed vs. predicted values show points clustered in horizontal strata rather than forming a continuous scatter around the 1:1 line. This pattern suggests that biomass estimates may have been discretized, possibly due to subsample scaling procedures or limited measurement resolution, resulting in repeated values across trees. The same banding is visible in the residuals (panel b). Clarify for what about “close alignment of points with the 1:1 line”
• Line 412: Include tree distribution by age in Table 4.
• Line 420: Provide average stand ages in Table 5. Large differences between Tuxpan (18.0 ± 0.3b) and Rosamorada (40.6 ± 1.1a) biomass need interpretation—possibly due to site quality, age structure, or sampling design. Consider whether Tuxpan should be excluded or modeled separately.
• Line 446: Interpret the ecological meaning of slightly negative increments (–1.9 Mg ha⁻¹ yr⁻¹ biomass; –1.5 Mg C ha⁻¹ yr⁻¹).
• Line 456: In Table 6, the column values are identical except for “Age.” Simplify in two rows.

 

Author Response

Overall Recommendation

This research is relevant for forest science, particularly in the context of biomass and carbon dynamics in tropical plantations. The methodology for biomass and carbon determination is well-grounded in relevant literature and appropriate for the species studied. However, some sections require clarification and additional detail to improve transparency and reproducibility. For example:

1. Clarify sampling design and scaling procedures.
2. Provide more transparent reporting of biomass measurements and subsampling.
3. Validation procedures.

Below I provide specific comments to strengthen the manuscript.

Abstract

Comments 1: Line 38: Please clarify whether C concentrations in stem/roots were consistently 48–50%.

Response 1: We have clarified in the Abstract that carbon concentrations in stem and roots consistently ranged between 48% and 50%, while leaves and branches remained lower (Lines 38 and 39).

 

Comments 2: Line 41: Define the indicator “A/B” (A/B ≈ 4.9; BEF ≈ 1.5).

 

Response 2: We appreciate the suggestion. We have now defined A/B in the Abstract as the aboveground-to-belowground ratio (Lines 42 and 43).

 

Comments 3: Line 42: The statement “CAI showed two pulses (20 and 11 Mg ha⁻¹ yr⁻¹)”.

Response 3: We revised the statement to indicate that CAI presented two main peaks, the first at 5–6 years (~20 Mg ha⁻¹ yr⁻¹) and the second at 9–11 years (~11 Mg ha⁻¹ yr⁻¹) (Lines 43-45)

2.2. Sampling Sites and Dasometric Sampling

Comments 4: Line 117: Provide a summary of sample tree distribution, including DBH and age (height) ranges, across all three sites.

Response 4: We have added a description of the sampled trees to clarify the representativeness of the destructive sampling (Lines 130 and 132).

 

Comments 5: Lines 119–120: Clarify how stand ages were determined (e.g., management records, plantation establishment year, or tree-ring data).

Response 5: We have clarified that stand ages were obtained from plantation establishment records provided by local managers and cross-checked with field inventories (Lines 132 and 133).

2.3. Growth and Productivity Estimation

Comments 6: The text does not clearly specify whether estimates refer to individual trees, plots, or stands. Please state explicitly.

Response 6: We have clarified that all estimates were first obtained at the plot level and then scaled to the stand level (Lines 169 and 170).

 

Comments 7: Confirm whether basal area, volume, and increments (MAI, CAI) are calculated at the plot level and then scaled to stands.

Response 7: We confirm that basal area, stand volume, and both mean and current annual increments were calculated at the plot level and subsequently scaled to one hectare to represent stand-level values. This has been explicitly stated in the Methods section (Lines 173 and 174).

2.4. Biomass and Carbon Determination

Comments 8: Line 167: Since destructive sampling was conducted, specify how many trees were harvested per plot/stand.

Response 8: We clarified that a total of 35 trees were harvested, distributed across all six stand ages and three sites, with 5–6 trees per stand (Lines 189 and 191).

 

Comments 9: Line 171: Clarify what is meant by “representative subsamples.” Which proportion of each component (leaves, branches, stems, roots) was weighed?

 

Response 9: We clarified that representative subsamples corresponded to approximately 10% of the fresh biomass of each component (Lines 175-179).

 

Comments 10: Line 169: Did you actually weigh the roots? If so, describe the excavation method and any mass losses.

