Evidence map›Paper›PMID 41611125›Full record

ArticleJournal of advanced research2026

Optimizing water allocation in irrigation districts for enhanced sustainability and climate resilience.

Yingshan Chen, Mo Li, Qiang Fu, Xiao Liu, Lei Yu, Vijay P Singh, Luchen Wang

Abstract read
In one paragraph

Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yingshan ChenSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang 150030, China; Heilongjiang Province Key Laboratory of Smart Water Network, Northeast Agricultural University, Harbin, Heilongjiang 150030, China.
Mo LiSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang 150030, China; State Key Laboratory of Smart Farm Technologies and Systems, Harbin, Heilongjiang 150030, China; Heilongjiang Province Key Laboratory of Smart Water Network, Northeast Agricultural University, Harbin, Heilongjiang 150030, China; International Cooperation Joint Laboratory of Health in Cold Region Black Soil Habitat of the Ministry of Education, Harbin, Heilongjiang 150030, China; Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang 150030, China. Electronic address: limo0828@neau.edu.cn.
Qiang FuSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang 150030, China; International Cooperation Joint Laboratory of Health in Cold Region Black Soil Habitat of the Ministry of Education, Harbin, Heilongjiang 150030, China.
Xiao LiuChina Institute of Water Resources and Hydropower Research, Beijing 100038, China; Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, Beijing 100038, China.
Lei YuSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.
Vijay P SinghDepartment of Biological & Agricultural Engineering, Department of Civil & Environmental Engineering, Texas A & M University, 77843-2117, USA.
Luchen WangSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang 150030, China; Heilongjiang Province Key Laboratory of Smart Water Network, Northeast Agricultural University, Harbin, Heilongjiang 150030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIrrigation plays a crucial role in enhancing agricultural productivity. However, it simultaneously increases water consumption and greenhouse gas (GHG) emissions, a challenge that is further exacerbated by climate change. While many irrigation strategies have been proposed, their potential to balance crop yield growth, water productivity improvement and carbon emission reduction in large-scale irrigation districts remains unclear.

objectivesThis study aims to establish a decision-relevant, scalable basis for climate-resilient irrigation management by testing whether coordinated irrigation allocation across large irrigation districts can jointly sustain food production while improving water-use efficiency and reducing GHG emissions under climate change. An integrated optimization perspective is used to delineate the achievable potential and limits of irrigation allocation for aligning food security with water conservation and carbon-neutrality goals.

methodsIn this study, the DSSAT crop model was used to simulate the biophysical response of main crops under irrigation and climate conditions, and water production function was extracted from random forest to realize rapid prediction. Then, the Pareto optimal irrigation strategy among grain yield, water productivity and greenhouse gas emission reduction was identified by NSGA-II, and it was calibrated and verified under the data of typical irrigation areas.

resultsUsing an integrated framework, we show that irrigation optimization increases irrigation water productivity by 11.84%, while reducing water use by 8.6% and GHG emissions by 45.3%. Under climate change scenarios, optimized allocation decreases irrigation volume by 6.9-9.3%, with crop yields rising in two-thirds of scenarios, reaching gains of up to 31.3%. Nationwide, crop water productivity is projected to rise by 0.68%-18.55%, accompanied by a reduction in GHG emissions of 7.49%-32.90%. These results demonstrate that coordinated irrigation optimization can effectively decouple agricultural production from water consumption and carbon emissions, highlighting its potential as a robust and scalable strategy for safeguarding food security while advancing agricultural sustainability and carbon-neutrality objectives.

Indexed as

Crop simulationDrought identificationEmission reductionMulti-objective coordinationOptimization of agricultural resourcesTypical irrigation areas in China

Identifiers

PMID41611125
PMCPMC13669935

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.