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通过机器学习,最大限度地提高中国大米系统的碳汇功能

Jin Wang¹, Qingguan Wu¹, Yong He²

¹Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang Province 310058, China; Institute of Zhejiang University-Quzhou, 99 Zheda Road, Quzhou, Zhejiang Province 324000, China.

The Science of the total environment

|November 19, 2023

概括

在中国的水系统中优化农业管理实践 (AMP) 显著提高了土壤有机碳捕获率 (SOCSR). 完全返回残留物和不耕作是促进农业碳汇的关键策略.

科学领域:

农业科学农业科学
土壤科学土壤科学
环境科学环境科学

背景情况:

低碳农业对于解决全球碳危机至关重要.
米系统在碳捕获中发挥着至关重要的作用.
准确评估农业管理实践 (AMP) 对土壤有机碳捕获率 (SOCSR) 的影响至关重要.

研究的目的:

开发一个数据驱动的模型,用于预测中国大米系统中的SOCSR.
确定提高SOCSR的关键AMP.
提供最佳策略,最大限度地提高米系统的碳汇功能.

主要方法:

编制了一个全面的SOCSR数据库,来自中国143个出版物的1388个实验数据组.
评估了各种回归,机器学习和深度学习模型.
开发并验证了一种用于SOCSR预测的随机森林模型.

主要成果:

随机森林模型获得了高精度 (R2 = 0.71,RMSE = 0.53 Mg ha-1).
无机N肥,无机K肥,有机肥,耕作和废弃物管理被确定为关键的AMP.
为了最大限度地提高SOCSR,确定了最佳的肥料应用率和残留物管理实践 (完全返回残留物,不耕作).

结论:

开发的模型可以在区域范围内对米系统进行快速和精确的SOCSR评估.
最佳的AMP可以显著提高SOCSR,为中国的低碳农业目标做出贡献.
这项研究为评估米系统的SOCSR和大米生产的碳足迹提供了一种新方法.

关键词:

农业管理实践农业管理实践碳水槽的碳排放方式机器学习机器学习麦子系统的麦子系统.区域范围的评估评估.土壤有机碳土壤有机碳

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