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  Eco-evolutionary agriculture: host-pathogen dynamics in crop rotations

Bargués-Ribera, M., & Gokhale, C. S. (2020). Eco-evolutionary agriculture: host-pathogen dynamics in crop rotations. PLoS Computational Biology, 16(1): e1007546. doi:10.1371/journal. pcbi.1007546.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-7227-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-0A98-4
Genre: Journal Article

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journal.pcbi.1007546.pdf (Publisher version), 2MB
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 Creators:
Bargués-Ribera, Maria1, Author              
Gokhale, Chaitanya S.1, Author              
Affiliations:
1Research Group Theoretical Models of Eco-Evolutionary Dynamics, Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_2355692              

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Free keywords: agriculture, crop rotations, eco-evolutionary dynamics, plant-pathogen coevolution
 Abstract: Since its origins, thousands of years ago, agriculture has been challenged by the presence of evolving plant pathogens. In response, current practices have started relying on computational tools to design efficient prospective planning, but further efforts for multi-criteria assessment are needed. Here, we present a methodology for developing cultivation strategies optimal for control or eradication of pathogens. This approach can integrate both, traditionally used criteria in crop rotations and the analysis of host-pathogen coevolution systems where hosts are artificially selected. Our analysis shows that prospective planning can maximise cash yield in the long run by investing consecutively in soil quality during initial sea-sons. Importantly, rational application of crop rotation patterns can minimise yield loss in infected fields, despite the evolution of pathogen virulence. Our results provide strategies for optimal resource investment for increased food production and lead to further insights into minimisation of pesticide use in a society demanding efficient agriculture.

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Language(s): eng - English
 Dates: 2019-08-022019-11-142020-01-162020-01
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1371/journal. pcbi.1007546
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 16 (1) Sequence Number: e1007546 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1