Simulating the impact of marker-assisted selection in a wheat breeding program

2005 ◽  
Author(s):  
Narelle Lee Kruger
Keyword(s):  
Marker Assisted Selection ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
The Impact

scholarly journals The Impact of the Kansas Wheat Breeding Program on Wheat Yields, 1911-2006

2008 ◽  
Vol 40 (3) ◽  
pp. 913-925 ◽  
Author(s):  
Lawton Lanier Nalley ◽  
Andrew Barkley ◽  
Forrest Chumley
Keyword(s):  
Wheat Breeding ◽  
Breeding Program ◽  
Average Increase ◽  
Agricultural Experiment Station ◽  
Wheat Varieties ◽  
Public And Private ◽  
Wheat Breeding Program ◽  
Agricultural Experiment ◽  
Growing Conditions ◽  
The Impact

This paper quantifies advances of the Kansas Agricultural Experiment Station (KAES) wheat breeding program for two time periods: (1) 1911 to 2006 and (2) 1977 to 2006. Using multiple regression, increases in yields of wheat varieties grown in Kansas are quantified, holding growing conditions and other improvements in productivity constant. Differences in KAES variety yields and those released by other public and private breeders are quantified. During the “new age” of wheat breeding (1977-2006), wheat breeding alone is found to have increased yields by 6.182 bushels per acre, or an average increase of 0.206 bushels per year.

scholarly journals Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program

2018 ◽  
Vol 8 (8) ◽  
pp. 2735-2747 ◽  
Author(s):  
Vikas Belamkar ◽  
Mary J. Guttieri ◽  
Waseem Hussain ◽  
Diego Jarquín ◽  
Ibrahim El-basyoni ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Genomic Selection ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Yield Trials

scholarly journals Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

2021 ◽  
Author(s):  
Karansher S Sandhu ◽  
Meriem Aoun ◽  
Craig Morris ◽  
Arron H Carter
Keyword(s):  
Deep Learning ◽  
Grain Yield ◽  
Cross Validation ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Quality Traits ◽  
Learning Models ◽  
Breeding Programs ◽  
Wheat Breeding Program ◽  
End Use

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Hence, testing is delayed until later stages in the breeding program. Delayed phenotyping results in advancement of inferior end-use quality lines into the program. Genomic selection provides an alternative to predict performance using genome-wide markers. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron), were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models performed superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trailing. Furthermore, the superior performance of machine and deep learning models strengthen the idea to include them in large scale breeding programs for predicting complex traits.

Evaluation of Lines from a Farmer Participatory Organic Wheat Breeding Program

Crop Science ◽  
2018 ◽  
Vol 58 (6) ◽  
pp. 2433-2443 ◽  
Author(s):  
M. H. Entz ◽  
A. P. Kirk ◽  
M. Carkner ◽  
I. Vaisman ◽  
S. L. Fox
Keyword(s):  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program
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