Non Technical Summary
This project utilizes a blend of detailed scientific methods and practical applications to tackle the significant issue of tar spot in corn, aiming to effect substantial improvements in agricultural practices and crop health through innovative approaches and robust evaluation methods.

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
807	1510	1170	50%
807	1510	1040	20%
807	1510	2080	30%

Knowledge Area
807 - Disaster Preparedness, Mitigation, Response, and Recovery;

Subject Of Investigation
1510 - Corn;

Field Of Science
1040 - Molecular biology; 2080 - Mathematics and computer sciences; 1170 - Epidemiology;

Goals / Objectives
We have two objectives: Objective one focuses on studying tar spot epidemiology using traditional and state-of- the-art methods, and objective two delves into the intricacies of corn-P. maydis interactions.

Project Methods
The project employs a comprehensive approach to address vulnerabilities in the agricultural system, specifically targeting the issue of tar spot in corn. It combines statistical machine learning, functional characterization, gene expression techniques, and observational and experimental research methodologies to establish potential cause-and-effect relationships crucial for agricultural improvements. Control groups and randomization are key elements of the experimental design, ensuring the integrity and unbiased nature of the data collected. The project takes the form of a case study, focusing deeply on a single disease to provide detailed insights, while leveraging a cross-disciplinary approach to enhance the reliability, validity, and overall quality of the findings.Unique aspects of the project include the rigorous spatial and temporal dynamics analysis, which utilizes data from specifically chosen geographic locations and time periods to evaluate the impact of environmental conditions and disease dynamics on crop health. The data collection integrates assessments by human raters and UAV imagery with continuous environmental data monitoring, creating rich, multi-faceted datasets. The validation of predictive models is thorough, involving steps like data splitting, fine-tuning, calibration, cross-validation, and potentially external validation with independent datasets.The project aims to significantly impact its target audience--stakeholders and agricultural practitioners--by delivering science-based knowledge through the development and distribution of practical materials. These efforts include detailed reports and guidelines on disease management and crop protection, along with experiential learning opportunities that demonstrate the practical applications of the research findings in real-world settings.The success and impact of the project are evaluated using several methods. Planned evaluation studies will collect various types of data, linking milestones and indicators of success to the expected outcomes and accomplishments. Quantitative indicators such as disease intensity measurements and environmental impacts will measure the tangible benefits of the project. Feedback from stakeholders, including farmers and agricultural experts, will provide insights into the practical applicability and effectiveness of the research outputs. Additionally, the project will produce detailed visualizations and comprehensive reports to clearly communicate the findings and their significance, ensuring that the results are both understandable and useful to all stakeholders involved.