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National crop loss assessment network: quality assurance program.
D S Coffey1, J C Sprenger, D T Tingey
1Northrop Services, Inc., Corvallis, Oregon 97333, USA.
Environmental Pollution (Barking, Essex : 1987)
|January 1, 1988
Summary
A quality assurance program improved data for assessing air pollution impacts on crops. Standardized protocols and audits ensured reliable data for agricultural economic assessments.
Area of Science:
- Environmental Science
- Agricultural Science
- Data Quality Management
Background:
- The National Crop Loss Assessment Network (NCLAN) program assesses economic impacts of air pollution on US agriculture.
- US Environmental Protection Agency (EPA) mandates known and documented data quality for environmental studies.
- A robust quality assurance (QA) program is essential for reliable agricultural economic impact assessments.
Purpose of the Study:
- To implement and evaluate a comprehensive quality assurance program within the NCLAN.
- To ensure environmental data collected were of known quality and suitable for intended use.
- To quantitatively assess data quality in terms of precision, accuracy, completeness, representativeness, and comparability.
Main Methods:
- Developed and implemented standardized research and monitoring protocols across NCLAN sites.
- Established a range of audit and review procedures, including independent on-site audits.
- Quantitatively measured data quality parameters such as precision and accuracy.
Main Results:
- Project data quality objectives proved valuable for validating data from diverse research sites.
- Standardized protocols successfully ensured data comparability across different research locations.
- On-site audits effectively evaluated adherence to established research protocols.
- Precision and accuracy measurements facilitated data quality assessment and instrument maintenance.
Conclusions:
- The QA program successfully enhanced the reliability and comparability of NCLAN data.
- Standardized protocols and audits are critical for maintaining data integrity in multi-site environmental studies.
- Data quality objectives and quantitative measurements are essential for ensuring data acceptability and guiding research improvements.