The manuscript entitled ‘Phenology, fluxes and their drivers in major Indian agroecosystems: A modeling study using the Community Land Model (CLM5)’ benchmarked model simulated carbon, water, and energy fluxes across croplands in India at both site and regional level; following model validation, the authors examined impacts of climate change, elevated CO2, nitrogen fertilization, and irrigation on the long-term trends (1970-2014) of matter and energy fluxes across Indian croplands by conducting four model simulations. The authors concluded that N fertilization and irrigation are top drivers for the increasing trends of carbon fluxes with additional effects from elevated CO2. This work has potential management implications for agricultural production and food security under changing climate. The work is straightforward but needs some clarifications in modeling methods; the results section can also be improved to be more concise. See my comments below.

1. CTRL experiment setup: it seems that the control experiment is using transient climate and CO2 concentration but unclear to me which years of data is used for driving the simulations. Was it 1965-2014? And what forcing data was used for the spin-up runs? In addition, in lines 201-203, field observations were collected from experiments with varying N addition and irrigation treatments, but only control treatment was used. Does this baseline treatment match with the control settings used to drive model simulations regarding N addition and irrigation frequency and intensity?

1. Cropland definition in model vs reality (L324-327): when compare model simulated matter and energy fluxes FLUXCOM products at regional scale, the authors came up with a 10% crop cover threshold to define grid cells for model-data comparison. This brings up a question: are we confident that 10% threshold is good enough to ensure observed ecosystem fluxes (of which ~ 80% can come from non-crop vegetation in real world if only 20% of the grid cell is covered by crops) can represent the average behavior of cropland? It seems to me that in the model, there are only crops being simulated? Also, please cite the data source used to define cropland (e.g., what data is used to classify vegetation cover)

1. Results need to be shortened and tighten up: L260-270 are results from Reddy et al. 2025. While I understand the purpose is to convince readers to believe that the current model parameters are much improved for simulating Indian crops, I’m not sure if it is necessary to highlight this here as this is the not results of the current work. My suggestion is to use one sentence to state that current model parameters have been calibrated and validated in Reddy et al. 2025 and move all the details into supplementary if needed.

There are a lot of figure caption-style of writing when documenting results (e.g., L274-275, L301, L330 etc.). These should be moved to the corresponding figures as caption instead of being documented as results.

When documenting the trends and drivers on simulated trends (e.g., section 3.2 and 3.3), most of the patterns in carbon fluxes and crop physiology are consistent. So I think it can be more concise to combine results instead of documenting them separately. For instance, L390-430 can be combined into one paragraph by documenting the trends of GPP, AR, and NPP, and then how climate, CO2, N fertilization, and irrigation influence them correspondently. For instance, CO2 has significant positive influences on all carbon fluxes with effect size being 9, 3, 6 gC/m2/year for GPP, AR, and NPP respectively.

In several places the authors have stated how well CLM can capture the seasonality of cropland function (e.g., L314, L618, L621), but seasonal pattern is not shown in any of the figures included in main. Maybe there can be a better way to visualize the results if that’s something authors would like to highlight.

Some other comments:

1. In abstract, the authors kind of sell themselves short when speak about the broader impacts of the work (L23-26). I think using cross-scale data for model benchmark is not the most important impact (as a lot of other work has done). The authors can focus on their findings of changing climate, rising CO2, and management practices effects on crop yield, and how this can help agriculture activity planning in the face of global change.
2. L50: this sentence reads a bit awkward in terms of its logic. Maybe rephrase as “the investigation … revealed a significantly longer growing period compared to….”
3. L185: equilibrium states regarding what? Soil carbon stock? Aboveground biomass? Please specify
4. 2.3.1 Crop physiological parameters: when I saw parameters, I thought it is model parameters associated with crop physiology (e.g., Vcmax, SLA). But I then realized that it refers to model variables such as LAI, dry matter. Please consider changing to crop physiological variables to avoid confusion.
5. L240: change to ‘corresponding fluxes observations’ to be concise.
6. L303: CLM5 simulations are close to observations only in Feb. and Apr.?
7. L392: 18gC/m2/year? Same typos in L399, 400 and many other places throughout the manuscript.

8.L488: not wheat but rice?

1. Fig. 10, 12, 14, 15, 16 don’t have captions. All figures should have its stand-alone figure captions.
2. L618: I don’t see any figures demonstrating the simulated seasonality of crop physiology.

General comments

This manuscript presents a well-executed and ambitious modeling study that substantially advances our understanding of long-term phenological and biogeochemical trends across India’s major agroecosystems. However, several weaknesses limit its clarity and impact, including:

1. Abstract: The Abstract is somewhat long, scattered, and lacks a clear structure. I suggest following the standard mini-paper structure, including:
   1. brief background introduction,
   2. the research questions or issues to be addressed,
   3. a brief description of methodology, and
   4. the key numerical results.
2. Introduction: The Introduction is too long and needs to be more concise. Some terms/phrases are repeated. For instance, the description of knowledge gaps overlaps substantially with the objectives, and knowledge gaps are usually followed by specific objectives. These two parts should be placed closer together and rephrased to avoid redundancy.
3. M&M: The Methodology is described in great detail, but the structure is cumbersome, and essential elements—such as model limitations in flooded rice systems—appear too late in the paper.
4. Results: The Results section is extremely long and difficult to follow, dominated by dense figure panels with minimal synthesis.
5. Discussion: The Discussion section is combined with the Conclusion section, which differs from the standard format of scientific papers. Please explain why these two sections were merged.

Specific comments

1. Introduction: I suggest reframing this section into 4–5 paragraphs. Begin with why understanding the dynamics of crop phenology, fluxes, and their drivers in Indian agroecosystems is so important. Follow this with the challenges—specifically that current evaluations are limited to regional-scale estimates, which restrict our understanding of carbon fluxes, crop phenology, etc. The introduction to the CLM5 model should be mentioned earlier.
2. M&M: Although the information in the M&M section is important, I suggest adding a conceptual figure and a summary table for clarity. For example, consider the conceptual figure of the DNDC (Denitrification–Decomposition) model—this type of figure is a good example for summarizing Sections 2.1.1 to 2.1.4. For Section 2.3 “Evaluation of CLM5,” I suggest adding a summary table describing the data sources used for validation (e.g., papers, theses, public datasets, or data repositories).
3. The Results section is technically rich but dense, and could be streamlined with short summary paragraphs and/or reorganized figures.
4. Verb tense inconsistencies further distract from the narrative flow. Past tense should generally be used throughout the paper; present tense can be used in the Conclusion section.
5. Avoid starting a sentence with an abbreviation. Instead, use the full term.
6. P4 L119 – “The overall bias in simulating crop growth has improved from 0.51 to 0.24 and 0.48 to 0.25.” These improvements need clarification (e.g., units). If these are unitless values, that should be stated explicitly.
7. P8 L224 – The citation for Patel et al. should be modified to: “Patel et al., 2011 and 2021.”
8. P10 L277 – Please change to “p < 0.05.”
9. P11 L296 – Please explain the statement “The CLM5 simulations overestimate the fluxes during the early months and are close to observation estimates in the reproductive and maturity months” in the Discussion section.