‘AR11LE24’, a Soft Red Winter Wheat Adapted to the Mid‐South Region of the USA

Mason, Richard E.; Johnson, J. W.; Mohamed, Maryati; Miller, R. G.; Moon, David E.; Carlin, J. F.; Harrison, Stephen A.; Babar, Ali; Murphy, Patrick M.; Ibrahim, Amir M. H.; Sutton, Russell; Blount, Ann R.

doi:10.3198/jpr2017.09.0060crc

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…The University of Arkansas soft red winter wheat (SRWW) breeding program makes over 800 unique crosses per year. Progenies are then tested over the following 10 seasons prior to releasing a new cultivar (Mason et al, 2018). Breeding lines are not evaluated for FHB resistance traits until the F 4:7 advanced (ADV) and F 4:8 elite (ARE) trials, where they are evaluated in misted and inoculated FHB disease nurseries at two locations in a RCBD design with two replications.…”

Section: Introductionmentioning

confidence: 99%

“…The UE and US nurseries include approximately 36 elite breeding lines from public and private SRWW breeding programs in the Southern and Eastern US, grown between 22 and 36 locations with between one and three replications per location annually (Boyles et al, 2019). The Sungrains cooperative consists of Southeastern US SRWW breeding programs that performs regional testing within the Southeastern US (Harrison et al, 2017;Johnson et al, 2017;Mason et al, 2018;Boyles et al, 2019). Select breeding lines from the ADV and ARE are grown in these regional Sungrains nurseries.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection

et al. 2021

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Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4:7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection

et al. 2021

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“…It seems more feasible and appropriate to form regional collaborations between breeding programs to leverage existing infrastructure and enable germplasm sharing. Some established small grains breeding programs have formed such a collaboration called the Southeastern University Grains (SunGrains) cooperative, which has been successful in accelerating cultivar development (Harrison et al, 2017; Johnson et al, 2017, Mason et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Yield Data from the Uniform Southern Soft Red Winter Wheat Nursery Emphasize Importance of Selection Location and Environment for Cultivar Development

Boyles

Marshall

Bockelman³

2019

Crop Science

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Yield and agronomic data from a regional soft red winter wheat (Triticum aestivum L.) nursery—consisting of 604 advanced breeding lines (ABLs) and 36 testing locations over a 21‐yr period—were evaluated to understand recent genetic gains in wheat and determine the impact of selection location and environment on cultivar performance and adaptation. Relative mean yield improvement of ABLs with respect to historical cultivar AGS 2000 was 106 kg ha−1 yr−1 (1.58 bu acre−1 yr−1), equating to an annual genetic gain of 1.6%. Yield gains for wheat during this timespan were attributed to an increase in both yield potential and stability across environments. However, a strong tradeoff (r = −0.36, p < 2.2 × 10−16) was observed between yield potential and stability. Additionally, distance between selection and evaluation environments was significantly correlated with yield, with yield decreasing as distance between locations increased. Advanced breeding lines had a +221, +126, and −29.6 kg ha−1 yr−1 (+3.29, +1.88, and −0.44 bu acre−1) yield difference over the location mean when the selection location was within, adjacent, and nonadjacent to the trial location zone, respectively. Advanced breeding lines in general performed poorly in production environments west of their selection site. Based on data analyzed, elevation and latitude are significant geographic parameters to consider when determining optimal selection location for a production environment. Meanwhile, change in growing degree days between selection and evaluation location had a stronger influence on yield than precipitation. Findings demonstrate the importance and benefits of breeder collaborations and multienvironment testing on crop improvement, which will be needed to maximize yield gains in the 21st century.

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“…Locally adapted check cultivars and the recurrent parent KS05HW14‐3 were replicated at least twice in each block and used for yield comparisons and block adjustments. The nine locally adapted check cultivars included ‘Everest’ (PI 659807), ‘Lyman’ (PI 658067), IL07‐19334, ‘Ernie’ (PI 584525) (McKendry et al., 1995), ‘Joe’ (PI 676270) (Zhang et al., 2016), AR11LE24 (PI 678970) (Mason et al., 2018), ‘Jasper’ (PI 678442) (Carter et al., 2017), ‘AC Mountain’ (SeCan), and Ambassador. A mixed linear model including genotype as a fixed effect and block as a random effect was used to calculate grain yield least squares means and 95% confidence intervals and to generate p values for pairwise comparisons using R version 3.2.1 (R Core Team, 2019) and the packages lme4 v.1.1‐12 (Bates et al., 2015) and lsmeans v.2.25‐5 (Lenth, 2016).…”

Section: Methodsmentioning

confidence: 99%

Description of U6719‐004 wheat germplasm with YrAS2388R stripe rust resistance introgression from Aegilops tauschii

Lopez

Wiersma

Strauss

et al. 2022

J of Plant Registrations

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In recent years, stripe rust has become an increasingly problematic disease affecting wheat (Triticum aestivum L.). Although stripe rust resistance genes Yr28 and YrAS2388R have been successfully transferred from Aegilops tauschii Coss. to hexaploid wheat, these genes have not been widely used for stripe rust resistance breeding largely due to a lack of publicly available and well‐adapted germplasm. Here we describe U6719‐004 (Entry 538 of D‐genome nested association mapping population, Reg. No. MP‐14, NSL 536301 MAP), a hexaploid wheat germplasm carrying YrAS2388R stripe rust resistance derived from Ae. tauschii accession TA1718 that is well adapted to U.S. wheat production environments. U6719‐004 (KS05HW14‐3/3/KS05HW14‐3/TA1718//KS05HW14‐3) was developed using a combination of direct hybridization of TA1718 with the hard white winter wheat line KS05HW14‐3 followed by two rounds of backcrossing and three generations of single seed descent. U6719‐004 stripe rust resistance was characterized under controlled and natural conditions. Using gene‐based markers, TA1718 and U6719‐004 were confirmed to be YrAS2388R carriers. Grain yield for U6719‐004 was evaluated in 11 year‐by‐location environments across the United States. Grain quality parameters were measured in four year‐by‐location environments. Compared with the recurrent wheat parent KS05HW14‐3 and locally adapted check cultivars, the Ae. tauschii introgression had little to no impact on U6719‐004 grain yield or quality. The availability of U6719‐004 will enable the use of YrAS2388R for stripe rust resistance breeding efforts.

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‘AR11LE24’, a Soft Red Winter Wheat Adapted to the Mid‐South Region of the USA

Cited by 5 publications

References 9 publications

Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection

Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection

Yield Data from the Uniform Southern Soft Red Winter Wheat Nursery Emphasize Importance of Selection Location and Environment for Cultivar Development

Description of U6719‐004 wheat germplasm with YrAS2388R stripe rust resistance introgression from Aegilops tauschii

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