The goal of this project is to advance basic and applied research in the biology, epidemiology, and management of plant-parasitic nematodes (PPN) problematic in North Carolina and the Southeastern Unites States, including the emerging Guava root-knot nematode (Meloidogyne enterolobii).The objectives of this project are:To add to scientific knowledge of PPN biology and epidemiology by studying current and emerging nematode threats in the Southeastern United States, particularly through investigating key biological characteristics influencing PPN reproduction, movement, and survival.In addition,increasing understanding of how environmental and edaphic factors influencePPN populations, and to asessing spatial andspatiotemporal dynamics of nematodeepidemics. These insights into PPN biological capabilities and spatial attributes will help to direct targeted soil sampling strategies and management decisions.To model the relationships between PPN populations and crop loss in agronomic and vegetable crops, including sweetpotato, cotton, tobacco, soybean, and corn. Additionally, to assess the influence of soil physical and chemical properties on PPN populations, establishment of infection, and yield responses. This will support development of pertinent economic thresholds and other risk assessment tools for PPN in North Carolina cropping systems. To investigate integrative pest management strategies for control of PPN, including crop rotation, host resistance, cultural and chemical control, biopesticides, and biofumigation. This will promote robust and durable management strategies, and support the economic and environmental resiliency of rural farming communities of North Carolina and the Southeastern United States. To advance molecular-based methods for the diagnosis and quantification of PPN in field soil and tissue samples, to complement manual, morphological-based quantification methods that are currently widely used. This will lay key groundwork in the development of a pre-plant, DNA-based soil test for timely detection of PPN, species-level identification, and development of risk prediction models.