Chen et al. (2026) Optimal water supply and irrigation indicators for winter wheat in the main producing regions of China: Insights from the WMAIP integrated model

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-01-06
• Authors: Xianguan Chen, Huiqing Bai, Mengqi Fu, Wenran Yu, Yabo Sun, Xueqing Ma, Liping Feng
• DOI: 10.1016/j.agwat.2025.110061

Research Groups

• College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
• Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China
• Member Institutions of the Cross-Strait Science and Technology Industry Cooperation Base Under the Ministry of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou, China
• Institute for Development and Programme Design, China Meteorological Administration, Beijing, China
• College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
• State Key Laboratory of Smart Farm Technologies and Systems, Harbin, China

Short Summary

This study developed an integrated multi-model platform (WMAIP) to determine optimal water supply and irrigation indicators for winter wheat in China's main producing regions under varying precipitation patterns, finding that optimal water supply ranges from 214 mm to 416 mm depending on precipitation year-type and region, with corresponding irrigation indicators significantly lower than current practices.

Objective

• To systematically evaluate region-specific irrigation indicators for winter wheat under different precipitation patterns across China's main producing regions (MPC) using the Wheat Model Algorithm Integration Platform (WMAIP) and a composite indicator integrating high stability coefficients for yield and water productivity (WP).
• To simulate winter wheat yield and WP under three precipitation patterns and multiple irrigation schemes using WMAIP.
• To explore irrigation thresholds for achieving a high-yield stability coefficient (Y-HSC) and a high-WP stability coefficient (WP-HSC).
• To determine region-specific water supply and irrigation indicators at multiple spatial scales to maximize yield and WP with minimal water input.

Study Configuration

• Spatial Scale: Main producing regions of winter wheat in China (MPC), specifically focusing on Hebei, Henan, Shandong provinces, and Beijing, Tianjin municipalities. Six representative meteorological stations (Beijing, Tianjin, Xingtai, Jinan, Xihua, Nanyang) were selected for detailed analysis, and 48 stations were used for regional optimal water supply and irrigation indicator identification.
• Temporal Scale: Historical meteorological data covering the period 1959–2018 (60 years) for precipitation pattern classification and simulations.

Methodology and Data

• Models used: Wheat Model Algorithm Integration Platform (WMAIP), a multi-model ensemble framework integrating key modules and algorithms from leading domestic and international wheat models. WMAIP combines two phenological development algorithms ("clock" model and thermal time method), two biomass accumulation algorithms (radiation use efficiency and carbon assimilation methods), two yield formation algorithms (harvest index and biomass remobilization methods), and two water stress algorithms (ratio of actual to potential transpiration and ratio of water supply to demand), resulting in 16 distinct algorithmic configurations. The FAO 56 Penman-Monteith equation was used to calculate reference evapotranspiration (ETo).
• Data sources:
  • Meteorological data: Daily records of maximum temperature (℃), minimum temperature (℃), sunshine duration (h), precipitation (mm), relative humidity (%), and wind speed (at 2 m height) (m⋅s⁻¹) from the China Meteorological Data Sharing Service (http://data.cma.cn/) for 1959–2018.
  • Soil data: Soil hydraulic parameters (bulk density, permanent wilting point, field capacity, saturation water content) from national and regional soil surveys in China, including Zhang (2012), Soil Chronicles of China, Chen et al. (2021a), and the China Soil Scientific Database (http://www.soil.csdb.cn).
  • Crop data: Measured values of leaf area index (LAI), biomass, and yield from field experiments and literature corresponding to Shangzhuang, Quzhou, Huangfanquan, and Wuqiao stations for model calibration and validation.
  • Cultivar parameters: Specific parameters for Jimai 22 and Handan 6172 wheat varieties (Table S1 in supplementary material).

