the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 May 2026
Improving photosynthate allocation dynamic simulations of crops under water stress conditions
Abstract. Accurately simulating crop photosynthate allocation under water stress is critical for predicting both food security and ecosystem carbon sequestration (SOC inputs). Current crop models often rely on static or growth-stage-fixed partitioning coefficients, which limits their ability to capture the physiological plasticity of crops responding to fluctuating environmental conditions. This study develops a water-driven, stage-dependent carbon allocation scheme within the Agro-C model to better represent crop responses to soil moisture variability. The scheme dynamically adjusts photosynthate partitioning coefficients for roots (PR) and leaves (PL), integrates the yellow-to-green leaf ratio (YGR), and incorporates a water stress–induced leaf senescence module. By linking carbon allocation to both soil moisture and crop development, the approach improves the representation of physiologically regulated allocation processes and enhances the realism of crop simulations. Model evaluation using extensive datasets for maize and wheat demonstrates substantial improvements in simulation accuracy, with R2 values reaching 0.77–0.95 for aboveground biomass (AGB) and 0.62–0.83 for belowground biomass (BGB). These results underscore the importance of dynamically representing carbon allocation under water stress and offer an improved framework for simulating carbon–water interactions in agroecosystems.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-2303', Anonymous Referee #1, 15 Jun 2026
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RC2: 'Comment on egusphere-2026-2303', Anonymous Referee #1, 15 Jun 2026
This study provides a comprehensive analysis of photosynthate allocation dynamics between aboveground and belowground organs under varying water stress conditions. Using an improved process-based model, Agro-C, optimized by integrating water- and phenology-dependent allocation coefficients, the study shows a significant improvement in simulation accuracy and reliability compared with the original framework. Furthermore, the study underscores the critical role of physiological plasticity in regulating soil carbon inputs, offering useful insights into crop–environment interactions. These findings not only fill an important knowledge gap in current crop growth and terrestrial carbon-cycle modeling, but also provide valuable implications for agricultural carbon management and climate adaptation strategies. Overall, the manuscript has substantial scientific value, and I recommend it for publication after Minor revision.
Major comments:
- Apart from water stress, have the authors considered other critical factors affecting photosynthate allocation, such as nitrogen fertilization? In agroecosystems, water and nitrogen dynamics are tightly coupled and often interactively affect crop physiology. While I acknowledge the complexity of modeling this coupled process and understand the specific focus of the current study on water-driven partitioning, the impact of nitrogen cannot be overlooked. Could the authors elaborate in the Discussion section on how they plan to incorporate nitrogen effects, particularly water–nitrogen interactions, in future modeling efforts?
- The manuscript highlights the importance of improved photosynthate allocation for estimating soil carbon inputs. However, this link could be discussed more clearly. The authors should briefly explain how improved AGB and BGB simulations may reduce uncertainty in residue and root carbon input estimates, while noting that long-term SOC sequestration would require further coupling with SOC turnover processes.
- The empirical equations for ΔPR, ΔPL, and ΔYGR in Tables 1 and 2 contain relatively large polynomial coefficients, especially during the reproductive and maturity stages. The authors should briefly clarify whether physiological bounds were imposed on PR, PL, and YGR, and whether these equations are only applicable within the listed ΔW ranges.
- After model optimization, how is the effect of changing irrigation regimes on photosynthate partitioning physically represented during the simulation? Please provide a clear description of this process.
Some minor issues:
- The abstract mentions “ecosystem carbon sequestration”, but the manuscript mainly evaluates crop biomass and soil carbon inputs rather than actual SOC stock changes. I suggest revising this phrase to “soil carbon inputs” or “potential implications for SOC dynamics.”
- In Tables 1 and 2, the fitted equations are useful but quite dense. The authors may consider moving some detailed parameter information to the Supplement or adding a short explanation of how readers should interpret the equations and their applicable ranges.
- The figures are generally informative, but some captions could be improved. For example, Fig. 2 should more clearly explain the meaning of the fitted curves and whether PR/PL and YGR use different axes.
- Some language polishing is needed. For example, “Up on the datasets…” should be revised to “Based on the datasets…”, and “Totally we collected…” could be changed to “In total, we collected…”.
Citation: https://doi.org/10.5194/egusphere-2026-2303-RC2 -
RC3: 'Comment on egusphere-2026-2303', Anonymous Referee #2, 01 Aug 2026
This study presents a valuable and timely contribution to the field of agroecosystem modelling by introducing a water-driven, stage-dependent carbon allocation scheme within the Agro-C model. The authors address a critical limitation in current crop models about the reliance on static or growth-stage-fixed partitioning coefficients, by dynamically linking photosynthetic allocation to soil moisture and crop development. The integration of root water uptake processes and the yellow-to-green leaf ratio (YGR) adds physiological realism. The model demonstrates substantial improvements in simulating aboveground and belowground biomass for maize and wheat across diverse water regimes. The manuscript is well-structured, the methodology is sound, and the results are compelling.
Some specific comments are shown as follows:
- The manuscript claims to "improve the water module" by integrating root water uptake characteristics, but the actual implementation relies heavily on empirical constants and literature-derived parameters. The authors should clarify what constitutes the novelty beyond the empirical calibration.
- There are several minor errors and inconsistencies in the equations and text. Please check them.
- The manuscript does not describe how soil hydraulic parameters were obtained for each site. Were these derived from the reported soil texture using pedotransfer functions, or were they directly measured? This affects the reproducibility of the model results.
- The Introduction packs excessive information into single sentences, making it difficult to follow the logical thread. For example, lines 36–51. I suggest the author to refine the statements.
- The authors use multiple terms interchangeably: "photosynthetic allocation," "assimilate partitioning," "carbon allocation," and "photosynthate allocation." While these are closely related, the lack of consistent terminology may create confusion.
- Although limitations in the present study are mentioned sporadically, they are scattered rather than consolidated. A dedicated, clearly headed subsection in Discussion would improve transparency and provide a clear roadmap for future research.
Citation: https://doi.org/10.5194/egusphere-2026-2303-RC3
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This study provides a comprehensive analysis of photosynthate allocation dynamics between aboveground and belowground organs under varying water stress conditions. Using an improved process-based model, Agro-C, optimized by integrating water- and phenology-dependent allocation coefficients, the study shows a significant improvement in simulation accuracy and reliability compared with the original framework. Furthermore, the study underscores the critical role of physiological plasticity in regulating soil carbon inputs, offering useful insights into crop–environment interactions. These findings not only fill an important knowledge gap in current crop growth and terrestrial carbon-cycle modeling, but also provide valuable implications for agricultural carbon management and climate adaptation strategies. Overall, the manuscript has substantial scientific value, and I recommend it for publication after Minor revision.
Major comments:
Some minor issues: