Sino-French Engineer School, Beihang University, Beijing, China.
Stroud Water Research Center, Avondale, PA, USA.
Commun Biol. 2024 Oct 18;7(1):1349. doi: 10.1038/s42003-024-07059-8.
Agricultural practices affect soil microbes which are critical to soil health and sustainable agriculture. To understand prokaryotic and fungal assembly under agricultural practices, we use machine learning-based methods. We show that fertility source is the most pronounced factor for microbial assembly especially for fungi, and its effect decreases with soil depths. Fertility source also shapes microbial co-occurrence patterns revealed by machine learning, leading to fungi-dominated modules sensitive to fertility down to 30 cm depth. Tillage affects soil microbiomes at 0-20 cm depth, enhancing dispersal and stochastic processes but potentially jeopardizing microbial interactions. Cover crop effects are less pronounced and lack depth-dependent patterns. Machine learning reveals that the impact of agricultural practices on microbial communities is multifaceted and highlights the role of fertility source over the soil depth. Machine learning overcomes the linear limitations of traditional methods and offers enhanced insights into the mechanisms underlying microbial assembly and distributions in agriculture soils.
农业实践影响土壤微生物，而土壤微生物对土壤健康和可持续农业至关重要。为了了解农业实践下的原核生物和真菌组合，我们使用基于机器学习的方法。我们表明，肥力来源是微生物组合的最显著因素，特别是对真菌而言，其影响随着土壤深度的增加而减小。肥力来源还通过机器学习揭示了微生物共生模式，导致受肥力影响的真菌主导模块一直延伸到 30cm 深的土层。耕作在 0-20cm 土层中影响土壤微生物组，增强了扩散和随机过程，但可能危及微生物的相互作用。覆盖作物的影响不那么明显，也缺乏深度相关的模式。机器学习揭示了农业实践对微生物群落的影响是多方面的，并强调了肥力来源在土壤深度上的作用。机器学习克服了传统方法的线性限制，为农业土壤中微生物组合和分布的机制提供了更深入的见解。
