Screening and Evaluation of the SAMNYT Germplasm for Yield and Disease Resistance and its mplication in the Indigenous Wheat Breeding Program

2005 ◽  
Vol 5 (1) ◽  
pp. 76-80
Author(s):  
Nazeer Hussain Shah . ◽  
Inamullah . ◽  
Fateh Ullah Khan .
Keyword(s):  
Disease Resistance ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program

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

Evaluation of chemical desiccation as a selection technique for drought resistance in a dryland wheat breeding program

1993 ◽  
Vol 44 (8) ◽  
pp. 1683 ◽  
Author(s):  
KL Regan ◽  
BR Whan ◽  
NC Turner
Keyword(s):  
Grain Yield ◽  
Drought Resistance ◽  
Variance Component ◽  
Wheat Breeding ◽  
Kernel Weight ◽  
Breeding Program ◽  
Thousand Kernel Weight ◽  
Wheat Breeding Program ◽  
Coefficients Of Variation ◽  
Variance Component Estimates

Previous studies have shown that some chemical desiccants and senescing agents, when applied to adequately watered cereals 10 to 14 days after anthesis, can be used to select lines with stable grain size (kernel weight) under post-anthesis water deficits. The present study evaluated the potential of one of these chemicals, potassium iodide (KI), to select for this character in a dryland wheat breeding program. Ninety-six F2-derived lines grown in the F6and F7 generations and 11 cultivars of wheat (Triticum aestzvum L.) were grown in two experiments at two medium-rainfall sites in 1988 and 1989 and sprayed with a 0.3% solution of KI when the grains had developed one-third in the lemma. Reductions in grain yield and thousand kernel weight due to treatment with KI were greater in 1988 than in 1989, probably due to the higher growing-season rainfall in that year. The reduction in grain yield as a result of desiccation was greater than the reduction in thousand kernel weight, but the correlation between the two was low (0.09 to 0.58) and non-significant in five out of the eight comparisons. There were considerable differences among genotypes in response to the desiccation treatment in the wetter 1988. Genetic coefficients of variation ranged from 5.6 to 12.9% for yield and 2.5 to 9.5% for thousand kernel weight. The ratio of the variance component estimates for the interaction between genotypes and desiccation treatment to the variance component estimates for genotypes was generally less than one. However, genetic differences in response to the desiccation treatment could be demonstrated in some experiments, particularly at one site and in the wetter of the two years. We conclude that the chemical desiccation technique can be used to select for post-anthesis drought resistance in a dryland breeding program. However, there are some limitations to the technique, and selection needs to be confined to wetter sites and seasons.

Evaluation of the Sedimentation Test in a Wheat Breeding Program 1

Crop Science ◽  
1965 ◽  
Vol 5 (5) ◽  
pp. 381-385 ◽  
Author(s):  
I. M. Atkins ◽  
Earl C. Gilmore ◽  
Paschal Scottino ◽  
O. G. Merkle ◽  
K. B. Porter
Keyword(s):  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program
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