Main Results

• Yield Enhancement: Irrigation significantly increased winter wheat yields across the MPC, with the highest improvements (up to 90%) observed in the northern region under dry conditions, compared to less than 20% in the south.
• Water Productivity (WP):
  • In dry years, the highest WP values under irrigation were achieved in the northern and central regions, ranging from 1.56 to 1.85 kg⋅m⁻³.
  • In contrast, rainfed conditions in the southern region resulted in the highest WP across the MPC, reaching 1.88–2.06 kg⋅m⁻³.
  • Irrigation in the northern MPC significantly improved WP (13.6%–20.6% in dry years, 9.6%–11.4% in normal years, and 1.8%–3.8% in wet years) compared to rainfed conditions.
  • In the southern MPC, higher irrigation (W4) led to a reduction in WP (2.2%–4.8% in dry years, 5.1%–6.7% in normal years, and 8.8%–15.5% in wet years) compared to rainfed conditions.
• Optimal Total Water Supply: By integrating high-yield stability coefficient (Y-HSC) and high-WP stability coefficient (WP-HSC), the optimal total water supply was determined to be:
  • Dry years: 237–416 mm (mean 319 mm)
  • Normal years: 231–393 mm (mean 309 mm)
  • Wet years: 214–388 mm (mean 299 mm)
• Optimal Irrigation Indicators: The corresponding irrigation indicators were:
  • Dry years: 6–335 mm (mean 191 mm)
  • Normal years: 0–257 mm (mean 126 mm)
  • Wet years: 0–176 mm (mean 57 mm)
• Correlation with Water Deficit: The optimal water supply was strongly correlated (R² = 0.96) with the gap between potential evapotranspiration and available soil water, indicating its value as a predictive indicator for water management.
• Regional Differentiated Strategies:
  • Northern MPC: Three irrigations for dry years, two to three for normal years, and one to two for wet years for high yield stability. For high WP stability, W1–W2 for dry years, W1 for normal years, and W0–W1 for wet years.
  • Central MPC: Two irrigations for dry and normal years, and one to two for wet years for high yield stability. For high WP stability, W0–W1 for dry years, with no irrigation needed in normal and wet years.
  • Southern MPC: One irrigation for dry and normal years, and zero to one for wet years for high yield stability. No irrigation requirement across all year types for high WP stability.

Contributions

• Developed the Wheat Model Algorithm Integration Platform (WMAIP), a novel multi-model ensemble framework that enhances simulation accuracy and robustness by integrating diverse algorithmic modules for key crop processes, addressing inherent uncertainties of single-model approaches.
• Established a composite evaluation indicator integrating high stability coefficients for both yield (Y-HSC) and water productivity (WP-HSC) to comprehensively assess irrigation strategies under variable conditions.
• Provided region-specific and precipitation-pattern-dependent optimal water supply and irrigation indicators for winter wheat across China's main producing regions, offering quantitative benchmarks for sustainable water management.
• Demonstrated a strong correlation between optimal water supply and the difference between potential evapotranspiration and available soil water, enabling a predictive approach for water resource management.
• Proposed an irrigation decision-support system framework that translates climatological water-supply optimization into current-season adjustments, providing actionable guidance for both strategic water allocation and tactical irrigation scheduling.

Funding

• Open Research Fund of CMA⋅Henan Agrometeorological Support and Applied Technique Key Laboratory (AMF202405)
• Agricultural Science and Technology Innovation Program (ASTIP) grant, grant number CAAS–ASTIP–2025-IEDA
• Agricultural Science and Technology Innovation Program (ASTIP) grant, grant number CAAS–ZDRW202519

Citation

@article{Chen2026Optimal,
  author = {Chen, Xianguan and Bai, Huiqing and Fu, Mengqi and Yu, Wenran and Sun, Yabo and Ma, Xueqing and Feng, Liping},
  title = {Optimal water supply and irrigation indicators for winter wheat in the main producing regions of China: Insights from the WMAIP integrated model},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2025.110061},
  url = {https://doi.org/10.1016/j.agwat.2025.110061}
}