N2OSSA

Combining measurements, modelling and machine learning to improve N₂O accounting for sustainable agricultural development in sub-Saharan Africa

Food security is a major issue in sub-Saharan Africa, with 25-40% of households classified as food insecure. In recent decades, agricultural production in sub-Saharan Africa has strongly increased, however the majority of production increases come from the expansion of cultivated area, while yield per area (productivity) remains low. One of the major causes of low productivity is insufficient use of nitrogen fertiliser: Fertiliser application is often 10 times lower than recommended due to high costs and lack of access to fertiliser.

In the coming decades, fertilizer use in sub-Saharan Africa is predicted to increase in order to achieve increasing agricultural productivity. If fertilizer application is carefully controlled, emissions of greenhouse gases from agricultural soils can be minimized while ensuring high productivity and food security: Climate-smart agriculture. The success of climate-smart agricultural practices depends on detailed knowledge of nutrient cycles in soils.

Nitrous oxide: A potent greenhouse gas and an indicator of agricultural nitrogen use efficiency

Nitrous oxide (N₂O ) is a powerful greenhouse gas and the most potent stratospheric ozone-depleting substance released this century. The strongest anthropogenic source of N₂O is microbial production in agricultural soils following fertiliser nitrogen (N) addition: This source accounts for 65% of Switzerland’s total N₂O emissions. While anthropogenic N₂O emissions are a direct environmental threat, N₂O is also a marker for N waste in agriculture. To reduce agricultural N₂O emissions, farmers need to maximise the transfer of fertiliser N into crops, known as Nitrogen Use Efficiency. This can be achieved through climate-smart agricultural management practices, which aim to precisely match the needs of crops with management practices such as fertiliser application and timing.

Measuring nitrous oxide fluxes and isotopes using laser spectroscopy

Measuring the flux of a greenhouse gas like N₂O tells us how strongly the gas is emitted from or taken up by the soil, however it does not tell us about the processes responsible for the gas production and consumption. The isotopic composition of the gas, in contrast, can act like a fingerprint for different emission processes. In the case of N₂O , the isotopic site preference is particularly useful to distinguish between the major microbial pathways of nitrification (high SP) and denitrification (low SP).

The four most common isotopocules of N₂O are ¹⁴N-¹⁴N-¹⁶O, ¹⁴N-¹⁵N-¹⁶O (𝛼), ¹⁵N-¹⁴N-¹⁶O (𝛽), and ¹⁴N-¹⁴N-¹⁸O. The isotopic site preference (SP) is the difference between the isotopic composition at the two sites (𝛼 - 𝛽) and the bulk isotopic composition is the average of the two sites.

Fluxes of greenhouse gases like N₂O can be monitored in the field using chambers, which close over the soil to trap gases which are released from the soil. We can measure the increase of N₂O in the closed chamber to estimate the flux: When N₂O builds up quickly, this indicates a high flux. Using laser spectroscopy, we can also measure the isotopic composition of N₂O in the chamber headspace, because the different isotopocules absorb laser light at different wavelengths. Through repeated chamber measurements we can construct a timeseries of the flux and isotopic composition of emitted N₂O.

Measuring N₂O flux and isotopic composition. a) When the chambers close, N₂O builds up in the chamber headspace. When the chamber is closed, measurements of N2O concentration (upper) and isotopic composition (lower) allow us to estimate the flux and the isotopic composition of emitted N₂O. b) Repeated measurements are used to construct a time series of flux and N₂O isotopic composition. This example shows a strong N₂O peak at around 17:00. The low site preference indicates the peak was due to microbial denitrification.

Although the example shown in the figure looks simple, interpreting these isotopic datasets is challenging. The TimeFRAME tool, recently developed at SDSC, is an easy-to-use Bayesian hierarchical modeling approach to quantify the pathways producing and consuming trace gases based on isotopic measurements. You can find more information about TimeFRAME on the project repository and in an upcoming publication.

Project tasks and goals

In N2O-SSA, we will use a combined measurement-data science-modeling approach to understand the drivers of soil N₂O emissions, in particular the influence of precipitation and soil moisture.

Specific project tasks include:

• Develop TREX-QCLAS-chamber system for a high precision monitoring of N₂O fluxes and emitted N₂O isotopic composition from soils (completed)

• 2024-2025: Conduct a 12-month measurement campaign in Eldoret, Kenya, to understand soil N₂O sources and sinks across dry-wet seasonal transition under the influence of difference fertiliser regimes

• Compile N₂O flux and isotope data from studies in sub-Saharan Africa and other tropical regions, and – in combination with campaign measurements – analyze data to understand the drivers of N₂O production and consumption.

• Use data science approaches for modeling and spatiotemporal upscaling, in order to predict future emissions and promote the development of targeted mitigation strategies

Field work at the N2OSSA site (clockwise from top left): Marking out plots at the experimental farm site, Outreach and International Students Centre, University of Eldoret; the view of the site from inside the experimental trailer; chambers and the trailer installed at the site; maize grains during planting.

Project scientists

Climate and Environmental Science, University of Bern: Prof. Dr. Eliza Harris (Project PI)

Swiss Data Science Center, ETHZ: Dr. Phillip Agredazwyzcuk, Turry Ouma (PhD student)

Sustainable Agroecosystems, ETHZ: Prof. Johan Six (Co-PI), Dr. Matti Barthel

University of Eldoret: Dr. Abigael Otinga, Dr. Ruth Njoroge, Kevin Churchil Oluoch (MSc student)

ILRI: Dr. Sonja Leitner, Dr. Yuhao Zhu, Collins Oduor

This project is funded by the Swiss National Science Foundation (200021_207348).