Regression Models vs. Variance Measures as Stability Parameters of Some Soybean Genotypes (original) (raw)
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Turkish Journal Of Field Crops, 2019
Seed yields of 15 soybean genotypes were evaluated in three locations i.e. Bursa, Samsun and Konya under main crop conditions through summer seasons from 2014 to 2016. The used design was a randomized complete block design with four replications. This research is aimed to estimate the stability parameters of seed yield of 15 soybean genotypes by used different stability analysis methods over nine environmental conditions and to study interrelationship among these stability methods. Genotypes, environments and genotype by environment interactions (GEI) played a significant role in terms of seed yield in this study. The genotypes KAMD 03, BATEM 306, BDUS 04, ARISOY and ATAEM 07 had higher seed yields and regression coefficient values above 1.0. These genotypes are sensitive to environmental variations and would be suggested for cultivation under favourable conditions, whereas KAMD 01, KASM 02 and KASM 03 with bi<1 and lowest average yields were poorly adapted across unfavourable environment conditions. The genotype BDSA 05 having regression coefficient below 1.0 and higher seed yield than average yield were goodly adapted to unfavourable environment conditions. The results of most parametric and non-parametric stability analyses showed that genotypes BDUS 04, KASM 02, KASM 03, KAMD 03 and BDSA 05 were stable genotypes. These genotypes were demonstrated superior adaptability with high yield performances in many environments. Results of correlation analysis indicated that seed yield was significantly correlated with Ri 2 (P<0.05), Si(3) (P<0.05), Di (P<0.01), Si(6) (P<0.01), TOP (P<0.01) and showed a negative and significant correlation with Pi and RS (P<0.01). The coefficient of regression (bi) had positively significant associated with CVi, αi, Si(3) and Si(6) (P<0.01) and with the superiority parameter (TOP) (P<0.05).
sciencedomain international, 2019
The objectives of this study were to investigate the comparison among non-parametric stability statistics and to evaluate seed yield stability of the sixteen soybean genotypes across four locations during the 2016, 2017 and 2018 growing seasons in Egypt. All trials were laid down in a randomized complete block design (RCBD) with three replications. The AMMI analysis showed ahighly significant effect of genotype (G), environment (E) and G x E interaction (GEI). The major contributions to treatment sum of squares were GEI, followed by G and E. The AMMI analysis also partitioned the total GEI component into eleven PCAs and Residual. The first eight PCAs were highly significant and accounted for about 99.56% of the total GEI. Based on the static and dynamic concepts, the results of spearman's rank correlation and PCA showed that stability measures could be classified into three groups. The non-parametric stability statistics i.e., YSi, KR, TOP, RSM and δgy related to the dynamic concept and strongly correlated with mean seed soybean yield of stability. While, the other non-parametric stability statistics (() , () , () and () , () , () , () and () , δr, MID, LOW) represented the concept of static stability, which were influenced simultaneously by both yield and stability. The non-parametric stability statistics in each the groups I, II, and III were positively and significantly correlated with each other, thus; any of these Original Research Article El-Hashash et al.; AJRCS, 4(4): 1-16, 2019; Article no.AJRCS.50983 2 parameters could be considered as appropriate alternatives for each other. According to cluster analysis, soybean genotypes G6, G4, G8, G11, G9, G1, G7 and G2 were more stable varieties on the basis of mean seed yield and non-parametric stability statistics. In conclusion, both yield and stability should be considered simultaneously to exploit the useful effect of GEI and to make the selection of genotypes more precise and refined. Thus, the YS i , KR, TOP, RSM and δgy were more useful statistics in soybean breeding programmes and could be useful alternatives to parametric stability statistics. According to most non-parametric stability statistics, the genotypes G6 and G11 were more stable coupled with high seed yield; therefore, these genotypes might be used for genetic improvement of soybean and they must be released in studied regions and other regions in Egypt.
