Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions

Nitrous oxide (N₂O) is a potent greenhouse gas emitted during soil nitrogen cycling. Excess nitrogen fertilization leads to increased N₂O emissions, which is a waste of applied nitrogen. Optimized nitrogen fertilizer management (4R nutrient management:  right product, right rate, right time, right method/place)  can enhance nitrogen use efficiency and reduce N₂O emissions without reducing crop yields, mitigating the climate impact of agriculture. This is particularly relevant in developing regions like sub-Saharan Africa where fertilizer use is expected to increase over coming decades. Effective fertilizer management offers multiple benefits: Boosting food security while safeguarding the environment and minimizing input costs for farmers.

Quantifying N₂O emissions at the field and farm level is challenging. Therefore, N₂O is often not included in agroecosystem assessments, which may focus on variables such as the CO₂ budget or soil carbon balance. Typical methods to quantify N₂O fluxes – such as automated chamber measurements and eddy covariance – are expensive and require advanced knowledge and infrastructure. Moreover, N₂O emissions are highly heterogeneous in space and time, thus many measurements are needed to quantify emissions. Novel measurements, models and machine learning can be used in combination with existing techniques to understand drivers, increase spatial coverage, and extrapolate to new locations.

Measurement innovations focusing on low-cost sensing of N₂O will provide much needed data in remote and developing regions. Low-cost sensing is particularly suited in direct soil gas measurements, where N₂O concentrations and variability are much higher than in free air. Specialised algorithms are needed to estimate fluxes based on soil gas measurements. Machine learning and process modelling approaches can furthermore be used to understand drivers and create simple simulations of N₂O emissions, to extrapolate in space and time based on existing (sparse) measurements. These approaches can also leverage proxies, such as isotopic composition, to estimate emissions. Measurement campaigns in data-poor regions should prioritise calibration, collection of ancillary data (such as soil moisture, temperature and nitrogen content), robust metadata reporting, and open data sharing, to maximise the impact of measurements and facilitate data-driven analyses. Development of these tools and approaches will allow N₂O emissions to be estimated for different sites and scenarios, opening the way for simple emission accounting and the inclusion of N₂O in agroecosystem assessments.