SREL Reprint #3787

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

Yujie Mo1, Raven Bier2,3, Xiaolin Li4, MelindaDaniels2, Andrew Smith5, Lei Yu1,
and Jinjun Kan2

1Sino-French Engineer School, Beihang University, Beijing, China
2Stroud Water Research Center, Avondale, PA, USA
3Savannah River Ecology Laboratory, University of Georgia, Aiken, SC, USA
4Zibo Vocational Institute, Zibo, Shandong, China
5Rodale Institute, Kutztown, PA, USA

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 30cm depth. Tillage affects soil microbiomes at 0-20cm 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.

SREL Reprint #3787

Mo, Y., R. Bier, X. Li, M. Daniels, A. Smith, L. Yu, and J. Kan. 2024. Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning. Communications Biology 7(1349).

This information was provided by the University of Georgia's Savannah River Ecology Laboratory (srel.uga.edu).