Article(id=1305139344340181376, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, articleNumber=null, orderNo=null, doi=10.7606/j.issn.1009-1041.2026.03.12, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743350400000, receivedDateStr=2025-03-31, revisedDate=1747756800000, revisedDateStr=2025-05-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1789099068007, onlineDateStr=2026-09-11, pubDate=1773504000000, pubDateStr=2026-03-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789099068007, onlineIssueDateStr=2026-09-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789099068007, creator=13701087609, updateTime=1789099068007, updator=13701087609, issue=Issue{id=1305139324555649224, tenantId=1146029695717560320, journalId=1303687221787852879, year='2026', volume='46', issue='3', pageStart='F2', pageEnd='424', issueExtLink='null', onlineDate='null', pubDate='1773504000000', pubDateStr='2026-03-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789099063289, creator='13701087609', updateTime=1789103147683, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1305156455804465602, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1305156455804465603, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=393, endPage=403, ext={EN=ArticleExt(id=1305139344528925057, articleId=1305139344340181376, tenantId=1146029695717560320, journalId=1303687221787852879, language=EN, title=Effects of Planting Density on Stem Lodging Resistance Under Different Compost Substitutions for Chemical Fertilizers and UAV-Based Lodging Resistance Prediction of Wheat, columnId=1305133133406433355, journalTitle=Journal of Triticeae Crops, columnName=Physiology, Ecology and Cultivation, runingTitle=null, highlight=null, articleAbstract=

This study investigated the effects of different compost substitution ratios for chemical fertilizers and planting densities on wheat stem lodging resistance, and explored the feasibility of using vegetation indices derived from UAV-based multispectral imagery for lodging prediction, aiming to provide scientific guidance for fertilizer-density management and lodging forecasting in wheat production. Using the wheat cultivar Huaimai 43, a split-plot design was implemented during 2022-2024 with four compost substitution levels［0(T0), 10% (T1), 20% (T2), 30% (T3)］ and three planting densities (D1: 1.5 million plants·hm²; D2: 3.0 million plants·hm²; D3: 4.5 million plants·hm²). Stem lodging resistance traits were measured, and UVA multispectral images were acquired for lodging prediction. The results demonstrated that, under the same planting density, as the proportion of compost substitution increases, the height of wheat plants showed a trend of first decreasing and then increasing, the center of gravity height showed a fluctuating trend, and all were at the lowest in the T2 treatment;the lodging resistance index, fresh weight, mechanical strength, the number and area of large and small vascular bundles in the second basal internode, as well as hemicellulose, cellulose and lignin contents in basal internodes all first increased and then decreased, and peaked under T2. Under the same compost substitution level, as planting density increased, plant height and center of gravity height showed an increasing trend; whereas the lodging resistance index, fresh weight, mechanical strength, and the number and area of large and small vascular bundles in the second basal internode decreased. In contrast, hemicellulose, cellulose and lignin contents in basal internodes showed a trend of first increased and then decreased, reaching maxima under D2. Correlation analysis revealed that the lodging resistance index showed the strongest association with vegetation indices. Accordingly, key vegetation indices (NDVI, RVI, TVI, DVI, and RDVI) were selected to construct lodging resistance prediction models using three regression methods: Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR), and Principal Component Regression (PCAR). The SMLR model achieved the highest accuracy, with calibration and validation R² values of 0.43 and 0.76, respectively. In conclusion, a compost substitution ratio of 20% combined with a planting density of 1.5 million plants·hm^-2 conferred superior lodging resistance in wheat. The SMLR-based model utilizing vegetation indices effectively predicted the lodging resistance index.

, authors=Chenhui SHU, Zihan YUE, Kaiwei LI, Lantian REN, Jiacheng ZHENG, Fengxian ZHEN, Qingqin SHAO, authorsList=Chenhui SHU, Zihan YUE, Kaiwei LI, Lantian REN, Jiacheng ZHENG, Fengxian ZHEN, Qingqin SHAO, authorCompany=null, correspAuthors=Qingqin SHAO, 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=1305139346277949827, articleId=1305139344340181376, tenantId=1146029695717560320, journalId=1303687221787852879, language=CN, title=堆肥替代化肥比例及种植密度对小麦茎秆抗倒伏性能的影响及基于无人机影像的抗倒预测研究, columnId=1305133133590982733, journalTitle=麦类作物学报, columnName=生理、生态与栽培, runingTitle=null, highlight=null, articleAbstract=

为探究肥替代化肥比例及种植密度对小麦茎秆抗倒伏性能的影响，于2022—2024年以小麦品种淮麦43为材料，采用裂区设计，设置4个堆肥替代化肥比例［0（T0）、10%（T1）、20%（T2）、30%（T3）］和3个种植密度［150万株·hm^-2（D1）、300万株·hm^-2（D2）、450万株·hm^-2（D3）］，探究其对茎秆抗倒性能的影响并采集无人机多光谱影像数据进行抗倒预测。结果表明，同一种植密度下，随着堆肥替代比例的增加，小麦株高呈先降后升趋势，重心高度呈波动变化，且均在T2处理下最低；抗倒伏指数、鲜重、机械强度及基部第二节间的大、小维管束数目和面积、基部节间半纤维素、纤维素、木质素含量均呈先升后降趋势，且在T2处理下最大。同一堆肥替代比例下，随着种植密度的增加，小麦株高和重心高度整体上均呈上升趋势；抗倒伏指数、鲜重、机械强度、基部第二节间的大、小维管束数目和面积均呈下降趋势；基部节间半纤维素、纤维素和木质素含量均呈先升后降趋势，且在D2处理下最高。选用植被指数（NDVI、RVI、TVI、DVI、RDVI）为自变量，运用3种模型方法（SMLR、PLSR、PCAR）共同构建抗倒伏指数的预测模型，得出通过SMLR构建抗倒伏指数的模型精度和反演效果均最优，R²分别为0.43和0.76。综上所述，在20%堆肥替代化肥比例和150万株·hm^-2的种植密度下小麦具有较好的抗倒伏能力；基于SMLR模型，利用植被指数可实现对小麦抗倒伏指数的预测。

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