Managing and Analyzing Pesticide Use Data for Pest Management, Environmental Monitoring, Public Health, and Public Policy

The purpose of this book is to share with a wider public the fascinating insights derived from pesticide use data. The book also provides an overview of the systems used to gather and manage these data. The ultimate goal is to inspire researchers and the public to utilize pesticide use data in diverse fields including pest management, environmental monitoring, public health, and public policy. Most pesticide use surveys are aggregated at a geographic scale larger than the county level (e.g., state). However, the quality of this type of use data can vary greatly. Further, detailed usage information based on market surveys is not available for every type of pesticide use, though it is generally available for most crop uses. First-order risk assessment typically assumes that any registered product would be used at the maximum labeled rate, and the maximum number of allowable times per season or year. Though intended to protect against worst-case scenarios, this approach might lead to an overestimation of real-world risks.

This first-order assessment is helpful as a screen, but resources may be wasted evaluating the impact of a product in a region where it is used only rarely, if at all. To incorporate real-world use patterns into risk assessments, one approach is to use data on where a particular pesticide has been sold. All pesticide manufacturers have a basic idea of where their products are being sold, but these data are often imprecise because of distributor sales networks that buy in bulk and redistribute product. In order to understand the markets they work in, manufacturers have used companies that survey end-users, because their own sales networks cannot provide detailed information.
In an effort to further the science of pesticide use, this book explains the systems used for managing pesticide use data, and presents a selection of the compelling questions that have been investigated using that data.