Impacts of long‐term fertilization and cultivation were evaluated on nematode communities associated with tall fescue turfgrass following 11 years of treatment applications. Fertilizer treatments of biosolid, synthetic, and plant‐based fertilizers and cultivation treatments of 0×, 1×, and 2× aerification passes were applied to randomized and replicated tall fescue plots at the University of Maryland Paint Branch Turfgrass facility in College Park, Maryland. Free‐living and plant‐parasitic nematodes were identified, enumerated, and categorized into functional groups. Nematode count data were compared using generalized linear mixed modeling with negative binomial distribution and two‐way ANOVA was used to compare nematode ecological indices. Biosolid treatments resulted in lower omnivore‐predator densities than plant‐based fertilizer treatments (p ≤ .001) and significantly greater Hoplolaimus densities than plant‐based fertilizer plots (p ≤ .05). Synthetic fertilizer applications resulted in the greatest Eucephalobus (p ≤ .05) and total bacterivore densities (p ≤ .001) of all fertilizer treatments. Plant‐based fertilizer‐treated plots had the largest Maturity Index cp 2‐5 and Structure Index (p ≤ .05). Cultivation of 1× resulted in fewer total bacterivore densities than 2× (p ≤ .01) while omnivore‐predator densities were greater in 1× than 0× (p ≤ .001). Plant health, as measured by NDVI, was lowest in biosolid‐treated turfgrass (p ≤ .05). These findings suggest that long‐term turfgrass management practices can have variable impacts on nematode abundance and community structure in tall fescue and provide insights into ecological impacts of turfgrass management practices.

Meloidogyne enterolobii is a virulent species of root-knot nematode that threatens watermelon (Citrullus lanatus) production in the southeastern United States. There are no known sources of root-knot nematode resistance in cultivated C. lanatus. Specific genotypes of a wild watermelon relative, C. amarus, are resistant against M. incognita but the genetics that underly this resistance are still unknown and it is not clear that this same resistance will be effective against M. enterolobii. To identify and characterize new sources of resistance to M. enterolobii, we screened 108 diverse C. amarus lines alongside a susceptible C. lanatus cultivar (Charleston Gray) for resistance against M. enterolobii. Different C. amarus genotypes ranged from resistant to susceptible for the three resistance phenotypes measured; mean percent root system galled ranged from 10 to 73%, mean egg mass counts ranged from 0.3 to 64.5, and mean eggs per gram of root ranged from 326 to 146,160. We used each of these three resistance phenotypes combined with whole-genome resequencing data to conduct a genome-wide association scan that identified significant associations between M. enterolobii resistance and 11 single-nucleotide polymorphisms (SNPs) within the C. amarus genome. Interestingly, SNPs associated with reduced galling and egg masses were located within a single quantitative trait locus (QTL) on chromosome Ca03, while reductions in nematode eggs per gram of root were associated with separate QTL on chromosomes Ca04 and Ca08. The results of this study suggest that multiple genes are involved with M. enterolobii resistance in C. amarus and the SNPs identified will assist with efforts to breed for M. enterolobii resistance in watermelon.

Root-knot nematodes (RKNs) in the genus Meloidogyne have long been considered one of the most destructive plant pathogens on earth. Meloidogyne enterolobii is an emergent species that is highly virulent and has arisen as a serious agricultural threat that has spread across tropical and subtropical growing regions around the world. As of 2024, M. enterolobii has been reported in 28 countries on 5 continents, and its distribution now includes 4 states in the southeastern United States and Puerto Rico. Due to the cryptic nature of this species, it is likely to be even more widespread than is currently known. M. enterolobii has raised concerns among growers and regulatory agencies in recent years due to its high rate of reproduction and its ability to infect many RKN-resistant crop varieties and hybrids. With a broad host range that rivals the most damaging RKN species, M. enterolobii is likely to continue to spread and cause significant damage on an extensive list of horticultural and agricultural crops. Indeed, M. enterolobii has already become one of the most prevalent RKN species in some crop growing regions. There is a great need for additional research and management research to help growers manage this nematode. This National Plant Disease Recovery Plan is meant to provide a detailed overview of what is currently known about the biology, distribution, and management of M. enterolobii and provide a reference for those seeking more information or resources regarding this nematode species.

Plant-parasitic nematodes are an important threat to turfgrass. Left unmanaged, they can cause serious reductions in the quality and playability of golf courses and sports fields. Effective nematode management depends on accurate identification of the nematode genera extracted from soil samples. However, this process requires specialized expertise in nematology, which is often limited in plant diagnostic laboratories. Recent advancements in deep learning models offer promising solutions for the future of nematode identification. In this study, we evaluated the performance of EfficientNet V2-S, MobileNetV3-L, ResNet101, and Swin Transformer V2-B convolutional neural network model architectures in the classification of seven nematode taxa associated with turfgrass. Models were trained using a dataset of 5406 plant-parasitic nematode images where the dataset was split into 70, 15, and 15% for training, testing, and validation, respectively. Data augmentation and hyperparameter optimization using a combined Bayesian optimization and Hyperband algorithm (BOHB) approach were used to improve the model performance. Balanced classification accuracy on the test set was highest for EfficientNet V2-S and Swin Transformer V2-B at 94.63% and 94.34%, respectively. MobileNetV3-L and ResNet101 had lower balanced accuracies of 90.83% and 86.33%, respectively. Testing the models on an additional dataset using a user-end platform indicated the superiority of EfficientNet V2-S to other models with 82.47% accuracy. The findings of this study indicate the potential application of deep learning tools for accurate nematode identification to aid in diagnostics.

Subsurface seep irrigation is a common practice in Florida histosol soils to supply water to crops by utilizing high-water tables. This study evaluated the effect of subsurface and overhead irrigation methods on plant-parasitic and free-living nematode densities in 46-cm soil lysimeters in a greenhouse study. Lysimeters were planted with bahiagrass in 2019 and wheat in 2020 and irrigated with subsurface or overhead irrigation for 12 weeks. Nematodes were extracted and quantified at 5-cm depth increments. Nematode counts were subjected to analysis of variance to compare treatment effects. Among nematode functional groups, omnivores were the most affected by irrigation method, their abundance being lower in subsurface-irrigated lysimeters than in overhead-irrigated ones. The results of this study show that omnivore nematode density and depth distribution are impacted by irrigation method.

Jute leaf (Corchorus olitorius) is an emerging ethnic crop in the Mid-Atlantic United States. No information is available on nematode associations and nematode community responses to jute leaf grown with ground cover. We conducted a preliminary field study in the summer of 2023 in Beltsville, Maryland to evaluate the responses of endemic nematode communities to three cultivars of jute leaf (Firebird, Molokhia, and USDA PI 404029) and four ground cover treatments (compost, compost and landscape fabric, compost and straw mulch, and compost and wood mulch). We extracted nematodes from soil samples collected before planting, at midseason, and at harvest. By the end of the season, plots with straw had higher counts of Prismatolaimus, Mononchus and total plant-parasites and plots with wood chips had higher counts of Helicotylenchus. Structure index and maturity index 2-5 were also higher in plots with straw at the end of the season. Cultivar had a relatively small impact on the nematode community with USDA PI 404029 plots having the highest Plectus counts and Firebird plots having the highest predator counts at midseason only. The channel and enrichment indexes showed a shift occurred with all four treatments: the soil environment became dominated by bacterial decomposition pathways with nutrient enriched conditions. However, plant biomass was not different between treatments. These results suggest ground covers can influence soil nematode communities in jute leaf production.