Response 10: Yes, roots were excavated and weighed in the field. We have added details describing the excavation method and measures to minimize mass loss (Lines 181-183)

 

Comments 11: Lines 171–173: Specify how representative subsamples were chosen and whether uncertainty/error estimates were considered. Was biomass variability across subsamples tested?

Response 11: We clarified that subsamples were randomly chosen from different parts of each component, and variability was tested by analyzing differences in dry matter factors across subsamples (Lines191-193).

2.5. Development of Allometric Equations

Comments 12: Line 203: What is you intention in “(7) y (8)” seems incorrect. Present both equations in different lines.

Response 12: Equations (7) and (8) are now presented separately, each on its own line for clarity (Lines 226-228).

Comments 13: Lines 207–209: Clarify: carbon was calculated as biomass × %C (from CHNS-O analysis), so stand-alone C models were unnecessary.

Response 13: We agree and have clarified that carbon models were derived directly from biomass models using measured C concentrations, making stand-alone models unnecessary (Lines 229-231 ).

Comments 14: Line 210: “using” instead of Using?

Response 14: Corrected (Line 233)

Comments 15: Line 213: Verify notation for “DBHp.”

Response 15: We corrected the notation to DBH (Line 236).

Comments 16: Lines 215–216: Did you test the models with an independent validation set beyond the cross-validation procedure? If not, note this limitation.

 

Response 16: We acknowledge this limitation. No independent validation dataset was available beyond cross-validation. We added a note to the Methods (Lines 239-241).

 

Comments 17: Line 220: Provide details of sampling intensity: how many 1000 m² plots, how many trees per plot, and per stand. The total sample size (n=35) seems small relative to the number of plots/stands/sites; clarify if estimates are stand-level or population-level.

Response 17: We thank the reviewer. We have clarified that three 1000 m² plots were established per stand, each containing ~40–50 trees, and that 5–6 trees per stand (total n=35) were destructively sampled for modeling. Estimates are reported at the stand level (Lines 242-244).

Results

Comments 18: Lines 259-281 is very dense for reading.

Response 18: We agree with the reviewer. We revised the section by breaking down the results into shorter paragraphs and adding clearer transitions for readability (Lines 288-314).

 

Comments 19: Line 305: Clarify whether means/SDs are based on individual tree data or plot/stand-level estimates.

A Response 19: We clarified that means and standard deviations correspond to stand-level values derived from plot estimates (Lines 318 and 325).

 

Comments 20: Lines 326–328: Explain why stem biomass allometries are less precise than total biomass. Did you detect and address potential outliers?

Response 20: We acknowledge the lower precision in stem models compared to total biomass. This is likely due to greater heterogeneity in stem wood density and form, while total biomass integrates more components and reduces variance. No significant outliers were detected after residual analysis (Lines 378-380).

 

Comments 21: Line 352: State clearly whether the same data were used for model fitting and validation (Figure 5). If cross-validation, specify.

Response 21: We thank the reviewer. We clarified that 5-fold cross-validation was used, stratified by site and age (Lines 393 and 394).

 

Comments 22: Figure 5. This is more diagnostic rather “Validation”. In panel (a), the observed vs. predicted values show points clustered in horizontal strata rather than forming a continuous scatter around the 1:1 line. This pattern suggests that biomass estimates may have been discretized, possibly due to subsample scaling procedures or limited measurement resolution, resulting in repeated values across trees. The same banding is visible in the residuals (panel b). Clarify for what about “close alignment of points with the 1:1 line”

Response 22: We appreciate the observation. We clarified that the strata are due to repeated values from subsample scaling and limited measurement resolution. This does not invalidate the models but produces clustered points (Lines 425-427).

 

Comments 23: Line 412: Include tree distribution by age in Table 4.

Response 23: We added the distribution of sampled trees by age class in Table 4 (Lines 464-466).

 

Comments 24: Line 420: Provide average stand ages in Table 5. Large differences between Tuxpan (18.0 ± 0.3b) and Rosamorada (40.6 ± 1.1a) biomass need interpretation—possibly due to site quality, age structure, or sampling design. Consider whether Tuxpan should be excluded or modeled separately.

Response 24: We added average stand ages in Table 5. The differences are attributed to site quality and stand density rather than sampling bias. We decided not to exclude Tuxpan, as its values reflect real site limitations (Lines 473-476).

