Grain yield stability of spring safflower (Carthamus tinctorius L.)

2008 ◽  
Vol 59 (6) ◽  
pp. 546 ◽  
Author(s):  
Reza Mohammadi ◽  
Sayyed Saeid Pourdad ◽  
Ahmed Amri
Keyword(s):  
Coefficient Of Variation ◽  
Rank Correlation ◽  
Type A ◽  
Carthamus Tinctorius ◽  
Coefficient Of Determination ◽  
Environment Interaction ◽  
Type B ◽  
Stability Parameters ◽  
Spearman’S Rank Correlation ◽  
Genotype Environment Interaction

The additive main effect and multiplicative interaction (AMMI) model and the phenotypic stability parameters, ecovalence (W2), regression coefficient (b), coefficient of determination (R2), coefficient of variation (CV), stability variance (S2), AMMI stability value (ASV), and TOP (proportion of environments in which a genotype ranked in the top third), were used to evaluate simultaneously the yield performance and stability of 17 spring safflower genotypes and to evaluate 26 rainfed environments during 2003–05 in Iran. These parameters were designated as Type-A and Type-B for genotypes and environments, respectively. Among Type-B parameters, Spearman’s rank correlation showed that the AMMI stability value (ASVj), ecovalence (Wj2), genotypic variance (Sj2), and coefficient of variation (CVj) were significantly and positively associated (P < 0.01), indicating that one of these parameters can be used as an alternative to the others, but were significantly and negatively correlated with the genotypic selectivity (bj) parameter. The results showed that none of the Type-A statistics per se was useful for selecting high-yielding and stable genotypes. Based on these parameters, the genotypes G9, G10, and G11 combined high and stable yields while the highest yielding genotypes G1 and G17 were the most instable. Type-A and Type-B stability parameters are useful to identify genotypes with specific and large adaptations and the contrasting environments with high contribution to genotype × environment interaction.

scholarly journals Assessment of Genotype × Environment Interaction of Safflower (Carthamus tinctorius L.) Genotypes by Parametric and Non-Parametric Methods

2021 ◽  
Vol 3 (1) ◽  
pp. 112-118
Author(s):  
İlhan Subaşı ◽  
Dilek Başalma
Keyword(s):  
Seed Yield ◽  
Positive Association ◽  
Stability Index ◽  
Oil Yield ◽  
Coefficient Of Determination ◽  
Mean Deviation ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Stability Parameters ◽  
Genotype Environment Interaction

Genotype-environment interaction is a significant factor for finding and selecting stable and productive varieties in safflower breeding programs. This study was conducted at three locations over two years (2016-2017) to determine the extent of genotype by environment (GE) interaction in seed and oil yield. 20 safflower lines and cultivars were evaluated in terms of stability in 3 environments. Considering the stability and performance, the most suitable genotypes were determined as Remzibey-05 and Genotype-125 in seed yield, Genotype-8 and Genotype-155 in oil yield. In terms of stability and performances of genotypes, the environment of Ikizce 2017 (E4) was prominent. Correlation analysis among parametric and nonparametric features was given only for seed yield. The following stability parameters were calculated: the coefficient of variation (CV), regression constant (ai), regression coefficient (bi), mean deviation squares from regression (S2di), coefficient of determination (Ri2), stability variance (σi2), ecovalance value (Wi), stability index (Pi) and as nonparametric stability measures Si(1) and Si(2) values. This analysis indicated that seed yield was significantly positively correlated only with Pi (P<0.01). CV showed a positively significant correlation with ai. S2di and ri2 had a positive association with Ri2, σi2, Wi, Pi, Si(1), Si(2), and between each other.

scholarly journals Inter-Regional Variability in the Incidence of Skin and Subcutaneous Tissue Diseases of Children Aged 0–14 Years in the Russian Federation (а Population-Based Study)

