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

Abstract

Sub-Saharan Africa continues to grapple with food insecurity due to low crop yields. While an increase in synthetic fertilisers could potentially increase agricultural productivity in the region, it would lead to an increase in emissions of nitrous oxide (N2O). Moreover, in this region, the lack of quantification of parameters and documentation of the processes relevant to N2O emissions have hampered the adoption of climate-smart agricultural practices and advancement of N2O inventories. This study aims to conduct the first online measurements of N2O fluxes and isotopic composition from agricultural soils in Uasin Gishu County, Kenya, using the TREX-QCLAS system: quantum cascade laser absorption spectrometer (QCLAS) coupled to a preconcentration unit- TRace gas EXtractor (TREX). The isotopic measurements obtained will be useful in the inference of N2O production and consumption rates for different pathways and will improve understanding of the key drivers of variability in tropical cropland N2O fluxes. Further, a collation and analysis of available N2O flux and isotope data along with campaign measurements and data science approaches will enhance the potential to predict future emissions and promote the development of targeted mitigation strategies.