PCLM-D-25-00244

Maximising the economic value of water through adaptive, climate-informed irrigation scheduling for sugarcane

PLOS Climate

Dear Dr. An-Vo,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’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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Zhipin Ai

Academic Editor

PLOS Climate

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Dear authors,

Your manuscript has received four review reports: three recommending major revision and one recommending minor revision. I agree with the reviewers’ assessments. Please revise your manuscript accordingly.

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Based on the APSIM model, this study simulated the long-term sugarcane growth, yield, and water consumption under two irrigation frequencies from 1889 to 2022. Its objective was to maximize the economic value of irrigation water for the sugarcane industry in the Burdekin region, Queensland, Australia, by constructing an integrated simulation-optimization analysis model. While the research premise holds certain value, a comprehensive review of the full text reveals that the authors appear to have failed to achieve their intended goals.

In essence, the "sugarcane water-production function" and "sugarcane profit function" are computational models constructed via mathematical formulas. Their core role is to quantitatively calculate sugarcane yield by integrating different seasonal climatic conditions (such as variations in seasonal rainfall, temperature, and sunshine duration) and further estimate planting profits based on the calculated yield. Therefore, the authors merely conducted simple calculations of irrigation amounts and yields across different periods, and proposed the required irrigation volume based on water deficit. No technological breakthrough in irrigation optimization was achieved.

Optimization generally refers to the process of solving for independent variables under certain constraints using optimization algorithms (e.g., PSO, NSGA-II). For this study, optimization should presumably involve determining the optimal irrigation amounts (x₁, x₂, x₃, ...) for different growth stages of sugarcane. Within a specific irrigation range ([min x₁, min x₂, min x₃, ...], [max x₁, max x₂, max x₃, ...]), the goal would be to find the irrigation solution that maximizes both yield and profit. However, this objective was not accomplished by the authors.

The author is asked to modify the research plan, or to answer these questions in detail and give a convincing excuse.

My major concerns are:

(1) In the study, the starting point is proposed as "Improving water use efficiency in agriculture is crucial, given the escalating scarcity of water resources in semi-arid regions." Logically, the core problem to be addressed should therefore center on "how to enhance water use efficiency." However, the authors have instead defined their research objective as improving the economic benefits of water resources—a discrepancy that gives rise to obvious logical incoherence and confusion.

(2) Essentially, this study merely used the APSIM model to simulate two irrigation schemes, analyzed the water resource economic benefits of these two schemes, and then drew the conclusion that "using the same water volume, a more frequent irrigation schedule (Schedule 1) improved potential cane yield and profitability by reducing water stress." Such a research process and conclusion lack persuasiveness.

Specific comments:

(1) “However, water availability for food production often has a low priority compared with other sectors, such as domestic consumption or environmental services (e.g., Janjua et al. 2024). Solutions that help achieve the best economic outcome per unit of water use in agriculture are thus urgently required.” There seems to be no clear causal relationship between these two sentences. From a common-sense perspective, what can be inferred from the first sentence is "improving the efficiency of agricultural water use, i.e., producing more crop yield per unit of water consumed". However, what does this have to do with the economy? The authors need to present direct evidence to demonstrate the importance of the economy to water conservation.

(2) “According to Barnett et al. (2005), rising temperatures and excessive evaporation increase the demand for irrigation.” The references are too old, and it is recommended to use the references of nearly 5 years. References elsewhere in the paper should also pay attention to this problem.

(3) “Water use must be optimised to achieve the best economic outcome with limited available water, given that there are various factors associated with climate change that have a direct impact on irrigation scheduling and the amount of water that is needed for irrigation.” 'limited available water 'should be able to deduce 'need to improve water use efficiency ', rather than 'economic benefits of water resources '. Logic doesn 't work.

(4) The article should set the line number for reviewers to refer to when making comments. Fig.1 should have latitude and longitude ; figure 1 should highlight the study area, and the non-study area part does not need to be expanded. In addition, the map has too little information, and elevation, land, water and other research-related elements are not presented.

