EVALUATION OF AN ARTIFICIAL TEST FOR FROST HARDINESS IN WHEAT

1973 ◽  
Vol 53 (1) ◽  
pp. 53-59 ◽  
Author(s):  
D. B. FOWLER ◽  
D. SIMINOVITCH ◽  
M. K. POMEROY
Keyword(s):  
Minimum Temperature ◽  
Dry Weight ◽  
Freezing Temperature ◽  
Genotype By Environment Interaction ◽  
Frost Hardiness ◽  
Environment Interaction ◽  
Breeding Programs ◽  
Preliminary Screening ◽  
Genotype By Environment ◽  
Highly Correlated

A frost hardiness test that differentiates between cultivars on the basis of resistance to injury from a single minimum freezing temperature was evaluated. Four measurements of frost hardiness: survival, modal height at 14 and 21 days, and dry weight at 21 days, gave highly correlated ratings by this test. The level of repeatability of the test was considered sufficient to allow for the detection of meaningful differences in frost hardiness. However, a significant genotype by environment interaction resulted in heritability ratios that were smaller than comparable repeatability ratios. It was therefore concluded that frost tests employing a single minimum temperature can be utilized to advantage, in breeding programs, only for preliminary screening of genotypes that differ considerably in hardiness potential.

scholarly journals Deciphering Genotype-By-Environment Interaction for Target Environmental Delineation and Identification of Stable Resistant Sources Against Foliar Blast Disease of Pearl Millet

2021 ◽  
Vol 12 ◽  
Author(s):  
S. Mukesh Sankar ◽  
S. P. Singh ◽  
G. Prakash ◽  
C. Tara Satyavathi ◽  
S. L. Soumya ◽  
...  
Keyword(s):  
Pearl Millet ◽  
Magnaporthe Grisea ◽  
Resistance Breeding ◽  
Genotype By Environment Interaction ◽  
Blast Disease ◽  
Environment Interaction ◽  
Breeding Programs ◽  
Genotype By Environment ◽  
Desirability Index ◽  
Resistant Genotypes

Once thought to be a minor disease, foliar blast disease of pearl millet, caused by Magnaporthe grisea, has recently emerged as an important biotic constraint for pearl millet production in India. The presence of a wider host range as well as high pathogenic heterogeneity complicates host–pathogen dynamics. Furthermore, environmental factors play a significant role in exacerbating the disease severity. An attempt was made to unravel the genotype-by-environment interactions for identification and validation of stable resistant genotypes against foliar blast disease through multi-environment testing. A diversity panel consisting of 250 accessions collected from over 20 different countries was screened under natural epiphytotic conditions in five environments. A total of 43 resistant genotypes were found to have high and stable resistance. Interestingly, most of the resistant lines were late maturing. Combined ANOVA of these 250 genotypes exhibited significant genotype-by-environment interaction and indicated the involvement of crossover interaction with a consistent genotypic response. This justifies the necessity of multi-year and multi-location testing. The first two principal components (PCs) accounted for 44.85 and 29.22% of the total variance in the environment-centered blast scoring results. Heritability-adjusted genotype plus genotype × environment interaction (HA-GGE) biplot aptly identified “IP 11353” and “IP 22423, IP 7910 and IP 7941” as “ideal” and “desirable” genotypes, respectively, having stable resistance and genetic buffering capacity against this disease. Bootstrapping at a 95% confidence interval validated the recommendations of genotypes. Therefore, these genotypes can be used in future resistance breeding programs in pearl millet. Mega-environment delineation and desirability index suggested Jaipur as the ideal environment for precise testing of material against the disease and will increase proper resource optimization in future breeding programs. Information obtained in current study will be further used for genome-wide association mapping of foliar blast disease in pearl millet.

scholarly journals Genomic Prediction and Genotype-by-Environment Interaction Analysis of Crown and Stem Rust in Ryegrasses in European Multi-Site Trials

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1119
Author(s):  
Mattia Fois ◽  
Marta Malinowska ◽  
Franz Xaver Schubiger ◽  
Torben Asp
Keyword(s):  
Stem Rust ◽  
Genomic Prediction ◽  
Interaction Analysis ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Forage Crops ◽  
Breeding Programs ◽  
Genotype By Environment ◽  
The Given ◽  
Better Than

Climate change calls for novel approaches to include environmental effects in future breeding programs for forage crops. A set of ryegrasses (Lolium) varieties was evaluated in multiple European environments for crown rust (Puccinia coronata f. sp. lolii) and stem rust (P. graminis f. sp. graminicola) resistance. Additive Main Effect and Multiplicative Interaction (AMMI) analysis revealed significant genotype (G) and environment (E) effects as well as the interaction of both factors (G × E). Genotypes plus Genotype-by-Environment interaction (GGE) analysis grouped the tested environments in multiple mega-environments for both traits suggesting the presence of an environmental effect on the ryegrasses performances. The best performing varieties in the given mega-environments showed high resistance to crown as well as stem rust, and overall, tetraploid varieties performed better than diploid. Furthermore, we modeled G × E using a marker x environment interaction (M × E) model to predict the performance of varieties tested in some years but not in others. Our results showed that despite the limited number of varieties, the high number of observations allowed us to predict both traits’ performances with high accuracy. The results showed that genomic prediction using multi environmental trials could enhance breeding programs for the crown and stem rust in ryegrasses.

