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Cited by 447

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

Plant Microbiome Engineering: Expected Benefits for Improved Crop Growth and Resilience

Arif, Inessa, Batool, Maria, Schenk, Peer M.·2020·Trends in Biotechnology

Abstract

Plant-associated microbiomes can boost plant growth or control pathogens. Altering the microbiome by inoculation with a consortium of plant growth-promoting rhizobacteria (PGPR) can enhance plant development and mitigate against pathogens as well as abiotic stresses. Manipulating the plant holobiont by microbiome engineering is an emerging biotechnological strategy to improve crop yields and resilience. Indirect approaches to microbiome engineering include the use of soil amendments or selective substrates, and direct approaches include inoculation with specific probiotic microbes, artificial microbial consortia, and microbiome breeding and transplantation. We highlight why and how microbiome services could be incorporated into traditional agricultural practices and the gaps in knowledge that must be answered before these approaches can be commercialized in field applications.

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Crossref · 10.1016/j.tibtech.2020.04.015

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Metadata

Authors
Arif, I., Batool, M., & Schenk, P. M.
Venue
Trends in Biotechnology
Publisher
Elsevier BV
Published
2020-12
Volume
38
Issue
12
Pages
1385-1396
ISSN
0167-7799 (ISSN-L)
OpenAlex
W3028124889
Cited by
447
References
78
FWCI
6.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Plant Microbiome Engineering: Expected Benefits for Improved Crop Growth and Resilience. https://app.cochat.ai/papers/10.1016/j.tibtech.2020.04.015
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Arif, I., Batool, M., & Schenk, P. M. (2020). Plant Microbiome Engineering: Expected Benefits for Improved Crop Growth and Resilience. Trends in Biotechnology. https://doi.org/10.1016/j.tibtech.2020.04.015

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.