Chris Jones PhD

Rapid responses and data-driven decision support tools are essential for understanding and mitigating threats posed by yield-damaging agricultural pests and pathogens. However, sparse data are well-known challenges limiting the accuracy and iterative improvement of pest spread models. This research will couple advances in image classification with vetted crowdsourced and satellite imagery to build an automated, repeatable pipeline for scaling host mapping efforts essential to forecasting pest spread. The resulting spatially-explicit maps of host species at scale will improve pest risk forecasting by addressing sparse data concerns and reducing data latency, thereby enabling iterative updating of the model parameters as new data become available and shortening time to decision making. We will specifically focus on fruit and tree nuts that represent an economically and culturally significant crop in the United States threatened by emerging pests and climate change. Throughout all aspects of the project, we will collaborate and build on our partnerships with USDA APHIS and ARS, state departments of agriculture, and growers associations to identify key threats to fruit and tree nut crops, iteratively validate host species maps and model forecasts, and co-develop a user-friendly decision support tool and alert system that translates forecasts and simulations into actionable insights. Our iterative near-term forecasting system coupled with data inputs created using machine learning will reduce costs for pest surveys and help growers identify when and where to intervene to protect their crops, thus reducing production losses and chemical inputs.������������������

Epidemic invasions have substantial impacts on both ecosystem function and human welfare (1,16,31,67,91), and may become more frequent owing to globalization (116). Understanding the establishment and spread of such diseases can contribute significantly to identifying appropriate disease control strategies (35,96,115). Pathogens demonstrating long-distance dispersal (LDD) are of particular concern, owing to their potential to rapidly spread over large spatial scales. This includes pathogens with propagules that have the potential for long-distance transport through air, such as foot-and-mouth disease (FMD) (60), West Nile Virus (90), avian influenza (62), white-nose syndrome of bats (4) and many diseases of plants (8), through water, such as Aspergillosis of coral (134), and perhaps also pathogens spread through human transport systems, such as influenza (66) and Ebola virus (40). Bird migration can result in fat-tailed, LDD dispersal patterns, with dispersal over hundreds or thousands of kilometers (95,130). Similarly, "anomalous diffusion" has been suggested to result in fat-tailed distributions and superdiffusive spread of a range of organisms (6,131). Developing effective models for such large-scale processes remains a challenge, and will likely require a range of approaches and comparative studies encompassing a diversity of pathogens and hosts.

The primary purpose of this agreement is to cooperatively develop a framework to allow for integration of geospatial information systems as well as geospatial products into a Geospatial Hub hosted within an existing platform at USDA. All constituent parts of USDA (mission areas, agencies, services) have geospatial investments and use. There is a need for a platform to 1. communicate the impact of geospatial programs and 2. allow for data sharing within USDA and between USDA, academia, private sector, other Federal agencies, and the public.

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world and in the US. Climate change is exacerbating weather events that affect crop production and food access for vulnerable areas. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover and evolution of new pathogen genetic lineages. Prediction of plant disease pandemics is unreliable due to the lack of real-time detection, surveillance and data analytics to inform decisions and prevent spread. In order to tackle these grand challenges, a new set of predictive tools are needed. In the PIPP Phase I project, our multidisciplinary team will develop a pandemic prediction system called ����������������Plant Aid Database (PAdb)��������������� that links pathogen transmission biology, disease detection by in-situ and remote sensing, genomics of emerging pathogen strains and real-time spatial and temporal data analytics and predictive simulations to prevent pandemics. We plan to validate the PAdb using several model pathogens including novel and host resistance breaking strains of lineages of two Phytophthora species, Phytophthora infestans and P. ramorum and the cucurbit downy mildew pathogen Pseudoperonspora cubensis Adoption of new technologies and mitigation interventions to stop pandemics require acceptance by society. In our work, we will also characterize how human attitudes and social behavior impact disease transmission and adoption of surveillance and sensor technologies by engaging a broad group of stakeholders including growers, extension specialist, the USDA APHIS, Department of Homeland Security and the National Plant Diagnostic Network in a Biosecurity Preparedness workshop. This convergence science team will develop tools that help mitigate future plant disease pandemics using predictive intelligence. The tools and data can help stakeholders prevent spread from initial source populations before pandemics occur and are broadly applicable to animal and human pandemic research.

