Evidence map›Paper›PMID 36620746›Full record

ArticlePeerJ2023

Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China.

Zhidong Wang, Xining Zhao, Jinglei Wang, Ni Song, Qisheng Han

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhidong WangCollege of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, China.
Xining ZhaoCollege of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, China.
Jinglei WangFarmland Irrigation Research Institute of Chinese Academy of Agriculture Sciences/Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural affairs, Xinxiang, China.
Ni SongFarmland Irrigation Research Institute of Chinese Academy of Agriculture Sciences/Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural affairs, Xinxiang, China.
Qisheng HanFarmland Irrigation Research Institute of Chinese Academy of Agriculture Sciences/Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural affairs, Xinxiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: We quantified and evaluated the allocation of soil and water resources in the Aksu River Basin to measure the consequences of climate change on an agricultural irrigation system. Methods: We first simulated future climate scenarios in the Aksu River Basin by using a statistical downscaling model (SDSM). We then formulated the optimal allocation scheme of agricultural water as a multiobjective optimization problem and obtained the Pareto optimal solution using the multi-objective grey wolf optimizer (MOGWO). Finally, optimal allocations of water and land resources in the basin at different times were obtained using an analytic hierarchy process (AHP). Results: (1) The SDSM is able to simulate future climate change scenarios in the Aksu River Basin. Evapotranspiration (ET

Indexed as

Climate ChangeWaterAgricultural IrrigationAgricultureChinaWaterAllocation of land and water resourcesAnalytic hierarchy processClimate scenariosGrey wolf optimizationMulti-objective programmingSemi-arid land

Identifiers

PMID36620746
PMCPMC9817936

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.