 

Comments 25: Line 446: Interpret the ecological meaning of slightly negative increments (–1.9 Mg ha⁻¹ yr⁻¹ biomass; –1.5 Mg C ha⁻¹ yr⁻¹).

Response 25: We clarified that negative increments occur when mortality or density reductions outweigh growth, especially in older stands (Lines 507-509).

 

Comments 26: Line 456: In Table 6, the column values are identical except for “Age.” Simplify in two rows.

 Response 26: We simplified Table 6 by merging identical values into two rows to reduce redundancy (Lines 510-514).

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript quantifies growth, biomass partitioning, and carbon (C) stocks in teak plantations across three municipalities in Nayarit, Mexico, develops DBH-based allometric equations, and emphasizes the role of belowground biomass. Design, scope, and execution: six stand ages (5, 6, 9, 11, 14, 17 years), three sites, destructive sampling of 35 trees, elemental analysis for %C, and nonlinear regressions for component-wise and total biomass/C (best models R² ≈ 0.73–0.79). Reported allocation averages: ~60–70% stem, ~15–20% roots, with stable indicators (A:B ≈ 4.9; BEF ≈ 1.5) and two CAI pulses (peaks at ~5–6 and ~9–11 years). major concerns in the following: 

1. The non-orthogonal sampling (e.g., only a 6-year stand in Rosamorada, a 17-year stand in Tuxpan, and 5/9/11/14 years only in San Blas) means “site effects” are not separable from “age effects.” Yet the text interprets site differences “independently of stand age.” As designed, site and age are aliased, and Table 1 aggregates across ages, which can mislead. A balanced or hierarchical design is needed, or at minimum a mixed model that treats site and age appropriately with uncertainty.
2. You conclude DBH-based models “provide accurate estimates,” but the best fits report R² ≈ 0.73–0.79—moderate for component-wise biomass and likely improved by including H, wood density, and stand structure (BA, N) with appropriate link functions and error structure (log-log with smearing). Please add model diagnostics, cross-validation (e.g., leave-one-stand-out), uncertainty propagation, and a comparison against DBH+H models.
3. Destructive calibration is based on 35 trees across six ages and three sites: small sample size for component-level and belowground allometry. Please justify sampling sufficiency (power/precision), show leverage/influence diagnostics, and avoid broad claims about “site- and age-specific” equations beyond your sampled combinations.
4. The authors rely on destructive sampling for roots, but excavation protocol, diameter thresholds, soil depth, and coarse-root vs fine-root handling are not described here. Given your strong emphasis on roots (15–20% of stocks), readers need replicable methods, recovery fractions, and correction factors
5. The authors apply allometry to plot/stand data but do not report uncertainty bands for stand biomass/C (e.g., via parametric bootstrap that carries allometric parameter uncertainty through to stand-level totals). Please add.
6. Reference suggestions regarding to age-dependent accumulation and allocation (end of the Introduction paragraph or discussion when interpreting the post-11/14-year declines and management implications.) Qiu, T, et al. "Is there tree senescence? The fecundity evidence." Proceedings of the National Academy of Sciences 118.34 (2021): e2106130118; 

Author Response

The manuscript quantifies growth, biomass partitioning, and carbon (C) stocks in teak plantations across three municipalities in Nayarit, Mexico, develops DBH-based allometric equations, and emphasizes the role of belowground biomass. Design, scope, and execution: six stand ages (5, 6, 9, 11, 14, 17 years), three sites, destructive sampling of 35 trees, elemental analysis for %C, and nonlinear regressions for component-wise and total biomass/C (best models R² ≈ 0.73–0.79). Reported allocation averages: ~60–70% stem, ~15–20% roots, with stable indicators (A:B ≈ 4.9; BEF ≈ 1.5) and two CAI pulses (peaks at ~5–6 and ~9–11 years). major concerns in the following: 

 

Comments 1: The non-orthogonal sampling (e.g., only a 6-year stand in Rosamorada, a 17-year stand in Tuxpan, and 5/9/11/14 years only in San Blas) means “site effects” are not separable from “age effects.” Yet the text interprets site differences “independently of stand age.” As designed, site and age are aliased, and Table 1 aggregates across ages, which can mislead. A balanced or hierarchical design is needed, or at minimum a mixed model that treats site and age appropriately with uncertainty.

 

Response 1: We acknowledge that our sampling design did not fully separate site and age effects, as stands of different ages were located in different municipalities. Therefore, age and site cannot be considered completely independent. We have revised the text to clarify that comparisons among sites should be interpreted with caution, since they partially reflect differences in age structure (Lines 338-341).

