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Title: Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning

Journal Article · · Communications Biology

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.

Research Organization:
Savannah River Ecology Laboratory (SREL), Aiken, SC (United States)
Sponsoring Organization:
USDOE Office of Environmental Management (EM)
Grant/Contract Number:
EM0005228
OSTI ID:
2477930
Journal Information:
Communications Biology, Journal Name: Communications Biology Journal Issue: 1 Vol. 7; ISSN 2399-3642
Publisher:
Springer NatureCopyright Statement
Country of Publication:
United States
Language:
English

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