the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jun 2025
Phenology, fluxes and their drivers in major Indian agroecosystems: A modeling study using the Community Land Model (CLM5)
Abstract. Agroecosystems are the largest land use category, covering more than half of the land surface in India, yet the understanding of spatio-temporal variability of the terrestrial fluxes over these ecosystems is limited. Previous studies are mostly at the site scale, relying on eddy covariance observations that fail to capture the spatial variations across diverse climatic regions of India. The only regional-scale study, Reddy et al. (2023), is limited to wheat crops and lacks the robust model calibration, leading to higher uncertainties in simulated crop physiology and carbon uptake across diverse climatic regions. This study is the first to comprehensively investigate long-term trends (1970–2014) in crop physiological parameters and terrestrial fluxes across major croplands of India. This study uses a robustly calibrated Community Land Model version 5 (CLM5) to conduct numerical experiments for understanding the influence of natural and management factors on crop physiology and terrestrial fluxes. CLM5 simulations show Pearson's correlation coefficients exceeding 0.6 for regional carbon fluxes and 0.95 for regional yield estimates. The results show that crop physiology parameters have increased more than twofold since the 1970s, with crop carbon uptake by agroecosystems doubling, while respiratory losses decreased due to improved nitrogen fertilization. The largest impact is due to nitrogen fertilizer usage and nitrogen-related processes, which contributed to more than 50 % of the observed trend in crop physiology parameters and carbon uptake in both rice and wheat. Followed by irrigation application and increasing atmospheric carbon concentration. The results further reveal that CLM5 performs particularly well in estimating carbon fluxes during the cold, dry rabi season and simulates water and energy fluxes more accurately during the warm, wet kharif season. The results highlight the need to investigate the stomatal activity for crops in CLM5 and understand the reason for comparatively poor simulation of carbon fluxes in the kharif season and water and energy fluxes in the rabi season. This is the first study to address both the spatial and temporal variations in agroecosystem physiology and fluxes in India using a robustly calibrated and evaluated land model. Given the scarcity of studies on terrestrial fluxes in tropical agroecosystems, this work demonstrates the importance of using limited site-scale data to improve regional-scale models and enhance our understanding of tropical agroecosystems.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-1987', Anonymous Referee #1, 05 Oct 2025
- AC2: 'Reply on RC1', Narender Reddy, 18 Dec 2025
- AC3: 'Reply on RC1', Narender Reddy, 18 Dec 2025
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RC2: 'Comment on egusphere-2025-1987', Anonymous Referee #2, 29 Nov 2025
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:
- Abstract: The Abstract is somewhat long, scattered, and lacks a clear structure. I suggest following the standard mini-paper structure, including:
- brief background introduction,
- the research questions or issues to be addressed,
- a brief description of methodology, and
- the key numerical results.
- 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.
- 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.
- Results: The Results section is extremely long and difficult to follow, dominated by dense figure panels with minimal synthesis.
- 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
- 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.
- 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).
- The Results section is technically rich but dense, and could be streamlined with short summary paragraphs and/or reorganized figures.
- 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.
- Avoid starting a sentence with an abbreviation. Instead, use the full term.
- 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.
- P8 L224 – The citation for Patel et al. should be modified to: “Patel et al., 2011 and 2021.”
- P10 L277 – Please change to “p < 0.05.”
- 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.
Citation: https://doi.org/10.5194/egusphere-2025-1987-RC2 - AC1: 'Reply on RC2', Narender Reddy, 18 Dec 2025
- AC3: 'Reply on RC1', Narender Reddy, 18 Dec 2025
- Abstract: The Abstract is somewhat long, scattered, and lacks a clear structure. I suggest following the standard mini-paper structure, including:
Data sets
Dataset for manuscript - Phenology, fluxes, and their drivers in major Indian agroecosystems: A modeling study using the Community Land Model (CLM5) Narender Reddy and Somnath Baidya Roy https://doi.org/10.5281/zenodo.15291023
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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.
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:
8.L488: not wheat but rice?