European Journal of Plant Pathology

, Volume 135, Issue 3, pp 479–497 | Cite as

Impacts of plant growth and architecture on pathogen processes and their consequences for epidemic behaviour

  • A. CalonnecEmail author
  • J-B. Burie
  • M. Langlais
  • S. Guyader
  • S. Saint-Jean
  • I. Sache
  • B. Tivoli


As any epidemic on plants is driven by the amount of susceptible tissue, and the distance between organs, any modification in the host population, whether quantitative or qualitative, can have an impact on the epidemic dynamics. In this paper we examine using examples described in the literature, the features of the host plant and the use of crop management which are likely to decrease diseases. We list the pathogen processes that can be affected by crop growth and architecture modifications and then determine how we can highlight the principal ones. In most cases, a reduction in plant growth combined with an increase in plant or crop porosity reduces infection efficiency and spore dispersal. Experimental approaches in semi-controlled conditions, with concomitant characterisation of the host, microclimate and disease, allow a better understanding and analysis of the processes impacted. Afterwards, the models able to measure and predict the effect of plant growth and architecture on epidemic behaviour are reviewed.


Canopy structure Disease transmission Architectural traits Microclimate Host-pathogen models 



This work was funded by Agence Nationale de la Recherche (ANR): project ARCHIDEMIO grant ANR-08-STRA-04.


