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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #418427
Research Project: Linkages Between Crop Production Management and Sustainability in the Central Mississippi River Basin

Location: Cropping Systems and Water Quality Research

Title: USDA LTAR Common Experiment measurement: best practices for collection, handling, and analyses of water quantity measurements

Author
item Baffaut, Claire
item Schomberg, Harry
item Cosh, Michael
item O'Reilly, Andrew - Andy
item SAHA, AMARTYA - Archbold Biological Station
item SALIENDRA, NICANOR - Retired ARS Employee
item Schreiner-Mcgraw, Adam
item Snyder, Keirith

Submitted to: Protocols.io
Publication Type: Research Notes
Publication Acceptance Date: 7/29/2024
Publication Date: 7/29/2024
Citation: Baffaut, C., Schomberg, H.H., Cosh, M.H., O'Reilly, A.M., Saha, A., Saliendra, N.Z., Schreiner-Mcgraw, A.P., Snyder, K.A. 2024. USDA LTAR Common Experiment measurement: best practices for collection, handling, and analyses of water quantity measurements. Protocols.io. https://doi.org/10.17504/protocols.io.eq2lyw14wvx9/v1
DOI: https://doi.org/10.17504/protocols.io.eq2lyw14wvx9/v1

Interpretive Summary: This protocol is part of a larger set published by and for the Long-term Agroecosystem Research Common Experiment, which is an experiment that is similar at all the sites of the network. This protocol outlines quality control/quality assurance practices for water quantity measurements. This is crucial to the agriculture sector because it is important to report high quality data for the common experiment. The goal is to provide repeatable guidelines to achieve consistent data collection, instrument maintenance, data processing, and quality control for obtaining these data at cropland sites across the United States.

Technical Abstract: Although each of the developed water quantity protocols addresses specific issues concerning quality assurance (QA) and quality control (QC), basic QA/QC procedures apply to many of these protocols and have relevance in field research. QA is a set of processes or steps taken to ensure that protocols are developed and adhered to in a way that minimizes inaccuracies in the data produced. QA produces high-quality data while minimizing the need for corrective measures to improve data quality. QC occurs after data generation and tests whether the data meet the requirements for quality outlined by the end users. QA is a proactive or preventive process to avoid problems; QC is a process to identify and flag suspicious data.