(5) “The Burdekin region has a low level of average annual rainfall of 650mm which requires and benefits significantly from applied irrigation.” Where is the data source ? It is best to add Figure 1b in Figure 1 to draw a multi-year change map of rainfall.

(6) “The Burdekin Falls Dam, with a storage capacity of 1860 GL, is an extremely reliable water source and annual allocations throughout the Burdekin River Irrigation Area (BRIA), leading to typically full water allocation with full irrigation practices.” Where is the data source ? The location of the dam is marked on the figure.

(7) “The Burdekin sugar cane growing region contains approximately 80,000 hectares of irrigated sugarcane and other crops.” Where is the data source ?

(8) “To examine the effect of irrigation timing, fixed intervals of 5-day (Schedule 1) and 10-day (Schedule 2) between irrigation applications were implemented.” Why choose these two programs.

Reviewer #2: The article is very well-written and addresses a highly relevant topic: maximizing the economic value of water and water use efficiency WUEin sugarcane irrigation systems within semi-arid regions characterized by water scarcity and climate variability.

The novelty of the article lies in the integration of a reference biophysical model (APSIM-Sugarcane) with a bio-economic optimization within a stochastic framework. The quantification of the economic water value AUD ML-1 across different climate scenarios and the identification of an optimization hierarchy WUE < Profitability < Yield represents a significant advance over irrigation scheduling practices based solely on yield.

The methodology is exceptionally well-structured and robust. The Main Method, based on Simulation-Optimization using the APSIM-Sugarcane model, provides high scientific rigor. The step-by-step process (tests of Schedule 1 vs. Schedule 2 and the three optimization goals) is clear, with sufficient references (e.g., Holzworth et al., 2014) confirming the replicability of the framework.

Required Revisions (Data and Model Transparency):

1. Data Foundation: The foundation for the quantitative data utilized in the model was not fully clarified in the text. It is crucial to include the specific input data used for the estimation model and its source (e.g., meteorological station data, historical yield data, economic cost data).

2. Model Explanation: An explicit paragraph explaining the APSIM-Sugarcane model is needed, detailing its advantages and disadvantages, the input data for the calculation (specifically, whether it was collected in situ or obtained from the nearest meteorological station), its validation process, and its limitations. Since many articles point to potential flaws in its validation for specific contexts, this needs to be addressed and elucidated.

3. Future works: I believe this methodology could be utilized for other applications, for example, in a city with an arid climate, to estimate the amount of water needed for gardens and trees and the associated cost to the municipality, or for use in family or urban agriculture in arid climates

In general, this paper is very well strutured and bring a very nice conclusions.

Reviewer #3: The manuscript generally meets publication criteria as it presents a technically sound study with conclusions largely supported by the data. The study utilizes a proposed integrated simulation-optimisation analysis, combining a calibrated APSIM-Sugarcane growth model with a novel bio-economic analysis. This approach aims to maximize the economic value of irrigation water in the sugarcane industry by informing seasonal irrigation scheduling. Sugarcane growth was simulated using the Agricultural Production Systems Simulator (APSIM)-Sugar model (version 7.10), a point-scale, modular modeling framework that incorporates soil, water, nitrogen, crop growth, and their interactions, driven by daily climate data. Long-term historical daily climate data from 1889 to 2022 were used for these simulations. Two irrigation schedules were simulated: a 5-day scenario (Schedule 1) and a 10-day scenario (Schedule 2), with varying applied water volumes from 5 mm to 100 mm per irrigation event.

Reviewer #4: This manuscript develops an integrated simulation–optimization framework (APSIM-Sugar + bio-economic analysis) to identify stage- and season-specific irrigation schedules for sugarcane in the Burdekin, quantifying trade-offs among yield, gross margin, and water value (AUD ML⁻¹). The long historical climate record and explicit comparison of 5- vs 10-day schedules are valuable, and the stage-wise optimal depths (Table 3) are practically useful.

Major comments.

The Abstract

The abstract claims forecast-informed/climate-informed scheduling, but methods use historical seasonal classes only; please align claims with methods or explain how forecasts are explicitly integrated.

