177-5 Maximizing Cost-Effectiveness of Soil N2O Monitoring.

See more from this Division: ASA Section: Environmental Quality
See more from this Session: Improving Accuracy and Precision of Soil Carbon and Greenhouse Gas Emission Measurements and Quantification: I

Tuesday, November 17, 2015: 9:00 AM
Minneapolis Convention Center, M101 A

Robert P. Anex, Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI and Jordi Francis Clar, Wisconsin, University of Wisconsin Madison, Madison, WI
Abstract:
The episodic nature of soil N2O fluxes limits our ability to accurately quantify N2O emissions. Soil chambers that are manually sampled at infrequent intervals can lead to significant errors in estimates of cumulative soil N2O flux. Although the temporal correlation of N2O flux with events such as fertilization and rainfall is well-known, application of this knowledge to guide trace gas sampling has been largely overlooked.  We used a genetic algorithm to develop sampling strategies that minimize error in cumulative N2O flux estimates for discrete numbers of total samples collected. We developed heuristics-based sampling protocols that are keyed by triggers such as rainfall and fertilization events. We “evolved” these rules using 6 decades of simulated N2O flux data from a corn-soybean system in Arlington, Wisconsin generated with a calibrated biogeochemical process model (i.e., DAYCENT). Performance of the optimized sampling protocols was verified by simulated sampling of high-frequency measured flux data. Using measured and simulated data we derive estimates of the probability of obtaining N2O flux estimates with a given level of confidence for the level of sampling effort employed. Evaluation of the evolved sampling protocol shows that for a fixed number of samples, the accuracy of cumulative flux estimates will vary from year-to-year depending on the degree of periodicity of emissions. Testing of the heuristics-based sampling protocols using calibrated simulation data representing several other sites in the Corn Belt showed that performance depends on local conditions such as soil texture and climate. Heuristics-based sampling protocols, however, can be developed for any site using these methods. These sampling protocols can then be used prospectively to produce minimum error estimates of annual soil N2O emissions and maximize the cost-effectiveness of N2O sampling campaigns.

See more from this Division: ASA Section: Environmental Quality
See more from this Session: Improving Accuracy and Precision of Soil Carbon and Greenhouse Gas Emission Measurements and Quantification: I