The Global N2O Database – Open & collaborative science for addressing epic N2O issues

Nitrous Oxide (N2O) emissions are notoriously variable – through time, across space, and with management and environmental conditions.

Full time, gap-free sampling is a rarity in N2O emissions due to time, cost, equipment and other constraints. These gaps in data lead to uncertainty that requires gap-filling methods to improve N2O estimates and better examine the mitigation potentials from various management practices. The creation of a Global N2O Database allows for consolidation and compatibility of data sets, which can serve as a catalyst for methods improvement, improved process understanding and emissions estimates, and model improvement within the N2O field.

Nitrous oxide emissions are highly episodic with large proportions of annual emissions often released in a small number of events (Davidson et al., 2000). Due to this episodicity, full year continuous measurements are best. When these continuous measurements are not possible, strong statistically based gap-filling methods are necessary to provide accurate flux estimates. However, these methods are under developed and poorly utilized within the N2O field. More advanced statistical methods (random forest, neural networks) provide an opportunity to better estimate or gapfill emissions by using our process knowledge of N2O to utilize associated meta-data (mineral N, climate, soil temperature/moisture, etc) to better estimate emissions (Taki et al., 2018).