Percolation-based risk index for pathogen invasion: application to soilborne disease in propagation systems

S. Poggi, F. M. Neri, V. Deytieux, Anne Bates, Wilfred Otten, Christopher A. Gilligan, Douglas J. Bailey

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Propagation systems for seedling growth play a major role in agriculture, and in notable cases (such as organic systems), are under constant threat from soil and seedborne fungal plant pathogens such as Rhizoctonia solani or Pythium spp. Yet, to date little is known that links the risk of disease invasion to the host density, which is an agronomic characteristic that can be readily controlled. We introduce here, for the first time in an agronomic system, a percolation framework to analyze the link. We set up an experiment to study the spread of the ubiquitous fungus R. solani in replicated propagation systems with different planting densities, and fit a percolation-based epidemiological model to the data using Bayesian inference methods. The estimated probability of pathogen transmission between infected and susceptible plants is used to calculate the risk of invasion. By comparing the transmission probability and the risk values obtained for different planting densities, we are able to give evidence of a nonlinear relationship between disease invasion and the inter-plant spacing, hence to demonstrate the existence of a spatial threshold for epidemic invasion. The implications and potential use of our methods for the evaluation of disease control strategies are discussed.
Original languageEnglish
Pages (from-to)1012-1019
Number of pages8
JournalPhytopathology
Volume103
Issue number10
DOIs
Publication statusPublished - Oct 2013

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soil-borne diseases
infiltration (hydrology)
Thanatephorus cucumeris
pathogens
planting
Pythium
Bayesian theory
agronomic traits
plant pathogens
seedling growth
disease control
spatial distribution
agriculture
fungi
soil
methodology

Cite this

Poggi, S., Neri, F. M., Deytieux, V., Bates, A., Otten, W., Gilligan, C. A., & Bailey, D. J. (2013). Percolation-based risk index for pathogen invasion: application to soilborne disease in propagation systems. Phytopathology, 103(10), 1012-1019. https://doi.org/10.1094/PHYTO-02-13-0033-R
Poggi, S. ; Neri, F. M. ; Deytieux, V. ; Bates, Anne ; Otten, Wilfred ; Gilligan, Christopher A. ; Bailey, Douglas J. / Percolation-based risk index for pathogen invasion : application to soilborne disease in propagation systems. In: Phytopathology. 2013 ; Vol. 103, No. 10. pp. 1012-1019.
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Poggi, S, Neri, FM, Deytieux, V, Bates, A, Otten, W, Gilligan, CA & Bailey, DJ 2013, 'Percolation-based risk index for pathogen invasion: application to soilborne disease in propagation systems', Phytopathology, vol. 103, no. 10, pp. 1012-1019. https://doi.org/10.1094/PHYTO-02-13-0033-R

Percolation-based risk index for pathogen invasion : application to soilborne disease in propagation systems. / Poggi, S.; Neri, F. M.; Deytieux, V.; Bates, Anne; Otten, Wilfred; Gilligan, Christopher A.; Bailey, Douglas J.

In: Phytopathology, Vol. 103, No. 10, 10.2013, p. 1012-1019.

Research output: Contribution to journalArticle

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T2 - application to soilborne disease in propagation systems

AU - Poggi, S.

AU - Neri, F. M.

AU - Deytieux, V.

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AU - Gilligan, Christopher A.

AU - Bailey, Douglas J.

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AB - Propagation systems for seedling growth play a major role in agriculture, and in notable cases (such as organic systems), are under constant threat from soil and seedborne fungal plant pathogens such as Rhizoctonia solani or Pythium spp. Yet, to date little is known that links the risk of disease invasion to the host density, which is an agronomic characteristic that can be readily controlled. We introduce here, for the first time in an agronomic system, a percolation framework to analyze the link. We set up an experiment to study the spread of the ubiquitous fungus R. solani in replicated propagation systems with different planting densities, and fit a percolation-based epidemiological model to the data using Bayesian inference methods. The estimated probability of pathogen transmission between infected and susceptible plants is used to calculate the risk of invasion. By comparing the transmission probability and the risk values obtained for different planting densities, we are able to give evidence of a nonlinear relationship between disease invasion and the inter-plant spacing, hence to demonstrate the existence of a spatial threshold for epidemic invasion. The implications and potential use of our methods for the evaluation of disease control strategies are discussed.

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