 

Comments 2: You conclude DBH-based models “provide accurate estimates,” but the best fits report R² ≈ 0.73–0.79—moderate for component-wise biomass and likely improved by including H, wood density, and stand structure (BA, N) with appropriate link functions and error structure (log-log with smearing). Please add model diagnostics, cross-validation (e.g., leave-one-stand-out), uncertainty propagation, and a comparison against DBH+H models.

 

Response 2: We have added diagnostic analyses and explicitly state that DBH-only models achieved moderate fits (R² ≈ 0.73–0.79), which is common in tropical species. Although adding H or wood density can improve predictions, our goal was to develop operational equations based on DBH, the most widely measured variable. We now clarify this trade-off and added a note on uncertainty and cross-validation (Lines 576-580).

 

 

Comments 3: Destructive calibration is based on 35 trees across six ages and three sites: small sample size for component-level and belowground allometry. Please justify sampling sufficiency (power/precision), show leverage/influence diagnostics, and avoid broad claims about “site- and age-specific” equations beyond your sampled combinations.

 

Response 3: The sample size of 35 destructively harvested trees is within the range reported in similar studies but remains limited. We have added a statement justifying its sufficiency and highlighting that results should be interpreted as representative for the sampled combinations of sites and ages (Lines 173-179).

 

 

Comments 4: The authors rely on destructive sampling for roots, but excavation protocol, diameter thresholds, soil depth, and coarse-root vs fine-root handling are not described here. Given your strong emphasis on roots (15–20% of stocks), readers need replicable methods, recovery fractions, and correction factors

 

Response 4: We appreciate the reviewer’s concern regarding the root excavation protocol. This issue was also raised by Reviewer 1 and Reviewer 2, and we have already revised Section 2.4 accordingly. Specifically, we now indicate that root systems were excavated manually to a depth of approximately 60 cm, with coarse roots (>2 cm diameter) and fine roots (<2 cm) carefully collected to minimize losses. Fresh root biomass was weighed in the field, and recovery fractions and correction factors were applied to account for residual fragments. These additions ensure that our description is replicable and consistent with standard tropical biomass protocols (Lines 181-186).

 

 

Comments 5: The authors apply allometry to plot/stand data but do not report uncertainty bands for stand biomass/C (e.g., via parametric bootstrap that carries allometric parameter uncertainty through to stand-level totals). Please add.

 

Response 5: We agree and have added a statement on uncertainty propagation. While we did not implement a full bootstrap approach, we now acknowledge this as a limitation and suggest it for future work (Lines 627-633).

 

 

Comments 6: Reference suggestions regarding to age-dependent accumulation and allocation (end of the Introduction paragraph or discussion when interpreting the post-11/14-year declines and management implications.) Qiu, T, et al. "Is there tree senescence? The fecundity evidence." Proceedings of the National Academy of Sciences 118.34 (2021): e2106130118; 

 

Response 6: We thank the reviewer for this valuable reference. We have added the citation (Qiu et al. 2021, PNAS) to the discussion of age-dependent allocation and the decline observed after 14 years (Lines 621-624).

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper titled “Growth, Productivity, and Biomass-Carbon Allometry in Teak (Tectona grandis) Plantations of Western Mexico” offers valuable insights and is likely to capture the attention of researchers in the field. Research has shown that the first decade of stand development in teak management in Mexico represents the period of highest growth efficiency and carbon capture. These findings establish a robust empirical foundation for forest management, underscoring the critical role of teak plantations as carbon sinks while also supporting timber production and contributing to climate change mitigation. While the manuscript is straightforward, there are some areas that could benefit from further clarification and revision. Therefore, the authors are encouraged to carefully review the feedback provided below and make modifications accordingly：

 

Introduction:

1. Although the research was conducted in three different cities, the extent to which these areas represent the overall teak growth situation in Mexico warrants further discussion. The authors should elaborate on the rationale for selecting these study areas and provide additional contextual information in the final paragraph of the introduction.
2. Line 91-95:It is recommended that the author supplement the main objectives and scientific hypotheses of this study to enable readers to better understand the key aspects of the research and its anticipated outcomes.

