埃塞俄比亚燕麦(Avena sativa L.)基因型的参数和非参数统计分析

Gezahagn Kebede, Walelign Worku, Habte Jifar, Fekede Feyissa
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引用次数: 2

摘要

背景由于生物和非生物因素的差异,不同环境下燕麦基因型的表现不同。因此,在不同环境中评估燕麦基因型对于确定优越和稳定的基因型以提高产量非常重要。方法本研究旨在评估埃塞俄比亚燕麦(Avena sativa L.)基因型之间的相互作用(基因型与环境的相互作用;GEI)效应,并使用参数和非参数稳定性统计来确定燕麦基因型的粮食产量稳定性。采用重复三次的随机完全区组设计,在九个环境中评估了24种燕麦基因型。结果粮食产量方差的集合分析显示,不同基因型、不同环境及其交互作用之间存在显著差异。显著的GEI揭示了基因型在不同环境中的等级顺序变化。环境主效应占总产量方差的44.62%,基因型效应和GEI效应分别占总产量变异的28.84%和26.54%。基于12个参数和两个非参数稳定性统计对粮食产量稳定性进行了评估。结果表明,基于基因型优势指数(Pi)、Perkins和Jinks调整线性回归系数(Bi)和产量稳定性指数(YSI)的稳定性参数,具有优异粮食产量的基因型表现稳定,表明使用这些稳定性参数的选择对于燕麦基因型的谷物产量提高是有效的。Spearman秩相关系数还表明,Pi、Bi和YSI的稳定性参数与粮食产量呈正相关。然而,粮食产量与稳定性参数标准差、回归偏差、Hernandez合意指数(Dji)、Wricke生态价(Wi)、Shukla稳定性方差(σi2)、AMMI稳定性值(ASV)和环境方差呈负相关,这表明使用这些稳定性参数的燕麦基因型选择对于提高产量是无效的,因为与高产基因型相比,这些稳定性参数更倾向于高产基因型。结论基于Pi、Bi和YSI的稳定性参数,G5、G8、G11、G12、G14、G16、G17、G19和G22基因型在所有9种环境中都具有适应性,选择这些优良基因型将提高燕麦基因型的产量。然而,这个结果的有效性应该通过在相同的环境中重复实验两年或更长时间来确认。
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Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia
The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.The study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.The pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression , the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance , indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones.Therefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.
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