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Cited by 217Plant-Microbe Interactions and ImmunityPlant Disease Management TechniquesSoil Carbon and Nitrogen Dynamics

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Linking the Belowground Microbial Composition, Diversity and Activity to Soilborne Disease Suppression and Growth Promotion of Tomato Amended with Biochar

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Jaiswal, Amit K., Elad, Yigal, Paudel, Indira, Graber, Ellen R., Cytryn, Eddie, Frenkel, Omer·2017·Scientific Reports
DOI 10.1038/srep44382PMC PMC5347032
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Abstract

Biochar, in addition to sequestering carbon, ameliorating soil, and improving plant performance, can impact foliar and soilborne plant diseases. Nevertheless, the mechanisms associated with suppression of soilborne diseases and improved plant performances are not well understood. This study is designed to establish the relationships between biochar-induced changes in rhizosphere microbial community structure, taxonomic and functional diversity, and activity with soilborne disease suppression and enhanced plant performance in a comprehensive fashion. Biochar suppressed Fusarium crown and root-rot of tomato and simultaneously improved tomato plant growth and physiological parameters. Furthermore, biochar reduced Fusarium root colonization and survival in soil, and increased the culturable counts of several biocontrol and plant growth promoting microorganisms. Illumina sequencing analyses of 16S rRNA gene revealed substantial differences in rhizosphere bacterial taxonomical composition between biochar-amended and non-amended treatments. Moreover, biochar amendment caused a significant increase in microbial taxonomic and functional diversity, microbial activities and an overall shift in carbon-source utilization. High microbial taxonomic and functional diversity and activity in the rhizosphere has been previously associated with suppression of diseases caused by soilborne pathogens and with plant growth promotion, and may collectively explain the significant reduction of disease and improvement in plant performance observed in the presence of biochar.

