普通油茶岑软3号苗木分级研究 |
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引用本文: | 唐维龙,杨利平,王华宇,陈元松,赵文东,韦长江,吴红英,覃雯霞,赵森,黄达军,许尤厚,王鹏良. 普通油茶岑软3号苗木分级研究[J]. 安徽农业科学, 2016, 0(32): 155-157. DOI: 10.3969/j.issn.0517-6611.2016.32.054 |
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作者姓名: | 唐维龙 杨利平 王华宇 陈元松 赵文东 韦长江 吴红英 覃雯霞 赵森 黄达军 许尤厚 王鹏良 |
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作者单位: | 1. 广西国有三门江林场,广西柳州,545006;2. 钦州市林业科学研究所,钦州市植物生物技术重点实验室/工程中心,广西钦州535099;3. 钦州学院海洋学院,广西北部湾海洋生物养护重点实验室,广西钦州535011;4. 钦州学院海洋学院,广西北部湾海洋生物养护重点实验室,广西钦州535011; 广西壮族自治区林业科学研究院,广西特色经济林开发和利用重点实验室,广西南宁530002 |
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基金项目: | 国家自然科学基金项目(31460208);广西优良用材林资源培育重点实验室开放课题(12A0301);广西优良用材林资源培育重点实验室自主课题(14-A-03-01);广西北部湾海洋珍稀动物养护重点实验室项目(2016ZB02);广西自然科学基金项目(2013GXNSFBA019104);广西高校科学技术研究项目(2013YB252)。 |
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摘 要: | [目的]对岑软3号苗木分级进行研究,进一步规范其出圃规格,提高苗木质量。[方法]以1年生的岑软3号嫁接苗为材料,测量其苗高、地径、叶片数等8个性状,利用主成分分析和逐步聚类分析法对苗木质量进行分级。[结果]28对性状组合中有22对存在(极)显著相关;主成分分析提取了前4个主成分,保留了82.8%的信息,地径、苗高、叶片数和上部鲜重为主要指标;逐步聚类将岑软3号苗木分成3类;确定其苗木分级:Ⅰ级苗的苗高H≥32.739 cm,地径D≥0.410 cm;Ⅱ级苗的苗高H≥20.666 cm,地径D≥0.323 cm。[结论]逐步聚类将岑软3号苗木分为3类,其中Ⅰ级苗5株,Ⅱ级苗29株,Ⅲ级苗38株。
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关 键 词: | 普通油茶 岑软3号 苗木分级 主成分分析 逐步聚类 |
Study on Seedling Grading of the Oil -tea Cultivar Cenruan No.3 |
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Abstract: | Objective]Seedling grading of the oil-tea cultivar Cenruan No.3 was studied to standardize planting specification ,improve the seed-ling’s quality.[Method]8 traits such as height, ground diameter,leaf number,etc.were measured based on the one-year old grafting seedlings of Cenruan No.3.Seedlings grading was carried out using the principal component analysis and clustering analysis .[ Result]The results indicated that 22 of 28 pairwise traits were (extremely) significant between 8 traits;4 principal component analysis were extractedwith 82.8%information. Height,ground diameter,leaf number and above fresh weight were major indices.The seedlings of Cenruan No.3 were divided into 3 groups by stepwise clustering;seedling grading was determined as following:Ⅰgrade H≥32.739 cm,D≥0.410 cm,Ⅱgrade H≥20.666 cm,D≥0.323 cm. [Conclusion]The seedlings of Cenruan No.3 were divided into 3 groups by stepwise clustering,Ⅰgrade 5 plants,Ⅱ grade29 plants,IIIgrade 38 plants. |
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Keywords: | Camellia o leifera Abel Cenruan No.3 Seedling grading Principal component analysis Stepwise cluster |
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