2018 ◽  
Vol 15 (4) ◽  
pp. 349-353
Author(s):  
Eleonora I. Pilguy ◽  
Leyla S. Namazova-Baranova ◽  
Igor A. Gundarov
Keyword(s):  
Russian Federation ◽  
Coefficient Of Variation ◽  
Subcutaneous Tissue ◽  
Rank Correlation ◽  
Population Based ◽  
Regional Variability ◽  
Spearman’S Rank Correlation ◽  
Risk Aggregation ◽  
The Russian Federation ◽  
Range Of Variation

Background. An effective system for management of pediatric dermatological care in Russia requires taking into account the peculiarities of regional morbidity. According to modern literature, this issue is not sufficiently developed. Our aim was to study the characteristics of variability in the incidence of skin and subcutaneous tissue diseases of children aged 0–14 years in the Russian Federation. Methods. The data of the state medical statistics on 83 territorial entities of the Russian Federation for 2009–2015 was used. The analysis implemented the risk aggregation method. The degree of variability in regional morbidity was assessed by the range of variation and the coefficient of variation. Region rankings were carried out by the annual incidence. The Spearman’s rank correlation coefficient was used to study the stability of regions belonging to a certain ranked place. Results. In 2009, the incidence of skin and subcutaneous tissue diseases of children aged 0–14 years in the Russian Federation had a pronounced variability: the range of variation was 7.7 times, the coefficient of variation was 28%. The annual region rankings were carried out. In 2010–2015, the annual variability indices remained high. The coefficients of rank correlation when distributing regions by morbidity were in the range of 0.75–0.94 compared to 2009. Quintiles of regions with different levels of pediatric dermatological morbidity were singled out. Conclusion. The incidence of skin and subcutaneous tissue diseases in children aged 0–14 years in the territorial entities of the Russian Federation during 2009–2015 had a pronounced variability. Regions had a stable preference for a certain ranking place. The division of territorial entities of the Russian Federation into groups of pediatric dermatological morbidity are presented.

Selecting stable and high-yielding sorghum cultivars for the semi-arid tropics

1994 ◽  
Vol 74 (4) ◽  
pp. 759-762
Author(s):  
O. P. Dangi ◽  
R. I. Hamilton ◽  
C. S. Lin ◽  
D. Andre ◽  
J. J. Johnson
Keyword(s):  
High Yield ◽  
Annual Rainfall ◽  
Environment Interaction ◽  
Mean Square ◽  
Stability Parameters ◽  
The North ◽  
Genotype Environment Interaction ◽  
Northern Cameroon ◽  
The Stability ◽  
Extreme North

A sorghum breeding program was reactivated in 1981 and selected cultivars, along with local checks, were evaluated in two experiments in the sorghum growing region of northern Cameroon. Experiment 1 was conducted in the Extreme North Province where annual rainfall ranges from 450 to 850 mm. Experiment 2 was conducted in the North Province where annual rainfall exceeds 850 mm. The objective of the study was to select a high yield and high stability sorghum cultivar for each region. The cultivar's responses were investigated using two analyses: the adaptability analysis and the stability analysis. The former used the method of superiority measure, defined by distance mean square between the test cultivar and the maximum (the highest yield in the location), and the latter used type 4 stability parameter, defined by the years within location mean square averaged over all locations. The conceptual separation of adaptability and stability facilitated the cultivars assessment. The results showed that in exp. 1, three cultivars S–35, CS–54 and CS–61 had similar adaptability and stability, while in the exp. 2, S–34 was best in terms of yield but was unstable due to susceptibility to grain mold. In contrast, the second best cultivar CS–63 was poorer in the high-yielding environments but was more stable than S–34. Key words: Sorghum, genotype-environment interaction, adaptability, stability parameters

scholarly journals Genotype x environment interaction of wheat genotypes under salinity environments

2017 ◽  
Vol 3 (1) ◽  
pp. 38-43
Author(s):  
Md Saleh Uddin ◽  
Md Sultan Alam ◽  
Nasrin Jahan ◽  
Kazi Md Wayaz Hossain ◽  
Md Ali Newaz
Keyword(s):  
Grain Yield ◽  
Grain Weight ◽  
Environment Interaction ◽  
Stability Parameters ◽  
Wheat Genotypes ◽  
Genotype X Environment ◽  
Genotype Environment Interaction ◽  
Days To Heading ◽  
Salinity Levels ◽  
1000 Grain Weight