Define “economic value of water” at first mention and state included/excluded costs. Report that profitability gains currently exclude extra operational costs from higher irrigation frequency.

Comments on the Introduction

The text repeatedly refers to forecast-informed/climate-informed scheduling, but the method uses historical seasonal classes (wet/normal/dry). Either (i) explain how probabilistic forecasts are integrated into the objective/decision rule, or (ii) soften to seasonal-conditioned optimisation.

End the section with a crisp paragraph stating the unmet gap (e.g., explicit comparison of fixed 5–10-day frequency against economic water value across plant and ratoon stages) and your specific contribution (definition of water value, adaptive production/profit functions).

Condense the global food security/climate paragraphs to 3–4 sentences tied directly to Australian sugarcane and water markets; move operational anecdotes to Study Area/Methods.

Standardize APSIM-Sugar; define PAWC, BRIA, plant cane, ratoon at first use; keep units consistent (t ha⁻¹, mm, ML, AUD ML⁻¹) and avoid mixing “tons” and “t”.

The “+10 t ha⁻¹ per 100 mm water” statement needs a precise, context-appropriate citation or local calibration caveat. For industry statistics, add the reference year and a consistent, recent source.

Organize the review around (1) optimisation/simulation for irrigation scheduling, (2) APSIM/SWAP applications in sugarcane, and (3) bio-economic models and economic water value. Remove duplicate entries (e.g., Kang et al., 2009) and fix formatting/DOIs.

Explicitly state the three objectives compared (yield, profit, economic water value) and define water value as gross margin per ML of seasonal irrigation, noting included/excluded costs.

Roadmap sentence: Close with a brief paper roadmap (study area, methods overview, 5- vs 10-day scenarios, evaluation metrics) and a short hypothesis (e.g., higher frequency improves outcomes until operational costs offset gains).

Suggested wording (examples):

“We benchmark irrigation schedules using historical seasonal condition classes (wet/normal/dry); integrating probabilistic seasonal forecasts is discussed as future work.”

“Given capped seasonal allocations, growers must choose frequency and depth that maximise profit and the economic value of water across plant and ratoon stages.”

Section 2 (Study area) & Figure 1

Move operational details (irrigation priorities, 9–12-day stretch, abandoning older ratoons) to Methods/Participatory approach or an appendix; keep Study Area descriptive (location, climate, soils, irrigation systems, water sources/allocations).

Define BRIA and DPI at first use; standardize terms (plant cane, ratoon).

Add core climate stats (mean/range rainfall, Tmax/Tmin, ET₀), dominant soil types with approximate PAWC, and shares of irrigation systems.

Use spaces and thousand separators: 650 mm, 1,860 GL, ha, ML; keep “t” (not “tons”).

Dam/allocations sourcing: Provide reference year and source for Burdekin Falls Dam capacity and “typically full allocation”; if historical, state the percentage of years.

Specify sample frame (number of growers/experts, selection criteria, interview dates, analysis approach) and ethics/consent status; move direct quotes to an appendix with participant codes.

Keep the note on limited seasonal-forecast adoption, but relocate the link to “forecast-adaptive scheduling” to Methods (how seasonal classes are formed) or Discussion (future integration).

Avoid repeating “maximising economic water value” here—reserve for Methods/metrics.

Figure 1—cartographic improvements

State the CRS/projection in the caption; optionally add a light graticule.

Increase resolution (≥300 dpi) and line weights; ensure the Burdekin boundary has consistent stroke and high contrast.

Legend: use “Sugarcane cropping area”; include data sources and year/licence for boundaries, catchment, cropping extent, and station.

Label Ayr DPI climate station with approximate coordinates and improve the symbol contrast.

Use a kilometres-only scale bar and colour choices that are colour-blind-safe.

Ensure the red catchment outline does not obscure coastlines; consider semi-transparent fill with a clear border.

Methodology

1. Specify the exact APSIM build (7.10.x), the .apsim file(s), cultivar parameters for Q117n, and APSoil #682 full profile table (layers, θsat/θfc/θwp, BD, CN, OC). Provide these as supplementary CSVs + a Git repo/Zenodo DOI.

