Anders Huseth

The CleanSEED project aims to develop a research and extension proposal that will address the critical needs of U.S. sweetpotato certified seed programs using stakeholder input to identify priority research areas and build relationships between industry representatives, top research scientists, and clean plant organizations. The project will include a collaborative process that brings together multi-state and multi-institutional teams of biological, physical, and social scientists to promote a trans-disciplinary systems-based approach, create a plan to address USDA priorities, and a plan for disseminating the results. The following SCRI program legislatively mandated focus areas will be addressed: a) Pest and disease management - sweetpotato clean seed is integral to management not only of systemic pathogens such as viruses and soft rot bacteria, but also to soilborne pathogens that infect roots such as the storage roots used for sweetpotato seed; b)Emerging and invasive species - black rot caused by the root and soilborne fungus Ceratocystis fimbriata re-emerged in 2013-14 and was apparently spread to other states on seed roots. GRKN, Meloidogyne enterolobii, was first found in Florida in 2001, then reported in North Carolina in 2011, and was intercepted on sweetpotato seed roots in interstate shipments in 2018. It is an invasive threat that poses a serious problem to vegetable and row crop industries throughout the U.S. and sweetpotato seed roots are an ideal vehicle for its dissemination; c)To improve production efficiency, handling and processing, productivity, and profitability over the long term - common U.S. sweetpotato viruses can reduce yields 25-40%, affect skin color and uniformity of shape. Black rot and GRKN can render sweetpotatoes unmarketable and quarantines for GRKN and sweetpotato weevil restrict efficient movement of sweetpotatoes to various markets; d)Improved monitoring systems for agricultural pests - breeding lines entered into therapy programs are routinely tested for viruses present, improving methods of seed inspection could provide an additional opportunity to detect new or re-emerging problems; e) Effective systems for pre-harvest and postharvest management of quarantine pests - clean seed of sweetpotato is a proven means of managing a long list of pathogens and pests that can infect or infest storage roots, but improved delivery systems and education programs will be needed to take advantage of this opportunity.

A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain

An objective of our project is to identify new pheromone compounds from live unmated pestiferous click beetle females. A second objective is to field screen possible pheromone compounds to determine which chemicals or blends are attractive to pestiferous click beetle species.

Brown stink bug, Euschistus servus, is the costliest and most problematic insect pest of corn in the southeastern US, and a major pest of soybean and cotton across the southeastern US and Midsouth. Our objectives are to 1. Measure stink bug populations in suitable host crops during the autumn where corn will be planted during the spring 2. Characterize overwintering habitats based on the categorization of host plants or forest structure 3. Measure brown stink bug colonization into spring corn adjacent to non-crop overwintering habitats and annual crops. 4. Estimate stink bug injury in focal corn fields 5. Assess corn yields relative to stink bug density and landscape features 6. Identify landscapes at risk for infestation by brown stink bug and create a risk map for the southeastern US 7. Document baselines for management of brown stink bug in field crops and disseminate brown stink bug risk management recommendation to relevant stakeholders

Accurate monitoring for changes in pest susceptibility to insecticidal toxins expressed in genetically engineered agronomic crops is currently an ineffective process limited by both scale and scope of deployment. Although long-term scientific and social change will be necessary to minimize pest resistance evolution, understanding near-term shifts in susceptibility through novel monitoring will also be essential to enable more effective resistance management strategies. To address this limitation on resistance monitoring, we propose to develop and deploy real-time pheromone-based sensor platforms to indicate patterns of lepidopteran pest activity in landscapes. We will use cotton bollworm (Helicoverpa zea Boddie) as a case study to develop and refine automated monitoring tools designed to detect shifts in pest susceptibility.

The modern availability of novel data analytics and cost-effective high-performance computing creates unique opportunities to tap into the wellspring of potential offered by big data for creating decision-making tools that inform sustainable agroecosystem management. When coupled with climate, land use, and policy-related data streams through analytics, long-term monitoring data can be applied to develop data-driven decision-support tools designed for land and water resource managers, but foundational research is needed to develop such data-rich decision-support platforms. As a case study, this research will develop a data-to-decision pipeline for nearshore water quality management in support of shellfish agroecosystem protection. Shellfish growing areas are regularly screened for coliform bacteria to inform on-the-fly decision-making by regulators who are evaluating the sanitation of cultured shellfish, which has led to the accrual of a vast record of spatiotemporal bacterial observations. These national-scale data remain poorly explored and underutilized due to challenges associated with analyzing big, multi-scale data, but could be mined to develop critically-needed decision-support platforms.

This project will addressing agronomic and pest challenges to sucessful organic sunflower production in North Carolina.

This project will develop integrated strategies to address trade barriers for export sweetpotatoes in the United States.

This project aims to survey stink bugs and natural enemies in North Carolina soybeans. The threat of stink bug damage to soybean production has been increasing in multiple NC field crops over the past several years. We do not understand what environmental or crop patterns are driving this change. Moreover, the last comprehensive statewide survey of stink bugs in soybean was conducted several decades ago. As a result, we do not understand how the distribution of stink bug species vary at the field, farm, or regional level across the state. Generating this information is important because susceptibility to common insecticides varies among species and, in turn, efficacy of spray decisions. Preservation of natural enemies that are predators of stink bugs and other pests is another component of sustainable soybean production systems. We do know that several stink bug egg parasitoids are important predators of these pests, however, we do not know how common they are across the state. To address this knowledge gap, this project proposes an on-farm survey of stink bugs and associated natural enemies in soybean from the mountains to the coast. Results from this survey will complement several other NC State projects focused on stink bug management. Together, our collective efforts will help guide extension efforts focused on regionally specific scouting and pesticide recommendations.

Unmanned Aircraft (drones) are marketed in the agricultural sector as a ‘revolutionary’ technology. Although the technology and corresponding data are truly unique, the application of data outputs for agricultural management decisions (e.g., re-plant, pest management) remain unclear. This interdisciplinary project will investigate the use of drones in five key production areas 1) re-plant decisions, 2) incidence of fungal disease, 3) severity of insect-related defoliation, 4) weed identification and management, and 5) nutrient deficiencies. The project will evaluate common commercial drone technology to document baseline potential for decision support in soybean. The information generated by this project will be used to provide robust training to County Extension Agents and farmers across North Carolina on the use of these technologies to enhance profitability.