‘DT-1’, a Drought-tolerant Triploid Turf Bermudagrass

Schwartz, Brian M.; Hanna, W. W.; Baxter, Lewis R.; Raymer, Paul L.; Waltz, F. Clint; Kowalewski, Alec; Chandra, Ambika; Genovesi, A. D.; Wherley, Benjamin; Miller, Grady L.; Milla‐Lewis, Susana R.; Reynolds, William C.; Wu, Yanqi; Martin, Dennis L.; Moss, Justin Q.; Kenna, Michael P.; Unruh, J. Bryan; Kenworthy, Kevin E.; Zhang, Jing; Muñoz, Patricio

doi:10.21273/hortsci13083-18

Cited by 23 publications

(22 citation statements)

References 6 publications

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“…, Tifway, TifSport, Celebration, TifGrand, T-11, and TifTuf) were planted in randomized complete block designs with three replicates at Tifton, GA and Griffin, GA on 9 and June 12, 2010, respectively. Schwartz et al (2018) provides detailed phenotypic observations made on the control varieties.…”

Section: Methodsmentioning

confidence: 99%

Molecular Dissection of Quantitative Variation in Bermudagrass Hybrids (Cynodon dactylonxtransvaalensis): Morphological Traits

Khanal

Dunne

Schwartz

et al. 2019

G3 Genes|Genomes|Genetics

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Bermudagrass ( Cynodon (L.)) is the most important warm-season grass grown for forage or turf. It shows extensive variation in morphological characteristics and growth attributes, but the genetic basis of this variation is little understood. Detection and tagging of quantitative trait loci (QTL) affecting above-ground morphology with diagnostic DNA markers would provide a foundation for genetic and molecular breeding applications in bermudagrass. Here, we report early findings regarding genetic architecture of foliage (canopy height, HT), stolon (stolon internode length, ILEN and length of the longest stolon LLS), and leaf traits (leaf blade length, LLEN and leaf blade width, LW) in 110 F 1 individuals derived from a cross between Cynodon dactylon (T89) and C. transvaalensis (T574). Separate and joint environment analyses were performed on trait data collected across two to five environments (locations, and/or years, or time), finding significant differences ( P < 0.001) among the hybrid progeny for all traits. Analysis of marker-trait associations detected 74 QTL and 135 epistatic interactions. Composite interval mapping (CIM) and mixed-model CIM (MCIM) identified 32 main effect QTL (M-QTL) and 13 interacting QTL (int-QTL). Colocalization of QTL for plant morphology partially explained significant correlations among traits. M-QTL qILEN-3-2 (for ILEN; R 2 = 11–19%), qLLS-7-1 (for LLS; R 2 = 13–27%), qLEN-1-1 (for LLEN; R 2 = 10–11%), and qLW-3-2 (for LW; R 2 = 10–12%) were ‘stable’ across multiple environments, representing candidates for fine mapping and applied breeding applications. QTL correspondence between bermudagrass and divergent grass lineages suggests opportunities to accelerate progress by predictive breeding of bermudagrass.

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Section: Methodsmentioning

confidence: 99%

Molecular Dissection of Quantitative Variation in Bermudagrass Hybrids (Cynodon dactylonxtransvaalensis): Morphological Traits

Khanal

Dunne

Schwartz

et al. 2019

G3 Genes|Genomes|Genetics

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“…In addition, the genetic correlation of the same trait under different environments (Type B) is useful to quantify the genotype × environment (GE) interaction, and its magnitude ranges from 0 to 1, indicating high and low GE interaction, respectively (Yamada, 1962). The warm‐season turfgrass breeding programs listed above have focused on improvement of several traits such as turfgrass quality (TQ; Schwartz, Kenworthy, Engelke, Genovesi, & Quesenberry, 2009), salt and drought tolerance (Fuentealba et al., 2016; Meeks & Chandra, 2020; Schwartz et al., 2009; Schwartz et al., 2018; Xiang et al., 2017; Zhang et al., 2017), tolerance to extreme temperature (Dunne et al., 2019; Fontanier et al., 2020; Kimball, Isleib, Reynolds, Zuleta, & Milla‐Lewis, 2016; Yu et al., 2019), shade tolerance (Chhetri, Fontanier, Koh, Wu, & Moss, 2019), and biotic stress resistance (Milla‐Lewis et al., 2011; Milla‐Lewis, Youngs, Arrellano, & Cardoza, 2017). The goal of all programs is to develop regionally adapted cultivars; however, most programs are limited to more localized evaluations within their specific agroecosystems.…”