2. You cite An-Vo et al. (2019), but this study should still report local calibration/validation performance (RMSE/MAE/ME/EF/R²) against observed yields at Ayr/BRIA for plant + ratoons, with a split (e.g., 70/30 by years) and a brief discussion of residuals.

3. Clarify application method and efficiency (furrow/center-pivot/drip; application efficiency/Deep drainage/Runoff assumptions). State whether irrigation water is added to the top soil layer or via an “irrigation efficiency” factor, and whether conveyance losses are costed.

4. Why 5- vs 10-day intervals? Justify these two fixed schedules (industry norm? pump/logistics constraint?) and explain how fixed-interval rules interact with rainfall events (e.g., skip if soil above threshold?).

5. Reconcile unit switches (mm per event, ML ha⁻¹ per season). Add a column in Tables 1–2 linking “mm per event” to expected seasonal total irrigation under each schedule and climate class.

6. Describe SILO variable versions, infilling, and any homogenization/QC. Explain how PET/evaporation was used in APSIM (e.g., FAO-56 vs SILO “Class A pan” conversions).

7. Define “wet/normal/dry” rigorously (e.g., terciles of total in-season rainfall; exact season months). Provide thresholds in a small table.

8. Justify fixed N rates across climates and crop ages; confirm that N is non-limiting (show N stress index ≈ 0). State assumptions for other limits (P, K, pests, weeds) and discuss implications.

9. Provide sources/years for the following costs: cane price, water price, electricity/diesel, labor, maintenance, and depreciation. Add a price sensitivity (±20–30%) and a brief stochastic analysis (e.g., triangle distributions) because results hinge on costs.

10. You compute profit/ML (economic water value). State clearly this is not biophysical WUE (yield/ET). Consider renaming to “economic water value (EWV)” throughout to avoid confusion.

11. In Methods (not Study Area), summarize farmer-interview protocol (sample size, dates, ethics/consent, question themes) and how insights informed schedules/constraints.

12. Report interannual variability (IQR/SD) of yields and profits; show confidence bands across 1889–2022 rather than single means only.

13. Note whether more frequent small irrigations increase drainage; report modeled drainage or soil water exceedance days.

14. : If frequency increases, pumping events increase; include an estimate of energy cost/emissions or acknowledge as a limitation.

15. Where you claim “greater” yields/gross margins, back with tests across years (paired Wilcoxon or mixed-effects with year as random effect). Report effect sizes and 95% CIs.

16. When stating thresholds (e.g., 20 mm in Sched-1; 35 mm in Sched-2), show how they were derived (marginal response ≤ ε). Consider derivative plots (Δyield/Δmm).

17. You report up to 50 t ha⁻¹ and AUD 1500 ha⁻¹ gaps; add ranges by crop stage and schedules, and indicate for which irrigation amounts these maxima occur.

18. Table 3 lists optima for yield, gross margin, and EWV. Add a short multi-objective view (e.g., Pareto points) and practical guidance: “If water-limited and aiming for EWV, choose X; if land-limited and aiming for tonnage, choose Y.”

19. Interpret why ratoon 1 sometimes exceeds plant-crop profitability (no planting cost); quantify by schedule and climate.

20. Replace long numeric tables with: (a) response curves per crop stage (yield & gross margin vs mm/event), (b) violin/box plots across climate classes, (c) heatmaps of profit by (mm/event × schedule). Keep Tables in Supplement.

17. Table 1/2: Add units in headers (“Average cane yield, t ha⁻¹”; “Average gross margin, AUD ha⁻¹”). Indicate seasonal totals alongside mm/event. Use consistent significant figures (1 decimal) and align. Keep bold for maxima but add footnotes explaining bolding criterion. Consider moving the exhaustive grids to Supplementary (main text show top 5 candidates per stage/goal).

18. Table 3: Title: “Optimal irrigation amount per event (mm) by objective, schedule, crop stage, and climate class.” Add a row with seasonal irrigation total (ML ha⁻¹) at the optimum for context.

19. Figure 1 (map): Add CRS, north arrow is present, include scale bar (it’s there—improve contrast), label BRIA and Ayr DPI more clearly, and ensure the sugar area layer source/date is cited.

