Evaluate disparate crop-related data sets and adapt LANL analytics and models for the purposes of crop disease forecasting.
The goal of the effort focuses around the NPMTI hypothesis, which is the risk for plant disease development can be reduced (through near real-time surveillance and forecasting) by developing a comprehensive and coherent modeling platform. This platform will attempt to integrate pre-season pathogen density, crop/host genetics, effects of soil type, air monitoring, and meteorological information to provide precise predictive and forecasting capability for in-season crop disease risk. The overarching goal is the delivery of a comprehensive forecasting platform to the agriculture community that includes multiple models and analytics.
- Identify, collect, and understand/characterize data being collected on agricultural crops for NPMTI initiative (cotton, corn, wheat).
- Identify relevant available data sets and develop road map for integration of data from cotton, corn, and wheat NPMTI member data.
- Develop road map for tool development and model modification/adaptation.
- Engage with a formal user group.
- Integrate disease information into database for centralized delivery to stakeholders.
- Develop comprehensive forecasting platform for the agricultural community.
- Identify the essential data streams and data types to be included in a shared platform to be used for agricultural disease forecasting – this will include evaluating the utility and scope of use of data sets together with operational criteria for data processing and storage.
- Collect necessary data in a database, build database.
- Identify end-user community and develop collaborations.
- Develop requirements for forecasting tools, integration approach for data, AND user interface and access.
- Identify gaps in data and forecasting to inform approaches to leverage and adapt existing forecasting tools and analytics developed at LANL.
The primary research needs include understanding the types of available data and gaps therein that would need to be addressed to develop effective crop disease forecasting tools. LANL will, in collaboration with other NPMTI members (cotton, corn, wheat), establish an end user group to inform requirements for a modeling framework and decision support platform. For example, incorporation of necessary input data for pathogens being evaluated (cotton, corn, wheat focus). Evaluation of current meteorological data for informing models, understanding limitations of data sharing, and disclosure, intellectual property and other issues related to housing data and sharing data are all relevant research needs. LANL will also work to provide information to inform “commercialization” or model distribution plan.
LANL will also identify current available models, analytics, and gaps therein that would need to be addressed through adaptation of LANL models.
Finally, LANL will collect and understand end-user needs and salient information for forecasting. It will be important to involve end-users early during model and analytic adaptation to ensure tools are useful and user-friendly.
- Data sets and road map for integration of data from cotton, corn, and wheat NPMTI members.
- Forecasting models and analytics that are adapted for use in agricultural forecasting.
- Web-based interface for deployment of data and models for forecasting and decision support.
- Comprehensive, integrated forecasting platform and decision support tool.
- Commercialization or model distribution plan/path forward.
Leverage resources within NPMTI and data from each crop initiative. Los Alamos National Laboratory modeling team, LANL computer scientists, and utilization of LANL developed models.
A comprehensive, accessible, user-friendly platform that incorporates data and models and allows for informed, science-based, decision-making.
Development of new pathogen detection assays for implementation in field and as new data streams for models, developed internally and through NPMTI partnerships.
- Working with NPMTI partners, critical gaps in pathogen detection, as well as data for forecasting models and analytics, will be identified. Based on these needs, LANL will develop and evaluate new, relevant assays for pathogen detection (nucleic acid signature-based or affinity reagent-based). The data from these field-tested and validated assays will be incorporated in the NPMTI platform, as appropriate.
- Identification of gaps in pathogen detection to inform assay design and development.
- Design and development of pathogen detection assays.
- Laboratory evaluation of pathogen detection assay performance.
- Field testing and evaluation of newly developed assays.
- Evaluation of the utility of data generated by new assays, for crop disease surveillance.
- Development of pipelines for ingestion of new data types into NPMTI database.
- If appropriate, incorporation of assay data and results in crop disease forecasting platform.
- It will be important to identify current gaps in crop disease detection and surveillance and define approaches to address them. Assays could be field based or laboratory based. LANL will conduct a survey of community needs for this research need.
- Once identified, LANL will need to evaluate and prioritize gaps in pathogen detection to be able to define an approach to new assay development. This would include technical and operational gaps. LANL will work with NPMTI partners to determine the most relevant new assays to design and develop.
- Understanding of needs/gaps for pathogen detection and prioritization.
- New assay(s) for pathogen detection that have been validated and transferred to end-users.
A team of plant pathologists and epidemiologists that will collaborate to develop elite forecasting tools for wheat diseases in critical wheat-producing regions of the US. Each of the participating programs already has established extension programs and audiences. These extension programs provide a ready audience for the new information and tools developed by the Wheat NPMTI team.
The effort will provide additional information to allow for improved pathogen detection in the field and to inform the next generation of forecast models. This will include development of new technologies and information to provide science/data to decision-makers.