Li et al. (2026) Shifting climatic sensitivities of drought-related yield gaps signal potential increases in irrigation reliance in the Yellow River Basin

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Authors: Linchao Li, Zhongshan Xu, Yajie Zhang, Ning Yao, Yi Li, Qiang Yu, Hao Feng, Guijun Yang, Qinsi He
• DOI: 10.1016/j.agwat.2026.110137

Research Groups

• College of Agronomy, Inner Mongolia Agricultural University, China.
• State Key Laboratory of Soil and Water Conservation and Desertification Control, Northwest A&F University, China.
• State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, China.
• State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, China.
• College of Water Resources and Architectural Engineering, Northwest A&F University, China.
• College of Geological Engineering and Geomatics, Chang’an University, China.
• Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, China.

Short Summary

This study quantifies the evolving sensitivity of the irrigated–rainfed yield gap to climate drivers in the Yellow River Basin, finding that rising atmospheric demand reduces the effectiveness of precipitation in narrowing this gap. The results indicate a projected increase in yield gaps for maize, soybean, and rice, signaling a heightened future reliance on irrigation to stabilize food production.

Objective

• To quantify how the sensitivity of drought-related crop yield gaps (the difference between irrigated and rainfed yields) to key climatic drivers shifts under different global warming scenarios (SSP126, SSP245, and SSP585).

Study Configuration

• Spatial Scale: Yellow River Basin (YRB), China (95°53′E–119°5′E; 32°10′N–41°50′N), analyzed at a 5-arc-minute (~9.25 km) grid resolution.
• Temporal Scale: Historical baseline (1980–2010) and future projections (2021–2099).

Methodology and Data

• Models used: Ensemble of 9 global gridded crop model emulators (CARAIB, EPIC-TAMU, JULES, GEPIC, LPJ-GUESS, LPJmL, pDSSAT, PEPIC, PROMET).
• Climate Data: 38 Global Climate Models (GCMs) from CMIP6 under three Shared Socioeconomic Pathways (SSP126, SSP245, SSP585).
• Statistical Methods:
  • Random Forest (RF) for identifying dominant climatic drivers.
  • Dynamic Linear Model (DLM) to estimate time-varying sensitivities.
  • Standardized Precipitation–Evapotranspiration Index (SPEI) for drought characterization.
• Data sources: CRU time series, WorldClim climatology, and high-resolution harvest-area datasets.

Main Results

• Yield Projections: Maize yields are projected to decline by 11.2% to 35.5% under SSP585 by 2099. Wheat yields show a mixed signal, with increases in winter wheat but declines in spring wheat.
• Yield Gap Expansion: Yield gaps for maize, soybean, and rice increase across scenarios, with the largest gaps occurring under SSP585.
• Precipitation Sensitivity: The negative association between precipitation and the yield gap is weakening (shifting toward zero) in 69.8% (SSP126) to 77.8% (SSP585) of maize grid cells, meaning rainfall is becoming less effective at mitigating yield losses as atmospheric demand rises.
• CO2 Fertilization: Atmospheric CO2 sensitivity is negative (reducing the yield gap), particularly in the arid upstream regions, due to improved water-use efficiency (WUE).
• Evapotranspiration (ET): ET is a dominant driver in the upstream Hetao Irrigation District; however, in extremely arid areas, the ET-yield gap coupling weakens due to excessive atmospheric demand.

Contributions

• Provides a process-based quantification of how the sensitivity of crop yields to drought evolves over time, rather than just measuring static yield losses.
• Identifies spatiotemporal "hotspots" where irrigation reliance is expected to intensify, offering a basis for targeted water-infrastructure investment.
• Integrates machine learning (Random Forest) with dynamic statistical modeling (DLM) to isolate the directional effects of climate drivers on the irrigated–rainfed yield gap.

Funding

• National Natural Science Foundation of China (No. 42501370 and 42501067).
• Open Research Fund of the National Key Laboratory of Soil and Water Conservation and Desertification Control (No. Z2010025001-KJ2520).
• Inner Mongolia Autonomous Region Education Special Fund Project (2025).

Citation

@article{Li2026Shifting,
  author = {Li, Linchao and Xu, Zhongshan and Zhang, Yajie and Yao, Ning and Li, Yi and Yu, Qiang and Feng, Hao and Yang, Guijun and He, Qinsi},
  title = {Shifting climatic sensitivities of drought-related yield gaps signal potential increases in irrigation reliance in the Yellow River Basin},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110137},
  url = {https://doi.org/10.1016/j.agwat.2026.110137}
}

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