Xun et al. (2026) Whole agricultural-year modeling for irrigation strategies and crop responses in seasonally frozen regions

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-07-16
• Authors: Yihao Xun, Chen Sun, Barret L. Kurylyk, Dongyang Ren, Quanzhong Huang, Guanhua Huang, Xu Xu
• DOI: 10.1016/j.agwat.2026.110639

Research Groups

• State Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing, PR China
• Center for Agricultural Water Research, China Agricultural University, Beijing 100083, PR China
• Bayannaoer Research Institute, China Agricultural University, Beijing 100083, PR China
• Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
• Department of Civil and Resource Engineering and Centre for Water Resources Studies, Dalhousie University, Halifax, Nova Scotia, Canada

Short Summary

This study developed an enhanced agricultural-year (agr-year) scale model, SHAW-SC, to evaluate the effects of non-crop season irrigation on crop performance and water use in arid, seasonally frozen regions. The improved model was validated against field observations for three major crops (sunflower, spring wheat, and maize) in northwest China.

Objective

• To develop a robust, physically based decision-support tool for sustainable irrigation planning in arid, seasonally frozen agroecosystems.
• To evaluate the combined effects of non-crop season irrigation and crop season irrigation on crop performance and water use throughout the agr-year.

Study Configuration

• Spatial Scale: Field-scale numerical model to simulate the Soil-Plant-Atmosphere Continuum (SPAC) systems in a semi-arid to arid continental climate with seasonal soil freeze-thaw cycles.
• Temporal Scale: Agricultural year (agr-year), defined as the period from 1 October to 30 September of the following year.

Methodology and Data

• Models used: SHAW-SC, an enhanced version of the SHAW model that integrates modifications of SHAW with the Erosion Productivity Impact Calculator (EPIC) crop growth module.
• Data sources: Field observations of soil temperature, moisture, salinity, and crop growth for three major crops (sunflower, spring wheat, and maize) in the Hetao Irrigation District, northwest China.

Main Results

• The enhanced SHAW-SC model was able to accurately simulate soil water-solute-heat dynamics, canopy growth, and yield formation for sunflowers over two agr-years.
• Scenario simulations revealed that non-crop season irrigation is critical for securing crop emergence and mitigating crop stress, while also affecting agr-year crop water productivity through off-season evaporation losses.

Contributions

• The study contributes to the development of a robust, physically based decision-support tool for sustainable irrigation planning in arid, seasonally frozen agroecosystems.
• The enhanced SHAW-SC model offers a unified framework that integrates both freeze-thaw and crop growth processes within an agricultural-year scale approach.

Funding

• This research was supported by the National Natural Science Foundation of China (Grant No. 51879215) and the Chinese Ministry of Education's New Century Excellent Talents in University (NCET-13-0923).

Citation

@article{Xun2026Whole,
  author = {Xun, Yihao and Sun, Chen and Kurylyk, Barret L. and Ren, Dongyang and Huang, Quanzhong and Huang, Guanhua and Xu, Xu},
  title = {Whole agricultural-year modeling for irrigation strategies and crop responses in seasonally frozen regions},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110639},
  url = {https://doi.org/10.1016/j.agwat.2026.110639}
}