Historical Pesticide Applications in the Forests of Atlantic Canada

The data rescue intern: 
Keerthikrutha Seetharaman

Internship Agency: Atlantic Forestry Centre, Natural Resources Canada (NRCan)
 
Project Title: Historical Pesticide Applications in the Forests of Atlantic Canada
 
Project background and Internship goal/s: Starting around the 1950s, insecticides were sprayed aerially across the Boreal forests of Canada to control insects that threatened the growth of commercially viable trees. Even though this spraying stopped around the 1980s, the action of spraying harmful pesticides such as DDT may have had long-lasting effects. Thus, the aim of this project is to understand legacy effects of pesticide use across the different provinces of Canada. The main goal of this internship is the Quality Assurance /Quality Control (QA/QC) of historical pesticide spraying data for Newfoundland and Labrador (NFL) in Canada.

Summary of Activities:

• Conducted the QA/QC by looking through an already created dataset by NFL authorities from georeferenced maps and identified errors and mismatches in it by comparing it with available references.
• Modified it to match an already existing dataset created for the province of New Brunswick. 
• I also created metadata tables elaborating table attributes for the NFL dataset. 
• Rectified errors and added information in R.

 
List of Deliverables:

• Cleanup datasheet (See email attachments with screenshots of before and after of dataset)
• Reproducible R-code for data clean-up
• Creating a ‘How-to’ document. This is more of a guideline document for conducting the process for any future personnel involved in the QA/QC of similar datasets.

 
Challenges: Lack of references for deciphering the information obtained in the source document. 
 
Learning/Gains:
I gained a lot in terms of learning about aerial spraying and the harmful effect of pesticides, (which was very interesting to me as an ecologist), how to carry out QA/QC, programming experience, especially coding in R, and how to find and detect data entry errors using R. The discussions with the agents at the internship agency were also very insightful and added to my knowledge.