scholarly journals Leveraging probability concepts for genotype by environment recommendation

2021 ◽  
Author(s):  
Kaio O.G. Dias ◽  
Jhonathan P.R. dos Santos ◽  
Matheus D. Krause ◽  
Hans-Peter Piepho ◽  
Lauro J.M. Guimarães ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Statistical Models ◽  
Genotype By Environment Interaction ◽  
Decision Making Process ◽  
Environment Interaction ◽  
Informed Decision Making ◽  
Breeding Programs ◽  
Specific Adaptation ◽  
Genotype By Environment ◽  
Probability Methods

AbstractStatistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for distinct environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process towards cultivar recommendation in multi-environment trials.

scholarly journals Monte Carlo Approach To Genotype By Environment Interaction Models

2020 ◽  
Vol 1 (2) ◽  
pp. 26-33
Author(s):  
Oyamakin S. Oluwafemi ◽  
Durojaiye M. Olalekan
Keyword(s):  
Monte Carlo ◽  
Mixed Model ◽  
Real Life ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Data Types ◽  
Breeding Programs ◽  
Genotype By Environment ◽  
Gxe Interaction ◽  
Real Life Data

Understanding the implication of Genotype-by-Environment (GXE) interaction structure is an important consideration in plant breeding programs. Traditional statistical analyses of yield trials provide little or no insight into the particular pattern or structure of the GXE interaction. In this study, efforts were made to solve these problems under different level of data occurrence. We employed the simulation process of Monte Carlo in generating since use of a real-life data may pose a serious difficulty. In this paper, we simulated for two data Types of Balance and Unbalance designs with different Levels of generations (3X3, 7X7, 10X10, and 3X7, 7X3, 7X10, 10X7 , , respectively). We therefore check the performance of GXE interaction on four different models (AMMI, FW, GGE and Mixed model), and also their stability and adaptability. The findings revealed that, when the assumption was maintained, AMMI outperformed Finlay-Wilkinson model, GGE Biplot model and Mixed model.

scholarly journals Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench)

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258211
Author(s):  
Muluken Enyew ◽  
Tileye Feyissa ◽  
Mulatu Geleta ◽  
Kassahun Tesfaye ◽  
Cecilia Hammenhag ◽  
...  
Keyword(s):  
Grain Yield ◽  
Plant Height ◽  
Agronomic Traits ◽  
Genotypic Variation ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
Genotype By Environment ◽  
And Cluster Analysis

Genotype by environment (G×E) interaction is a major factor limiting the success of germplasm selection and identification of superior genotypes for use in plant breeding programs. Similar to the case in other crops, G×E complicates the improvement of sorghum, and hence it should be determined and used in decision-making programs. The present study aimed at assessing the G×E interaction, and the correlation between traits for superior sorghum genotypes. Three hundred twenty sorghum landraces and four improved varieties were used in alpha lattice experimental design-based field trial across three environments (Melkassa, Mieso and Mehoni) in Ethiopia. Phenotypic data were collected for days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT) and grain yield (GY). The results revealed that the variance due to genotype, environment and G×E interaction were highly significant (P < 0.001) for all traits. GY and PAWT were highly affected by environments and G×E whereas DTF, PALH, PAWD and PH were mainly affected by genotypic variation. Therefore, multi-environment testing is needed for taking care of G × E interaction to identify high yielding and stable sorghum landraces. GY and PAWT revealed highly significant positive correlations indicating the possibility of effective selection of the two traits simultaneously. Among the studied populations, South Wello, West Hararghe and Shewa zones had highly diverse genotypes that were distributed across all clusters. Hence, these areas can be considered as hotspots for identifying divergent sorghum landraces that could be used in breeding programs. Melkassa was the most representative environment whereas Mieso was the most discriminating. Five genotypes (G148, G123, G110, G203 and G73) were identified as superior across the test environments for grain yield with farmer-preferred trait, such as plant height. The identified stable and high yielding genotypes are valuable genetic resources that should be used in sorghum breeding programs.

Using AMMI approach to delineate genotype by environment interaction and stability of barley (Hordeum vulgare L.) genotypes under northern Indian shivalik hill conditions

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Om Prakash Yadav ◽  
A. K. Razdan ◽  
Bupesh Kumar ◽  
Praveen Singh ◽  
Anjani K. Singh
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Principal Component ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Principal Component Axis ◽  
Ammi Analysis

Genotype by environment interaction (GEI) of 18 barley varieties was assessed during two successive rabi crop seasons so as to identify high yielding and stable barley varieties. AMMI analysis showed that genotypes (G), environment (E) and GEI accounted for 1672.35, 78.25 and 20.51 of total variance, respectively. Partitioning of sum of squares due to GEI revealed significance of interaction principal component axis IPCA1 only On the basis of AMMI biplot analysis DWRB 137 (41.03qha–1), RD 2715 (32.54qha–1), BH 902 (37.53qha–1) and RD 2907 (33.29qha–1) exhibited grain yield superiority of 64.45, 30.42, 50.42 and 33.42 per cent, respectively over farmers’ recycled variety (24.43qha–1).

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