Local Network Properties of Soil and Rhizosphere Microbial Communities in Potato Plantations Treated with a Biological Product Are Important Predictors of Crop Yield.
Agricultural Inoculants
/ metabolism
Agriculture
/ methods
Bacteria
/ genetics
Biological Products
/ pharmacology
Crops, Agricultural
Fungi
/ genetics
High-Throughput Nucleotide Sequencing
Microbiota
/ genetics
RNA, Ribosomal, 16S
Rhizosphere
Soil
/ chemistry
Soil Microbiology
Solanum tuberosum
/ microbiology
United States
agricultural biological
machine learning
soil microbiome
yield prediction
Journal
mSphere
ISSN: 2379-5042
Titre abrégé: mSphere
Pays: United States
ID NLM: 101674533
Informations de publication
Date de publication:
25 08 2021
25 08 2021
Historique:
pubmed:
12
8
2021
medline:
14
1
2022
entrez:
11
8
2021
Statut:
ppublish
Résumé
Understanding the effectiveness and potential mechanism of action of agricultural biological products under different soil profiles and crops will allow more precise product recommendations based on local conditions and will ultimately result in increased crop yield. This study aimed to use bulk soil and rhizosphere microbial composition and structure to evaluate the potential effect of a Bacillus amyloliquefaciens inoculant (strain QST713) on potatoes and to explore its relationship with crop yield. We implemented next-generation sequencing (NGS) and bioinformatics approaches to assess the bacterial and fungal biodiversity in 185 soil samples, distributed over four different time points-from planting to harvest-from three different geographical locations in the United States. In addition to location and sampling time (which includes the difference between bulk soil and rhizosphere) as the main variables defining the microbiome composition, the microbial inoculant applied as a treatment also had a small but significant effect in fungal communities and a marginally significant effect in bacterial communities. However, treatment preserved the native communities without causing a detectable long-lasting effect on the alpha- and beta-diversity patterns after harvest. Using information about the application of the microbial inoculant and considering microbiome composition and structure data, we were able to train a Random Forest model to estimate if a bulk soil or rhizosphere sample came from a low- or high-yield block with relatively high accuracy (84.6%), concluding that the structure of fungal communities gives us more information as an estimator of potato yield than the structure of bacterial communities.
Identifiants
pubmed: 34378980
doi: 10.1128/mSphere.00130-21
pmc: PMC8386434
doi:
Substances chimiques
Biological Products
0
RNA, Ribosomal, 16S
0
Soil
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0013021Références
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