Genotypes x environment interaction as well as stability of performance were determined for grain yield and yield contributes of 12 wheat genotypes under four salinity levels of environments (control, 8, 12, 16 dS/m). Significant genotype-environment interaction (linear) for days to heading, plant height, number of spikes per plant and grains per spikes, 1000-grain weight and grain yield per plant at 1% level of probability when tested against pooled deviation. Both the environment (linear) and genotype x environment (linear) components of variation for stability were also significant indicating that prediction of the genotypes on the environment appeared feasible for all the characters. The variance due to pooled deviation was significant for only days to heading. Considering all the three stability parameter, genotype G11 was found most stable among all the genotypes for grain weight of wheat. Among the genotypes G11, G22, G24, G33 and G40 were most desirable for yield per plant. The genotype G32 showed more responsiveness to changing environment and was suited only for highly favorable environments. Based on three stability parameters, G11, G22 and G37 were the most stable and desirable genotypes with reasonable good yield among the all.Asian J. Med. Biol. Res. March 2017, 3(1): 38-43

scholarly journals Weaning performance of beef Hungarian Fleckvieh calves: 3. Genotype × environment interaction

2010 ◽  
Vol 53 (2) ◽  
pp. 123-129
Author(s):  
A. Fördős ◽  
I. Füller ◽  
S. Bene ◽  
F. Szabó
Keyword(s):  
Random Effect ◽  
Rank Correlation ◽  
Cattle Breed ◽  
Least Square ◽  
Environment Interaction ◽  
Birth Season ◽  
Genotype Environment Interaction ◽  
Population Interaction ◽  
Association Data ◽  
Two Populations

Abstract. The interaction of sire and population in Hungarian Fleckvieh beef cattle breed were examined in this study on data from the Hungarian Fleckvieh Breeders Association. Data of 2 345 progeny (1 260 male and 1 085 female), born between 1992 and 2003, of 35 sires from two populations were evaluated. Preweaning daily gain (PDG) and 205-day weight (205-dw) were analysed. Population, age of cows, year of birth, season of birth and sex of calves as fixed, sire and sire × population were treated as a random effect. Among the same performance data in the two populations (A, B) genetic correlation (rg), while by the gradiation of sires rank correlation (rrank), were evaluated. Data were analysed with HARVEY’S (1990) Least Square Maximum Likelihood Computer Program and SPSS 9.0 for Windows. Results were as follows: rg=PDGA−PDGB: 0.31(P<0.01); 205-dwA−205-dwB: 0.22(P<0.01) and rrank=PDG: −0.04(P>0.05); 205-dw: 0.078(P>0.05). According to the result of examination important and significant (P<0.001) sire × population interaction were found in case of the two traits in Hungarian Fleckvieh breed.

scholarly journals Using interpolation and correlation to evaluate the quality of cement composites exposed to a thermally aggressive environment

Vestnik MGSU ◽  
2021 ◽  
pp. 1473-1482
Author(s):  
Irina N. Maksimova ◽  
Irina V. Erofeeva ◽  
Victor V. Afonin ◽  
Denis V. Emelyanov
Keyword(s):  
Quality Evaluation ◽  
Rank Correlation ◽  
Operating Conditions ◽  
Atmospheric Environment ◽  
Coefficient Of Determination ◽  
Cement Composites ◽  
Spearman’S Rank Correlation ◽  
Negative Temperatures ◽  
Cyclic Changes

