秋葵(Abelmoschus esculentus L.Moench)种子产量和形态特征的多元分析

Abdulsalam Mosobalaje Murtadha, S. Adetoro, K. Shittu
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引用次数: 0

摘要

摘要提高秋葵果实产量需要鉴定具有良好种子产量属性的基因型,因此,在2019年的雨季和旱季,在奥孙州立大学农业学院埃吉博校区的教学与研究农场,采用多元技术揭示了16个不同秋葵品系的遗传和形态属性。在随机完全块区设计中,将种子播种在沙壤土上,长5米,间隔0.70米,内部0.50米的单列地块中。旱季作物通过每周施用12毫米的水来支持。使用统计分析系统(SAS,2018)对收集的生长和种子性状数据进行一般线性模型(GLM)、主成分分析(PCA)和聚类分析。根据分析前转化的IPGRI(1991)平方根对叶柄颜色进行评分。结果显示,几乎所有性状都具有高度显著的品系、季节及其相互作用。四个PCA占85.77%,前两个PCA占总变异的51%。主成分分析和聚类分析都将品系分为四个,并揭示了SAHARI F1、NGB01197和LD-88的高产潜力。结果表明,这些品系可纳入秋葵产量改良计划。
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Multivariate analysis of seed yield and morphological characters of Okra (Abelmoschus esculentus L. Moench) accessions
Abstract. Enhancement of okra fruit production requires identification of genotypes with promising seed yield attributes, thus multivariate techniques were employed to reveal genetic and morphological attributes of sixteen diverse okra lines during wet and dry seasons in 2019 at Teaching and Research Farm, College of Agriculture, Osun State University, Ejigbo Campus. Seeds were sown in single-row plots of 5 m long, spaced 0.70 m apart and 0.50 m within on sandy loam soil in a randomized complete block design. The dry season crop was supported by the application of 12 mm water weekly. Data collected on growth and seed traits were subjected to the General Linear Model (GLM), principal component analysis (PCA), and cluster analysis using the Statistical Analysis System (SAS, 2018). The petiole color was scored according to IPGRI (1991) square root transformed prior to the analysis. Results showed highly significant lines, season, and their interactions for almost all traits. Four PCAs accounted for 85.77% and the first two PCA captured 51% of the total variations. Both PCA and cluster analysis grouped the lines into four and revealed the potentials of SAHARI F1, NGB01197, and LD-88 for high seed yield. It is concluded that these lines can be incorporated into okra yield improvement program.
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发文量
24
审稿时长
11 weeks
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