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Can epidemic control be achieved by altering landscape connectivity in agricultural systems?

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  • Papaïx, Julien
  • Touzeau, Suzanne
  • Monod, Hervé
  • Lannou, Christian

Abstract

Few investigations on host diversification at the landscape scale to control plant disease in agricultural systems are available in the literature. At this scale, landscape connectivity measures how the landscape structure facilitates or impedes the disease spread among host patches. We developed a simulation model, giving a particular attention to the representation of the landscape structures, and we characterized the landscape connectivity by the proportion and the aggregation level of the host varieties, the ability for the pathogen to develop on each host and its ability to disperse. The pathogen dynamics was represented by a matrix population model designed for a generic air-borne foliar disease. This framework was used to establish a detailed assessment of the influence of each landscape connectivity variable on the pathogen population dynamics. When deploying a host with complete resistance to the pathogen along with a susceptible host, mixed landscapes were always found to be more efficient to hamper the disease spread. However, when using a quantitatively resistant host, aggregating the hosts in different regions could result in a better control of the pathogen spread, depending on the proportion and level of resistance of the resistant host and according to a source–sink dynamics between the two hosts. The ability of the pathogen to disperse did not change the results from a qualitative point of view. By accounting explicitly for the landscape features, our approach can be used to guide further data analysis or to evaluate the effectiveness of control strategies designed at the landscape scale.

Suggested Citation

  • Papaïx, Julien & Touzeau, Suzanne & Monod, Hervé & Lannou, Christian, 2014. "Can epidemic control be achieved by altering landscape connectivity in agricultural systems?," Ecological Modelling, Elsevier, vol. 284(C), pages 35-47.
    • Handle: RePEc:eee:ecomod:v:284:y:2014:i:c:p:35-47
    DOI: 10.1016/j.ecolmodel.2014.04.014
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    References listed on IDEAS

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    1. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    2. Youyong Zhu & Hairu Chen & Jinghua Fan & Yunyue Wang & Yan Li & Jianbing Chen & JinXiang Fan & Shisheng Yang & Lingping Hu & Hei Leung & Tom W. Mew & Paul S. Teng & Zonghua Wang & Christopher C. Mundt, 2000. "Genetic diversity and disease control in rice," Nature, Nature, vol. 406(6797), pages 718-722, August.
    3. Kimberly A. With, 2004. "Assessing the Risk of Invasive Spread in Fragmented Landscapes," Risk Analysis, John Wiley & Sons, vol. 24(4), pages 803-815, August.
    4. Kinezaki, Noriko & Kawasaki, Kohkichi & Shigesada, Nanako, 2010. "The effect of the spatial configuration of habitat fragmentation on invasive spread," Theoretical Population Biology, Elsevier, vol. 78(4), pages 298-308.
    5. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
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    Cited by:

    1. Martin Drechsler & Julia Touza & Piran White & Glyn Jones, 2016. "Agricultural landscape structure and invasive species: the cost-effective level of crop field clustering," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 8(1), pages 111-121, February.
    2. David, Olivier & Lannou, Christian & Monod, Hervé & Papaïx, Julien & Traore, Djidi, 2017. "Adaptive diversification in heterogeneous environments," Theoretical Population Biology, Elsevier, vol. 114(C), pages 1-9.
    3. Bourhis, Yoann & Poggi, Sylvain & Mammeri, Youcef & Le Cointe, Ronan & Cortesero, Anne-Marie & Parisey, Nicolas, 2017. "Foraging as the landscape grip for population dynamics—A mechanistic model applied to crop protection," Ecological Modelling, Elsevier, vol. 354(C), pages 26-36.
    4. Bourhis, Yoann & Poggi, Sylvain & Mammeri, Youcef & Cortesero, Anne-Marie & Le Ralec, Anne & Parisey, Nicolas, 2015. "Perception-based foraging for competing resources: Assessing pest population dynamics at the landscape scale from heterogeneous resource distribution," Ecological Modelling, Elsevier, vol. 312(C), pages 211-221.

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