The near-full-length 18S ribosomal DNA (rDNA) gene and internal transcribed spacer 1 region were amplified and sequenced from 52 nematode populations belonging to 28 representative species in 13 families recovered from turfgrasses in North Carolina (38 populations) and South Carolina (14 populations). This study also included 13 nematode populations from eight other plant hosts from North Carolina for comparison. Nematodes were molecularly characterized and the phylogenetic relationships were explored based on 18S rDNA sequences. Phylogenetic analysis using Bayesian inference was performed using five groups of the plant-parasitic nematode populations Tylenchids, Criconematids, Longidorids, Xiphinematids, and Trichodorids. The 65 nematode populations were clustered correspondingly within appropriate positions of 13 families, including Belonolaimidae, Caloosiidae, Criconematidae, Dolichodoridae, Hemicycliophoridae, Hoplolaimidae, Heteroderidae, Longidoridae, Meloidogynidae, Paratylenchidae, Pratylenchidae, Telotylenchidae, and Trichodoridae. This study confirms previous morphological-based identification of the plant-parasitic nematode species found in turfgrasses and provides a framework for future studies of plant-parasitic nematodes associated with turfgrasses based upon DNA sequences and phylogenetic relationships.

Root-knot nematodes (Meloidogyne spp.) are the most common and destructive plant-parasitic nematode group worldwide and adversely influence both crop quality and yield. In this study, a total of 51 root-knot nematode populations from turfgrasses were tested, of which 44 were from North Carolina, 6 from South Carolina and 1 from Virginia. Molecular characterisation was performed on these samples by DNA sequencing on the ribosomal DNA 18S, ITS and 28S D2/D3. Species-specific primers were developed to identify turfgrass root-knot nematode through simplex or duplex PCR. Four species were identified, including M. marylandi Jepson & Golden in Jepson, 1987, M. graminis (Sledge & Golden, 1964) Whitehead, 1968, M. incognita (Kofoid & White, 1919) Chitwood, 1949 and M. naasi Franklin, 1965 through a combined analysis of DNA sequencing and PCR by species-specific primers. M. marylandi has been reported from North Carolina and South Carolina for the first time. Molecular diagnosis using PCR by species-specific primers provides a rapid and cheap species identification approach for turfgrass root-knot nematodes.

Plant growth regulators (PGRs) are commonly applied to ultradwarf hybrid bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy] putting greens during the growing season. Trinexapac‐ethyl (TE) and prohexadione‐Ca (PH) are PGRs that inhibit gibberellic acid biosynthesis and are used to reduce clipping yield and improve turfgrass visual quality. Growing degree day (GDD) models have optimized the timing of PGR reapplications to creeping bentgrass (Agrostis stolonifera L.) putting greens, but no information is available regarding proper PGR reapplication timing on bermudagrass putting greens. The objective of this research was to develop a GDD model to determine optimal TE and PH application frequencies on bermudagrass putting greens. Field research was conducted on three ultradwarf cultivars at separate locations in the southeastern United States: ‘MiniVerde’ in Knoxville, TN, ‘Champion’ in Durham, NC, and ‘TifEagle’ in Starkville, MS. Peak yield suppression was 49 to 65% after TE application at 0.034 kg a.i. ha−1 and 50 to 54% after 0.154 kg PH ha−1. Peak suppression occurred later for TE (166–177 GDD calculated using 10°C as the base temperature [GDD10C]) than for PH (92–97 GDD10C), which resulted in an estimated PGR reapplication interval of 216 to 230 for TE and 120 to 126 GDD10C for PH. Enhanced clipping yield and rebound did not follow clipping yield suppression. The use of a GDD model to schedule PGR applications on bermudagrass putting greens has the potential to maximize PGR benefits; however, season‐long implementation of this GDD model needs comparison with current PGR programs used on ultradwarf putting surfaces.

Dollar spot is one of the most common diseases of golf course turfgrass and numerous fungicide applications are often required to provide adequate control. Weather-based disease warning systems have been developed to more accurately time fungicide applications; however, they tend to be ineffective and are not currently in widespread use. The primary objective of this research was to develop a new weather-based disease warning system to more accurately advise fungicide applications to control dollar spot activity across a broad geographic and climactic range. The new dollar spot warning system was developed from data collected at field sites in Madison, WI and Stillwater, OK in 2008 and warning system validation sites were established in Madison, WI, Stillwater, OK, Knoxville, TN, State College, PA, Starkville, MS, and Storrs, CT between 2011 and 2016. A meta-analysis of all site-years was conducted and the most effective warning system for dollar spot development consisted of a five-day moving average of relative humidity and average daily temperature. Using this model the highest effective probability that provided dollar spot control similar to that of a calendar-based program across the numerous sites and years was 20%. Additional analysis found that the 20% spray threshold provided comparable control to the calendar-based program while reducing fungicide usage by up to 30%, though further refinement may be needed as practitioners implement this warning system in a range of environments not tested here. The weather-based dollar spot warning system presented here will likely become an important tool for implementing precision disease management strategies for future turfgrass managers, especially as financial and regulatory pressures increase the need to reduce pesticide usage on golf course turfgrass.

Fungi in the genus Clarireedia are widespread and destructive pathogens of grasses worldwide, and are best known as the causal agents of dollar spot disease in turfgrass. Here we report genome assemblies of seven Clarireedia isolates, including ex-types of the two most widespread species, C. jacksonii and C. monteithiana. These datasets provide a valuable resource for ongoing studies of the dollar spot pathogens that include population diversity, host/pathogen interactions, marker development and disease control.