Nitrous Oxide Emissions Across Sub‐Saharan Africa: Meta‐Analysis and Data‐Driven Modeling

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Abstract
Food security and avoiding land use change in Sub‐Saharan Africa (SSA) requires increasing agricultural productivity, necessitating greater fertilizer use. This may increase soil nitrous oxide (O) emissions, a potent greenhouse gas. This study used Machine learning (ML) models to predict O emissions under future climatic and fertilizer scenarios across SSA. Three models were trained (Random Forest (RF), XGBoost (XGB), and feedforward neural networks (FNN)) on existing forest, grassland, and cropland O measurements. The analysis identified the main drivers influencing O emissions: temperature, soil moisture, rainfall, and fertilizer (cropland). SSA O emissions totaled 253–538 Gg N (1 Gg = g), with forests contributing 119–342 Gg N , grasslands 70–132 Gg N , and croplands 63–64 Gg N . Ranges reflect the full uncertainty across all models. Unexpectedly, severe climate change (SSP5‐8.5 scenario) may decrease total O emissions by 4%–37% across SSA, possibly due to drier soils. However, when climate change was combined with tripled fertilizer use (0–63 to 0–189 kg N ), the models predicted a wide range of cropland emission increases of 6%–23% (XGB–RF), with FNN predicting 139% from baseline projections. These findings highlight that while climate change may reduce overall O emissions from forests and grasslands, agricultural intensification will likely become an increasingly significant emission source. To improve prediction accuracy, more comprehensive O monitoring across SSA is needed. This work underscores the need for international investment in monitoring infrastructure and data repositories to guide sustainable agricultural development and SSA climate policy.
Funding Information
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (200021_207348)
  • Universiteit Gent

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