|Polumsky, Robert - Wayne|
|YAO, CHUNMEI - Former ARS Employee|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/31/2012
Publication Date: 10/5/2012
Citation: Gollany, H.T., Polumsky, R.W., Yao, C. 2012. Comparison of four approaches for estimating soil-atmosphere trace gas fluxes. ASA-CSSA-SSSA Annual Meeting Abstracts. p. 132.
Technical Abstract: Accurate measurements of trace gas fluxes are critical in estimating the impact of agricultural management practices on greenhouse gas (GHG) emission. Our objectives were to compare several approaches to estimate gas fluxes and to select a proper model for each gas. Gas samples were collected on 48 days from an agricultural field with three cropping systems using static-chamber technique, and N2O, CO2 and CH4 concentrations were determined. A total of 1536 measurements were processed using four approaches: linear regression (LR), Hutchinson-Mosier (HM), Quadratic (QD), and Hutchinson-Mosier-Revised (HMR); and their performance was evaluated. The HMR is based on an implementation of the R statistical analysis package. The HM method requires three observations with equi-spaced time intervals, while HMR can be used with more than three observations. The HMR provided the best approach to estimate N2O and CO2 fluxes in a single method and eliminated underestimation and overestimation of flux. The HM underestimated CO2 flux compared to the HMR, and HMR and QD models were a best fit. The LR failed to estimate N2O, CH4 and CO2 fluxes in 38, 47, and 73% of total measurements, respectively, compared to HMR. The HM failed to estimate CO2, N2O and CH4 fluxes in 27, 74, and 75% of total measurements, respectively. The QD failed to estimate N2O, CO2, and CH4 fluxes in 21, 27, and 45% of total measurements, respectively. Comparison of LR, QD and HM with the HMR procedure revealed that HMR was the best approach for N2O and CO2 flux estimation under a dryland cropping system while LR provided the best estimate for CH4 flux estimation within the detection limit. Compared to LR, QD and HM, HMR is the most comprehensive, robust and flexible.