Bringing a Needle to a Laser Fight: Comparing Greenhouse Gas Sampling Methods with Gas Chromatography and Fourier Transform Infrared Spectroscopy.

As scientists, producers, policymakers, and the general public become more concerned about impacts of climate change, there is an increasing need to understand and quantify greenhouse gas emissions from agricultural practices, which often feed into global, multi-institution databases. Current best practices allow for a variety of sampling and analytical techniques, including approaches such as syringe sample collection followed by gas chromatography (GC) in the laboratory, and in-field Fourier transform infrared spectroscopy (FTIR). However, to date only limited analysis has been undertaken to investigate the comparability of data generated through these differing approaches. This is of particular relevance where large databases – such as those used in crop production and climate modeling – will be populated by multiple data streams possibly derived through varying techniques. The goal of this study was to determine if two technologies, GC and FTIR, produce comparable results both in measurement of absolute gas concentration above the soil as well as rate of flux from the soil. The study was conducted using insulated stainless steel closed static chambers, with modified fittings to allow samples to be taken simultaneously from the same chamber headspace with both sampling methods. Comparisons were made on four soil types (Plano silt loam, St. Charles silt loam, barnyard sand bedding, and barnyard mulch bedding) and under three agricultural production practices (continuous corn, a corn-alfalfa rotation, and dairy barnyards). Both methods measured CO₂, N₂O, and CH₄ emissions from soil. Results of 130 paired sampling instances indicate that no significant differences exist between the two techniques in measured CO₂ concentrations, while slight differences were detected in both CH₄ and N₂O concentrations (P >0.10). Calculated flux comparisons, with Hutchison Mosier, quadratic, and linear regressions will also be used to assess comparability of GC and FTIR data.