Dear Dr.  Teh,

Please submit your revised manuscript by Nov 10 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Salim Heddam

Academic Editor

PLOS ONE

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Additional Editor Comments:

Reviewer 1#:

Dear authors,

I have just few comments to your interesting manuscript:

1) Please, check the keywords: L. 39 there should be "distribution", and not use just abbreviation "MsiaGen".

2) In the chapter Study Sites, L. 87, please introduce there why just 23 stations are considered (it is later in L. 102-103.

Please, introduce there the climate zonation according to the Koppen classification, the potential evapotranspiration

and a potential water budget.

3) L. 220, please, introduce the definition of dry and wet days.

4) The model Msia Gen should be well described in Methodology, do not do it in Conclusions (L.767-770 are not necessary),

in the chapter Conclusions go directly to your achievements.

Reviewer 2#:

The manuscript introduces MsiaGen, a stochastic daily weather generator tailored for Malaysia’s tropical climate. It emphasizes computational simplicity, use of daily data (instead of hourly), site-specific calibration, and application in oil palm yield modeling. The model employs Skew Normal distributions for temperature, Weibull for wind speed, and Gamma (with GEV-derived shape) for rainfall, with temporal dependence modeled via AR(1) processes and a first-order Markov chain. Validation across 23 sites shows good agreement with observations, though performance weakens for extremes (heavy rainfall and high wind speeds).

Strengths: The paper addresses the lack of simple, robust weather generators designed for tropical agricultural contexts. The paper also adapts statistical techniques (Skew Normal, Weibull, Gamma+GEV) to Malaysian climate, bridging a gap between advanced but data-hungry generators (e.g., AWE-GEN) and practical agricultural applications. Multi-site calibration/validation, metrics (NMAE, NMBE, KGE, AD test), and diagnostic plots (QQ, ECDF, hex-bin) are used for validation. Coupling MsiaGen with Sawit.jl for oil palm yield simulations strengthens the case for applied agricultural relevance.

Weaknesses: Only 3–5 years of data per site. This limits robustness, particularly for extremes. Authors acknowledge this, but stronger justification (or sensitivity analysis) would help. The Gamma distribution + Markov chain may not adequately capture heavy-tailed rainfall and long wet/dry spells. Authors briefly discuss alternatives (higher-order Markov, non-Gamma models), but don’t attempt even exploratory comparison. While validated across sites, it is unclear how MsiaGen performs outside Malaysia (e.g., equatorial Southeast Asia more broadly). This could affect generalizability. The choice of AR(1) and first-order Markov is justified for parsimony, but at the cost of realism during monsoon persistence. More quantitative discussion of wet/dry spell length reproduction is warranted. Since calibration sites had lower rainfall extremes than validation sites, performance differences may partly stem from this imbalance. A stratified validation approach might yield clearer insights. The manuscript is dense in statistical detail, which may overwhelm readers unfamiliar with distribution fitting. A summary table of all chosen distributions, parameters, and rationales would improve readability. Figures are strong, but some (ECDF panels for each site) are too numerous for the main text—consider moving to supplementary materials.

Suggestions: Test an alternative rainfall distribution (e.g., Generalized Pareto, mixed-exponential) for comparison, even if briefly, to show MsiaGen’s trade-offs. Add explicit metrics for wet/dry spell length reproduction and compare to observations. If possible, include one or two sites with ≥20 years of data to test parameter stability. Provide more detail on how inaccuracies in extremes (e.g., rainfall >100 mm) might impact oil palm yield modeling outcomes. Move highly technical derivations (e.g., parameter transformations for Skew Normal) to appendices/supplementary material. This keeps the main text accessible. Typo in keywords (“distibution” → “distribution”), improve figure captions (add sample sizes, error bars). The study is well-motivated and promising, but before acceptance the authors should:

• Better address the limitations in modeling extremes and persistence,

• Provide clearer evidence of MsiaGen’s stability and generalizability, and

• Streamline statistical detail for readability.

