Early Generation Selection Under Different Environments as It Influences Adaptation of Barley

St-Pierre, C.; Klinck, H. R.; Gauthier, F. M.

doi:10.4141/cjps67-091

Cited by 28 publications

(17 citation statements)

References 2 publications

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“…Thus, in high-yielding areas, a clone with high response to the stresses common to the area would be used as a standard. This would also serve as a biological indicator (St Pierre, Klinck & Gauthier, 1967) to show when a testing period may bo unsuitable for selection to be made, perhaps owing to unusually adverse weather conditions. Sebastiampillai & Solomon (1976) suggested that the clonal testing period be reduced to 1 year of yield assessments in order to accelerate the selection process.…”

Section: Discussionmentioning

confidence: 99%

Genotype-environment interactions in tea (Camellia sinensis L.) and their implications in tea breeding and selection

Wickramaratne

1981

J. Agric. Sci.

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An analysis of yield data from 31 tea clones grown in replicated trials at four locations revealed the presence of significant genotype-environment interactions. Joint regression analysis showed that a significant proportion of these interactions could be attributed to differences in the linear response of each genotype (clone) to the range of environments. However, estimates of the deviations from regression were also significant, indicating the presence of some unpredictable variation.The response of each individual clone was estimated by computing the regression coefficient of individual yield on environmental mean yield at each location. The clones used in this study had regression values ranging from 0-19 to 2-08, indicating considerable genotypic difference in response to environmental change. Genetic variation in adaptation was also found to be present. Clones of the Assam type were found to possess general adaptation whereas the one clone of Indo-Chinese origin was specifically adapted to low-yielding environments.Some known features of several tea clones are explained on the basis of the linear regression model and a more scientific basis is provided for the recommendations of clones for different districts. The implications of genotype-environment interactions in practical tea breeding and selection are also discussed.

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Section: Discussionmentioning

confidence: 99%

Genotype-environment interactions in tea (Camellia sinensis L.) and their implications in tea breeding and selection

Wickramaratne

1981

J. Agric. Sci.

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“…1.00 Si (2) 0.99** 1.00 Si (3) 0.67** 0.64** 1.00 Si (6) 0.65** 0.63** 0.99** 1.00 NPi (1) 0.96** 0.94** 0.77** 0.74** 1.00 NPi (2) 0.79** 0.78** 0.89** 0.90** 0.85** 1.00 NPi (3) 0.77** 0.77** 0.90** 0.92** 0.85** 0.96** 1.00 NPi (4) 0.83** 0.82** 0.57* 0.53* 0.84** 0.68** 0.66** 1.00 *P< 0.05, **P<0.01 † Symbols: Y-Mean yield (t ha -1 ), YSD-Yield standard deviation, RM-Rank mean, RSD-Rrank's standard deviation (Ketata, 1988), YS-Yield stability statistic (Kang and Magari, 1995), PA-Percentage of adaptability (St-Pierre et al, 1967), R1 and R2-Range indexes (Langer et al, 1979), TOP-Proportion of environments in which a genotype ranked in the top third (Fox et al, 1990), Si (1) , Si (2) , Si (3) and Si (6) -Ranks of adjusted yield means of genotypes (Huehn, 1979), NPi (1) , NPi (2) NPi (3) and NPi (4) -Ranks of adjusted yield means of genotypes (Thennarasu, 1995).…”

Section: Relationships Among the Non-parametric Stability Statisticsmentioning

confidence: 99%

Evaluation of Genotype by Environment Interaction for Grain Yield in Durum Wheat Using Non-Parametric Stability Statistics

Kaya¹,

Türköz²

2016

Turkish Journal of Field Crops

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If genotype (G) ranks change from one environment (E) to another, genotype by environment interactions (GEI) reflects the need for testing Gs in numerous Es in order to obtain reliable results. The aim of this study was to compare 16 non-parametric stability statistics (NPSSs) for GEI on grain yields of 15 durum wheat genotypes, consisting of 11 advanced lines selected from Turkish National Durum Wheat Breeding Program (TNDWBP) and four checks, tested in 12 rain-fed environments during the 2 cropping seasons (2009-2010 and 2010-2011) in Turkey. The combined ANOVA indicated that G, E and GEI effects were significant for grain yield. According to analyses of NPSSs, the highest in ranking (TOP), percentage of adaptability (PA), rank mean (RM) and yield-stability (YS) statistics were positively associated with grain yield and therefore characterized under the dynamic concept of stability. In addition, spearman rank correlation analysis revealed that only TOP, PA, RM and YS statistics would be useful for simultaneous selection for high grain yield and stability. Based on the 16 NPSSs used in this study, Dumlupinar cultivar (G15) was both the most stable and one of the highest yielding ones. On the other hand, G5 and G7 were the most stable ones among the advanced lines tested, but their yield performances were lower. As a result, this study showed that the crossing block of TNDWBP should be enriched by germplasm being capable of dynamic stability, wide adaptation and higher yielding.

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“…As indicated above, it must be important to allow natural or artificial selection to operate under the conditions for which wide adaptation is required. St-Pierre et al (1967) support the importance of the practical use of genotype-environment interactions for obtaining varieties with wider adaptation and for increasing the efficiency of selection. However, Choo et al (1980) failed to demonstrate that alternation between sites broadened adaptation in composite populations.…”

Section: Agro No Mic Perfo Rma N Ce O F Comp Osite Cro Ss Pop Ula Tio Nsmentioning

confidence: 83%

Evolutionary plant breeding for low input systems

2005

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Heritable variation is at the heart of the process of evolution. However, variation is restricted in breeding for uniform crop populations using the pedigree line approach. Pedigree lines are successful in agriculture because synthetic inputs are used to raise fertility and control weeds, pests and diseases. An alternative method promoted for exploring the value of variation and evolutionary fitness in crops is to create composite cross populations. Composite cross populations are formed by assembling seed stocks with diverse evolutionary origins, recombination of these stocks by hybridization, the bulking of F1 progeny, and subsequent natural selection for mass sorting of the progeny in successive natural cropping environments. Composite cross populations can provide dynamic gene pools, which in turn provide a means of conserving germplasm resources: they can also allow selection of heterogeneous crop varieties. The value of composite cross populations in achieving these aims is dependent on the outcome of mass trials by artificial and natural selection acting upon the heterogeneous mixture. There is evidence to suggest that composite cross populations may be an efficient way of providing heterogeneous crops and of selecting superior pure lines for low input systems characterized by unpredictable stress conditions.

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Early Generation Selection Under Different Environments as It Influences Adaptation of Barley

Cited by 28 publications

References 2 publications

Genotype-environment interactions in tea (Camellia sinensis L.) and their implications in tea breeding and selection

Genotype-environment interactions in tea (Camellia sinensis L.) and their implications in tea breeding and selection

Evaluation of Genotype by Environment Interaction for Grain Yield in Durum Wheat Using Non-Parametric Stability Statistics

Evolutionary plant breeding for low input systems

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