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Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming.

TLDR
It is found that about 10% of variation in microbial communities was explained by the tested cropping practices, which presents the basis towards developing microbiota management strategies for smart farming.
Abstract
Harnessing beneficial microbes presents a promising strategy to optimize plant growth and agricultural sustainability. Little is known to which extent and how specifically soil and plant microbiomes can be manipulated through different cropping practices. Here, we investigated soil and wheat root microbial communities in a cropping system experiment consisting of conventional and organic managements, both with different tillage intensities. While microbial richness was marginally affected, we found pronounced cropping effects on community composition, which were specific for the respective microbiomes. Soil bacterial communities were primarily structured by tillage, whereas soil fungal communities responded mainly to management type with additional effects by tillage. In roots, management type was also the driving factor for bacteria but not for fungi, which were generally determined by changes in tillage intensity. To quantify an “effect size” for microbiota manipulation, we found that about 10% of variation in microbial communities was explained by the tested cropping practices. Cropping sensitive microbes were taxonomically diverse, and they responded in guilds of taxa to the specific practices. These microbes also included frequent community members or members co-occurring with many other microbes in the community, suggesting that cropping practices may allow manipulation of influential community members. Understanding the abundance patterns of cropping sensitive microbes presents the basis towards developing microbiota management strategies for smart farming. For future targeted microbiota management—e.g., to foster certain microbes with specific agricultural practices—a next step will be to identify the functional traits of the cropping sensitive microbes.

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Keystone taxa as drivers of microbiome structure and functioning

TL;DR: A definition of keystone taxa in microbial ecology is proposed and over 200 microbial keystoneTaxa that have been identified in soil, plant and marine ecosystems, as well as in the human microbiome are summarized.
Journal ArticleDOI

Plant–microbiome interactions: from community assembly to plant health

TL;DR: This Review explores how plant microbiome research has unravelled the complex network of genetic, biochemical, physical and metabolic interactions among the plant, the associated microbial communities and the environment and how those interactions shape the assembly of plant-associated microbiomes and modulate their beneficial traits.
Journal ArticleDOI

A review on the plant microbiome: Ecology, functions, and emerging trends in microbial application

TL;DR: In this paper, the importance and functionalities of the bacterial plant microbiome and discusses challenges and concepts in regard to the application of plantassociated bacteria. But, the authors do not consider the impact of farming practices and genotype on the microbial community.
Journal ArticleDOI

Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots

TL;DR: It is demonstrated that agricultural intensification reduces network complexity and the abundance of keystone taxa in the root microbiome, and this is the first study to report mycorrhizal keystoneTaxa for agroecosystems.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
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