Dear Dr. Pittendrigh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Yang (Jack) Lu, PhD

Academic Editor

PLOS ONE

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This work was funded in part by funds provided to JBB and BRP by Purdue University. This work was funded (BRP and JWM) in part by the United States Agency for International Development (USAID) under Agreement No. 7200AA18LE00003 as part of Feed the Future Innovation Lab for Legume Systems Research. This work was funded in part as part of the SAWBO RAPID research, supported by the United States Agency for International Development (USAID) under the terms of the agreement (BRP, JWM, and JBB). Any opinions, findings, conclusions, or recommendations expressed here are those of the authors alone and do not necessarily reflect the views or policies of USAID or the Food and Agriculture Organization of the United Nations.

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If this statement is not correct you must amend it as needed.

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Reviewers' comments:

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: Yes

Reviewer #3: Partly

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

Reviewer #1: N/A

Reviewer #2: Yes

Reviewer #3: Yes

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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

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: 1. Lack of Heterogeneity and Parameter Correlation

The model assumes that key parameters such as adoption rates, income gains, and campaign costs are fixed and independent across contexts. However, in practice, these factors are often highly heterogeneous and interdependent. For example, adoption rates vary widely depending on farmers’ literacy levels, cultural norms, prior exposure to digital tools, and socioeconomic conditions. Similarly, income gains differ substantially by crop type, farming system, and baseline productivity. Furthermore, multilingual adaptation does not guarantee equal comprehension — regional dialects, education levels, and digital literacy gaps often affect knowledge transfer effectiveness. Ignoring such heterogeneity and parameter correlations risks biasing ROI estimates and overstating scalability. I recommend that the authors expand the discussion to clarify how these correlations may impact overall findings.

2. Writing Quality and Clarity

The manuscript would benefit from improved English writing to enhance clarity, especially in the methodology and results sections. Certain assumptions and parameter derivations are difficult to follow, which may limit reproducibility and interpretability. Providing clearer explanations, illustrative tables, and stronger linkage between model assumptions and referenced literature would significantly improve readability. Prior PLOS ONE studies on agricultural investment modeling demonstrate the value of transparent parameter reporting and plain-language summaries when communicating findings to broader audiences.

3. Lack of Sensitivity and Robustness Analysis

The reported 30-year IRR of 657% is extraordinarily high compared to typical ex-post evaluations of agricultural extension programs, which often report returns closer to 30–50%. While the authors attribute this to cost efficiencies enabled by digital dissemination, the manuscript lacks sufficient sensitivity analyses and robustness checks to support this conclusion. For example, how would the IRR change if adoption rates were lower, income gains were overestimated, or translation and campaign costs were higher? Including formal sensitivity testing or Monte Carlo simulations would greatly improve the credibility and policy relevance of the results.

Reviewer #2: Strengths:

1. Analyze and recognize key control components for evaluating such programs.

2. Clearly point out the problems they want to solve and define the scenarios.

3. Correct and sound mathematical deductions during the model’s set-up section.

4. Comprehensive experiments with discussion and conclusion.

Major concerns

1. Methodology: Consider using a hybrid systems model instead of purely SI approach. The SI approach could be a sub-component and think about how to integrates it with cost-benefit dynamics, various groups of farmers (with different backgrounds) and time-varying decay.

2. Writing & Presentation: Consider better highlighting Subtitles like “Adoption base model” to improve readibility.

3. Experiment & Observations: Concerns raised on equations’ parameters in Table 1, some key parameters like SAWBO animation and YouTube cost per informed, only citing one paper may cause bias.

Minor comments & suggestions

Line 170, “indicate” typesetting or typo issues.

Requests for clarification

The paper ignores any social and policy-related factors and only focuses on technical modeling. Is that by deliberation?

Reviewer #3: The manuscript presents a framework for evaluating the economic impact of scalable agricultural learning initiatives.

- While the manuscript applies a systems modeling framework, it lacks comprehensive coverage of recent developments in this area. More recent applications of system approaches could be discussed to highlight what is novel in this work.

- The model used in the article relies on digital media, but lacks the nature of digital media.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: PONE-D-25-41099-Note.pdf

Dear Dr. Pittendrigh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Feb 12 2026 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 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,

Yang (Jack) Lu, PhD

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.

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

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #4: (No Response)

Reviewer #5: (No Response)

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

Reviewer #4: (No Response)

Reviewer #5: (No Response)

**********

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

Reviewer #4: (No Response)

Reviewer #5: (No Response)

**********

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

The PLOS Data policy

Reviewer #4: (No Response)

Reviewer #5: (No Response)

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

Reviewer #4: (No Response)

Reviewer #5: (No Response)

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Reviewer #4: 1.The original method section specifies that "Simulations varied system inputs (i.e., production cost, deployment cost, adoption cost) and parameters (i.e., sharing rate, adoption rate, annual income, income gain) by 1/4 x, 1/2 x, 2 x, and 4 x of their base model values to evaluate their impact on overall returns." However, it does not provide the basis for selecting the range of parameter variations. It is recommended to briefly supplement this explanation

2.The original text clearly states that "to accommodate regional linguistic diversity, voice-overs are used to produce language-specific versions of animations, in this case, 20 language variants. The initial cost for animation creation is $20,000, with an additional expenditure of $500 per language variant, which is used for the base model." It also confirms the economic feasibility of multi-language adaptation through simulation, but does not specify the contribution of the 20 language variants to the overall Internal Rate of Return (IRR). It is recommended to provide additional explanations.

3.The original text mentions that "the cost of digital learning (1.8 USD per informed individual) is lower than that of farmer field schools (9-35 USD per farmer)", but the comparison of economic efficiency between the two models is briefly discussed in the conclusion. It is suggested to supplement the conclusion with data from Table 1 in the original text.

Reviewer #5: The manuscript proposes a systems modeling-based framework for assessing the economic impact of scalable agricultural learning programs. The research questions are of practical significance, however the manuscript still has several areas for improvement.

1. The contribution of the manuscript should be further elaborated in the Introduction.

2. The economic impact of the manuscript should be fully analyzed in conjunction with existing literature, such as Potential of large language models in blockchain-based supply chain finance. Enterprise Information Systems, 19(11), 2541199 and Decentralized finance (DeFi): a paradigm shift in the Fintech. Enterprise Information Systems, 18(9), 2397630.

3. It is recommended to further analyze the limitations of the manuscript in Conclusions.

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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 #4: No

Reviewer #5: No

**********

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Investment Modeling for Scalable Agricultural Learning

PONE-D-25-41099R2

Dear Dr. Pittendrigh,

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.

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Kind regards,

Yang (Jack) Lu, PhD

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments: