Creeping bentgrass (Agrostis stolonifera) is a widely used cool-season turfgrass valued for its fine texture and ability to form dense, uniform turfs. However, its complex allotetraploid genome and high repetitive content have posed challenges for genomic research and molecular breeding. Here, we report a haplotype-resolved chromosome-level genome assembly generated using PacBio HiFi and Oxford Nanopore sequencing with Omni-C scaffolding. The final assembly spans 5.4 Gb, with a scaffold N50 of 187.9 Mb and comprises 28 pseudochromosomes representing fully phased haplotypes (2n = 4x = 28). BUSCO analysis indicated 98.8% completeness, indicating the high quality of the assembly. We annotated 146,216 protein-coding genes and found that transposable elements account for 79.8% of the genome, dominated by LTR-Gypsy elements. Subgenome-specific LTR clustering and comparative genomic alignments supported an allopolyploid origin involving two diverged progenitors. This high-quality genome provides a foundational resource for functional genomics and breeding efforts to improve disease resistance, abiotic stress tolerance, and turf quality.

Creeping bentgrass ( Agrostis stolonifera ) and colonial bentgrass ( A. capillaris ) naturally occupy wetter and drier environments, respectively. Hybridization between these species offers valuable insights into drought tolerance and could enhance breeding strategies for developing water-deficit–tolerant bentgrasses. A greenhouse dry-down study was conducted using 52 interspecific bentgrass lines, including two parent cultivars, BCD (colonial bentgrass; drought-tolerant) and Providence (creeping bentgrass; drought-susceptible). The study revealed that the drought-tolerant hybrid plants exhibited more efficient mechanisms for drought stress management, including optimized carbon allocation, reduced oxidative stress, and enhanced water conservation. These plants were able to thrive under stress with lower levels of certain metabolites such as citric acid, malic acid, pyruvic acid, α-ketoglutaric acid, indicating a more efficient drought response compared to the susceptible group.

Extreme weather events due to climate change threaten sustainable crop production and quality, and developing crop varieties with resilient yield and quality traits through breeding is needed. One of the major issues for turfgrass management is excessive water use for irrigation. In the face of climate change, water availability is becoming increasingly limited and more costly, and water conservation in turfgrass culture has become extremely important; development of drought tolerant turfgrass germplasm is therefore critical. Precise phenotypic assessment of large mapping populations, comprising a few thousand plants, is time-consuming and labor-intensive. Depending on the trait being phenotyped, results may be subjectively assessed and variable due to environmental effects. This is particularly the case when evaluating genetic responses to stresses which may involve multiple measurements over time as well as a range of induced stress levels. A machine learning (ML)-based imaging analysis system offers an efficient and precise means to capture temporal progression of stress symptoms within a large genetic population that can be interpreted through computer vision algorithms. In this study, we developed an automatic image analysis system through ML methods that can automatically (1) capture images, (2) segment an image containing numerous plants into sub-images with individual turfgrass plants, (3) label each image with the corresponding sample information, (4) quantify stress symptoms (i.e. percent green cover), and (5) classify breeding lines based on the progression pattern of drought stress symptoms. This system enabled effective and precise evaluation of genetic performance of drought tolerance based on temporal progression of drought symptoms in a large turfgrass hybrid population (about 1,000 lines including three replications), with a processing time to quantify drought stress symptoms from 345,600 images was accomplished within 30 min. Such a machine learning (ML)-based imaging processing/analysis platform along with a low-cost automated greenhouse-based RGB imaging system can significantly boost the effectiveness of germplasm evaluation, quantitative trait locus (QTL) mapping and candidate gene identification to develop potential molecular markers that will aid in faster development of improved germplasm.

Colonial bentgrass (Agrostis capillaris) is a tetraploid rhizomatous bentgrass that generally exhibits improved resistance to the important fungal disease dollar spot and has more tolerance to water-deficit stress than the more widely grown creeping bentgrass (Agrostis stolonifera). Interspecific hybridization between these two species is possible and efforts to understand the genetic mechanisms of enhanced biotic and abiotic stress resistance in colonial bentgrass are proceeding. To gain a better understanding of the level of genetic diversity in cultivated colonial bentgrasses and to determine if genotypes with improved interspecific hybridization potential can be identified, nine colonial bentgrass cultivars and one creeping bentgrass cultivar were screened with 48 colonial bentgrass-derived simple sequence repeat markers. Twenty-one primer pairs were found to produce reliable amplification, and these markers were scored using high-resolution melt analysis to generate haplotypes that were used to evaluate cultivar relationships. The nine colonial bentgrass cultivars could be placed into two groups and overall showed limited levels of diversity. The creeping bentgrass cultivar was distinct and individual genotypes within each species exhibited haplotypes more common in the alternate species, suggesting that these markers may be useful for selecting genotypes with enhanced interspecific hybridization potential.

Poa annua L. is a globally distributed grass with economic and horticultural significance as a weed and as a turfgrass. This dual significance, and its phenotypic plasticity and ecological adaptation, have made P. annua an intriguing plant for genetic and evolutionary studies. Because of the lack of genomic resources and its allotetraploid (2n = 4x = 28) nature, a reference genome sequence would be a valuable asset to better understand the significance and polyploid origin of P. annua. Here we report a genome assembly with scaffolds representing the 14 haploid chromosomes that are 1.78 Gb in length with an N50 of 112 Mb and 96.7% of BUSCO orthologs. Seventy percent of the genome was identified as repetitive elements, 91.0% of which were Copia- or Gypsy-like long-terminal repeats. The genome was annotated with 76,420 genes spanning 13.3% of the 14 chromosomes. The two subgenomes originating from Poa infirma (Knuth) and Poa supina (Schrad) were sufficiently divergent to be distinguishable but syntenic in sequence and annotation with repetitive elements contributing to the expansion of the P. infirma subgenome.

ORCiD: [0000-0001-6507-9985 of Jinyoung Y. Barnaby], [0000-0001-9082-6583 of Scott E. Warnke] Precise assessment of large mapping populations, comprising a few thousand plants including replications (a prerequisite step for breeding) is time-consuming and labor-intensive. Furthermore, phenotyping results tend to be variable and subjective depending on who is doing the scoring. One way to overcome these limitations is by collecting more data in the form of digital images, and precisely evaluating phenotypic variation in stress severity as well as temporal progression of stress symptoms within the population through machine learning methods. 230,400 images representing temporal progression of drought stress symptoms of interspecific turfgrass hybrid mapping population were processed using Python OpenCV and NumPy packages for noise removal, edge-preserving smoothing, color space conversion, contrast enhancement, and identification mapping. Then machine learning-based algorithms and models were developed not only to quantify stress severity but also to monitor temporal progression rate of stress symptoms. Hierarchical clustering was then performed to assess genotypic variation in stress progression. Such machine learning-based high-throughput digital phenotyping platforms can significantly increase the success of quantitative trait locus mapping and candidate gene identification to develop potential molecular markers that will assist in a faster characterization of germplasm to ultimately breed for stress resilient cultivars.

Buffalograss [Buchloe dactyloides (Nutt.) Engelm. syn. Bouteloua dactyloides (Nutt.) Columbus] is a stoloniferous low input turfgrass species native to the central Great Plains of the United States. Several molecular marker techniques have been used to compare relationships among elite buffalograss germplasm, but most rely on random amplification, are transferred from other species, or are developed from limited DNA sequence information. The objectives of this study were to develop buffalograss derived simple sequence repeat markers (SSRs) from buffalograss sequence data using an in-silico bioinformatics pipeline and use high resolution melt analysis to screen the SSRs based on their melt profiles on buffalograss cultivars and germplasm. Transcriptomes from buffalograss cultivars '378' and 'Prestige' were mined for SSRs using the MIcroSAtellite tool. There were 259 conserved SSRs that were polymorphic between 378 and Prestige. Of those, 96 were tested by HRM on a panel of eight genotypes and 24 were selected for producing melt profiles that could distinguish the genotypes. The selected reactions produced 356 distinct melt profiles when tested on a panel of 96 genotypes, including 88 buffalograss germplasm selections, six buffalograss cultivars, and two blue grama [Bouteloua gracilis (H. B. K.) Lag. ex Steud.] selections, with melt profile frequencies ranging from 0.01 to 0.97. A subset of 86 melt profiles segregating among the blue grama and elite buffalograss cultivars distinguished each genotype. The buffalograss-specific SSR markers presented here will be useful to study buffalograss genetic diversity, line purity maintenance, and for marker supported plant breeding strategies.

Many efforts in the USA have focused on turfgrass tolerances to drought imposition as means to reduce their need for irrigation. As part of this effort, our research groups have subjected multiple species and varieties to drought and collected transcript expression profiles of genes that were differentially expressed under stress and control conditions. We generated lists of transcripts and gene families differentially expressed in Kentucky bluegrass, perennial ryegrass, and creeping bentgrass; and we compared each species’ differentially expressed transcript set across all three species. The gene families with differentially expressed transcript isoforms in each species and across all three species were annotated and characterized. By examining these datasets, we found key genetic mechanisms by which these species respond to drought stress. Specific genes, such as ABA responsive LEA homologs, show their crucial nature upon drought stress in all three species. Other gene families, such as E3 ubiquitin ligases, exhibited different gene family members in each species and highlight the species-specific responses to drought.

For the cool-season turfgrass Kentucky bluegrass, improving germplasm for drought tolerance is an increasingly important priority. Although genetic mechanisms behind drought tolerance have been characterized for model and agronomic plant species, the critical genes, gene families, and transcript isoforms important in Kentucky bluegrass are unclear. Using an RNAseq approach across three germplasm sources that differ in their regrowth, relative water content, and turf quality under drought stress, we have identified transcript isoforms exhibiting a shared response of all three germplasm sources to drought stress and transcript isoforms exhibiting a tolerance response where the more drought-tolerant germplasm sources exhibited higher transcript differences compared to the drought-susceptible cultivar Midnight. Annotation and transcript profile groupings both identified abundant chaperone gene families with protein folding and protective functions, such as heat shock proteins, DNAj, and late embryogenesis abundant genes. Transcript isoforms within these gene families were tolerance related and known to respond to abscisic acid. Two dehydrin genes, RAB15 and HVA1, were induced in several more drought-tolerant germplasm sources and show promise as candidate genes for selection.

Natural stands of creeping bentgrass (Agrostis stolonifera) are often found in wetland areas and exhibit very poor tolerance to dry soils. Colonial bentgrass (A. capillaris) is frequently found in drier habitats and has the ability to go dormant and recover quickly under water-deficit stress. Hybridization between creeping and colonial bentgrass is possible and a better understanding of gene regulation under water-deficit conditions of the two species could improve breeding strategies for water deficit-tolerant bentgrasses. A greenhouse dry-down study was conducted using two creeping bentgrass clones with differing water use profiles and one colonial bentgrass clone. The genotypes were exposed to water-deficit stress and well-watered control genotypes were included. Gravimetric evapotranspiration was determined, and control plants watered at 80% ET and deficit irrigation plants watered at 50% ET. Water use rates were similar among all plants with the creeping bentgrass genotypes exhibiting stress earlier and recovering more slowly than the colonial bentgrass. At the conclusion of the experiment, ribonucleic acid sequencing analysis was conducted and there were 975 differentially-expressed colonial bentgrass transcripts in response to water-deficit stress compared with an average 98 differentially-expressed creeping bentgrass transcripts. Among the colonial bentgrass upregulated transcripts in response to water-deficit stress were eight transcription factors previously shown to be involved in deficit water stress response and several transposon-related transcripts. This study characterized several transcripts with unique expression changes in colonial bentgrass compared with creeping bentgrass, which may explain why colonial bentgrass is more drought tolerant than creeping bentgrass.

Danthonia spicata (L.) Beauv., commonly known as poverty oatgrass, is a perennial bunch-type grass native to North America. Danthonia spicata is often managed as a turfgrass in areas of the United States where cool-season grasses are adapted, and it has potential for development as a low-input turfgrass option. Naturally occurring D. spicata plants occasionally exhibit "choke" of the flowering stems due to a proliferation of fungal hyphae by the ascomycete Atkinsonella hypoxylon (Peck) Diehl. (family Clavicipitaceae). Polymerase chain reaction (PCR) primers were designed to amplify a 116-bp A. hypoxylon-specific fragment within the 5.8S ribosomal RNA Internal Transcribed Spacer 1 (ITS 1) region. The primer set was then used to amplify the fragment from 24 A. hypoxylon isolates and 24 D. spicata terminal seed head DNA extractions collected from a greenhouse-maintained population that has never exhibited choke. The A. hypoxylon ITS 1 fragment was amplified from all fungal samples and all terminal seed head samples tested. The plant and fungal amplified fragments produced the same melt peak in high-resolution melt analysis, and sequence analysis of all terminal seed head samples showed highly significant blast hits with the ITS 1 sequence of A. hypoxylon GenBank Accession U57405.1 and the 24 A. hypoxylon isolates. These results provide evidence that all sampled D. spicata plants are associated with the fungus A. hypoxylon and provide a useful tool for the study A. hypoxylon and its association with D. spicata.

Poverty oat grass [Danthonia spicata (L.) P. Beauv. ex Roem. & Schult.] is a perennial bunch-type grass native to North America. Poverty oat grass is often present in managed turfgrass areas of the United States where cool-season grasses are adapted and has potential for development as a seeded low-input turfgrass option. Naturally occurring poverty oat grass plants occasionally exhibit choke on the flowering stems because of a proliferation of fungal hyphae by the ascomycete Atkinsonella hypoxylon (Peck) Diehl. (family Clavicipitaceae). Twenty-five A. hypoxylon isolates were cultured from choke-exhibiting poverty oat grass plants collected from five different populations. DNA variation was evaluated at the 5.8S ribosomal ribonucleic acid (rRNA) internal transcribed spacer (ITS) first (ITS-1) and second (ITS-2) regions and 10 simple sequence repeat (SSR) loci. Five single nucleotide polymorphisms (SNPs) in the ITS-1 region and an addition-deletion in the ITS-2 region identified two isolate groups present in four of the five populations. The 10 SSR loci exhibited 28 scorable alleles and support the two groupings with variation appearing as random mutations and high linkage disequilibrium providing no evidence for sexual recombination within or between the two identified groups.

AbstractBentgrasses (Agrostis spp.) are cool‐season turfgrasses that have utility on golf courses or other turf applications requiring low mowing heights. Interspecific hybridization of Agrostis species is a strategy that has been proposed to enhance biotic and abiotic stress tolerance in this genus. Non‐transgenic, efficient, and cost‐effective methods for interspecific hybrid detection are needed. In this study two bentgrass species creeping bentgrass (A. stolonifera L.), and colonial bentgrass (A. capillaris L.) were allowed to intercross under field conditions. Progeny plant DNA was extracted and stored using Flinders Technology Associates (FTA) PlantSaver cards followed by primer specific polymerase chain reaction (PCR) and high‐resolution melt analysis. Species identifying PCR primers were optimized for real‐time PCR through fragment resequencing of species‐specific sequence characterized amplified region (SCAR) markers. A total of 2289 potential interspecific hybrid bentgrass progeny were screened with 99.4% of the DNA extractions successful. The percent interspecific hybridization, on an individual plant basis, ranged from 1 to 85% and a total of 524 interspecific‐hybrid bentgrasses were identified representing 23% of plants tested. Interspecific hybridization is common between these bentgrass species and the proposed detection method provides a non‐transgenic, efficient, and cost‐effective method for interspecific hybrid identification.

Brown patch, caused by Rhizoctonia solani, is a destructive disease on tall fescue. Compared with R. solani, Rhizoctonia zeae causes indistinguishable symptoms in the field but varies in geographic distribution. This may contribute to geographic variability observed in the resistance response of improved brown patch–resistant cultivars. This study examined R. solani and R. zeae susceptibility of four cultivars, selected based on brown patch performance in the National Turfgrass Evaluation Program (NTEP), and nine plant introductions (PIs). Twenty genotypes per PI/cultivar were evaluated by using four clonal replicates in a randomized complete block design. Plants were inoculated under controlled conditions with two repetitions per pathogen. Disease severity was assessed digitally in APS Assess, and analysis of variance and correlations were performed in SAS 9.3. Mean disease severity was higher for R. solani (65%) than for R. zeae (49%) (P = 0.0137). Interaction effects with pathogen were not significant for PI (P = 0.0562) but were for genotype (P < 0.001). Moderately to highly resistant NTEP cultivars compared with remaining PIs exhibited lower susceptibility to R. zeae (P < 0.0001) but did not differ in susceptibility to R. solani (P = 0.7458). Correlations between R. solani and R. zeae disease severity were not significant for either PI (R = 0.06, P = 0.8436) or genotype (R = 0.11, P = 0.09). Breeding for resistance to both pathogens could contribute to a more geographically stable resistance response. Genotypes were identified with improved resistance to R. solani (40), R. zeae (122), and both pathogens (26).

There are more than 150 different bentgrass species, but only five that are routinely used in turf applications. Even among the common species, there is significant variation in morphology and turf performance, differences that are exacerbated with a broader representation of species. Many alternative bentgrasses do not have acceptable turf quality, making it difficult for bentgrass breeders to introduce stress tolerance from those sources without also compromising quality of their elite breeding material. This project builds from the previously funded USGA project, Low input performance of Highland, heat, and drought tolerant bentgrasses. In the previous study, 69 bentgrass accessions were obtained from the National Plant Germplasm System and evaluated under 5/8 inch and 3 inch mowing heights with minimal supplemental fertility or irrigation inputs following establishment in Mead, Nebraska. Results from the previous study identified accessions with traits that may benefit elite creeping bentgrasses used on golf courses. The current study is focused on preliminary breeding to move desirable traits (drought, heat, low fertility use, late season color retention) into elite bentgrass breeding stocks through introgression breeding to benefit bentgrass breeders.

Danthonia spicata (L.) Beauv., commonly known as poverty oatgrass, is a perennial bunch-type grass native to North America. D. spicata is often found in low input turfgrass areas on the East Coast of the United States and has potential for development as a new native low input turfgrass species. Roche 454 sequenced randomly sheared genomic DNA reads of D. spicata were mined for SSR markers using the MIcroSAtellite identification tool. A total of 66,553 singlet sequences (approximately 37.5 Mbp) were examined, and 3454 SSR markers were identified. Trinucleotide motifs with greater than six repeats and possessing unique PCR priming sites within the genome, as determined by Primer-BLAST, were evaluated visually for heterozygosity and mutation consistent with stepwise evolution using CLC Genomics software. Sixty-three candidate markers were selected for testing from the trinucleotide SSR marker sites meeting these in silico criteria. Ten primer pairs that amplified polymorphic loci in preliminary experiments were used to screen 91 individual plants composed of at least 3–5 plants from each of 23 different locations. The primer pairs amplified 54 alleles ranging in size from 71 to 246 bp. Minimum and maximum numbers of alleles per locus were two and 12, respectively, with an average of 5.4. A dendrogram generated by unweighted pair group method with arithmetic mean cluster analysis using the Jaccard’s similarity coefficient was in agreement with the grouping obtained by Structure v2.3. The analyses were dominated by clonal groupings and lack evidence for gene flow with some alleles present in a single plant from a single location. Fourteen multilocus genotype groups were observed providing strong evidence for asexual reproduction in the studied D. spicata populations.