Project is in support of PSI. We have developed faster and more reliable in-field detection methods for plant pathogens that will greatly reduce plant disease by reducing time from occurrence to detection and thus time to mitigation. Two new innovations in sensor technology have been developed including a smart-phone field-compatible molecular assay that uses a loop-mediated isothermal amplification (LAMP) sensor and a volatile-based sensor that will speed identification of plant pathogens in the field. In this project renewal, we will continue deploy and field test work a volatile organic compound (VOC) sensor and microneedle patch-supported LAMP sensors to differentiate two regulatory Phytophthora species of concern, P. ramorum and P. kernoviae. Phytophthora ramorum and P. kernoviae cause disease on nursery plants such as rhododendron, lilac and kalmia and important forestry tree species including oak and beech among others. Phytophthora kernoviae has not yet been found in the US. We will test the sensors in field tests and deploy them with inexpensive cartridges to run on a smartphone reader. We will also complete the modeling of historic late blight disease occurrence data using a near-real time mapping platform and the process based spatially explicit discrete time PoPS (Pest or Pathogen Spread) Forecasting Platform to develop predictive maps of pathogen risk of spread at regular intervals. The system will improve the response time of USDA APHIS PPQ and National Plant Diagnostic Network (NPDN) personnel to respond to emerging Phytophthora threats and improve economic return of growers as they use the digital diagnostic tools to prevent the spread of important Phytophthora diseases.

Emerging plant disease and pest outbreaks reduce food security, national security, human health, and the environment, with serious economic implications for North Carolina growers. These outbreaks may accelerate in coming decades due to shifts in the geographic distributions of pests, pathogens and vectors in response to climate change and commerce. Data-driven agbioscience tools can help growers solve pest and disease problems in the field more quickly but there is an urgent need to harness game-changing technologies. Computing devices are now embedded in our personal lives with sensors, wireless technology, and connectivity in the ����������������Internet of Things��������������� (IoT) but these technologies have yet to be scaled to agriculture. Our interdisciplinary team will build transformative sensor technology to identify plant pathogens, link local pathogen data and weather data, bioinformatics tools (pathogen genotypes), and use data driven analytics to map outbreaks, estimate pest and pathogen risk and economic damage, in order to coordinate response to emerging diseases, and contain threats. Sensor-supported early and accurate detection of pathogens before an outbreak becomes wide-spread in growing crops will significantly reduce pesticide use and increase crop yields.

The primary goal of this agreement is to characterize global agricultural trade pathways and evaluate the likelihood of alien species introductions. The TRACE (Trade Route Analytic Computation and Evaluation) framework assists with characterizing global scale pathways and determining the likelihood of new pest introductions, which have intensified in recent years due to increased global trade, transport, and travel. The multilateral pathways allow non-indigenous pests to move easily and expand habitats. Development of TRACE will establish the analytical foundations to help PPQ evaluate and manage global pathways by incorporating analytic tools. These tools assist analyzing global scale plant pest pathways to prevent pest and disease introductions.

The primary purpose of this agreement is to support further development of the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS). This framework integrates a vast amount of abiotic/biotic data with the capacity and flexibility to produce forecast and analysis models to support Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ).

The goal of this proposal is to build economic cost benefit analysis into spread model treatment scenarios. We will do this by adding an economic module and allow for multiple hosts with different values and abilities to increase the spread of the pest.

This is year three funding suggestion for USDA APHIS funding. In this project renewal, we will deploy in-field volatile organic compound (VOC) sensors and microneedle patch-supported LAMP sensors that can differentiate several important Phytophthora species of regulatory concern including P. ramorum and P. kernoviae. Phytophthora ramorum and P. kernoviae cause disease on nursery plants such as rhododendron, lilac and kalmia and important forestry tree species including oak and beech among others. Phytophthora kernoviae has not yet been found in the US. We are developing species-specific LAMP and VOC sensors and will deploy these sensors with inexpensive cartridges to run on a smartphone reader.