20. Use “sugarcane” consistently (avoid “sugar cane”).

21. Units: add spaces (e.g., “10 kg N ha⁻¹”), use “1 September”, not “1st September”.

22. Replace hyphen minus with proper minus (−) and en dashes for ranges (1889–2022).

23. Clarify “evaporation” vs “ET” terminology.

24. Ensure “Schedule 1 = 5-day; Schedule 2 = 10-day” is stated once early and used consistently everywhere.

Section 4.2 Water use efficiency:

Please define water use efficiency (WUE) precisely at the outset (yield per unit water, profit per unit water, or water value in AUD ML⁻¹) and clearly distinguish among yield productivity, profitability, and water value; the text occasionally conflates these.

Add the unit conversion linking mm to ML ha⁻¹ (e.g., 10 mm = 0.10 ML ha⁻¹) to keep economic metrics dimensionally consistent.

Quantitative claims (e.g., 40–50 t ha⁻¹ and AUD 1200–1500 ha⁻¹ gaps at 5 mm) should be backed by explicit citations to Table 1–2 and Fig. 2–3 (panel and depth), and provide uncertainty (SE/CI) if available.

The statement that the 5-day schedule has “higher efficiency” needs statistical support (e.g., comparing peak values/areas under curves or an ANOVA across treatments), not only visual interpretation.

Fix formatting/style: “5- day” → “5-day”; ensure consistent use of “Schedule 1/2” and “wet/normal/dry.”

Explain why ratoons 2–3 achieve the highest water values (shorter season → lower seasonal irrigation volume) and quote the corresponding peak values from Table 5 (≈200–250 AUD ML⁻¹), by crop stage and season.

When noting “> AUD 250 ML⁻¹ (ratoon 3, Fig. 5b),” also report the optimal depth (from Table 3) to link the outcome to a practical prescription.

Reconcile/clarify the claim “up to AUD 600 ML⁻¹ improvement at 5 mm” with the maxima shown in Fig. 5 (~250 AUD ML⁻¹). If 600 represents a difference (wet – dry) at very small depths, specify the exact depth and stage or revise the number.

Briefly interpret negative water values in Table 5/Fig. 4 (costs exceed returns) and indicate recommended grower actions under such conditions.

Include a short sensitivity analysis (or at least a statement) for cane price, water price, and harvesting cost to test robustness of optimal depths and water values.

Support the mechanism “longer interval → greater water stress” by pointing to the shift in peak/plateau and steeper post-peak decline across Fig. 2–4.

Replace “synergy effect” with a more precise phrasing such as “complementarity between rainfall and small irrigation depths,” and add a brief physiological rationale (higher soil/air humidity → lower evaporative loss → stronger yield response).

Where you state that seasonal differences “fade beyond > 40 mm,” give a consistent range (e.g., 40–50 mm across stages) and cite the relevant curves.

Add a cautionary generalization note: results are specific to the cultivar/soil/prices and require local calibration before extrapolation.

Consider tightening the narrative: (i) define WUE; (ii) present quantitative evidence with figure/table calls; (iii) conclude with actionable guidance (optimal depths by stage/season).

Section 4. Discussion

• Clearly differentiate yield productivity, profitability (gross margin), and water value (AUD ML⁻¹/WUE) at the outset, and keep the logic: study goal → measurement → quantitative outcomes per objective.

• There is a wording inconsistency: thresholds are said to be “20 mm for the short (5-day) schedule and 35 mm for the more regular (10-day) schedule.” A 10-day schedule is less frequent/less regular—please correct.

• When stating “substantial opportunities/clear improvements,” provide numbers with figure/table calls (e.g., Tables 1–3; Figs. 2–5) and, if available, uncertainty (SE/CI).

• Explain why ratoons 2–3 deliver the highest water values (shorter season → lower seasonal irrigation volume) and cite the corresponding peaks from Table 5.

• Support the claim “the 5-day schedule yields higher profitability” with statistical evidence (e.g., peak comparisons/area-under-curve tests or ANOVA/GLM), not only visual inspection.