Agrostis stolonifera L. (creeping bentgrass) and Agrostis capillaris L. (colonial bentgrass) are turfgrass species well adapted for golf course use in regions of the world where cool‐season grasses are grown. Interspecific hybrids between the species do form and have the potential to incorporate some of the beneficial characteristics of both species. Agrostis stolonifera has excellent quality at low mowing heights and recovers well from damage. Agrostis capillaris tends to exhibit more drought tolerance and a higher level of resistance to the common fungal pathogen Sclerotinia homoeocarpa. Fast and inexpensive methods of species differentiation could help seed certification agencies and enhance interspecific hybrid bentgrass development. Ninety‐six simple sequence repeat primer pairs were designed from Roche 454 sequencing of A. stolonifera and A. capillaris genomic DNA and were tested for their potential for bentgrass species differentiation. Real‐time polymerase chain reaction followed by high‐resolution melt analysis was tested for its potential to speed up analysis and lower costs. Simple sequence repeat primer pairs were identified that can differentiate bentgrass species.

Brown patch (Rhizoctonia solani Kuhn), a destructive disease of tall fescue (Festuca arundinacea Schreb.), is typically evaluated visually. The subjectivity of visual evaluations may be reduced using technology like digital image analysis (DIA). This study compared DIA and visual evaluations for accuracy and precision of brown patch ratings of glasshouse grown tall fescue plants. Across four experiments, 112 plants were inoculated with R. solani. Disease was rated visually and using DIA‐WP (digital image analysis whole plant canopy). In two experiments, disease evaluations were replicated using three images and three visual evaluations per pot. Absolute error was calculated as the difference between actual disease severity [calculated using an individual leaf DIA method previously quantified as highly predictive of actual brown patch disease severity on tall fescue (r2 = 0.99)] and DIA‐WP and visual evaluations, respectively. Standard deviations within repeated measures were also calculated. A mixed‐model ANOVA was used to determine differences (P < 0.05) in mean absolute error and mean standard deviation by method, disease range, and method by disease range. Disease ranged from 0 to 100%. Mean absolute error did not differ between methods but did by disease range, exhibiting a bell‐shaped curve from 0% to 100% disease severity. Mean standard deviation exhibited significant method by disease range interaction. Mean standard deviation did not differ across the disease range within DIA‐WP evaluations but did across the disease range within visual evaluations. The more consistent precision of DIA across the disease range could reduce variability in brown patch evaluations of tall fescue.

Background Kentucky bluegrass ( Poa pratensis L.) is a prominent turfgrass in the cool-season regions, but it is sensitive to salt stress. Previously, a relatively salt tolerant Kentucky bluegrass accession was identified that maintained green colour under consistent salt applications. In this study, a transcriptome study between the tolerant (PI 372742) accession and a salt susceptible (PI 368233) accession was conducted, under control and salt treatments, and in shoot and root tissues. Results Sample replicates grouped tightly by tissue and treatment, and fewer differentially expressed transcripts were detected in the tolerant PI 372742 samples compared to the susceptible PI 368233 samples, and in root tissues compared to shoot tissues. A de novo assembly resulted in 388,764 transcripts, with 36,587 detected as differentially expressed. Approximately 75 % of transcripts had homology based annotations, with several differences in GO terms enriched between the PI 368233 and PI 372742 samples. Gene expression profiling identified salt-responsive gene families that were consistently down-regulated in PI 372742 and unlikely to contribute to salt tolerance in Kentucky bluegrass. Gene expression profiling also identified sets of transcripts relating to transcription factors, ion and water transport genes, and oxidation-reduction process genes with likely roles in salt tolerance. Conclusions The transcript assembly represents the first such assembly in the highly polyploidy, facultative apomictic Kentucky bluegrass. The transcripts identified provide genetic information on how this plant responds to and tolerates salt stress in both shoot and root tissues, and can be used for further genetic testing and introgression.