评估土壤质量指数使用三个方法来评估土壤肥力
Hiba Chaudhry1, Hiteshkumar Bhogilal Vasava1, Songchao Chen2
1School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
可见近红外 (VIS-NIR) 光谱为评估土壤健康指标提供了一种快速,具有成本效益的方法. 使用这种技术直接预测土壤质量指数 (SQI) 证明比间接预测方法更准确.
科学领域:
- 农业科学 农业科学
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
背景情况:
- 土壤健康对全球粮食安全至关重要,影响作物产量和质量.
- 土壤质量指数 (SQI) 整合了多种土壤属性,但传统的实验室分析是昂贵和耗时的.
- 开发快速,非破坏性的土壤评估方法对于实际的土壤管理至关重要.
研究的目的:
- 评估可见近红外 (VIS-NIR) 光谱学在预测关键土壤肥力指标方面的有效性.
- 通过使用光谱数据,比较计算和预测土壤质量指数 (SQI) 的不同方法.
- 为传统的土壤分析建立一个具有成本效益和环保的替代方案.
主要方法:
- 专注于预测七个土壤指标:pH,有机物 (OM), (K), (Ca), (Mg),可用的 (P) 和总 (TN).
- 使用可见近红外 (VIS-NIR) 光谱学与立体模型相结合,用于属性预测 (R2 = 0.35-0.93).
- 我们比较了三种SQI计算方法:测量 (SQI_m),预测 (SQI_p) 和直接预测 (SQI_dp).
主要成果:
- 使用Vis-NIR光谱学准确地预测了使用立体模型的单个土壤指标.
- 直接预测SQI (SQI_dp) 实现了高精度 (R2 = 0.90).
- 使用光谱数据预测SQI (SQI_p) 与直接预测相比,显示精度较低 (R2 = 0.23).
结论:
- 视NIR光谱是一种可行的工具,用于快速,非破坏性地预测土壤特性和SQI.
- 使用光谱数据直接预测SQI提供了比先预测个体性质更准确的方法.
- 这种光谱方法为土壤健康监测提供了具有成本效益和可持续性的替代方案.
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