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

Abstract

BackgroundThe 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.MethodsThe 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.ResultsThe 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.ConclusionsTherefore, 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.

Multi-Traits Selection of Maize Hybrids under Sole-Crop and Multiple-Crops with Soybean

“…The selection of high-yielding varieties using a single stability measurement was considered inaccurate, so it is necessary to combine various stability measurements [14]. Various studies have reported the combined stability measurements, including those of black soybeans [26,27], durum wheat [28,29], sweet potatoes [30], stevia [31], and rice [32]. In combining stability measurements, biplot visualization is very important to use.…”

Technological Advancements and the Changing Face of Crop Yield Stability in Asia

“…Several other empirical results exist for crops not covered in our analysis, e.g., for oats in Ethiopia (Kebede et al 2023), for chickpeas in India (Kumar et al 2020), energy crops in the EU (Popp et al 2021), maize in several locations (Kang 1993), and cereals and vegetables in Europe and North America Hollósy 2019a, 2019b), using various methods for evaluating yield stability, but all agree that both climate impacts and the impacts of management decisions regarding farming technology interact in creating actual yield fluctuations. Shojaei et al (2021) used the AMMI methodology for maize in Iran, comparing 12 maize hybrids under different environmental conditions for a two-year study using similar stability measures as Bose et al (2014).…”

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

“…This was based on stability factors such as lower values of Wi 2 , σ 2i , P i , and s 2 di, a high BLUP factor, and bi equal to 1. The use of different parametric stability assessments has also been reported in wheat (Bornhofen et al, 2017), rice (Lee et al, 2023), safflower (Afzal et al, 2021), and oat (Kebede et al, 2023).…”