在西印度的机器学习下,在几个气候场景下,在农田中模拟土壤有机碳
Amanullah Adeel1, Mustafa Hasani2, A S Jadhav3
1Department of Environmental Science, Shivaji University, Kolhapur, Maharashtra, 416004, India. amanadeel2020@gmail.com.
Scientific reports
|January 17, 2026
概括
未来的气候变化场景 (SSPs) 显著威胁到农田土壤有机碳 (SOC) 水平. 尽管面临这些挑战,保护农业实践对于促进SOC封存和确保土壤健康至关重要.
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:
- 农田土壤有机碳 (SOC) 对土壤健康和农业生产力至关重要.
- 全球气候变化和共享的社会经济路径 (SSP) 为维持SOC水平带来了复杂的挑战.
- 了解气候,土地管理和SOC之间的相互作用对于可持续农业至关重要.
研究的目的:
- 调查SSP对农田土壤有机碳 (SOC) 趋势的影响.
- 分析历史气候,土壤特性和土地利用对SOC动态的影响.
- 在不同的气候场景下预测未来的SOC变化,并评估缓解策略.
主要方法:
- 使用机器学习模型 (随机森林,XGBoost,SVR) 来进行SOC预测.
- 通过谷歌地球引擎 (GEE) 和地理信息系统 (GIS) 处理的远程传感数据用于空间分析.
- 经过验证的模型预测,使用来自当地认证的土壤测试实验室的数据.
主要成果:
- 极端梯度增强 (XGB) 模型显示了高精度 (R2=0.998,RMSE=0.225 g/kg).
- 高SSP,特别是SSP5-8.5,预计到2100年平均SOC将大幅下降 (从46.4g/kg降至24.2g/kg).
- 自2018年以来,保护性农业 (不耕种,堆肥,堆肥) 已对SOC产生积极影响,尽管农田面积减少.
结论:
- 在高SSP下的气候变化对农田SOC构成重大风险.
- 保护农业技术有效地提高了SOC封存和土壤健康.
- 研究结果为气候适应性管理提供了框架,以促进可持续农业和土壤弹性.
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