Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Nitrous Oxide Emissions from Smallholders’ Cropping Systems in Sub-Saharan Africa

Domain:

agricultureclimate

Record type:

paper
Creator:
ShaColJosMil
Publisher:
WILEY
Host:
Increased concentration of atmospheric nitrous oxide (N2O), a potent greenhouse gas (GHG), is of great concern due to its impact on ozone layer depletion leading to climate change. Ozone layer depletion allows penetration of ultraviolet radiations, which are hazardous to human health. Climate change culminates in reduced food productivity. Limited empirical studies have been conducted in Sub-Saharan Africa (SSA) to quantify and understand the dynamics of soil N2O fluxes from smallholder cropping systems. The available literature on soil N2O fluxes in SSA is limited; hence, there is a pressing need to consolidate it to ease mitigation targeting and policy formulation initiatives. We reviewed the state of N2O emissions from selected cropping systems, drivers that significantly influence N2O emissions, and probable soil N2O emissions mitigation options from 30 studies in SSA cropping systems have been elucidated here. The review outcome indicates that coffee, tea, maize, and vegetables emit N2O ranging from 1 to 1.9, 0.4 to 3.9, 0.1 to 4.26, and 48 to 113.4 kg N2O-N ha-1 yr−1, respectively. The yield-scaled and N2O emissions factors ranged between 0.08 and 67 g N2O-N kg−1 and 0.01 and 4.1%, respectively, across cropping systems. Soil characteristics, farm management practices, and climatic and environmental conditions were significant drivers influencing N2O emissions across SSA cropping systems. We found that site-specific soil N2O emissions mitigation measures are required due to high variations in N2O drivers across SSA. We conclude that appropriate fertilizer and organic input management combined with improved soil management practices are potential approaches in N2O emissions mitigation in SSA. We recommend that (i) while formulating soil N2O emissions mitigation approaches, in SSA, policymakers should consider site-specific targeting approaches, and (ii) more empirical studies need to be conducted in diverse agroecological zones of SSA to qualify various mitigation options on N2O emissions, yield-scaled N2O emissions, and N2O emission factors which are essential in improving national and regional GHG inventories.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

Similar

Nitrous Oxide Emissions Across Sub‐Saharan Africa: Meta‐Analysis and Data‐Driven ModelingNitrous oxide emissions across Sub-Saharan Africa: meta-analysis and data-driven modellingNitrous oxide emissions from African GrasslandsNitrous oxide emissions from African CroplandsNitrous oxide emissions from African Forests and Plantationspzywczuk/n2o-ssa-models: Nitrous oxide emissions across Sub-Saharan Africa: meta-analysis and data-driven modelling

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)

Nitrous oxide emissions across Sub-Saharan Africa: meta-analysis and data-driven modelling

This comprehensive dataset collection presents nitrous oxide (N2O) flux measurements from 27 sites a

Nitrous oxide emissions from African Grasslands

This comprehensive dataset collection presents nitrous oxide (N2O) flux measurements from 27 sites a

Nitrous oxide emissions from African Croplands

This comprehensive dataset collection presents nitrous oxide (N2O) flux measurements from 27 sites a

Nitrous oxide emissions from African Forests and Plantations

This comprehensive dataset collection presents nitrous oxide (N2O) flux measurements from 27 sites a

pzywczuk/n2o-ssa-models: Nitrous oxide emissions across Sub-Saharan Africa: meta-analysis and data-driven modelling

This repository contains three machine learning models for predicting N2O emissions from di