机器学习分析了影响非洲粮食安全联系的温室气体来源
Adusei Bofa1, Temesgen Zewotir2
1School of Business and Applied Sciences, Garden City University College, Kumasi, Ghana. bofa.adusei@gcuc.edu.gh.
Scientific reports
|August 5, 2025
概括
确定影响非洲粮食安全的温室气体驱动因素至关重要. 机器学习和PCA揭示了食品消费 (CH4),作物残留物燃烧 (N2O) 和食品运输 (N2O) 是关键的排放来源.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 气候科学 气候科学
背景情况:
- 非洲的粮食安全受到气候变化驱动因素的重大影响.
- 确定温室气体排放的确切来源对于有效的减排战略至关重要.
研究的目的:
- 确定影响非洲粮食安全联系的关键温室气体排放来源.
- 为了补充机器学习的单变量重要性,主要组件分析 (PCA) 以全面了解CO2,CH4和N2O的影响.
主要方法:
- 利用粮农组织2000-2021年非洲的数据.
- 采用机器学习来单变量重要性和PCA来导出主要组件.
- 分析的主要变量:家庭食品消费 (CH4),燃烧作物残留物 (N2O) 和食品运输 (N2O).
主要成果:
- 机器学习确定了家庭食品消费 (CH4),作物残留物燃烧 (N2O) 和食品运输 (N2O) 作为关键排放贡献者.
- PCA揭示了关键的主要组成部分:生物质燃烧排放因子,土地管理排放因子和食品供应链排放因子.
- 该研究通过PCA分析了86个变量.
结论:
- 专注于已识别的变量和因素可以指导利益相关者制定有效的实践.
- 建议促进可持续的作物残留物管理和减少收获后损失.
- 未来的研究将探索对影响粮食安全的温室气体来源识别的空间影响.
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