ArticleCommunications biology2024
Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Soil-plant-animal properties and microbial reassembly distinguish cattle oral disease prevalence across converted Amazon pasture systems.Environmental geochemistry and health · 2026Article
- Recent advances in the defense mechanisms of arbuscular mycorrhizal fungi in antibiotic-contaminated soil ecosystems.Archives of microbiology · 2026Review
- Engineering Plant-Associated Soil Microbiomes for Sustainable and Climate-Resilient Agriculture: Mechanisms, Technologies, and Applications.Microorganisms · 2026Review
- From chaos to order: cross-kingdom coordination buffers microbial communities against typhoon impacts.BMC microbiology · 2026Article
- Beneath the surface: non-target effects of multiple pesticides on the soil microbiome in organic and conventional agricultural European fields.Environmental science and pollution research international · 2026Article
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Effects of Different Cropping Patterns on Soil Microbial Community and Function in Ningxia Irrigation-Silted Soil.Microorganisms · 2026Article
- Associations between soil microbiomes and carbon stabilization under long-term no-till farming systems in the Argentine Pampas.Scientific reports · 2026Article
- Soil microbiome prediction using traditional machine learning and deep learning models.Scientific reports · 2026Article
- Taxonomic filtering accompanies functional expansion during long-term soil restoration.The ISME journal · 2026Article
- Rhizosphere microbiome dynamics and plant adaptation to abiotic stress in major oilseed crops: a review.Frontiers in plant science · 2026Review
- Synergy between silicon and beneficial microorganisms enhances plant abiotic stress tolerance through rhizosphere restructuring and coordinated defense.Frontiers in plant science · 2026Review
- Application of glyphosate or paraquat before the emergence of maize plants (Frontiers in microbiology · 2026Article
- Orchard management alters citrus root and rhizosphere microbiomes with functional consequences for plant performance.ISME communications · 2026Article
- Metagenomic Tools as Predictive Approaches to Decipher Soil and Rhizosphere Microbial Communities and Their Functions for Sustainable Agriculture.International journal of genomics · 2026Review
- Wheat mycobiome dynamics driven by interseasonal crop-crop transfer and Fusarium head blight.Frontiers in microbiology · 2026Article
- Plant microbiome engineering: from inoculation to genome editing.Frontiers in microbiology · 2026Review
- Ecological Modulation of Soil Microbial Communities by Fertilization Regimes: Insights from Castor Bean Cake, Chemical Fertilizers, and Organic Fertilizer.Microorganisms · 2025Article
- Soil management strategies drive divergent impacts on pathogens and environmental resistomes.Scientific reports · 2025Article
- Functional Specialisation and Diversity Shape the Rhizosphere Microbiota of Cacao and Coffee in the Amazonas Region, Peru.Environmental microbiology reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.