Deel Heather L, Manter Daniel K, Moore Jennifer M
United States Department of Agriculture, Agricultural Research Service, Soil Management and Sugarbeet Research Unit, Fort Collins, Colorado, United States of America.
United States Department of Agriculture, Agricultural Research Service, Forage Seed and Cereal Research Unit, Corvallis, Oregon, United States of America.
PLoS One. 2024 Dec 2;19(12):e0314072. doi: 10.1371/journal.pone.0314072. eCollection 2024.
Soil health relies on the actions and interactions of an abundant and diverse biological community. Current soil health assessments rely heavily on a suite of soil biological, chemical, and physical indicators, often excluding molecular information. Soil health is critical for sustainable agricultural production, and a comprehensive understanding of how microbial communities provide ecosystem services can help guide management practices. To explore the role of microbial function in soil health, 536 soil samples were collected from 26 U.S. states, representing 52 different crops and grazing lands, and analyzed for various soil health indicators. The bacterial functional profile was characterized using 16S ribosomal RNA gene sequencing paired with PICRUSt2 to predict metagenome functions. Functional data were used as predictors in eXtreme Gradient Boosting (XGBoost), a powerful machine learning algorithm, and enzymes important to soil health indicators were compiled into a Molecular Index of Soil Health (MISH). The overall MISH score significantly correlated with non-molecular measures of soil health and management practice adoption. Additionally, several new enzymes were identified as potential targets to better understand microbial mediation of soil health. This low-cost, DNA-based approach to measuring soil health is robust and generalizable across climates.
土壤健康依赖于丰富多样的生物群落的活动和相互作用。当前的土壤健康评估严重依赖于一系列土壤生物学、化学和物理指标，通常不包括分子信息。土壤健康对于可持续农业生产至关重要，全面了解微生物群落如何提供生态系统服务有助于指导管理实践。为了探究微生物功能在土壤健康中的作用，从美国26个州采集了536份土壤样本，这些样本代表了52种不同的作物和放牧地，并对各种土壤健康指标进行了分析。利用16S核糖体RNA基因测序结合PICRUSt2对细菌功能谱进行表征，以预测宏基因组功能。功能数据被用作极端梯度提升（XGBoost）这一强大的机器学习算法中的预测因子，对土壤健康指标重要的酶被汇编成土壤健康分子指数（MISH）。MISH总分与土壤健康的非分子测量方法以及管理措施的采用显著相关。此外，还鉴定出几种新的酶作为潜在靶点，以更好地理解微生物对土壤健康的调节作用。这种基于DNA的低成本土壤健康测量方法具有稳健性，并且在不同气候条件下都具有通用性。
