Stability Analysis of Some Soybean Genotypes Using Parametric and Nonparametric Methods in Multi-Environments

Çubukçu, Pınar; Kocatürk, Mehmet; İlker, Emre; Kadiroğlu, Abdullah; Vurarak, Yasemin; Şahin, Yeşim Müge; Karakuş, Mehmet; Yıldırım, Ümran; Göksoy, Abdurrahim Tanju; Sincik, Mehmet

doi:10.17557/tjfc.1033363

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…Although the values of the stability parameters W i and σ i 2 were very different, the stability rankings of W i and σ i 2 in this study were similar, which is confirmed by the existence of a perfect correlation between them. This result is also in line with the findings of Temesgen et al (2015), Cubukcu et al (2021), and Erdogdu and Esendal (2021). Thus, using either of the two stability parameters can lead to similar stability results in the oat selection program.…”

Section: Discussionsupporting

confidence: 92%

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

Kebede,

Worku,

Jifar

et al. 2023

Grassland Research

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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.

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Section: Discussionsupporting

confidence: 92%

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

Kebede,

Worku,

Jifar

et al. 2023

Grassland Research

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“…In general, the effectiveness of the parameters is like the information already known about them in previous studies (El-Hashash et al, 2019;Sitaresmi et al, 2019;Cubukcu et al, 2021). In addition, the whole set of parameters gives a similar assessment of the varieties grown in different conditions (locations).…”

Section: Discussionmentioning

confidence: 81%

“…The advantages of nonparametric methods have been highlighted many times (El-Hashash et al, 2019;Cubukcu et al, 2021), which makes them desirable and suitable for analysis of the data collected in this study. According to Segherloo et al, (2008) and Mohammadi et al, (2016) the rank estimates of some of these parameters are generally assigned to the biological concept of evaluation [NP(2-4)], some to the agronomic one (Pi, YSi, KR, TOP), (Flores et al, 1998;Vaezi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Analysis of genotype by environmental interaction of common wheat (Triticum Aestivum L.) by non-parametric stability methods

Tsenov¹,

Gubatov²,

Yanchev³

2022

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Data from the Multi Environmental Field Trail (MET), which examined 24 varieties of common wheat, were divided into three "datasets" related to three of the five study locations. Using meta-analysis, these three data sets were compared with those from the whole experiment. The aim of the study is to determine whether a 4-year growing period in three country-specific locations, it is possible to establish a significant impact of the environment on the stability of a group of varieties. The analysis of the genotype x environment interaction (GEI) was performed in parallel in the three groups of data, which were compared with the entire MET database. A direct comparison was made on them regarding the possibilities of non-parametric methods to assess the stability of the variety. The analysis of the results of the four "datasets" is done through a number of statistical approaches, allowing them to be correctly compared at different levels. Genotype x environment interaction was found in each of the studied locations. The variation in yield in them is a result of the direct effect of the "year" and the combined effect of the genotype x year. At all three locations, the GEI is broken down into four main components, which is evidence of the strong linear and non-linear nature of the dispersion of grain yield. These results are a prerequisite for an objective assessment of genotype stability. All applied parameters give completely similar stability information for each of them, regardless of the test location. Data from one location are sufficient to assess the stability of each variety in a group. This may be the case if significant differences between the seasons of the trial, are found. The applied non-parametric methods for stability assessment give correct information about the varieties, in the presence of GEI, regardless of the conditions from which the data for analysis are selected.

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“…Meanwhile, one of the government's efforts to increase soybean production is to expand the planting area to marginal land (Aldillah, 2015). However, genetic and environmental factors affect soybean yield (Cubukcu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Application of bio and NPK fertilizer to improve yield soybean and acid sulfate soil properties in Indonesia

Maftu’ah¹,

Susilawati²,

Lestari³

et al. 2023

Chil. j. agric. res.

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In Indonesia, soybean (Glycine max (L.) Merr.) is the second most important staple food with low production compared to the demand. This problem can be overcome by using marginal land such as acid sulfate soil of tidal swampland, however, the optimal application of fertilizer in such soil is minimal. Therefore, this study aims to examine the application of fertilizer in acid sulfate soil in South Kalimantan, Indonesia. It was conducted using a split-plot design with three replicates and the main plot was biofertilizer, which consisted of no-biofertilizer (P1); biofertilizer consortium of fungi, P-solubilizing bacteria, N-fixing bacteria (P2), and biofertilizer consortium of N-fixing bacteria symbiotic and nonsymbiotic, P-solubilizing bacteria and phytohormone-producing bacteria (P3). Meanwhile, the subplot was NPK fertilizer dosage, consisting of nofertilizer (A1), dose of 150% (A2), dose of 100% (A3), and dose of 50% (A4) of the recommendation. The results showed that the biofertilizer and NPK fertilization dose did not show an interaction with soil pH and Al 3+ . The highest N and K nutrient uptake was in P2 at 50% NPK fertilizer dosage, while P in P3 at 150% NPK fertilization dose. The highest soybean yields were obtained in the application of biofertilizer P3 with 150% NPK, i.e., 1.6 t ha -1 , followed by the 1.5 t ha -1 in the application of P2 + 50% NPK. This showed that the use of P2 biofertilizer can reduce NPK fertilizer application to 50% in soybean plants in acid soil tidal lands. Furthermore, the application of biofertilizers can increase soybean productivity in acid sulfate soil in Indonesia.

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Stability Analysis of Some Soybean Genotypes Using Parametric and Nonparametric Methods in Multi-Environments

Cited by 7 publications

References 32 publications

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

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

Analysis of genotype by environmental interaction of common wheat (Triticum Aestivum L.) by non-parametric stability methods

Application of bio and NPK fertilizer to improve yield soybean and acid sulfate soil properties in Indonesia

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