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Figure 1 Effect of eucalyptus (EUC-600) (A) and greenhouse waste (GHW-350) (B) biochar amendments at concentrations of 0, 0.5, 1, and 3% on tomato plant collapse and final FORL disease severity of tomato plants (C). Columns labeled by a different capital letter and small letter are significantly different at P ≤ 0.05 according to Tukey Kramer HSD test within EUC-600 and GHW-350 biochars, respectively. Asterisk denotes the significant difference at P ≤ 0.05 according to Tukey Kramer HSD test between different biochar types at each concentration. Bars represent the standard error.
Figure 1 Effect of eucalyptus (EUC-600) (A) and greenhouse waste (GHW-350) (B) biochar amendments at concentrations of 0, 0.5, 1, and 3% on tomato plant collapse and final FORL disease severity of tomato plants (C). Columns labeled by a different capital letter and small letter are significantly different at P ≤ 0.05 according to Tukey Kramer HSD test within EUC-600 and GHW-350 biochars, respectively. Asterisk denotes the significant difference at P ≤ 0.05 according to Tukey Kramer HSD test between different biochar types at each concentration. Bars represent the standard error.CC BY · PubMed Central
Figure 2 Effect of eucalyptus (EUC-600) and greenhouse waste (GHW-350) biochar amendments at concentrations of 0, 0.5, 1, and 3% on canopy dry weight (A,B); fresh root weight (C,D), without FORL inoculation (A,C) and with inoculation (B,D). Columns labeled by a different capital letter and small letter are significantly different at P ≤ 0.05 according to Tukey Kramer HSD test within EUC-600 and GHW-350 biochars, respectively. Asterisk denotes the significant difference at P ≤ 0.05 according to Tukey Kramer HSD test between biochar types at the same concentration. Bars represent the standard error.
Figure 2 Effect of eucalyptus (EUC-600) and greenhouse waste (GHW-350) biochar amendments at concentrations of 0, 0.5, 1, and 3% on canopy dry weight (A,B); fresh root weight (C,D), without FORL inoculation (A,C) and with inoculation (B,D). Columns labeled by a different capital letter and small letter are significantly different at P ≤ 0.05 according to Tukey Kramer HSD test within EUC-600 and GHW-350 biochars, respectively. Asterisk denotes the significant difference at P ≤ 0.05 according to Tukey Kramer HSD test between biochar types at the same concentration. Bars represent the standard error.CC BY · PubMed Central
Figure 3 Effect of eucalyptus (EUC-600) and greenhouse waste (GHW-350) biochar amendments at concentrations of 0, 0.5, 1, and 3% on photosynthesis rate (A,B); stomatal conductance (C,D) without FORL inoculation (A,C) and with inoculation (B,D). Columns labeled by a different capital letter and small letter are significantly different at P ≤ 0.05 according to Tukey Kramer HSD test within EUC-600 and GHW-350 biochars, respectively. Asterisk denotes the significant difference at P ≤ 0.05 according to Tukey Kramer HSD test between biochar types at the same concentration. Bars represent the standard error.
Figure 3 Effect of eucalyptus (EUC-600) and greenhouse waste (GHW-350) biochar amendments at concentrations of 0, 0.5, 1, and 3% on photosynthesis rate (A,B); stomatal conductance (C,D) without FORL inoculation (A,C) and with inoculation (B,D). Columns labeled by a different capital letter and small letter are significantly different at P ≤ 0.05 according to Tukey Kramer HSD test within EUC-600 and GHW-350 biochars, respectively. Asterisk denotes the significant difference at P ≤ 0.05 according to Tukey Kramer HSD test between biochar types at the same concentration. Bars represent the standard error.CC BY · PubMed Central
Figure 4 Effect of greenhouse waste (GHW-350) biochar on relative abundances of rhizosphere bacterial composition at Phylum level (inner circle) and Order level (outer circle) as identified by using the Illumina sequencing of 16S rRNA gene amplicons.
Figure 4 Effect of greenhouse waste (GHW-350) biochar on relative abundances of rhizosphere bacterial composition at Phylum level (inner circle) and Order level (outer circle) as identified by using the Illumina sequencing of 16S rRNA gene amplicons.CC BY · PubMed Central
Figure 5 Beta diversity analysis to estimate the effect of greenhouse waste (GHW-350) biochar amendments on the rhizosphere microbial community composition and microbial metabolic potential. Taxonomic based NMDS ordination based on the Bray-Curtis similarity index, using most abundant OTUs (at least 5 sequences in total) (A) and Functional based NMDS ordination based on the Bray-Curtis similarity index of 95 carbon substrate utilization patterns obtained with Biolog Microplates (B).
Figure 5 Beta diversity analysis to estimate the effect of greenhouse waste (GHW-350) biochar amendments on the rhizosphere microbial community composition and microbial metabolic potential. Taxonomic based NMDS ordination based on the Bray-Curtis similarity index, using most abundant OTUs (at least 5 sequences in total) (A) and Functional based NMDS ordination based on the Bray-Curtis similarity index of 95 carbon substrate utilization patterns obtained with Biolog Microplates (B).CC BY · PubMed Central
Figure 6 Effect of greenhouse waste (GHW-350) biochar amendments on alpha diversity of rhizosphere microbial community composition and microbial metabolic potential. Taxon based Shannon’s diversity index (A), Taxon based phylotype richness, using OTUs (97% identity) as identified by the Illumina sequencing of 16S rRNA gene amplicons using QIIME (B), Functional Shannon’s diversity index (C), Functional richness, based on 95 carbon substrate utilization patterns obtained with Biolog Microplates (D). Columns labeled by a different capital letter are significantly different at P ≤ 0.05 according to Student’s t-test. Bars represent the standard error.
Figure 6 Effect of greenhouse waste (GHW-350) biochar amendments on alpha diversity of rhizosphere microbial community composition and microbial metabolic potential. Taxon based Shannon’s diversity index (A), Taxon based phylotype richness, using OTUs (97% identity) as identified by the Illumina sequencing of 16S rRNA gene amplicons using QIIME (B), Functional Shannon’s diversity index (C), Functional richness, based on 95 carbon substrate utilization patterns obtained with Biolog Microplates (D). Columns labeled by a different capital letter are significantly different at P ≤ 0.05 according to Student’s t-test. Bars represent the standard error.CC BY · PubMed Central
Figure 7 Effect of greenhouse waste (GHW-350) biochar amendments on rhizosphere microbial community carbon source utilization rates of specific carbon groups, based on 95 carbon substrate utilization patterns obtained with Biolog Microplates. Columns labeled by a different capital letter are significantly different at P ≤ 0.05 according to Student’s t-test. Bars represent the standard error.
Figure 7 Effect of greenhouse waste (GHW-350) biochar amendments on rhizosphere microbial community carbon source utilization rates of specific carbon groups, based on 95 carbon substrate utilization patterns obtained with Biolog Microplates. Columns labeled by a different capital letter are significantly different at P ≤ 0.05 according to Student’s t-test. Bars represent the standard error.CC BY · PubMed Central
Figure 8 Effect of greenhouse waste (GHW-350) biochar amendments on microbial activities estimated by Fluorescein di-acetate activity (FDA; hydrolytic activity) (A), dehydrogenase activity (DHA, oxidative activity) (B), and respiration rates (CO2 emission) (C). Columns labeled by a different capital letter are significantly different at P ≤ 0.05 according to Student’s t-test. Bars represent the standard error.
Figure 8 Effect of greenhouse waste (GHW-350) biochar amendments on microbial activities estimated by Fluorescein di-acetate activity (FDA; hydrolytic activity) (A), dehydrogenase activity (DHA, oxidative activity) (B), and respiration rates (CO2 emission) (C). Columns labeled by a different capital letter are significantly different at P ≤ 0.05 according to Student’s t-test. Bars represent the standard error.CC BY · PubMed Central
Figure 9 A rhizosphere microbiome centric view of the complex interactions between physical, chemical and biological components of the soil-biochar-plant-pathogen system. Biochar-induced changes in rhizosphere microbial structure, diversity and activity may be associated with direct or indirect effect of biochar on soil i) physical aspects (porous structure, soil moisture contents, aggregation and temperature); ii) chemical aspects (nutrients supply and availability, inorganic and organic compounds/toxins, pH, cation exchange capacity (CEC), electrical conductivity (EC), redox potential (Eh)); and iii) biological aspects (root morphology and exudations, interaction and signaling between microbes) of the rhizosphere. The increased microbial taxonomic and functional diversity and activity in the biochar amended rhizosphere is linked with suppression of soilborne diseases and with plant growth promotion.
Figure 9 A rhizosphere microbiome centric view of the complex interactions between physical, chemical and biological components of the soil-biochar-plant-pathogen system. Biochar-induced changes in rhizosphere microbial structure, diversity and activity may be associated with direct or indirect effect of biochar on soil i) physical aspects (porous structure, soil moisture contents, aggregation and temperature); ii) chemical aspects (nutrients supply and availability, inorganic and organic compounds/toxins, pH, cation exchange capacity (CEC), electrical conductivity (EC), redox potential (Eh)); and iii) biological aspects (root morphology and exudations, interaction and signaling between microbes) of the rhizosphere. The increased microbial taxonomic and functional diversity and activity in the biochar amended rhizosphere is linked with suppression of soilborne diseases and with plant growth promotion.CC BY · PubMed Central