Comments: The calibration is based on relatively short weather records (3–5 years per site). This limitation likely contributes to underperformance in simulating extremes. It may be justified more clearly why this truncation was necessary and, if possible, provide a sensitivity analysis or at least include one or two sites with longer datasets to test parameter stability. The manuscript acknowledges underestimation of high rainfall (>100 mm) and wind speeds (>3–4 m s⁻¹). Since these extremes are critical for agriculture, especially for perennial crops like oil palm, the authors should:

• Provide explicit quantitative evaluation of how frequently such events are misrepresented.

• Explore or at least compare with alternative distributions (e.g., Generalized Pareto, mixed-exponential) that may better capture heavy tails.

The first-order Markov chain may inadequately represent monsoonal persistence. The discussion notes this but does not present quantitative validation. Spell length statistics (mean wet/dry spell length, frequency distribution) comparing observed vs. simulated data may be included. This would greatly strengthen confidence in MsiaGen’s ability to reproduce tropical rainfall characteristics. While the model is designed for Malaysia, it would be valuable to discuss explicitly whether MsiaGen can be extended to other equatorial regions with similar Af climates (e.g., Indonesia, Papua New Guinea). Even if not tested, a short discussion would help readers assess applicability. The yield simulations show close agreement overall, but the manuscript does not quantify how misrepresentation of extremes propagates into yield estimates. The following would need clarification:

• Do errors in extreme rainfall or wind events materially affect modelled yields?

• Could yield risk assessments underestimate extreme-event-driven yield loss?

The level of mathematical detail (e.g., parameter derivations for the Skew Normal distribution) may overwhelm readers. Consider moving some of these derivations to supplementary material and instead provide a concise summary table of distributions, parameters, and rationales in the main text. Figures showing ECDFs for every site are informative but numerous. Suggest moving some panels to Supplementary Information and retaining representative sites in the main text. Add sample sizes and error bars (where applicable) in figure captions. The manuscript states that full datasets are available but relies heavily on short station records. It may be clarified whether raw station data are archived in a national database and if readers can access them directly, or whether only processed datasets are provided.

Recommendation: The manuscript makes a valuable contribution to agricultural modeling in tropical environments. However, I recommend major revisions to address the limitations in extreme event representation, spell length validation, and presentation clarity. Once revised, the paper will be a strong candidate for publication.

[Note: HTML markup is below. Please do not edit.]

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: Dear authors,

I have just few comments to your interesting manuscript:

1) Please, check the keywords: L. 39 there should be "distribution", and not use just abbreviation "MsiaGen".

2) In the chapter Study Sites, L. 87, please introduce there why just 23 stations are considered (it is later in L. 102-103.

Please, introduce there the climate zonation according to the Koppen classification, the potential evapotranspiration

and a potential water budget.

3) L. 220, please, introduce the definition of dry and wet days.

4) The model Msia Gen should be well described in Methodology, do not do it in Conclusions (L.767-770 are not necessary),

in the chapter Conclusions go directly to your achievements.

Reviewer #2: The manuscript introduces MsiaGen, a stochastic daily weather generator tailored for Malaysia’s tropical climate. It emphasizes computational simplicity, use of daily data (instead of hourly), site-specific calibration, and application in oil palm yield modeling. The model employs Skew Normal distributions for temperature, Weibull for wind speed, and Gamma (with GEV-derived shape) for rainfall, with temporal dependence modeled via AR(1) processes and a first-order Markov chain. Validation across 23 sites shows good agreement with observations, though performance weakens for extremes (heavy rainfall and high wind speeds).

Strengths: The paper addresses the lack of simple, robust weather generators designed for tropical agricultural contexts. The paper also adapts statistical techniques (Skew Normal, Weibull, Gamma+GEV) to Malaysian climate, bridging a gap between advanced but data-hungry generators (e.g., AWE-GEN) and practical agricultural applications. Multi-site calibration/validation, metrics (NMAE, NMBE, KGE, AD test), and diagnostic plots (QQ, ECDF, hex-bin) are used for validation. Coupling MsiaGen with Sawit.jl for oil palm yield simulations strengthens the case for applied agricultural relevance.

