The use of glyphosate, a widely-used herbicide in America’s farmlands, has been a topic of intense debate due to its classification as a probable human carcinogen by the International Agency for Research on Cancer (IRAC) since 2015. Despite the ongoing controversy, the usage of glyphosate has increased significantly, presenting a critical challenge: balancing agricultural productivity with environmental protection.
This project aims to optimize glyphosate usage, along with other herbicides, to maintain environmental sustainability in key agricultural regions of the United States.
We concentrate on Iowa, Illinois, and Nebraska - the states with the highest herbicide usage in the US. Our localized approach ensures a comprehensive understanding of herbicide dynamics in these areas, aligning with our goal of reducing herbicide overuse while maintaining major crop yields.
Our project employs a combination of mathematical modeling and optimization techniques, integrated through data analysis. This approach is geared towards developing strategies that balance reduced use of harmful herbicides with the need for increasing yields in vital crops like corn.
- Consumers: Ensuring safer, healthier produce by mitigating risks associated with herbicide exposure.
- Agricultural Community: Supporting farmers by fostering sustainable practices, preserving crop yields, and reducing financial burdens.
- Government and Regulatory Bodies: Providing data-driven insights to inform balanced regulations, addressing both agricultural needs and environmental conservation.
Our project transcends agricultural boundaries, promising significant benefits for societal well-being, environmental stewardship, and the sustainability of American agriculture through innovative optimization techniques.
[1] "Surge in glyphosate usage", National Pesticide Information Center. [15] "Herbicide usage statistics in the US", United States Department of Agriculture.
This README provides a conceptual overview of the project. For detailed methodologies, data analysis procedures, and results, please refer to the accompanying project documentation and reports.
Disclaimer: The information provided in this document is for academic and research purposes. The views and findings expressed are those of the project team and do not necessarily reflect those of any affiliated institutions or bodies.
© [2023] Decision Analytics Project Team. All Rights Reserved.