Turkish Journal Of Field Crops, 2021
Seed yields of 14 soybean genotypes were evaluated in four locations i.e. Adana, Şanlıurfa, Antalya and İzmir under second crop conditions through summer seasons from 2014 to 2016. The study aims to estimate the stability parameters in terms of seed yield of 14 soybean genotypes by using different stability analysis methods across eleven environmental conditions and to study interrelationships among these stability methods. The analysis of variance for seed yield revealed that the genotypes and the environments as well as the genotype x environment interactions (GEI) were statistically significant at P<0.01. Environmental effects were contributed 51.04% to the total sum of squares whereas GEI and genotype effects were 20.8% and 2.59%, respectively. According to most stability methods, BATEM 223, BATEM 306, BATEM 317 and KASM 02 were determined to be stable genotypes. These genotypes demonstrated superior adaptability with high yield performances in many environments. Results of c...
STABILITY ANALYSIS OF SOME SOYBEAN GENOTYPES USING A SIMPLIFIED STATISTICAL MODEL
The genotype × environment (G×E) interaction is considered a stumbling block to plant breeders, since the presence of significant GxE interaction component can complicate the identification of superior genotypes and reduce the usefulness of selection. Seed yields of 26 soybean genotypes were evaluated in three locations i.e. Sakha, Etay ElBaroud and Mallawy, through four successive summer seasons from 2012 to 2015. The used design was a randomized complete block design with three replications. This research is aimed to estimate the stability parameters of seed yield of 26 soybean genotypes over twelve environmental conditions and to examine the usefulness and validity of a new simple stability method comparing with four widely used methods. The four stability methods follow three main statistical models namely; regression, variance, and non-parametric approaches. Results showed highly significant mean squares for genotypes, environments and G×E interaction indicating that the tested genotypes exhibited different responses to environmental conditions giving the justification for running stability analysis. The terms of predictable (linear) and unpredictable (non-linear) interaction components were highly significant indicating that the tested soybean genotypes were different in their relative stability. The two soybean cultivars Giza 111 and Giza 21 in addition to their high mean yields, they met all the rules of stable genotypes. Therefore, both cultivars could be considered a good breeding material stock in any future breeding program. Also, when the simplified stability method was applied, the unstable eighteen genotypes were differentiated into three classes. These classes included three genotypes (L162, H29 L115, and H2 L12) were adapted to the unpredictable low yielding environments, while five others (H15 L273, L163, H3 L4, H4 L24 and DR 101) were adapted to high yielding environments. Whereas, the rest ten genotypes were unstable over the low, medium and high environmental groups. The results proved also that, the proposed stability method of Thillainathan and Fernandez (2002) is very simple and easy to apply, understand and interpret by agronomists and plant breeders than the other popular stability models. Also, it is possible to support the results of this stability method by a scatter plot diagram that enable the researchers to visually, directly and quickly compare the mean yield performance and stability of the tested genotypes.
Yield Stability of some Soybean Genotypes across Diverse Environments
The present investigation was carried out to study stability performance over eight environments for seed yield and its components in 40 genetically diverse genotypes (37 indigenous + 3 exotic) of soybean (Glycine max L.) using a completely randomized block design. The partitioning of (environment + genotype × environment) mean squares showed that environments (linear) differed significantly and were quite diverse with regards to their effects on the performance of genotypes for fodder yield and the majority of yield components.
Stability Analysis of Soybean ( Glycine Max L. Merrill) GenotypesAcross North West of Ethiopia
International Journal of Research Studies in Agricultural Sciences, 2016
Yield stability is an interesting feature of today’s soybean breeding programs, due to the high annual variation in mean yield, particularly in the areas across North West of Ethiopia. Nineteen soybean (Glycine max. L Merrill) genotypes sourced from Pawe Agricultural Research Center were tested for yield stability and performance in four environments between 2014 and 2016 using various stability statistics. The experiment of each environment was laid out in a randomized complete block design with four replications. Combined analysis of variance of grain yield showed highly significant differences among genotypes and environments. Significant GEI indicated differential performance of genotypes across environments. Considering coefficient of several linear regression models, including conventional, adjusted independent and Tai models as well as deviation variance from these models, genotype G18 was the most stable genotype. Stability analysis in basis of parameters like environmental ...
