Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia

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

Abstract

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.
埃塞俄比亚燕麦(Avena sativa L.)基因型籽粒产量稳定性的参数和非参数统计分析
由于生物和非生物因素的变化,不同环境下燕麦基因型的表现不同。因此,对不同环境下的燕麦基因型进行评价,对于确定优良的、稳定的基因型以提高产量具有重要意义。该研究旨在评估相互作用(基因型-环境相互作用;利用参数稳定性统计和非参数稳定性统计分析GEI对埃塞俄比亚燕麦(Avena sativa L.)籽粒产量稳定性的影响。采用随机完全区组设计,在9种环境中评估24种燕麦基因型,重复3次。对籽粒产量方差进行汇总分析,发现基因型、环境及其互作效应之间存在显著差异。显著的GEI揭示了基因型在不同环境下的等级变化。环境主效应解释了粮食产量总方差的44.62%,基因型和GEI效应分别解释了粮食产量总方差的28.84%和26.54%。利用12个参数稳定性统计量和2个非参数稳定性统计量对粮食产量稳定性进行了评价。结果表明,基于基因型优势指数(Pi)、Perkins和Jinks校正线性回归系数(Bi)和产量稳定指数(YSI)的稳定性参数,籽粒产量优越的基因型表现稳定,表明利用这些稳定性参数进行选择是提高籽粒产量的有效途径。Spearman等级相关系数也表明,稳定参数Pi、Bi和YSI与粮食产量呈显著正相关。籽粒产量与稳定性参数标准差、回归偏差、Hernandez可取性指数(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,表明利用这些稳定性参数进行燕麦基因型选择并不有效,因为这些稳定性参数更倾向于低产量基因型。与高产的相比。因此,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种环境下都具有适应性,选择这些优势基因型可以提高燕麦的产量。然而,这一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证实。
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