Visual Selection for Yielding Ability of F 3 Lines in a Hard Red Spring Wheat Breeding Program 1

Crop Science ◽  
1970 ◽  
Vol 10 (4) ◽  
pp. 400-402 ◽  
Author(s):  
K. G. Briggs ◽  
L. H. Shebeski
Keyword(s):  
Spring Wheat ◽  
Wheat Breeding ◽  
Visual Selection ◽  
Breeding Program ◽  
Hard Red Spring Wheat ◽  
Wheat Breeding Program ◽  
Yielding Ability ◽  
Selection For

scholarly journals Genomic Selection for Predicting Fusarium Head Blight Resistance in a Wheat Breeding Program

2015 ◽  
Vol 8 (3) ◽  
Author(s):  
Marcio P. Arruda ◽  
Patrick J. Brown ◽  
Alexander E. Lipka ◽  
Allison M. Krill ◽  
Carrie Thurber ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Genomic Selection ◽  
Fusarium Head Blight Resistance ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Blight Resistance ◽  
Head Blight ◽  
Wheat Breeding Program ◽  
Selection For

IMPLICATIONS CONCERNING THE FREQUENCY OF CONTROL PLOTS IN WHEAT BREEDING NURSERIES

1968 ◽  
Vol 48 (2) ◽  
pp. 149-153 ◽  
Author(s):  
K. G. Briggs ◽  
L. H. Shebeski
Keyword(s):  
Triticum Aestivum ◽  
Soil Fertility ◽  
Spring Wheat ◽  
Wheat Breeding ◽  
Control Plot ◽  
Good Measure ◽  
Hard Red Spring Wheat ◽  
Yielding Ability ◽  
University Of Manitoba ◽  
The University

Control plots of Triticum aestivum var. Manitou were grown adjacent to every plot of breeding material in three hard red spring wheat nurseries at the University of Manitoba. Simple correlations between yields were high (r =.88,.87 and.63) and significant (P.01) for control plots at 2.7-m (9-ft) centers but decreased rapidly to nonsignificance with increasing distance between plot centers. The data indicate that for the particular type of plot used, the yield of a control plot provides a good measure of the soil fertility in terms of the yielding ability of an adjacent plot.

scholarly journals Genomic Selection for Processing and End‐Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program

2016 ◽  
Vol 9 (2) ◽  
Author(s):  
Sarah D. Battenfield ◽  
Carlos Guzmán ◽  
R. Chris Gaynor ◽  
Ravi P. Singh ◽  
Roberto J. Peña ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Bread Wheat ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Quality Traits ◽  
Spring Bread Wheat ◽  
Wheat Breeding Program ◽  
Selection For ◽  
End Use

scholarly journals Genomic Selection for Grain Yield in the CIMMYT Wheat Breeding Program—Status and Perspectives

2020 ◽  
Vol 11 ◽  
Author(s):  
Philomin Juliana ◽  
Ravi Prakash Singh ◽  
Hans-Joachim Braun ◽  
Julio Huerta-Espino ◽  
Leonardo Crespo-Herrera ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Selection For

scholarly journals Gains through selection for grain yield in a winter wheat breeding program

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0221603 ◽  
Author(s):  
Dennis N. Lozada ◽  
Brian P. Ward ◽  
Arron H. Carter
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Selection For

scholarly journals Gains through selection for grain yield in a winter wheat breeding program

2019 ◽  
Author(s):  
Dennis N. Lozada ◽  
Arron H. Carter
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Response To Selection ◽  
Breeding Values ◽  
Wheat Breeding Program ◽  
Selection Strategies ◽  
Selection For ◽  
Estimated Breeding Values

AbstractIncreased genetic gains for complex traits in plant breeding programs can be achieved through different selection strategies. The objective of this study was to compare potential gains for grain yield in a winter wheat breeding program through estimating response to selection R values across several selection approaches including phenotypic (PS), marker-based (MS), genomic (GS), and a combination of PS and GS. Five populations of Washington State University (WSU) winter wheat breeding lines evaluated from 2015 to 2018 in Lind and Pullman, WA, USA were used in the study. Selection was conducted by selecting the top 20% of lines based on observed yield (PS strategy), genomic estimated breeding values (GS), presence of yield “enhancing” alleles of the most significant single nucleotide polymorphism (SNP) markers identified from genome-wide association mapping (MS), and high observed yield and estimated breeding values (PS+GS). Overall, PS compared to other individual strategies showed the highest response. However, when combined with GS, a 23% improvement in R for yield was observed, indicating that gains could be improved by complementing traditional PS with GS. Using GS alone as a selection strategy for grain yield should be taken with caution. MS was not that successful in terms of R relative to the other selection approaches. Altogether, we demonstrated that gains through increased response to selection for yield could be achieved in the WSU winter wheat breeding program by implementing different selection strategies either exclusively or in combination.

Evaluation of Semidwarf Selections from a Spring Wheat Breeding Program 1

1960 ◽  
Vol 52 (12) ◽  
pp. 710-712 ◽  
Author(s):  
F. H. Mcneal ◽  
M. A. Berg ◽  
M. G. Klages
Keyword(s):  
Spring Wheat ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program

scholarly journals Deep Learning for Predicting Complex Traits in Spring Wheat Breeding Program

2021 ◽  
Vol 11 ◽  
Author(s):  
Karansher S. Sandhu ◽  
Dennis N. Lozada ◽  
Zhiwu Zhang ◽  
Michael O. Pumphrey ◽  
Arron H. Carter
Keyword(s):  
Deep Learning ◽  
Spring Wheat ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Recombinant Inbred Lines ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Breeding Program ◽  
Wheat Breeding Program

Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014–2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder’s toolkit for use in large scale breeding programs.

scholarly journals Spring Wheat Breeding: Evaluation of Selected Adapted Spring Wheat Germplasm in Eastern Canada

2016 ◽  
Vol 5 (2) ◽  
pp. 1
Author(s):  
Shahrokh Khanizadeh ◽  
Harvey Voldeng ◽  
Xuelian Wang ◽  
Allen Xue ◽  
Mirko Tabori ◽  
...  
Keyword(s):  
Triticum Aestivum ◽  
Spring Wheat ◽  
Fusarium Head Blight Resistance ◽  
Wheat Breeding ◽  
Research Centre ◽  
Blight Resistance ◽  
Head Blight ◽  
Eastern Canada ◽  
Hard Red Spring Wheat ◽  
Wheat Breeding Program

Twenty-five hard red spring wheat (<em>Triticum aestivum</em>) lines, including three known cultivars used as checks, were grown in seven locations across Eastern Canada. The objective of this multi-location experiment was to evaluate selected Eastern Cereal and Oilseed Research Centre advanced lines (ECAD lines) from the Spring Wheat Breeding Program in order to identify the best lines for performance and grower trials. The lines from this trial performed very well compared to the check varieties, especially at the Ontario locations. Overall, the ECAD lines were on a par with or superior to the checks in terms of several attributes, including yield, protein content, and Fusarium head blight resistance.

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