Weaknesses: Only 3–5 years of data per site. This limits robustness, particularly for extremes. Authors acknowledge this, but stronger justification (or sensitivity analysis) would help. The Gamma distribution + Markov chain may not adequately capture heavy-tailed rainfall and long wet/dry spells. Authors briefly discuss alternatives (higher-order Markov, non-Gamma models), but don’t attempt even exploratory comparison. While validated across sites, it is unclear how MsiaGen performs outside Malaysia (e.g., equatorial Southeast Asia more broadly). This could affect generalizability. The choice of AR(1) and first-order Markov is justified for parsimony, but at the cost of realism during monsoon persistence. More quantitative discussion of wet/dry spell length reproduction is warranted. Since calibration sites had lower rainfall extremes than validation sites, performance differences may partly stem from this imbalance. A stratified validation approach might yield clearer insights. The manuscript is dense in statistical detail, which may overwhelm readers unfamiliar with distribution fitting. A summary table of all chosen distributions, parameters, and rationales would improve readability. Figures are strong, but some (ECDF panels for each site) are too numerous for the main text—consider moving to supplementary materials.

Suggestions: Test an alternative rainfall distribution (e.g., Generalized Pareto, mixed-exponential) for comparison, even if briefly, to show MsiaGen’s trade-offs. Add explicit metrics for wet/dry spell length reproduction and compare to observations. If possible, include one or two sites with ≥20 years of data to test parameter stability. Provide more detail on how inaccuracies in extremes (e.g., rainfall >100 mm) might impact oil palm yield modeling outcomes. Move highly technical derivations (e.g., parameter transformations for Skew Normal) to appendices/supplementary material. This keeps the main text accessible. Typo in keywords (“distibution” → “distribution”), improve figure captions (add sample sizes, error bars). The study is well-motivated and promising, but before acceptance the authors should:

• Better address the limitations in modeling extremes and persistence,

• Provide clearer evidence of MsiaGen’s stability and generalizability, and

• Streamline statistical detail for readability.

Comments: The calibration is based on relatively short weather records (3–5 years per site). This limitation likely contributes to underperformance in simulating extremes. It may be justified more clearly why this truncation was necessary and, if possible, provide a sensitivity analysis or at least include one or two sites with longer datasets to test parameter stability. The manuscript acknowledges underestimation of high rainfall (>100 mm) and wind speeds (>3–4 m s⁻¹). Since these extremes are critical for agriculture, especially for perennial crops like oil palm, the authors should:

• Provide explicit quantitative evaluation of how frequently such events are misrepresented.

• Explore or at least compare with alternative distributions (e.g., Generalized Pareto, mixed-exponential) that may better capture heavy tails.

The first-order Markov chain may inadequately represent monsoonal persistence. The discussion notes this but does not present quantitative validation. Spell length statistics (mean wet/dry spell length, frequency distribution) comparing observed vs. simulated data may be included. This would greatly strengthen confidence in MsiaGen’s ability to reproduce tropical rainfall characteristics. While the model is designed for Malaysia, it would be valuable to discuss explicitly whether MsiaGen can be extended to other equatorial regions with similar Af climates (e.g., Indonesia, Papua New Guinea). Even if not tested, a short discussion would help readers assess applicability. The yield simulations show close agreement overall, but the manuscript does not quantify how misrepresentation of extremes propagates into yield estimates. The following would need clarification:

• Do errors in extreme rainfall or wind events materially affect modelled yields?

• Could yield risk assessments underestimate extreme-event-driven yield loss?

The level of mathematical detail (e.g., parameter derivations for the Skew Normal distribution) may overwhelm readers. Consider moving some of these derivations to supplementary material and instead provide a concise summary table of distributions, parameters, and rationales in the main text. Figures showing ECDFs for every site are informative but numerous. Suggest moving some panels to Supplementary Information and retaining representative sites in the main text. Add sample sizes and error bars (where applicable) in figure captions. The manuscript states that full datasets are available but relies heavily on short station records. It may be clarified whether raw station data are archived in a national database and if readers can access them directly, or whether only processed datasets are provided.

