Pesticides Data in a nutshell is all information on pesticide types, residues in food or the atmosphere, and the methods of sampling and testing. In addition to providing suggested sampling procedures, authorities publishing this data have made it possible for people to report excessive pesticide use or falsified results.
Government and intergovernmental agencies collect and detail most of the pesticides data currently available. However, they also publish recommendations for sampling and measuring pesticides, so much individually-reported data follow these guidelines.
Despite the fairly standardized sampling and reporting guidelines, the collected data can be difficult to read. Most commonly, the USDA’s database shows columns of food type, chemical measured, and year, organized by a summary of findings (by country, etc.), presumptive tolerance, or the amount of chemical detected. Databases allow users to search by food, by food origin, and by chemical.
This data also uses a lot of government codes. In addition to codes for pesticides and food type, users can search by codes for:
Other databases are organized differently. The EU’s Pesticides Database, for instance, allows users to search by status (whether the EU has approved, not approved, or has not yet assessed the pesticide) and pesticide type (such as herbicide or rodenticide).
The main use of pesticides data is to improve or maintain the health of people and crops, with secondary uses being environmental conservation and sustainability.
There are other, less immediately obvious uses for this data. For example, businesses can use low pesticide residue reports as marketing tools. Competitors and watchdog groups can also use the federal sampling guidelines to check that these companies do not make fraudulent claims.
Since government and intergovernmental agencies collect and report most of this data, its quality is generally very good. For personal or private use, then, users should simply keep the ultimate goal in mind, to ensure their dataset’s relevance.
The data should regularly be cleansed to ensure it remains accurate and internally consistent.
Users could also sample soil, water, and food for pesticide residue themselves rather than rely entirely on reports.
With a good smart farming system, farmers can collect data on all aspects of their practices and discover where they might cut back or improve on certain elements to save money, improve crop yield or quality, or to lessen their environmental impact.
To make such a technological revolution possible, compatible smart-sensors must be developed that are capable of monitoring every possible point of interest for farmers, including the levels of pesticides that their crops are being exposed to.
Water-borne contaminants were monitored in 69 tributaries of the Laurentian Great Lakes in 2010 and 2014 using semipermeable membrane devices (SPMDs), and polar organic chemical integrative samplers (POCIS). Analyses included 185 chemicals (143 detected) including PAHs, legacy and current-use pesticides, fire retardants, pharmaceuticals, fragrances, and others. Hazard quotients were calculated by dividing detected concentrations by biological effect concentrations reported in the ECOTOX Knowledgebase (Toxicity quotients, TQs) or ToxCast database (Exposure Activity Ratios, EARs).This dataset is associated with the following publication: Alvarez, D., S. Corsi, L. De Cicco, D. Villeneuve, and A. Baldwin. Identifying chemicals and mixtures of potential biological concern detected in passive samplers from Great Lakes tributaries using high-throughput data and biological pathways. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola,
GC/MS data resulting from the metabolomic analysis of amphibian livers following exposure to common agrochemicals (fertilizer and pesticides) and their mixtures.This dataset is associated with the following publication: Van Meter, R.J., D. Glinski, T. Purucker, and M. Henderson. Induced Hepatic Glutathione and Metabolomic Alterations Following Mixed Pesticide and Fertilizer Exposures in Juvenile Leopard Frogs (Lithobates sphenocephala). ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 41(1): 122-133, (2022).
The U.S. Environmental Protection Agency (USEPA) and U.S. Department of Agriculture (USDA) are developing the VarroaPop+Pesticide model which simulates the dynamics of honey bee colonies and how they respond to multiple stressors, including weather, Varroa mites and pesticides. To evaluate this model, we used Approximate Bayesian Computation to fit field data from an empirical study where honey bee colonies were fed the insecticide clothianidin.Model input data (Minucci 2021a) are available on Figshare: https://doi.org/10.6084/m9.figshare.c.5402901.v1. Scripts (Minucci 2021b) to run this analysis are available on Zenodo: https://doi.org/10.5281/zenodo.4721797.This dataset is associated with the following publication: Minucci, J., R.J. Curry, G. DeGrandi-Hoffman, C. Douglass, K. Garber, and S. Purucker. Inferring pesticide toxicity to honey bees from a field-based feeding study using a colony model and Bayesian inference. ECOLOGICAL APPLICATIONS.