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

Lorsban has been the cornerstone of soil pest control in sweetpotato and white potato for decades. A recent decision to remove registrations for chlorpyrifos (Lorsban) has left a major gap in pest control plans for sweetpotato in the southern United States. The goal of this project is to build innovative Integrated Pest Management (IPM) programs that alleviate reliance on chlorpyrifos while increasing sustainability of the sweetpotato production system. Objectives will be to 1) build monitoring and modeling capabilities for adult click beetles, and 2) develop innovative strategies to control wireworms in sweetpotato production systems across the eastern coastal plain of the Carolinas and Virginia. Results will help improve management recommendations for multiple states.

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.

Weed management was identified as a high priority of organic sweetpotato producers who lack chemical control options available to conventional producers. This project will examine the effectiveness of multiple weed management techniques including 1) the use of advanced sweetpotato lines and cultivars with bunching shoot architecture to outcompete weeds for light resources and allow for more efficient use of between-row cultivation, 2) modified planting density to reduce the critical period for weed removal, 3) identification of weed suppressive (allelopathic) lines that can function in a production environment, and 4) utilization of fall-planted cover crops and reduced tillage transplanting operations to reduce the dependence on cultivation. Recognizing that these techniques may have non-target effects, this project will also investigate the insect pest pressure and plant disease occurrences in the test plots. Research-based findings will be shared with stakeholders and the greater scientific community via field days, production meetings, expos, conferences, peer-reviewed journal publications, Extension publications/fact sheets/bulletins, and electronic newsletters, webpages, and social media. Throughout the proposed project, investigators will remain engaged with the US Sweetpotato Stakeholder Advisory Panel to ensure the project remains aligned with industry goals and that meaningful results are effectively communicated to stakeholders nation-wide. Identifying best practices for weed management, in an integrated pest management context, will facilitate the development and improvement of organic sweetpotato production, in line with Goal 1 of the Organic Agriculture Research and Extension Initiative.

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.

Inconsistent quality and aesthetics in agricultural crops can result in increased consumer and producer food waste, reduced industry resiliency and decreased farmers’ and growers’ profit, poor consumer satisfaction, and inefficiencies across the supply chain. Although there are opportunities to characterize and quantify sources of phenotypic variability across the agricultural supply chain - from cultural practices of growers and producers to storage and handling by distributors - the data available to allow for assessment of horticultural quality drivers are disparate and disconnected. The absence of data integration platforms that link heterogeneous datasets across the supply chain precludes the development of strategies and solutions to constrain variability in produce quality. This project’s central hypothesis is that multi-dimensional produce data can be securely integrated and used to optimize management practices in the field while simultaneously adding value across the entire food supply chain. We propose to develop multi-modal sensing platform along with a trust-based, data management, integration, and analytics framework for systematic organization and dynamic abstraction of heterogeneous data across the supply chain of agricultural crops. The projects short term goals are to (1) engage growers to refine research and extension priorities; (2) develop a first-of-its-kind modular imaging system that responds to grower needs by analyzing existing and novel multi-dimensional data; (3) establish the cyberinfrastructure, including analytics and blockchain, to make meaningful inference of the acquired data as related to management practices while ensuring data security; (4) deploy the sensing system at NCSU’s Horticultural Crops Research Station in Clinton, NC and on a large-scale system at a major commercial farm and distribution facility, and (5) extend findings to producers and regulators through NC Cooperative Extension. The proposed sensing and cyberinfrastructure platforms will be crop-agnostic and our findings will be transferable to other horticultural crops produced in NC and beyond.

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