Soybean Yield Simulation and Sustainability Assessment Based on the DSSAT-CROPGRO-Soybean Model.

Lei Zhang, Zhenxi Cao, Yang Gao, Weixiong Huang, Zhuanyun Si, Yuanhang Guo, Hongbo Wang, Xingpeng Wang

Abstract read

In one paragraph

Article in Plants (Basel, Switzerland), 2024. 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

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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

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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.

Evaluating soybean yield responses to future climate change and irrigation regimes: a DSSAT multi-model assessment.Frontiers in plant science · 2026
Article

4 · The record

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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

8 authors.

Lei ZhangModern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, College of Water Hydraulic and Architectural Engineering, Tarim University, Alar 843300, China.

Zhenxi CaoModern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, College of Water Hydraulic and Architectural Engineering, Tarim University, Alar 843300, China.ORCID 0000-0002-4013-6768

Yang GaoModern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, College of Water Hydraulic and Architectural Engineering, Tarim University, Alar 843300, China.ORCID 0000-0003-3209-4788

Weixiong HuangHubei Key Laboratory of Yangtze Catchment Environmental Aquatic Science, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China.ORCID 0000-0002-1704-5550

Zhuanyun SiInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China.ORCID 0000-0002-1026-0696

Yuanhang GuoModern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, College of Water Hydraulic and Architectural Engineering, Tarim University, Alar 843300, China.

Hongbo WangModern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, College of Water Hydraulic and Architectural Engineering, Tarim University, Alar 843300, China.ORCID 0000-0002-8844-6677

Xingpeng WangModern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, College of Water Hydraulic and Architectural Engineering, Tarim University, Alar 843300, China.

Funding

the Major Science and Technology Project of Xinjiang Production and Construction Corps 2021AA003the Tarim University Graduate Student Research and Innovation Program TDGRI202363the Tarim University President's Fund Project TDZKYS202301

6 · The paper itself

Abstract

In order to ensure national grain and oil security, it is imperative to expand the soybean planting area in the Xinjiang region. However, the scarcity of water resources in southern Xinjiang, the relatively backward soybean planting technology, and the lack of a supporting irrigation system have negatively impacted soybean planting and yield. In 2022 and 2023, we conducted an experiment which included three irrigation amounts of 27 mm, 36 mm, and 45 mm and analyzed the changes in dry mass and yield. Additionally, we simulated the potential yield using the corrected DSSAT-CROPGRO-Soybean model and biomass based on the meteorological data from 1994 to 2023. The results demonstrated that the model was capable of accurately predicting soybean emergence (the relative root mean square error (nRMSE) = 0, the absolute relative error (ARE) = 0), flowering (nRMSE = 0, ARE = 2.78%), maturity (nRMSE = 0, ARE = 3.21%). The model demonstrated high levels of accuracy in predicting soybean biomass (R

Indexed as

biomassDSSAT modelirrigation water use efficiencysoybeanyield

Identifiers

PMID39274008

DOI10.3390/plants13172525

PMCPMC11397127

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LicenceCC BY

Read underepoch 390

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.