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

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.