{
  "abstract": "Saline irrigation represents a pivotal alternative strategy for enhancing crop production under freshwater scarcity. However, previous assessments have mainly quantified average yield responses, while how interacting environmental and management conditions shape region-specific irrigation-water salinity thresholds and associated spatial risks remain elusive across global major breadbaskets. This study combined a meta-analysis of 551 field observations with machine learning (ML) to quantify global wheat yield responses to saline irrigation, identify optimal region-specific thresholds, and evaluate spatial risks in four major wheat-producing areas: the North China Plain (NCP), Indo-Gangetic Plain (IGP), Middle East (ME), and Central California Valley (CCV). Meta-analysis revealed that saline irrigation led to significant average yield reductions ranging from 12.4% in the NCP to 31.4% in the CCV. ML further showed that irrigation water salinity, growing-season temperature, and irrigation amount emerged as the dominant drivers, collectively explaining more than 54% of yield variation. An optimum irrigation amount of 500 mm was optimal for maximizing yield under saline conditions. Region-specific best practices included border irrigation at flowering (FL) and grain-filling stages for the NCP, drip irrigation at these stages for the IGP, and drip irrigation at the FL stage for the ME and CCV. Spatial risk assessment revealed a mismatch between salinity tolerance thresholds and actual exposure. Under a conservative 5% yield-loss scenario, the IGP exhibited the highest model-derived salinity threshold of 4.14 dS m ⁻¹ but the largest risk area proportion due to elevated background salinity, whereas the ME faced the lowest risk, with a risk area of 7.7%, indicating high potential for saline water utilization. These findings highlight the limitations of universal water quality guidelines and demonstrate the necessity of localized management. By establishing a data-driven framework, this study provides actionable insights to safeguard global wheat supply, build climate resilience, and ensure agricultural sustainability in salt-affected environments.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences",
    "University of Chinese Academy of Sciences"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yimin Fang",
      "orcid": "0009-0008-2397-770X"
    },
    {
      "affs": [
        0
      ],
      "name": "Guoyong Leng",
      "orcid": "0009-0004-1862-4206"
    },
    {
      "affs": [
        0
      ],
      "name": "Linfei Yu",
      "orcid": "0000-0002-8255-0860"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiali Qiu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoyong Liao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4a7rgdabx1n54.bib",
  "cited_by": 0,
  "doi": "10.1088/1748-9326/aeaf6c",
  "fulltext_url": null,
  "id": "4a7rgdabx1n54",
  "issns": [
    "1748-9326"
  ],
  "landing_url": "https://iopscience.iop.org/article/10.1088/1748-9326/aeaf6c",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1111|Irrigation Practices and Water Management"
    ]
  ],
  "openalex": "W7216521022",
  "pdf_url": "https://iopscience.iop.org/article/10.1088/1748-9326/aeaf6c/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "IOP Publishing",
  "ris": "https://research.draconitic.com/paper/4a7rgdabx1n54.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:55Z",
      "native_id": "10.1088/1748-9326/aeaf6c",
      "source": "crossref",
      "url": "https://iopscience.iop.org/article/10.1088/1748-9326/aeaf6c"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1111|Irrigation Practices and Water Management",
      "subfield": "hydrology-soils"
    }
  ],
  "title": "Global meta-analysis and machine learning uncover region-specific salinity thresholds and risks for sustainable wheat production under saline irrigation",
  "title_text": "Global meta-analysis and machine learning uncover region-specific salinity thresholds and risks for sustainable wheat production under saline irrigation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:55Z",
  "url": "https://research.draconitic.com/paper/4a7rgdabx1n54",
  "venue": "Environmental Research Letters",
  "versions": []
}