Maximizing Cost-Effectiveness of Soil N2O Monitoring.

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.