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机器学习算法实现了土壤固体测量预测及其驱动因素的识别,在中国东部南北截面的密集农业生态系统中实现了这一目标

Xintong Xu¹, Chao Xiao², Yubing Dong³

¹Jiangsu Key Laboratory of Low Carbon Agriculture and GHGs Mitigation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China; Department of Agricultural Sciences, Natural Resources Institute Finland (LUKE), 00790 Helsinki, Finland.

The Science of the total environment

|October 1, 2023

概括

机器学习模型准确地预测土壤的营养物质 (碳,,) 和矿化. 极端梯度增强和梯度增强决策树在各种农田和气候中表现出高性能.

科学领域:

土壤科学土壤科学
生态生态学生态生态学
农业科学农业科学
数据科学数据科学数据科学

背景情况:

土壤中的碳 (C), (N) 和 (P) 对于生态系统的健康至关重要,但它们的直接测量是昂贵且耗时的.
准确的土壤营养固体测量对于有效的农业和自然生态系统管理至关重要.
开发用于土壤营养固态度和微生物动态的预测模型具有重要意义.

研究的目的:

为了比较四个机器学习模型 (支持向量机器,随机森林,极端梯度增强,梯度增强决策树) 的性能,用于预测土壤C:N:P固态度.
评估模型预测净N矿化率的能力.
评估这些模型在不同农业用地类型和气候区的适用性.

主要方法:

实施和比较四种不同的机器学习算法:支持向量机 (SVM),随机森林 (RF),极端梯度增强 (XGBoost) 和梯度增强决策树 (GBDT).
使用R2 (R2),根均平方误差 (RMSE) 和预测与偏差比率 (RPD) 等指标评估模型性能.
特性重要性分析,以确定影响不同土地使用类型和环境条件预测的关键土壤特性.

主要成果:

极端梯度增强 (XGBoost) 和梯度增强决策树 (GBDT) 模型在预测土壤C:N:P固态度方面表现出卓越的性能,准确度和稳定性高 (XGBoost的平均R2>0.81,GBDT的平均R2>0.77).
与田相比,机器学习模型在蔬菜田中实现了显著更高的预测准确度 (平均改善率为42.9%),C:N比率有所变化.
预测性能在寒冷气候地区比在温暖温带和亚热带地区更好. 关键预测指标因土地使用而异,大米田的电导率和总N很重要,蔬菜田的降雨量和总P很重要.

结论:

机器学习,特别是XGBoost和GBDT,提供了一种可靠和准确的方法来预测土壤C:N:P石化度和净N矿化率.
模型性能受土地使用类型和气候区域的影响,需要针对不同农业环境量身定制的方法.
这些发现支持推用于精确农业管理的机器学习模型,从而实现有效的营养评估和优化.

关键词:

气候区气候区的气候区.评估特征的重要性评估特征的重要性.机器学习是机器学习.营养物质的石化测量.预测业绩的预测.

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