Ultimately, NPMTI will be a national, multi-crop disease forecasting tool that will expand to include a diversity of row crops and scientific disciplines across the United States. The current endeavor brings together a network of scientists from Land Grant universities, USDA-ARS, and cooperating national laboratories. Over the life of this initiative, primary research will provide insight into management decisions pertaining to crop selection, hybrid/varietal selection, cover crop selection, tillage options, seed treatments, foliar fungicides, and other agronomic tools that focus on minimizing the impact of crop disease. The resulting research efforts will be published in newsletters, peer-reviewed journals, and other forms of communication to the immediate benefit of the agricultural community. As a result, this increasing knowledge base will provide site-specific (i.e., field, region) and evidence-based suggestions for on-farm disease management to an audience of farmers, Extension agents, USDA personnel, crop consultants and agronomic advisors. The information in aggregate will be disseminated by press releases to national agricultural media outlets and available on the Initiative’s website. NPMTI will stay abreast of and use the most up-to-date communication vehicles to reach its audiences, with particular emphasis on end users. Tools will include web-based graphical user-interfaces (GUIs), smartphone application (app) platforms, use of the online iPiPE Portal (Integrated Pest Information Platform for Extension and Education), and the continued development of new communication avenues.
Important to the ongoing progression of this Initiative is the pursuit and delivery of a comprehensive forecasting tool to the agricultural community. As collection and analysis of multiple variables begin to aggregate, the primary deliverable, a site-specific forecasting model, must be considered during the early stages of research and development of the Initiative. The success of this deliverable will require collaboration with data scientists and mathematicians skilled in computational analyses that provide longevity through continual maintenance, storage, and calibration of the forecasting tool. A successful forecasting tool of this magnitude and breadth is contingent on delivering simplicity (user-friendly interface for farmers and other end-users) out of complexity (advanced predictive modeling).
As a general guide, the following recommendations are briefly outlined with the objective of promoting information transfer and partnership:
A) Academic Laboratories shall include working groups within Land Grant and other research institutions. The success of the Initiative relies on their expertise in study design, development and validation of new methods and technology that provide a clearer understanding of disease prediction. At the forefront of scientific discovery, these “Academic Laboratories” will identify variables and thresholds associated with disease risk. As focal variables emerge for use within the forecasting tool, the next objective will be to transfer methods, where applicable, to Service Laboratories to minimize time and resources spent on highly repetitive sample testing and instrumentation calibrations.
B) Service Laboratories shall be defined as those laboratories that can provide additional support for data collection. They will be invited to participate in cases where Academic Laboratories may not have the equipment, personnel, or administrative authority to perform high-throughput testing of samples outside their primary research scope. Service Laboratories will collaborate with Academic Laboratories to develop standard operating procedures (SOPs) on focal variables (e.g., pathogen detection), which may require standardization for regional forecasting models or field-level specificity for more personalized and real-time user models. Service Laboratories must have Quality Control procedures in place. Ideally, Service Laboratories will be in good standing with the International Organization for Standardization (ISO) or hold an accreditation through another certification body. Service Laboratories should have SOP development experience to provide validations on the original or updated methods, as communicated by the Academic Laboratories. Additionally, Service Laboratories will conduct interlaboratory comparisons and implement proficiency tests on SOPs as new Service Laboratories join the Initiative. Participating Service Laboratories with the capacity for data aggregation and analyses will also provide resources for developing forecasting models.