Article(id=1305136353872732285, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305136340186718236, articleNumber=null, orderNo=null, doi=10.7606/j.issn.1009-1041.2026.04.14, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750348800000, receivedDateStr=2025-06-20, revisedDate=1751212800000, revisedDateStr=2025-06-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1789098355023, onlineDateStr=2026-09-11, pubDate=1776182400000, pubDateStr=2026-04-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789098355023, onlineIssueDateStr=2026-09-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789098355023, creator=13701087609, updateTime=1789098355023, updator=13701087609, issue=Issue{id=1305136340186718236, tenantId=1146029695717560320, journalId=1303687221787852879, year='2026', volume='46', issue='4', pageStart='F2', pageEnd='566', issueExtLink='null', onlineDate='null', pubDate='1776182400000', pubDateStr='2026-04-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789098351761, creator='13701087609', updateTime=1789103128365, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1305156374778900923, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305136340186718236, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1305156374778900924, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305136340186718236, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=550, endPage=558, ext={EN=ArticleExt(id=1305136354078253182, articleId=1305136353872732285, tenantId=1146029695717560320, journalId=1303687221787852879, language=EN, title=Calibration of Yield-Formation Parameters in the APSIM-Wheat Model Based on Chaos Particle Swarm Optimization Algorithm, columnId=1305133133406433355, journalTitle=Journal of Triticeae Crops, columnName=Physiology, Ecology and Cultivation, runingTitle=null, highlight=null, articleAbstract=

To address the challenges of time consumption, low accuracy, and inefficiency in the localization calibration of yield formation parameters for spring wheat in the APSIM model under dryland conditions, a Chaos Particle Swarm Optimization (CPSO) algorithm was employed. Based on meteorological data from Dingxi City, Gansu Province, spanning 1971 to 2023, as well as yield data extracted from the Dingxi Statistical Yearbook for the periods 1971—2013 and 2022—2023, along with field-measured data collected from Mazichuan Village, Anding District, Dingxi City, between 2014 and 2021, key parameters influencing spring wheat yield were calibrated using the CPSO algorithm. The results showed that after parameter optimization with CPSO, the root mean square error (RMSE) decreased from 39.21 kg·hm^-2 to 24.64 kg·hm^-2; the normalized RMSE (NRMSE) dropped from 2.32% to 1.65%; and the modeling efficiency (ME) increased from 0.965 to 0.991. Therefore, through parameter optimization with CPSO, the fitting degree of APSIM model to spring wheat yield was significantly improved, and the model had better adaptation to Dingxi City, Gansu Province.

, authors=Zhengqiang ZHAO, Qiang LIU, Rui MA, authorsList=Zhengqiang ZHAO, Qiang LIU, Rui MA, authorCompany=null, correspAuthors=Qiang LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1305136354363465855, articleId=1305136353872732285, tenantId=1146029695717560320, journalId=1303687221787852879, language=CN, title=基于混沌粒子群算法对APSIM-Wheat模型中春小麦产量形成参数的率定分析, columnId=1305133133590982733, journalTitle=麦类作物学报, columnName=生理、生态与栽培, runingTitle=null, highlight=null, articleAbstract=

为解决APSIM模型中旱地春小麦产量形成参数本地化率定过程中遇到的耗时长、精度低及效率不高等问题，利用混沌粒子群算法（chaos particle swarm optimization, CPSO），基于甘肃省定西市1971—2023年的气象数据以及1971—2013、2022—2023年甘肃省定西市统计年鉴中的产量数据和2014—2021年定西市安定区麻子川村的大田测定数据，对影响春小麦产量形成的6个关键参数进行率定。结果表明，与传统试错法率定参数结果相比，利用CPSO率定参数后，6个参数的数值均发生明显变化，模型对产量的模拟值与实际值更加接近，均方根误差（RMSE）从39.21 kg·hm^-2下降至24.64 kg·hm^-2，归一化均方根误差（NRMSE）从2.32%下降至1.65%，且模型有效指数（ME）从0.965升至0.991，说明参数经CPSO率定后APSIM模型对春小麦产量拟合度提高，对西北地区的适应性增强。

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