A data-driven topsoil classification framework to support soil health assessment in Minnesota

IF 1.3 Q3 AGRONOMY
Hava K. Blair, Jessica L. Gutknecht, Anna M. Cates, Ann Marcelle Lewandowski, Nicolas Adam Jelinski
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Abstract

Soil health assessments aim to quantify soil health status using indicators linked to ecosystem services such as yield, nutrient cycling, water cycling, or carbon storage. Many indicators are related to soil biological processes, which can be challenging to interpret because they are sensitive not only to management, but also to nonmanagement variables such as soil inherent properties, topography, and climate. Existing studies address this challenge by grouping similar soils by taxonomy, geography, or a combination of these and other variables for soil health assessment. We investigated whether grouping soils based on multiple quantitative topsoil properties could be an alternative to taxonomic or geographic groups. We used an unsupervised classification algorithm, k-means, to cluster publicly available soil and climate data for Minnesota. Clustering into eight conceptual groups (“clusters”) based on 10 topsoil properties was determined to be the optimal algorithm output. We evaluated the ability of our soil clusters and other grouping methods to explain variance in eight soil health indicators. We found the combination of Major Land Resource Area (MLRA) and soil cluster performed best, explaining as much or more variance than other groupings for five of the eight indicators. The clusters distinguish zones of topsoil variation at the field scale, and MLRAs account for broader scale variation in climate and other landscape factors. The approach we describe is flexible and could be applied at different locations and scales to produce conceptual soil groups and associated maps to support soil health test sampling and interpretation at the field scale.

Abstract Image

支持明尼苏达州土壤健康评估的数据驱动表土分类框架
土壤健康评估旨在利用与生态系统服务(如产量、养分循环、水循环或碳储存)相关的指标来量化土壤健康状况。许多指标都与土壤生物过程有关,解释这些指标可能具有挑战性,因为它们不仅对管理敏感,而且对土壤固有特性、地形和气候等非管理变量也很敏感。为了应对这一挑战,现有的研究通过分类学、地理学或这些变量和其他变量的组合对相似的土壤进行分组,以进行土壤健康评估。我们研究了根据表土的多种定量特性对土壤进行分组是否可以替代分类学或地理分组。我们使用无监督分类算法 k-means 对明尼苏达州公开可用的土壤和气候数据进行聚类。根据 10 种表土特性将其聚类为 8 个概念组("簇")被确定为最佳算法输出。我们评估了土壤聚类和其他分组方法解释八个土壤健康指标差异的能力。我们发现,主要土地资源区(MLRA)和土壤组群的组合表现最佳,在八个指标中,有五个指标的方差解释能力与其他分组方法相当或更强。群组区分了田间尺度的表土变化区域,而主要土地资源区则考虑了更大尺度的气候和其他景观因素的变化。我们所描述的方法非常灵活,可应用于不同地点和不同尺度,以产生概念性土壤组群和相关地图,从而为田间尺度的土壤健康测试取样和解释提供支持。
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来源期刊
Agrosystems, Geosciences & Environment
Agrosystems, Geosciences & Environment Agricultural and Biological Sciences-Agricultural and Biological Sciences (miscellaneous)
CiteScore
2.60
自引率
0.00%
发文量
80
审稿时长
24 weeks
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