Information on pesticide types, residues in food or the atmosphere, and the methods of sampling and testing it all constitute pesticides data. In addition to providing suggested sampling procedures, authorities publishing this data have opened lines to report excessive pesticide use or falsified results.
Government and intergovernmental agencies collect and report 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 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 pesticide type (such as herbicide or rodenticide) and status (whether the EU has approved, not approved, or has not yet assessed the pesticide).
Obviously, the main uses 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 of this data, however. 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.
Secondly, users should regularly cleanse the data to ensure it remains accurate and internally consistent.
If possible, users should 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.
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