• Specify how threshold levels were identified (rule for “plateau/decline” on response curves).

• Add a price sensitivity check (cane price, water price, harvesting cost) because economic conclusions hinge on these parameters.

• Briefly interpret negative water values (costs exceed returns) and add recommended grower actions.

• Style fixes: “5- day” → “5-day”; harmonize “wet/normal/dry”; replace “synergy effect” with “complementarity between rainfall and small irrigation depths.”

• Add a short paragraph on generalizability limits (cultivar, soil, APSIM calibration, local water markets).

Section 5. Conclusion & Recommendation

• Tie each recommendation to actionable numbers (optimal depths by stage/season from Table 3) instead of general statements.

• Summarize a practical decision framework: (1) classify the upcoming season (wet/normal/dry); (2) select stage-specific optimal depth/intervals; (3) compare water value vs market water price to decide buy/sell.

• State the study limitations (no pests/salinity/ET constraints; perfect delivery assumed) and implications for field deployment.

• Clarify whether ratoons should always be prioritized in drought or whether this depends on water price and operational capacity.

• Provide brief implementation steps: soil-water monitoring, periodic (2–3-week) schedule revision with seasonal forecast updates, rules for lengthening/shortening intervals.

• Add a note on data/code availability (APSIM configs, price tables) for reproducibility and adoption.

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Maximising the economic value of water through adaptive, climate-informed irrigation scheduling

PCLM-D-25-00244R1

Dear Dr. An-Vo,

We are pleased to inform you that your manuscript 'Maximising the economic value of water through adaptive, climate-informed irrigation scheduling' has been provisionally accepted for publication in PLOS Climate.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Climate.

Best regards,

Zhipin Ai

Academic Editor

PLOS Climate

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Additional Editor Comments (if provided):

Reviewer Comments (if any, and for reference):

Reviewer's Responses to Questions

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1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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-->2. Does this manuscript meet PLOS Climate’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->

Reviewer #1: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

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-->4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #3: Yes

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

PLOS Climate does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #3: Yes

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-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1 : The revised manuscript has comprehensively addressed all reviewer comments. Significant improvements have been achieved in logical consistency, methodological clarity, interpretation of results, and academic writing. The research design is rigorous, the models and datasets are standardized and reliable, and the conclusions carry practical implications for sugarcane irrigation management. The manuscript generally meets the acceptance criteria, and I recommend acceptance after addressing the following minor issues.

The sentence “Climate change will likely exacerbate the problem with the projected increase in rainfall variability” in the abstract is weakly related to the core contributions and is recommended for deletion.

Sugarcane is not a food crop; the frequent mentions of “food production / food security” in the first paragraph of the Introduction are irrelevant to the study subject. It is recommended to revise the relevant statements to avoid conceptual misalignment.

This study adopts fixed irrigation intervals of 5 days (Schedule 1) and 10 days (Schedule 2). Although this design is based on local farmers’ practices, soil moisture threshold triggered irrigation is widely applied in modern precision agriculture. It is recommended that the authors clearly justify the use of fixed intervals, or emphasize adaptive scheduling based on soil water potential / soil moisture as a priority in future work to enhance the practical relevance and timeliness of the study.

Tables 2, 3, and 5 are overly lengthy. It is suggested that the full tables be moved to the supplementary materials, with only condensed results or trend summaries retained in the main text to improve readability.

Some sentences are wordy. A thorough language trimming and polishing prior to acceptance is recommended to enhance readability.

The economic analysis does not account for operational costs such as energy consumption and machinery depreciation associated with high frequency irrigation. It is recommended to add a statement in the conclusion: Practical on farm application should consider additional operational costs.

Reviewer #3:  This research provides a comprehensive analysis of optimizing sugarcane irrigation, emphasizing the need for goal-specific, climate-adapted, and crop-stage-sensitive strategies. It effectively integrates simulation results with practical considerations, offering valuable insights for improving water use efficiency and profitability in sugarcane production, particularly in regions facing climate variability.

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Reviewer #1: No

Reviewer #3: No

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