scholarly journals An investigation of genotype-environment interaction and stability for pea (Pisum sativum L) seed yield

2000 ◽  
Vol 6 (1) ◽  
Author(s):  
L. Csizmadia ◽  
F. Kormos
Keyword(s):  
Linear Regression ◽  
Pisum Sativum ◽  
Seed Yield ◽  
Regression Technique ◽  
Environment Interaction ◽  
Stability Parameters ◽  
Breeding Lines ◽  
Genotype Environment Interaction ◽  
Pisum Sativum L ◽  
Stability Variance

Eighteen pea breeding lines and three check cultivars were tested in two years with and without irrigation. The linear regression can explain only a small part of GEI so the use of regression technique was not possible for interpreting the data. In this case the ecovalence, stability variance and superiority measure stability parameters cannot describe properly the genotype's response. With the AMMI method it is possible to group properly the genotypes according to their response.

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 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.

scholarly journals GENOTYPE-ENVIRONMENT INTERACTION FOR SEED YIELD AND YIELD CONTRIBUTING CHARACTERS IN CHICKPEA (Cicer arietinum L.)

2007 ◽  
Vol 20 (1) ◽  
pp. 09-12
Author(s):  
F. Mahmud ◽  
M. Z. Ullah ◽  
K. M. K. Huda
Keyword(s):  
Seed Yield ◽  
Significant Variation ◽  
Seed Weight ◽  
Environment Interaction ◽  
Stability Parameters ◽  
Cicer Arietinum L ◽  
Plant Seeds ◽  
Genotype Environment Interaction ◽  
Days To Maturity ◽  
100 Seed Weight

Genotype-environment interaction was studied in seven genotypes of chickpea under four different cultural environments. Significant variation for genotype (G), environment (E) and G × E interactions were found for the characters days to maturity, plant height, pods/plant, seeds/plant, 100-seed weight and seed yield/plant. On the basis of stability parameters the genotypes Barichola-2, Barichola-3, Barichola-4, Barichola-7 and Barichola-8 could be considered stable for seed yield but suitable only under poor environments where no fertilizers were used. The genotype Barichola-1 was highly responsive but suitable for favorable environments only. DOI: http://dx.doi.org/10.3329/bjpbg.v20i1.17012

Proximate and mineral composition of field peas

1997 ◽  
Vol 77 (1) ◽  
pp. 101-103 ◽  
Author(s):  
T. D. Warkentin ◽  
A. G. Sloan ◽  
S. T. Ali-Khan
Keyword(s):  
Pisum Sativum ◽  
Key Words ◽  
Mineral Composition ◽  
Seed Yield ◽  
Total Protein ◽  
Field Pea ◽  
Pisum Sativum L ◽  
Field Peas ◽  
Pea Seeds ◽  
Proximate And Mineral Composition

Field pea seeds from 10 cultivars grown at two locations in Manitoba in 1986 and 1987 were analyzed for proximate and mineral profiles. Cultivars differed significantly in their level of total protein, crude fat, ADF, and all minerals tested. However, differences were not extremely large and were comparable to European reports. Location-year also had a significant effect on the levels of total protein, ADF, and all minerals tested. In most cases, the warmest location-year produced relatively higher levels of minerals, ash, and total protein, and lower seed yield than the coolest location-year. Key words: Field pea, Pisum sativum L., mineral

Breeding for improved blanchability in peanut: phenotyping, genotype × environment interaction and selection

2018 ◽  
Vol 69 (12) ◽  
pp. 1237 ◽  
Author(s):  
G. C. Wright ◽  
M. G. Borgognone ◽  
D. J. O Connor ◽  
R. C. N. Rachaputi ◽  
R. J. Henry ◽  
...  
Keyword(s):  
Low Cost ◽  
Peanut Butter ◽  
Environment Interaction ◽  
Breeding Lines ◽  
Seed Supply ◽  
Genotype Environment Interaction ◽  
Effective Selection ◽  
The Usa ◽  
Speed Up ◽  
Very High

Breeding for improved blanchability—the propensity of the testa (skin) to be removed from the kernel following rapid heat treatment—is a priority for improvement in the Australian Peanut Breeding Program (APBP). Easy removal of the testa by blanching is required for processing of peanuts into peanut butter and various other confectionary products. Thus, blanchability is an economically important trait in any newly released cultivar in Australia. A better understanding of the range of genetic variation, nature of inheritance and genotype×environment (G×E) interactions, and the development of a low-cost method to phenotype in early generations, could speed up breeding for this trait. Studies were conducted to develop a low-cost, rapid method utilising minimal amounts of seed to phenotype in early generations, along with an assessment of G×E interactions over a range of years and environments to derive optimal selection protocols. Use of a smaller kernel sample size than standard (50 vs 200g) was effective for accurately assessing blanchability in breeding lines and could allow selection in early generations (e.g. in seed produced from a single F2 plant where seed supply is adequate). G×E interaction for blanchability was shown to be very low. Genotypic variance explained 62–100% of the total variance for blanchability, assessed in two diverse germplasm pools including 107 accessions in the USA mini-core over three environments and multiple APBP breeding lines grown over nine different years–environments. Genotypic correlations between all environments were very high (~0.60–0.96), with heritability for the blanchability trait estimated to be very high (0.74–0.97) across the 13 trials. The results clearly demonstrate that effective selection for improved blanchability can be conducted in early generations and in a limited number of contrasting environments to ensure consistency of results.

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