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Metadata

Authors
Jaiswal, A. K., Elad, Y., Paudel, I., Graber, E. R., Cytryn, E., & Frenkel, O.
Affiliations
  • Hebrew University of Jerusalem (IL)
  • Agricultural Research Organization (IL)
Corresponding author
Frenkel, Omer
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2017-03-13
Volume
7
Issue
1
Pages
44382-44382
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/srep44382
PMC
PMC5347032
OpenAlex
W2595184516
Cited by
217
References
83
Citations by year
2026: 16 · 2025: 25 · 2024: 22 · 2023: 32 · 2022: 28 · 2021: 34 · 2020: 28 · 2019: 21 · 2018: 9 · 2017: 2
FWCI
16.16
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
University of Illinois at Chicago
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant-Microbe Interactions and Immunity
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Disease Management Techniques
  • Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil Carbon and Nitrogen Dynamics
Keywords
biochar, tomato, Fusarium crown and root rot, rhizosphere microbiome, microbial diversity, plant growth promotion, carbon source utilization, soil amendment
MeSH terms
Bacteria, Charcoal, Fertilizers, Fungi, Fusarium, Plant Diseases, RNA, Ribosomal, 16S, Soil, Soil Microbiology, Solanum lycopersicum, Rhizosphere, Microbial Consortia, High-Throughput Nucleotide Sequencing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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