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适宜瓜棉套作模式陆地棉品种的评价与筛选
引用本文:刘翔宇,娄善伟,王瑞华,张鹏忠,巴哈尔古丽·先木西,彭华,任红松.适宜瓜棉套作模式陆地棉品种的评价与筛选[J].新疆农业科学,2020,57(6):1000-1008.
作者姓名:刘翔宇  娄善伟  王瑞华  张鹏忠  巴哈尔古丽·先木西  彭华  任红松
作者单位:1.新疆农业科学院吐鲁番农业科学研究所,新疆吐鲁番 838000; 2.国家棉花工程技术研究中心,乌鲁木齐 830091
基金项目:国家重点研发计划(2017YFD0201900)
摘    要:【目的】基于形态指标和品质指标,评价筛选适合瓜棉套作模式的棉花品种。【方法】以14个棉花品种为材料,测定在瓜棉套作模式下棉花株高、果枝节位、果枝数、吐絮铃数、有效铃数、单铃重、衣分、籽棉产量、皮棉产量和生育期10项数量性状,运用相关分析、主成分分析、聚类分析、逐步判别分析等对参试棉花品种进行评价和筛选。【结果】10个性状间的相关关系复杂。其中,3对数量性状相关性达到极显著水平(P<0.01),2对数量性状间相关性达到显著水平(P<0.05);前5个主成分代表了14个棉花的10项数量性状89.77%的信息,其贡献率分别为26.89%、21.94%、18.19%、13.23%和9.53%;当类间距离为4.75时,14个棉花品种被聚为3类。第Ⅰ类形态不繁茂且低产型有6个品种、第Ⅱ类形态较繁茂且高产型有2个品种、第Ⅲ类形态繁茂且产量较高型有6个品种;聚类分析的判对概率是100%,分类结果准确可靠;加权关联度高于岱字-80(CK)的品种包括:兴农7号、陆地棉系1号、新陆中49号、新陆中51号、新陆中40号和中棉63号。【结论】陆地棉品种兴农7号和新陆中51号在吐鲁番瓜套棉栽培模式下具有生产潜力和推广应用价值。陆地棉系1号、新陆中40号、新陆中49号,3个品种可作为储备品种。

关 键 词:瓜棉套作模式  陆地棉  主成分分析  聚类分析  判别分析  加权关联度分析  
收稿时间:2020-01-20

Evaluation and Selection of Upland Cotton Varieties Suitable for Melon-Cotton Intercropping Pattern
LIU Xiangyu,LOU Shanwei,WANG Ruihua,ZHANG Pengzhong,Bahargul Xamxi,PENG Hua,REN Hongsong.Evaluation and Selection of Upland Cotton Varieties Suitable for Melon-Cotton Intercropping Pattern[J].Xinjiang Agricultural Sciences,2020,57(6):1000-1008.
Authors:LIU Xiangyu  LOU Shanwei  WANG Ruihua  ZHANG Pengzhong  Bahargul Xamxi  PENG Hua  REN Hongsong
Institution:1.Turpan Research Institute of Agricultural Sciences, Xinjiang Academy of Agricultural Sciences, Turpan Xinjiang 838000 China; 2.National Cotton Engineering Technology Research Center,Xinjiang Academy of Agricultural Sciences,Urumqi 830091, China
Abstract:【Objective】 To evaluate and select upland cotton varieties suitable for melon-cotton intercropping patter based on the morphological indexes and yield indexes.【Methods】The evaluation of quantitative traits about plant height, fruiting sites and position, number of fruit branch, open bolls number per plant, effective bolls number per plant, boll weight, lint percentage, seed cotton yield, lint yield and breeding time of 14 cotton varieties under the condition of cotton and melon intercropping pattern were evaluated by correlation analysis, principal components analysis, hierarchical cluster analysis and Stepwise discriminant analysis, etc.【Result】The results of the correlation analysis showed that the correlations were complex between 10 quantitative traits,because 3 of these coefficients were extremely significantly correlated at P< 0.01 and 2 of these coefficients were significantly correlated at P< 0.05;The results of the principal components analysis showed that the first five principal components represent 89.77% of the information which was the 10 quantitative traits of the 14 cotton varieties. The contribution rates were respectively 26.89%, 21.94%, 18.19%, 13.23% and 9.53%;The results of the hierarchical cluster analysis showed that the 14 cotton cultivars were clustered into three categories when the class separation distance was 4.75. Among them, the first class, whose form was not luxuriant with low yield, had 6 varieties, the class Ⅱ, whose form was more luxuriant with the highest yield, had 2 varieties, the class Ⅲ, whose form was the most luxuriant with a higher yield, had 6 varieties;The results of the Stepwise discriminant analysis showed that the results of cluster analysis were accurate and reliable,whose identification rate was 100%;The results of the correlation analysis showed that the varieties with higher weighted incidence degree than daizi-80 (CK) included: Xingnong 7, Upland Cotton series 1, Xinluzhong 49, Xinluzhong 51, Xinluzhong 40 and Zhongmian 63.【Conclusion】According to hierarchical cluster analysis, Stepwise discriminant analysis and weighted correlation analysis, it was considered that: Xingnong No.7 and Xinluzhong No.51, had production potential and popularization and application value under the melon-cotton intercropping pattern in Turpan. Upland cotton No. 1, Xinluzhong No. 40 and Xinluzhong No. 49 could be used as reserve varieties.
Keywords:cotton and melon intercropping pattern  upland cotton  principal components analysis  hierarchical cluster analysis  discriminant analysis  weighted correlation analysis  
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