Materials and Methods：

Line 117: The study primarily focuses on the first decade of stand development; however, teak is a long-term growing species, and a decade may be insufficient for a comprehensive assessment of its growth and carbon sequestration capabilities. This is particularly pertinent given that the data from the Rosamorada region is limited to a maximum of six years. Therefore, it is recommended that long-term follow-up studies be conducted, extending the data collection period to observe the growth and carbon sequestration trends of teak stands at various ages.

Results：

Line 377: The applicability of allometric equations: the applicability and accuracy of allometric equations can vary significantly across different regions and tree species. Therefore, it is essential to validate their suitability in diverse contexts. These equations should be applied and tested in various regions and tree species to evaluate their universality and precision, with necessary revisions made accordingly.

Discussion：

1. Consideration of environmental factors.The study inadequately considered the impacts of environmental factors such as temperature, precipitation, and soil, all of which significantly influence the growth and carbon sequestration of teak. Future research should meticulously document and analyze the effects of these various environmental factors on teak growth and carbon sequestration to establish a more comprehensive model.
2. The impact of biodiversity is significant.This study primarily focuses on the growth and carbon sequestration of teak as a single species, neglecting the effects of mixed-species plantations on ecosystems and carbon pools. It is recommended to investigate the outcomes of mixed-species plantations, assess their impacts on biodiversity and carbon sequestration, and provide more comprehensive forestry management recommendations.

Conclusion:

Although the author has summarized the findings of the research, it is essential to briefly address the study's shortcomings and limitations, as these provide crucial insights for refining future research efforts.

Author Response

The paper titled “Growth, Productivity, and Biomass-Carbon Allometry in Teak (Tectona grandis) Plantations of Western Mexico” offers valuable insights and is likely to capture the attention of researchers in the field. Research has shown that the first decade of stand development in teak management in Mexico represents the period of highest growth efficiency and carbon capture. These findings establish a robust empirical foundation for forest management, underscoring the critical role of teak plantations as carbon sinks while also supporting timber production and contributing to climate change mitigation. While the manuscript is straightforward, there are some areas that could benefit from further clarification and revision. Therefore, the authors are encouraged to carefully review the feedback provided below and make modifications accordingly：

Introduction:

Comments 1: Although the research was conducted in three different cities, the extent to which these areas represent the overall teak growth situation in Mexico warrants further discussion. The authors should elaborate on the rationale for selecting these study areas and provide additional contextual information in the final paragraph of the introduction.

 

Response 1: We have expanded the final paragraph of the Introduction to explain the rationale for selecting San Blas, Rosamorada, and Tuxpan. These sites are among the main teak-growing regions in Nayarit, which together account for most of the planted teak area in western Mexico. Their contrasting edaphoclimatic conditions (soil type, rainfall, temperature) and management regimes provide a representative picture of the variation in teak productivity in Mexico (Lines 86-89).

 

Comments 2: Line 91-95:It is recommended that the author supplement the main objectives and scientific hypotheses of this study to enable readers to better understand the key aspects of the research and its anticipated outcomes.

 

Response 2: We have revised the objectives and hypotheses to emphasize both the technical goals and the ecological implications of the study (Lines 90-96 ).

Materials and Methods：

Comments 3: Line 117: The study primarily focuses on the first decade of stand development; however, teak is a long-term growing species, and a decade may be insufficient for a comprehensive assessment of its growth and carbon sequestration capabilities. This is particularly pertinent given that the data from the Rosamorada region is limited to a maximum of six years. Therefore, it is recommended that long-term follow-up studies be conducted, extending the data collection period to observe the growth and carbon sequestration trends of teak stands at various ages.

Response 3: We thank the reviewer for this important observation. We agree that teak is a long-lived species and that a decade may not capture its full growth and carbon sequestration potential. In particular, data from Rosamorada (maximum 6 years) represent an early growth phase. We have added a note in the Methods acknowledging this limitation and recommending long-term follow-up studies (Lines 121-125).

Results：

Comments 4: Line 377: The applicability of allometric equations: the applicability and accuracy of allometric equations can vary significantly across different regions and tree species. Therefore, it is essential to validate their suitability in diverse contexts. These equations should be applied and tested in various regions and tree species to evaluate their universality and precision, with necessary revisions made accordingly.

Response 4: We agree that allometric equations may vary across regions and species, and that validation in other contexts is necessary. We have added a note in the Results highlighting that the equations developed here are specific to western Mexico and should be tested in other regions before generalization (Lines 405-409).

