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<p>Agriculture is a major contributor to non-CO<sub>2</sub> greenhouse gas emissions, mainly methane (CH<sub>4</sub>) and nitrous oxide (N<sub>2</sub>O), whose global warming potentials (GWP) are 28 and 265 times higher than that of carbon dioxide (CO<sub>2</sub>), respectively. This study predicts CH<sub>4</sub> and N<sub>2</sub>O emissions from agriculture in Ethiopia (ETH), Chad (TCD), and Nigeria (NGA) using time series data from 1993 to 2022 sourced from FAOSTAT and the World Bank, with projections extending to 2050. Agricultural-environmental, and economic indicators were incorporated into the analysis. A stacking ensemble model combining Random Forest, XGBoost, CatBoost, and Gaussian Process Regression was applied, along with time series models (Prophet and LSTM) for future forecasting. The stacking model achieved accuracies of 0.986, 0.982, and 0.945 for ETH, TCD, and NGA, respectively. SHAP analysis showed that agricultural land area and manure nitrogen content were the main factors influencing emissions across the three nations. Prophet forecasts indicated a steady rise in emissions through 2050, likely driven by livestock intensification and land-use expansion. These findings highlight machine learning’s effectiveness in improving agricultural emission estimates and guiding data-driven mitigation strategies, offering insights into advancing climate-smart, low-emission agriculture in Africa.</p>

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Forecasting agricultural methane and nitrous oxide emissions in Ethiopia Chad and Nigeria using machine learning models for advancing climate smart agriculture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Abdullahi Mohamud Adam,
  • Omar Aweis Ali,
  • Sidnei Eduardo Lima‑Junior,
  • Nur Dahir Warsame,
  • Mahesh Prasad Awasthi

摘要

Agriculture is a major contributor to non-CO2 greenhouse gas emissions, mainly methane (CH4) and nitrous oxide (N2O), whose global warming potentials (GWP) are 28 and 265 times higher than that of carbon dioxide (CO2), respectively. This study predicts CH4 and N2O emissions from agriculture in Ethiopia (ETH), Chad (TCD), and Nigeria (NGA) using time series data from 1993 to 2022 sourced from FAOSTAT and the World Bank, with projections extending to 2050. Agricultural-environmental, and economic indicators were incorporated into the analysis. A stacking ensemble model combining Random Forest, XGBoost, CatBoost, and Gaussian Process Regression was applied, along with time series models (Prophet and LSTM) for future forecasting. The stacking model achieved accuracies of 0.986, 0.982, and 0.945 for ETH, TCD, and NGA, respectively. SHAP analysis showed that agricultural land area and manure nitrogen content were the main factors influencing emissions across the three nations. Prophet forecasts indicated a steady rise in emissions through 2050, likely driven by livestock intensification and land-use expansion. These findings highlight machine learning’s effectiveness in improving agricultural emission estimates and guiding data-driven mitigation strategies, offering insights into advancing climate-smart, low-emission agriculture in Africa.