Food insecurity in sub-Saharan Africa is widespread, with crop yields much lower than in many developed regions. Increased fertilizer application in the coming decades could improve yields, alleviating food stress; however, this can cause major increases in emissions of greenhouse gases such as nitrous oxide (N2O). The N2O-SSA project aims to use laser spectroscopy to measure fluxes and isotopic composition of N2O from maize and potato crops subjected to a range of fertilization levels. The data generated will allow us to develop a model to predict N2O emissions under different climate and management scenarios, to guide future measurements, reduce uncertainty in these predictions, and identify potential avenues for sustainable agricultural development in sub-Saharan Africa.
The need for climate-smart agriculture
Food security is a major issue in sub-Saharan Africa, with 25-40% of households classified as food insecure, meaning they lack regular access to sufficient, safe, and nutritious food for growth, development, and an active, healthy life [1,2]. In recent decades, agricultural production in sub-Saharan Africa has rapidly 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 fertilizer, with application rates often 10 times lower than recommended due to high costs and lack of access to fertilizer. In order to increase agricultural productivity, fertilizer use in sub-Saharan Africa is predicted to grow in the coming decades. Climate-smart agriculture requires careful fertilizer application control, minimizing greenhouse gas emissions from agricultural soils while ensuring high productivity and food security. However, 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 largest anthropogenic source of N2O is microbial production in agricultural soils following the addition of nitrogen (N) fertilizer. This source accounts for 65% of Switzerland’s total N2O emissions , and 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 must maximize the transfer of fertilizer 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 fertilizer application and timing.
The N2O-SSA project
We launched the N2O-SSA project with the full title “Combining measurements, modeling and machine learning to improve N2O accounting for sustainable agricultural development in sub-Saharan Africa” in 2021. The project aims to measure N2O emissions and isotopic composition from maize and potato crops over a range of fertilizer application levels. We will use data science techniques to understand drivers of N2O emission peaks and construct modeling approaches to predict N2O emissions in the coming decades. These models will provide a basis to develop sustainable agricultural practices that balance low greenhouse gas emissions with high yields while fostering soil and environmental health.
Measuring nitrous oxide fluxes and isotopes using laser spectroscopy
Measuring the flux of a greenhouse gas such as N2O tells us how much gas is emitted from or taken up by the soil; however, this does not tell us about the processes responsible for the gas production and consumption. In contrast, the isotopic composition of the gas (see Figure 1) can act like a fingerprint for different emission processes. In the case of N2O, the isotopic site preference (SP) is particularly useful for distinguishing between the major microbial pathways of nitrification (high SP) and denitrification (low SP).
Figure 1: The four most common isotopocules of N2O are 14N14N16O, 14N15N16O (𝛼), 15N14N16O (𝛽), and 14N14N18O. The isotopic site preference (SP) is the difference between the isotopic composition at the two sites 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 that are released from the soil (Figure 2).
We can measure the increase of N2O in the closed chamber to estimate the flux (Figure 3a): 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. As shown in Figure 3b, we can construct a time series of the flux and isotopic composition of emitted N2O through repeated chamber measurements.
Figure 3: 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 Figure 3 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.
Pilot measurement campaign in Eschikon, Switzerland
In June 2023, we installed a laser spectrometer coupled to a preconcentration device interfaced with twelve automated chambers (Figure 4a,b) at the ETHZ Research Station for Plant Sciences in Eschikon. During this pilot measurement campaign, we aim to test the complete measurement setup, troubleshoot for potential problems, and develop automated data management and analysis capabilities. The pilot campaign has been a great learning and problem-solving opportunity for PhD student Turry Ouma and postdoctoral researcher Phillip Agredazywczuk, as field measurements with cutting-edge instrumentation rarely run smoothly. Having faced many problems with the system ranging from laser meltdowns to power issues, the system is now finally running (nearly) successfully four months after installation.
Outlook: Nitrous oxide measurements in Eldoret, Kenya
Following shipping to Kenya at the end of 2023, the instrumentation will perform measurements at an experimental farm site at the University of Eldoret (Figure 5).
We aim to measure fluxes and isotopic composition from maize and potato plots with various fertilizer application levels over an entire growing season. This data will help us to understand the relationship between fertilizer application rate and N2O emissions. Using data from other regional sites to extrapolate, we will develop a model to predict cropland emissions under different climate and management scenarios. A key focus will be understanding the sources of uncertainty in our predictions and guiding future measurements to reduce these uncertainties.
Feel free to reach out if you are interested in the project.
Eliza has joined the academic team as a senior scientist. She previously worked as a postdoctoral researcher at the Massachusetts Institute of Technology (2012-2013), Empa (2013-2017) and the University of Innsbruck (2017-2020). Eliza received her PhD in Atmospheric Science from the Max Planck Institute for Chemistry in 2012, and her Bachelor degree with Honours in Antarctic Science from the University of Tasmania in 2008. Her previous research has centered around the use of novel isotopic measurements and modelling approaches in atmospheric and biogeosciences, in particular the nitrogen cycle. Her research at SDSC will focus on data analytics and machine learning approaches in environmental and natural sciences.