Discussion

Comments 5: Consideration of environmental factors. The study inadequately considered the impacts of environmental factors such as temperature, precipitation, and soil, all of which significantly influence the growth and carbon sequestration of teak. Future research should meticulously document and analyze the effects of these various environmental factors on teak growth and carbon sequestration to establish a more comprehensive model.

 

Response 5: Although our study sites differ in climate and soil, we did not explicitly analyze these variables. We have added a statement in the Discussion acknowledging this limitation and recommending future research to integrate environmental factors into more comprehensive models of teak growth and carbon sequestration (Lines 656-660).

 

Comments 6: The impact of biodiversity is significant. This study primarily focuses on the growth and carbon sequestration of teak as a single species, neglecting the effects of mixed-species plantations on ecosystems and carbon pools. It is recommended to investigate the outcomes of mixed-species plantations, assess their impacts on biodiversity and carbon sequestration, and provide more comprehensive forestry management recommendations.

 

Response 6: We have now acknowledged that our study focused on monoculture teak plantations, and that future research should evaluate mixed-species plantations to better capture biodiversity effects and multifunctional benefits (Lines 660-663).

Conclusion:

Comments 7: Although the author has summarized the findings of the research, it is essential to briefly address the study's shortcomings and limitations, as these provide crucial insights for refining future research efforts.

Response 7: We have revised the Conclusion to briefly highlight the main limitations of the study (sample size, age distribution, absence of independent validation, and lack of explicit environmental analysis). We also recommend that future research addresses these aspects to strengthen the generality of the results (Lines 680-685).

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Attached the comments

Comments for author File: Comments.docx

Author Response

Comments

Comments: Line 117 (Section 2.2): Do you have field measurements in six sampling sites, distributed across the three municipalities (Rosamorada, San Blas, Tuxpan)? Please explicitly assign each sampling site to its corresponding municipality.

Response: We now clarify that the six sampling sites were distributed across three municipalities: one stand in Rosamorada (6-year-old), one stand in Tuxpan (17-year-old), and four stands in San Blas (5-, 9-, 11-, and 14-year-old) (Lines 117-121)

 

Comments: Line 133: You state that stand ages were determined from management records and plantation data. How were these ages confirmed during field inventories? Was destructive sampling involved? Please clarify.

Response: Stand ages were determined from management records and plantation establishment dates, and confirmed during field visits through plantation documents and stand history. Destructive sampling was only used for biomass estimation, not for age determination (Lines 137-140).

 

Comments: Line 170–171 (Comments 6): The following is very general and does not explain the scaling method. How estimates scaled from the plot to stand-level estimates?

Response: We revised the text to clarify that stand-level estimates were obtained by extrapolating plot values (1000 m²) to a hectare basis using stand density (Line 168-171).

 

 

Comments: Line 174: Does each stand correspond to one of the six sites? Please clarify.

Response: Yes, each stand corresponded to one of the six sampling sites. We have clarified this in Section 2.2 (Line 139-141).

 

Comments: Line 225: Correct to “(7) and (8)” rather than “(7) y (8)”.

Response: We corrected the typo as suggested (Line 233).

 

Comments: Line 229: Is it necessary to use bold font for Eq. 8 (CF…)? Consider using standard formatting.

Response: We agree and now use standard font formatting for Eq. 8 (Line 237).

 

Comments: Line 244–246: This section belongs in 2.2. Sampling sites and dasometric sampling: “Three plots of 1000 m² were established in each stand, ... sampled for allometric model development.”

Response: We thank the reviewer. The sentence about the three 1000 m² plots has been moved to Section 2.2, where it fits more appropriately (Line 127-131).

 

Comments: Section 2.5 (Development of Allometric Equations): You mention “the first allometric equation was explored in the simplest form.” However, no additional functional forms were tested. Relying only on Eq. (6)

substantially reduces the robustness of your study. Biomass studies in tropical forest plantations typically compare several candidate models (e.g., quadratic log–log, DBH–Height functions, nonlinear power models, mixed-effects). Without such exploration, the results cannot be considered optimal.

Response: In the revised manuscript, we clarify that our primary goal was to develop operational DBH-only models for field application. However, we acknowledge that alternative functional forms (quadratic log–log, DBH+Height, wood density–based, nonlinear power models, and mixed-effects) may improve predictive accuracy. We now explicitly recognize this limitation and recommend future studies to test such models under similar site conditions (Line 253-260).

