Get reliable rangeland science

Producing quality data to inform land management decisions is the goal of every rangeland monitoring program, however the exact role of quality assurance (QA) and quality control (QC) in this process is rarely discussed. The purpose of QA and QC is to prevent and describe non-sampling errors, thereby increasing the repeatability, defensibility, and usability of the data collected. Quality assurance is a proactive process designed to prevent errors from occurring, while QC is a reactive process whereby the number, nature, and implications of errors are identified. We explored the QA and QC protocols of prominent rangeland, forest, and aquatic monitoring programs across federal agencies (BLM, USFS, NRCS, EPA) to develop a list of best QA and QC practices that are reasonable given the cost and efficiency constraints of most rangeland monitoring programs. Common QA practices include careful design of the monitoring programs; training and calibration of data collectors; and management of resulting data. The quantification of QC errors includes automated data checks, variance decomposition, and evaluation of signal-to-noise ratios. As a result, we present a list of practices and standards that yield an acceptable quality of data without overburdening data collectors or monitoring program managers. These practices should be implemented regardless of scale to ensure consistent, quality data for any type of rangeland monitoring.