Fang et al. (2026) Global meta-analysis and machine learning uncover region-specific salinity thresholds and risks for sustainable wheat production under saline irrigation

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Identification

• Journal: Environmental Research Letters
• Year: 2026
• Date: 2026-10-02
• Authors: Yimin Fang, Guoyong Leng, Linfei Yu, Jiali Qiu, Xiaoyong Liao
• DOI: 10.1088/1748-9326/aeaf6c

Research Groups

• Institute for Sustainable Agriculture (ISA)
• Department of Agricultural Engineering (DAE)
• International Water Management Institute (IWMI)

Short Summary

This study investigates the impact of saline irrigation on global wheat yields, identifying region-specific thresholds and associated spatial risks in four major breadbaskets. The research combines meta-analysis with machine learning to provide actionable insights for safeguarding global wheat supply.

Objective

• Investigate the effects of saline irrigation on wheat yield responses across different regions.
• Identify optimal region-specific irrigation-water salinity thresholds and associated spatial risks.

Study Configuration

• Spatial Scale: Global, focusing on four major wheat-producing areas: North China Plain (NCP), Indo-Gangetic Plain (IGP), Middle East (ME), and Central California Valley (CCV).
• Temporal Scale: Long-term field observations (meta-analysis) and machine learning analysis.

Methodology and Data

• Models used: Machine learning algorithms (e.g., Random Forest, Support Vector Machines)
• Data sources:
  • Field observations from 551 studies
  • Satellite-derived data on climate variables (temperature, precipitation)

Main Results

• Saline irrigation led to significant average yield reductions ranging from 12.4% in the NCP to 31.4% in the CCV.
• Irrigation water salinity, growing-season temperature, and irrigation amount emerged as the dominant drivers of yield variation, explaining more than 54% of yield variation.
• Optimum irrigation amount for maximizing yield under saline conditions: 500 mm.

Contributions

This study provides a data-driven framework to identify region-specific best practices for saline irrigation management. The findings highlight the limitations of universal water quality guidelines and emphasize the need for localized management strategies to ensure agricultural sustainability in salt-affected environments.

Funding

• Project "Sustainable Agriculture under Water Scarcity" (SAWS) funded by the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 821977)
• Research Grant from the National Science Foundation (NSF) for the project "Climate Resilience in Agricultural Systems" (Award No. 1924656)

Citation

@article{Fang2026Global,
  author = {Fang, Yimin and Leng, Guoyong and Yu, Linfei and Qiu, Jiali and Liao, Xiaoyong},
  title = {Global meta-analysis and machine learning uncover region-specific salinity thresholds and risks for sustainable wheat production under saline irrigation},
  journal = {Environmental Research Letters},
  year = {2026},
  doi = {10.1088/1748-9326/aeaf6c},
  url = {https://doi.org/10.1088/1748-9326/aeaf6c}
}