在印度-河平原的综合营养管理下,评估-小麦系统中的土壤质量框架
Tridiv Ghosh1, Bappa Das2, Debashis Chakraborty1,3
1Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, 110 012, India.
Scientific reports
|October 28, 2025
概括
使用主要成分分析 (PCA) 选择关键的土壤指标可以有效预测作物产量. 这种方法通过确定提高生产力的关键土壤健康因素来帮助可持续农业.
科学领域:
- 农业科学 农业科学
- 土壤科学 土壤科学
- 数据科学数据科学数据科学
背景情况:
- 在密集农业下维持土壤质量对于全球粮食安全至关重要.
- 选择土壤指标的最低数据集 (MDS) 对于准确地评估土壤质量至关重要.
- 由于土壤的复杂性,在理想的土壤指标上没有普遍的共识.
研究的目的:
- 评估机器学习和主要组件分析 (PCA) 以选择土壤指标的MDS.
- 开发一种与作物产量相关的土壤质量分析和索引 (SQI) 计算的新框架.
- 为了比较基于PCA和基于的权重方法来衡量指标的重要性.
主要方法:
- 应用机器学习功能选择技术与PCA一起用于MDS识别.
- 使用PCA和方法计算指标权重.
- 开发了一个土壤质量指数 (SQI) 框架,与大米和小麦产量相关联.
主要成果:
- 基于PCA的MDS选择和权重显示出与作物产量有很强的相关性 (R2高达0.93).
- 性能与PCA可比,使用基于的权重.
- 综合营养管理显著改善了土壤特性,提高了生产力.
结论:
- 基于PCA的MDS选择和权衡为土壤质量评估提供了有效的框架.
- 这种方法为生产系统提供了更好的表现,以最大限度地提高作物产量.
- 拟议的框架可适应各种种植系统,以实现可持续农业.
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