Section: Introductionmentioning

confidence: 99%

Multispecies genotype × environment interaction for turfgrass quality in five turfgrass breeding programs in the southeastern United States

et al. 2021

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In breeding programs, superior parental genotypes are used in crosses to generate novel genetic variability for new selection cycles. Genotypes are usually more adapted to environments where the breeding program is located, since selections are performed under specific agroecosystems. Thus, the objective of this study was to evaluate the performance of bermudagrass (Cynodon Rich. species), St. Augustinegrass [Stenotaphrum secundatum (Walter) Kuntze], seashore paspalum (Paspalum vaginatum Sw.), and zoysiagrass (Zoysia Willd. species) breeding lines from five different breeding programs (North Carolina State University, Oklahoma State University, Texas A&M University System, University of Florida, and University of Georgia) across the southeastern United States. Three breeding nurseries for each species were evaluated for 2 yr at eight locations: Citra and Hague, FL; College Station and Dallas, TX; Griffin and Tifton, GA; Stillwater, OK; and Jackson Springs, NC. Turfgrass quality (TQ) was evaluated (rated on a 1–9 scale) across repeated measurements over time. Data were analyzed using mixed models, and principal component analyses were performed using predicted genotypic values. The narrowest range in variation for TQ performance was observed in seashore paspalum breeding lines, whereas greater variation was observed for St. Augustinegrass and zoysiagrasses. St. Augustinegrass presented the lowest genotype × environment interaction in all nurseries. Specific adaptability was not observed for the lines developed by different breeding programs, with the exception of the bermudagrass lines from Oklahoma State University in Nursery 3.

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“…are warm‐season turfgrass species widely used on golf courses, athletic fields, and home lawns in the southern United States (Dunne, Tuong, Livingston, Reynolds, & Milla‐Lewis, 2019). These species are known for their superior turf quality (TQ), excellent durability, and superior heat and drought tolerances (Beard, 1973; Schwartz et al., 2018). In the public sector, bermudagrass breeding for turf is primarily conducted at the University of Georgia (Baxter & Schwartz, 2018) and Oklahoma State University (Taliaferro, Martin, Anderson, & Anderson, 2006; Wu, Martin, Taliaferro, Anderson, & Moss, 2012a, 2012b).…”

Section: Introductionmentioning

confidence: 99%

Genotype‐by‐environment interaction for turfgrass quality in bermudagrass across the southeastern United States

et al. 2020

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Estimation of genotype‐by‐environment interaction (GEI) is important in breeding programs because it provides critical information to guide selection decisions. In general, multienvironment trials exhibit heterogeneity of variances and covariances at several levels. Thus, the objectives of this study were (a) to find the best genetic covariance matrix to model GEI and compare changes in genotypic rankings between the best covariance structure against a compound symmetry structure, (b) to define mega‐environments for turfgrass performance across the southeastern United States, and (c) to estimate genetic correlations between drought or nondrought and growing or nongrowing conditions to determine the extent of GEI under specific environments. Three nurseries with 165, 164, and 154 genotypes were evaluated in 2011–2012, 2012–2013, and 2013–2014, respectively. These nurseries were conducted at eight locations (Citra, FL; Hague, FL; College Station, TX; Dallas, TX; Griffin, GA; Tifton, GA; Stillwater, OK; and Jackson Springs, NC). The response variables were averaged turfgrass quality (TQ), TQ under drought (TQD), nondrought TQ (TQND), TQ under actively growing months (TQG), and TQ under nongrowing months (TQNG). This study demonstrated that (a) the best variance structure varied among traits and seasons, and changes in genotype rankings were dependent on GEI; (b) considering TQ and TQND, mega‐environments formed between Jackson Springs and College Station, and between Citra, Dallas, and Griffin, whereas Stillwater, Hague, and Tifton represented unique environments across the southeastern United States; and (c) genetic correlations between drought or nondrought and growing or nongrowing conditions suggested that indirect selection can be efficient in multienvironment trials for contrasting environmental conditions.

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‘DT-1’, a Drought-tolerant Triploid Turf Bermudagrass

Cited by 23 publications

References 6 publications

Molecular Dissection of Quantitative Variation in Bermudagrass Hybrids (Cynodon dactylonxtransvaalensis): Morphological Traits

Molecular Dissection of Quantitative Variation in Bermudagrass Hybrids (Cynodon dactylonxtransvaalensis): Morphological Traits

Multispecies genotype × environment interaction for turfgrass quality in five turfgrass breeding programs in the southeastern United States

Genotype‐by‐environment interaction for turfgrass quality in bermudagrass across the southeastern United States

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