Modelling Nitrous Oxide Emissions from Croplands in sub-Saharan Africa Using the CN-Model

Abstract

Nitrous oxide (N₂O), a potent greenhouse gas, contributes significantly to climate change, with agricultural soils being a major source. In sub-Saharan Africa (SSA), increasing fertilization to boost productivity is expected to elevate N₂O emissions, however data scarcity and regional variability challenge accurate predictions. Thus, quantifying these fluxes remains a major challenge for both science and policy. Here, we present a process-based modelling study of N2O emissions using the CN-model, recently introduced as a mechanistic tool for simulating carbon-nitrogen coupling in terrestrial ecosystems [1], extended here for soil nitrogen transformations and N2O emissions. We apply the CN-model to an experimental maize cropping site in Eldoret, Kenya, as part of the N₂O-SSA project, which investigates greenhouse gas emissions in sub-Saharan African agroecosystems. The site in Eldoret (Kenya), features two annual rainfed maize and potato cropping seasons, with varied nitrogen fertilization regimes (0, 50, 100, and 125 kg N ha-¹ yr-¹). Our analysis covers the 2024 growing period (April 2024-January 2025), during which high-frequency flux measurements of N₂O, CH₄, and CO₂ were collected. The CN-model simulates microbial nitrification and denitrification pathways, soil moisture interactions, and fertilization impacts, providing process-level insights into observed N₂O flux dynamics. Model outputs are evaluated against measured greenhouse gas fluxes to assess predictive performance and to explore the effects of nitrogen input levels, precipitation patterns, and cropping cycles. Simulations under both current and future climate scenarios are used to assess potential trajectories under alternative management practices. This modeling framework is critical for improving nitrogen budgeting by enabling more precise and efficient fertilizer use, reducing unnecessary nitrogen losses, and supporting climate-smart agricultural practices. Preliminary results show that the CN-model captures both background and event-driven emissions effectively, highlighting the sensitivity of N₂O emissions to rainfall timing and nitrogen inputs. This work illustrates the value of combining mechanistic modelling with targeted field observations in sub-Saharan African smallholder systems to better constrain N₂O budgets and inform mitigation strategies under a changing climate.ACKNOWLEDGEMENTThis research was generously supported by the Swiss National Science Foundation (SNSF) under grant number 200021_207348.REFERENCE1. Stocker, B. D. & Prentice, I. C. CN-model: A dynamic model for the coupled carbon and nitrogen cycles in terrestrial ecosystems. bioRxiv, 2024.2004.2025.591063 (2024). https://doi.org/10.1101/2024.04.25.591063