Recommendation: The manuscript makes a valuable contribution to agricultural modeling in tropical environments. However, I recommend major revisions to address the limitations in extreme event representation, spell length validation, and presentation clarity. Once revised, the paper will be a strong candidate for publication.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: Yes:  Muttath Leo Franklin

**********

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Dear Dr. Teh,

Please submit your revised manuscript by Dec 17 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Salim Heddam

Academic Editor

PLOS ONE

Journal Requirements:

1. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Reviewer 1# :Dear authors,

Thank you for submitting your interesting manuscript. I have just the following comments:

1) Please, keep the limit of 300 words in the abstract.

2) In keywords, I think, it is better to use "oil palm yield".

3) Considering the structure of your paper, probably, better seems to use the standard structure required by the journal

guidlines: Material and methods ....

4) I prefer the chapter "Study site" (instead Study sites), descripting the area of your interest, those 23 meteorological stations

are just parts of this study site.

5) Please discuss the honogeinity of the analysed data-sets.

6) And, discuss also when the model performance has comparable value in all the climate district of your study.

7) The statement "MsiaGen is written in Julia programming language, and its full source code is available at:

github.com/cbsteh/MsiaGen" belongs not to the Conclusions, it should be introduced in the chapter describing the model.

Reviewer 2#: The development of MsiaGen fills a critical gap for tropical agricultural applications, where daily data are scarce and models like AWE-GEN are impractical.

The use of Skew Normal distributions for temperature and GEV-based variability in rainfall shape parameters is innovative and well justified.

The model was tested across 23 diverse sites, and validation metrics (KGE > 0.8; NMAE < 2.5%) are impressive.

The dual-scale evaluation (monthly and daily) and multiple diagnostics (KGE, QQ, ECDF, AD tests) demonstrate thorough statistical rigor.

The independent test via Sawit.jl simulations adds strong applied validation.

The manuscript is well structured, with equations clearly presented.

Data and model code availability via GitHub enhances transparency.

The manuscript is recommended for publication

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1:  Dear authors,

Thank you for submitting your interesting manuscript. I have just the following comments:

1) Please, keep the limit of 300 words in the abstract.

2) In keywords, I think, it is better to use "oil palm yield".

3) Considering the structure of your paper, probably, better seems to use the standard structure required by the journal

guidlines: Material and methods ....

4) I prefer the chapter "Study site" (instead Study sites), descripting the area of your interest, those 23 meteorological stations

are just parts of this study site.

5) Please discuss the honogeinity of the analysed data-sets.

6) And, discuss also when the model performance has comparable value in all the climate district of your study.

7) The statement "MsiaGen is written in Julia programming language, and its full source code is available at:

github.com/cbsteh/MsiaGen" belongs not to the Conclusions, it should be introduced in the chapter describing the model.

Reviewer #2: The development of MsiaGen fills a critical gap for tropical agricultural applications, where daily data are scarce and models like AWE-GEN are impractical.

The use of Skew Normal distributions for temperature and GEV-based variability in rainfall shape parameters is innovative and well justified.

The model was tested across 23 diverse sites, and validation metrics (KGE > 0.8; NMAE < 2.5%) are impressive.

The dual-scale evaluation (monthly and daily) and multiple diagnostics (KGE, QQ, ECDF, AD tests) demonstrate thorough statistical rigor.

The independent test via Sawit.jl simulations adds strong applied validation.

The manuscript is well structured, with equations clearly presented.

Data and model code availability via GitHub enhances transparency.

The manuscript is recommended for publication

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

**********

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NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

A stochastic daily weather generator for perennial crop simulations in tropical Malaysia

PONE-D-25-41380R2

Dear Dr. Teh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Lingye Yao, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #3: Yes

**********

Reviewer #1: Dear authors,

thank you for your kind revision of your paper, I very much appreciate your personal effort to improve the quality of your paper..

Reviewer #3: Thank you for submitting the revised manuscript and for the diligent effort you have made in addressing the reviewer's comments. I appreciate the time and effort you have devoted to revising the manuscript.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #3: No

**********