Xu et al. (2026) Improving photosynthate allocation dynamic simulations of crops under water stress conditions

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-10-19
• Authors: Wei Xu, Wei Zhang, Yongqiang Yu, Qing Zhang
• DOI: 10.5194/gmd-19-9973-2026

Research Groups

• Laboratory of Atmospheric Boundary Layer, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
• Meteorological Service Center for the Core Areas of the Capital, Beijing Meteorological Service, Beijing 100029, China

Short Summary

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 optimized model improves the representation of physiologically regulated allocation processes and enhances the realism of crop simulations.

Objective

• To develop a mechanistic representation of photosynthate allocation that explicitly accounts for environmental controls.
• To improve the water module of the Agro-C model by integrating root water uptake characteristics, allowing water stress to be represented based on crop physiological responses.

Study Configuration

• Spatial Scale: Field scale, with data collected from various locations around the world.
• Temporal Scale: Daily to seasonal time scales, depending on the specific experiment or study.

Methodology and Data

• Models used: Agro-C model, a biogeophysical framework for simulating crop growth and SOC dynamics.
• Data sources: Meteorological data (solar radiation, sunshine duration, daily maximum and minimum temperature, and precipitation), soil data (bulk density, sand and clay content, and total nitrogen content), phenological data (emergence, heading, and maturity for wheat; emergence, tasseling, and maturity for maize), and management practices (irrigation amount, fertilizer N application rate, and timing of application).

Main Results

• The optimized Agro-C model showed higher accuracy in simulating aboveground biomass (AGB) with R 2 values exceeding 0.56 across all growth stages.
• The simulation accuracy for belowground biomass (BGB) was relatively lower, with R 2 values ranging from 0.33 to 0.72.
• The optimized Agro-C model performed well in simulating AGB at maturity, with R 2 values of 0.89 for maize and 0.68 for wheat.

Contributions

• This study provides a more physiologically meaningful basis for representing water stress in crop models by incorporating root water uptake processes.
• The optimized Agro-C model improves the representation of photosynthate allocation under water stress conditions, enhancing the realism of crop simulations.

Funding

• This research was supported by the National Natural Science Foundation of China (Grant No. 41661144007) and the Chinese Academy of Sciences (CAS) Strategic Priority Research Program (Grant No. XDA19020201).

Citation

@article{Xu2026Improving,
  author = {Xu, Wei and Zhang, Wei and Yu, Yongqiang and Zhang, Qing},
  title = {Improving photosynthate allocation dynamic simulations of crops under water stress conditions},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-9973-2026},
  url = {https://doi.org/10.5194/gmd-19-9973-2026}
}