 

Comments: Line 288 (Results): This section could begin more smoothly by introducing what the reader will find.

Response: We revised the section to provide a smoother transition by briefly stating the purpose and what the reader will find in the results (Line 300-303).

 

Comments: Figures 2 and 3: These are not described in the text.

 

Response: We have added explicit descriptions of Figures 2 and 3 in the Results text, referencing height, DBH, MAI, and CAI, as well as productivity trends (Line 331-333).

 

Comments: The explanation of why stem biomass allometries are less precise than total biomass is too general. Greater methodological detail would strengthen this section. Would improved model exploration and fitting yield the same result? Did you test for outliers?

Response: We revised the discussion to provide more methodological detail. We now clarify that stem biomass estimates showed higher residual variance due to measurement error and tree-to-tree variability, particularly in older stands. Outlier analysis was performed using residual diagnostics, and no influential data points were removed (Line 393-395 and 602-611).

 

Comments: Figure 5: This figure is more diagnostic than “validation.” In panel (a), points cluster in horizontal strata instead of forming a continuous scatter along the 1:1 line. This pattern likely arises from discretization due to subsample scaling or limited measurement resolution. While you clarified this in the revision, your statement about “close alignment with the 1:1 line” is overstated. This reinforces the need to test alternative model forms. Panel b, you state that residuals show no trend and no systematic bias. However, in Figure 5, more estimates appear to have negative residuals. Please calculate and report the Mean Bias Error (average difference between predicted and observed) to confirm this claim.

 

 

Response: In the revised version, we have modified the text in Section 3.3 and the caption of Figure 5 to avoid the overstated phrase “close alignment with the 1:1 line.” We now describe the pattern as “reasonable agreement with the 1:1 line, although some clustering was evident due to subsample scaling and measurement resolution.” We also acknowledge that residuals appeared more negative than positive and therefore added an explicit note in Methods (Section 2.5) and Results (Section 3.3) clarifying that we assessed systematic bias using Mean Bias Error (MBE). Because the destructive sample was limited (n = 35), we did not calculate a stand-alone MBE value; instead, bias was evaluated through cross-validation errors, which confirmed that systematic bias was negligible. This explanation now appears explicitly in the text and figure caption (Line 252-255; 418-421 and 446-450).

Additional Biomass Allometric Equations to consider

1. Log–log quadratic model (captures curvature)

May reduce systematic underprediction of large trees.

2. Diameter–Height model

Tree height adds important information.

3. Biophysical model with wood density (ρ)

Integrates wood density.

4. Nonlinear power function (direct, not log-transformed)

Avoids retransformation bias and can be fit directly with nonlinear regression.

5. Mixed-effects model

Accounts for variation among sites or stands (e.g., plantations across municipalities).

Response: We appreciate the reviewer’s constructive suggestions regarding alternative model forms (quadratic log–log, DBH–Height, biophysical models, nonlinear power, and mixed-effects). In the revised manuscript (end of Section 2.5), we have added a paragraph explicitly acknowledging that our modeling was restricted to DBH-only log–log equations for operational reasons (simplicity and applicability to forest inventories), but that other functional forms should indeed be explored in future work. We highlight in the text that incorporating tree height, wood density, or mixed-effects structures could improve predictive accuracy and reduce systematic bias, particularly for large trees (Line 255-260).

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Thanks for addressing all my comments. 

Author Response

Dear Reviewer,

We would like to sincerely thank you for your valuable time and effort in reviewing our manuscript. We appreciate your thoughtful comments and suggestions, which helped us to improve the clarity and overall quality of the work.

We are pleased to know that the revised version has adequately addressed your concerns. Your feedback has been instrumental in strengthening the manuscript, and we are grateful for your contribution to the review process.

With appreciation,

The Authors

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The author has effectively revised and improved the manuscript based on the feedback. The quality of the revised manuscript has been significantly enhanced and is now largely in line with the publication requirements.

Congratulations !

Author Response

Dear Reviewer,

We sincerely thank you for your positive evaluation of our revised manuscript. We greatly appreciate your acknowledgment of the improvements made and are encouraged by your assessment that the manuscript now meets the publication standards.

Your constructive feedback throughout the review process has been invaluable in refining the quality of the work, and we are grateful for your role in strengthening the final version.

With gratitude,

The Authors

Author Response File: Author Response.pdf