International Journal of Forestry and Horticulture, 2019
1. INTRODUCTION Soybean (Glycine max L. Merrill), popularly and often called "miracle bean" as it is extraordinarily rich in protein (~40%) and oil (~20%). It is the world's foremost provider of high-quality protein and edible oil for both human food and animal feed; in addition, it can improve soil fertility through its capability to fix atmospheric nitrogen (Morsy et al., 1990). It contains well balanced 40% protein (Lysin rich) and 20 % oil enriched with essential fatty acids. According to Tesfaye et al. (2018), with its diverse agro-ecological and climatic conditions, Ethiopia is endowed with a very large area of land, where soybean can be suitably produced, especially in rotation with maize. The land suitability analysis shows that soybean is the second among legumes in terms of land area that is moderately and highly suitable for its production in the country, with an estimated 42,067,700 (37.2%) ha of land (According to EIAR (2017), soybean can be grown in altitudes ranging from 1250 to 2200 meters above sea level (m.a.s.l.); however, it performs well between 1300 and 1700 m.a.s.l. It can also be grown in an area receiving 450 to 1500 mm annual rainfall; however, to grow very well, and for optimum yields, soybean requires a minimum of 500 mm annual rainfall. Temperature ranging from 23-25oC is reported to be optimum for soybean production; however, it performs well at warm temperature and medium relative humidity. For strong breeding program of any crop such as soybean testing over diverse environment is very important to ensure that the selected genotypes have acceptable performance in variable environments within the target region (Ashraf et al., 2010). Effective interpretation and utilization of data in making
Grain yield stability analysis of soybean genotypes by AMMI method
The additive main effects and multiplicative interaction (AMMI) model was used to analysis the grain yield stability of 20 soybean genotypes in four locations (Karaj, Gorgan, Moghan and ShahreKord) of Iran. Experiments were carried out based on randomized complete block design (RCBD), with three replications in 2014-2015. Result revealed that the grain yield was significantly influenced by environments (E), genotypes (G) and G × E interactions. Principal component analysis (PCA) declared three components which explained up to 90% of G x E sum square (IPCA1, IPCA2 and IPCA3 with 70.72%, 18.99% and 10.60%, respectively). AMMI multivariate method identified two genotypes (No.13 and No.8) with grain yield of 2789 and 2702 kg.ha-1 respectively, which were stable genotypes in different environments. The study concluded that the AMMI model is a practical and effective alternative for crop breeders to screen stability of soybean genotypes for different environments.
Soybean productivity, stability, and adaptability through mixed model methodology
Ciência Rural, 2021
ABSTRACT: The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Thus, the present study aimed to estimate genetic parameters and to compare different selection strategies in the context of mixed models for soybean breeding. For this, data referring to the evaluation of 30 genotypes in 10 environments, regarding the grain yield trait, were used. The variance components were estimated through restricted maximum likelihood (REML) and genotypic values were predicted through best linear unbiased prediction (BLUP). Significant effects of genotypes and G×E interaction were detected by the likelihood ratio test (LRT). Low genotypic correlation was obtained across environments, indicating complex G×E interaction. The selective accuracy was very high, indicating high reliability. Our results showed that the most productive soybean genotypes have high adaptability and stability.
Stability of Soybean Genotypes and Their Classification into Relative Maturity Groups in Brazil
American Journal of Plant Sciences, 2013
The stability of soybean genotypes is very important in breeding programs for not only the evaluation, selection, and production of cultivars but also the establishment of parameters required for the classification of genotypes into relative maturity groups (RMG). The aim of this study was to define stable genotypes for traits, such as days to flowering, days to maturity, and length of the reproductive period, and to classify them into RMG. For this purpose, 20 commercial soybean cultivars were evaluated in 12 environments distributed in the major producing regions of Brazil. Assessments according to the Eberhart and Russell method and the additive main effects and multiplicative interaction (AMMI) method were effective in the identification of stable genotypes and their classification into RMG. These methods can also be used collectively for this purpose. Our results showed that the AMMI method led to a better interpretation of genotype-environment interactions. Thus, RMG obtained on the basis of stable genotypes represented a good estimate of the relative maturity of soybean crops throughout Brazil.