Introduction. The article addresses the quality evaluation of cement composites after their exposure to the atmospheric environment that features a rapid change in positive and negative temperatures. The article has a numerical assessment of the quality of cement composites. The quality assessment method encompasses the Spearman’s rank correlation, the coefficient of determination, and the adjustment ratio. Materials and methods. Testable cement composites are the compositions numbered from one to eight. They differ in the concentration of various additives, including superplasticizers and aggregates. Testable compositions were exposed to cyclic changes in positive and negative temperatures, and four of their properties were checked in the points of control on Day 0, Day 15, and Day 45. Linear interpolation was used to expand the data array. Changes in the properties of composites were compared with the benchmark values; the Spearman’s rank correlation and the coefficient of determination were calculated. Moreover, arrays of interpolated values were reduced to relative units to calculate the adjustment ratios. Entry values were employed to develop the evaluation metrics and assess the quality of cement composites. Results. The proposed numerical metrics is used to rank the testable samples and identify the best compositions. Library functions and transformations, available in the MATLAB system, are applied to each action, which can be implemented in nearly any software programming language. Conclusions. The proposed method of heuristic quality evaluation of cement composites may be applied to the cases when testable samples are exposed to versatile adverse and aggressive operating conditions.

scholarly journals Improvement of yield and yield stability in safflower using multivariate, parametric and non-parametric methods under different irrigation treatments and planting date

2020 ◽  
Vol 115 (2) ◽  
pp. 315
Author(s):  
Pooran GOLKAR ◽  
Nasrin RAHMATABADI ◽  
Seyyed Ali Mohammad MIRMOHAMMADY MAIBODY
Keyword(s):  
Seed Yield ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Genotype By Environment Interaction ◽  
Planting Date ◽  
Environment Interaction ◽  
Stability Parameters ◽  
Breeding Programs ◽  
Growing Seasons ◽  
Non Parametric

<p>Development of superior genotypes with high adaptability to different environments is considered as one of the most important goals in safflower breeding programs. In this study, ten parametric and six non-parametric measures along with the additive main effects and the relevant multiplicative interaction (AMMI) model were used to evaluate genotype by environment interaction (GE) in 15 safflower genotypes across 12 test environments ) combination of year, planting date and moisture conditions) during growing seasons in 2016 and 2017. AMMI analysis revealed significant differences among the genotypes and their GE interactions. The different stability statistics were substantiated by rank correlation coefficient. Rank-correlation coefficients revealed positive and significant correlations between mean seed yield and superiority index (r = 0.99**), and significant and negative correlation with bi, R2, Dij and non- parametric measures (NPi(2), NPi(3) and NPi(4)). Based on most stability parameters, the Mex.295 genotype (G10) was found to be the most stable for seed yield. IL.111 genotype (G9) recorded the highest mean yielding genotype regarded as the most favorable safflower genotype. In conclusion, both stability and seed yield should be simultaneously considered to exploit useful effects of G × E interactions in safflower breeding programs.</p>

scholarly journals Stability Analysis for Seed Yield over Environments in Coriander

2016 ◽  
Vol 4 (11) ◽  
pp. 1014
Author(s):  
Sangeeta Yadav ◽  
Arun Kumar Barholia
Keyword(s):  
Stability Analysis ◽  
Seed Yield ◽  
High Fertility ◽  
Environment Interaction ◽  
Stability Parameters ◽  
Genotype Environment Interaction ◽  
Ammi Analysis ◽  
Back Ground ◽  
Ammi Model ◽  
Main Effects

Thirty five genotypes of coriander (Coriandrum sativum L.) were tested in four artificially created environments to judge their stability in performance of seed yield. The differences among genotypes and environments were significant for seed yield. Stability parameters varied considerably among the tested genotypes in all the methods used. The variation in result in different methods was due to non-fulfillment of assumption of different models. However, AMMI analysis provides the information on main effects as well as interaction effects and depiction of PCA score gives better understanding of the pattern of genotype – environment interaction. The sum of squares due to PCAs was also used for the computation of AMMI stability values for better understanding of the adaptability behavior of genotypes hence, additive main effects and multiplicative interaction (AMMI) model was most appropriate for the analysis of G x E interactions for seed yield in coriander. Genotypes RVC 15, RVC 19, RVC 22, RVC 25 and Panipat local showed wider adaptability while, Simpo S 33 exhibited specific adaptability to favourable conditions of high fertility. These genotypes could be utilized in breeding programmers to transfer the adaptability genes into high yielding genetic back ground of coriander.

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