  1. Allen, M. T., Prusinkiewicz, P., & DeJong, T. M. (2005). Using L-systems for modeling source-sink interactions, architecture and physiology of growing trees: the L-PEACH model. New Phytologist, 166(3), 869–880. doi: 10.1111/j.1469-8137.2005.01348.x.PubMedCrossRefGoogle Scholar
  2. Analytis, S. (1980). Obtaining of sub-models for modeling the entire life cycle of a pathogen. Journal of Plant Diseases and Protection, 87, 371–382.Google Scholar
  3. Ando, K., Hammar, S., & Grumet, R. (2009). Age-related resistance of diverse cucurbit fruit to infection by Phytophthora capsici. Journal of the American Society for Horticultural Science, 134(2), 176–182.Google Scholar
  4. Austin, C. N., & Wilcox, W. F. (2011). Effects of fruit-zone leaf removal, training systems, and irrigation on the development of grapevine powdery mildew. American Journal of Enology and Viticulture, 62(2), 193–198. doi: 10.5344/ajev.2010.10084.CrossRefGoogle Scholar
  5. Aylor, D. E. (1990). The role of intermittent wind in the dispersal of fungal pathogens. Annual Review of Phytopathology, 28, 73–92.CrossRefGoogle Scholar
  6. Aylor, D. E., & Sanogo, S. (1997). Germinability of Venturia inaequalis conidia exposed to sunlight. Phytopathology, 87, 628–633.PubMedCrossRefGoogle Scholar
  7. Baccar, R., Fournier, C., Dornbusch, T., Andrieu, B., Gouache, D., & Rober, C. (2011). Modelling the effect of wheat canopy architecture as affected by sowing density on Septoria tritici epidemics using a coupled epidemic–virtual plant model. Annals of Botany, 108, 1179–1194.PubMedCrossRefGoogle Scholar
  8. Bannon, F. J., & Cooke, B. M. (1998). Studies on dispersal of Septoria tritici pycnidiospores in wheat-clover intercrops. Plant Pathology, 47(1), 49–56. doi: 10.1046/j.1365-3059.1998.00200.x.CrossRefGoogle Scholar
  9. Berryman, A. A. (2004). Limiting factors and population regulation. Oikos, 105(3), 667–670. doi: 10.1111/j.0030-1299.2004.13381.x.CrossRefGoogle Scholar
  10. Burie, J. B., Langlais, M., & Calonnec, A. (2011). Switching from a mechanistic model to a continuous model to study at different scales the effect of vine growth on the dynamic of a powdery mildew epidemic. Annals of Botany, 107(5), 885–895.PubMedCrossRefGoogle Scholar
  11. Burie, J. B., Calonnec, A., Langlais, M., & Mammeri, Y. (2012). Modeling the spread of a pathogen over a spatially heterogeneous growing crop. Paper presented at the 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA), Shanghai, China, oct 2012.Google Scholar
  12. Butzler, T., Bailey, J., & Beute, M. (1998). Integrated management of sclerotinia blight in peanut: utilizing canopy morphology, mechanical pruning, and fungicide timing. Plant Disease, 82, 1312–1318.CrossRefGoogle Scholar
  13. Calonnec, A., Cartolaro, P., Naulin, J. M., Bailey, D., & Langlais, M. (2008). A host-pathogen simulation model: powdery mildew of grapevine. Plant Pathology, 57, 493–508.CrossRefGoogle Scholar
  14. Calonnec, A., Cartolaro, P., & Chadoeuf, J. (2009). Highlighting features of spatiotemporal spread of powdery mildew epidemics in the vineyard using statistical modeling on field experimental data. Phytopathology, 99, 411–422.PubMedCrossRefGoogle Scholar
  15. Carisse, O., & Bouchard, J. (2010). Age-related susceptibility of strawberry leaves and berries to infection by Podosphaera aphanis. Crop Protection, 29, 969–978.CrossRefGoogle Scholar
  16. Casadebaig, P., Quesnel, G., Langlais, M., & Faivre, R. (2012). A generic model to simulate air-borne diseases as a function of crop architecture. PLoS ONE, PONE-D-12-03636-R1.Google Scholar
  17. Celette, F., Findeling, A., & Gary, C. (2009). Competition for nitrogen in an unfertilized intercropping system: the case of an association of grapevine and grass cover in a mediterranean climate. European Journal of Agronomy, 30(1), 41–51. doi: 10.1016/j.eja.2008.07.003.CrossRefGoogle Scholar
  18. Chang, K. F., Ahmed, H. U., Hwang, S. F., Gossen, B. D., Howard, R. J., Warkentin, T. D., et al. (2007). Impact of cultivar, row spacing and seeldling rate on ascochyta blight severity and yield of chickpea. Canadian Journal of Plant Science, 87, 395–403.CrossRefGoogle Scholar
  19. Cieslak, M., Seleznyova, A. N., & Hanan, J. (2011). A functional-structural kiwifruit vine model integrating architecture, carbon dynamics and effects of the environment. Annals of Botany, 107(5), 747–764. doi: 10.1093/aob/mcq180.PubMedCrossRefGoogle Scholar
  20. Cintron-Arias, A., Castillo-Chavez, C., Bettencourt, L. M. A., Lloyd, A. L., & Banks, H. T. (2009). The estimation of the effective reproductive number from disease outbreak data. Mathematical Biosciences and Engineering, 6(2), 261–282. doi: 10.3934/mbe.2009.6.261.PubMedCrossRefGoogle Scholar
  21. Cleland, E., Chuine, I., Menzel, A., & Mooney, H. (2007). Shifting plant phenology in response to global change. Trends in Ecology & Evolution, 22(7), 357–365.CrossRefGoogle Scholar
  22. Costes, E., Smith, C., Renton, M., Guedon, Y., Prusinkiewicz, P., & Godin, C. (2008). MAppleT: simulation of apple tree development using mixed stochastic and biomechanical models. Functional Plant Biology, 35(9–10), 936–950. doi: 10.1071/fp08081.CrossRefGoogle Scholar
  23. Dalla Marta, A., Magarey, R. D., & Orlandini, S. (2005). Modelling leaf wetness duration and downy mildew simulation on grapevine in Italy. Agricultural and Forest Meteorology, 132(1–2), 84–95. doi: 10.1016/j.agrformet.2005.07.003.CrossRefGoogle Scholar
  24. de Vallavieille-Pope, C., Giosue, S., Munk, L., Newton, A. C., Niks, R. E., Stergard, H., et al. (2000). Assessment of epidemiological parameters and their use in epidemiological and forecasting models of cereal airborne diseases. Agronomie, 20(7), 715–727. doi: 10.1051/agro:2000171.CrossRefGoogle Scholar
  25. de Vallavieille-Pope, C., Huber, L., Leconte, M., & Bethenod, O. (2002). Preinoculation effects of light quantity on infection efficiency of Puccinia striiformis and Puccinia triticina on wheat seedlings. Phytopathology, 92(12), 1308–1314. doi: 10.1094/phyto.2002.92.12.1308.PubMedCrossRefGoogle Scholar
  26. Dell, A. I., Pawar, S., & Savage, V. M. (2011). Systematic variation in the temperature dependence of physiological and ecological traits. Proceedings of the National Academy of Sciences of the United States of America, 108(26), 10591–10596. doi: 10.1073/pnas.1015178108.PubMedCrossRefGoogle Scholar
  27. Develey-Rivière, M.-P., & Galiana, E. (2007). Resistance to pathogens and host developmental stage: a multifaceted relationship within the plant kingdom. New Phytologist, 175, 405–416.PubMedCrossRefGoogle Scholar
  28. Deytieux-Belleau, C., Geny, L., Roudet, J., Mayet, V., Doneche, B., & Fermaud, M. (2009). Grape berry skin features related to ontogenic resistance to Botrytis cinerea. European Journal of Plant Pathology, 125(4), 551–563. doi: 10.1007/s10658-009-9503-6.CrossRefGoogle Scholar
  29. Fernandez-Aparicio, M., Amri, M., Kharrat, M., & Rubiales, D. (2010). Intercropping reduces Mycosphaerella pinodes severity and delays upward progress on the pea plant. Crop Protection, 29(7), 744–750. doi: 10.1016/j.cropro.2010.02.013.CrossRefGoogle Scholar
  30. Ferrandino, F. J. (2008). Effect of crop growth and canopy filtration on the dynamics of plant disease epidemics spread by aerially dispersed spores. Phytopathology, 98(5), 492–503. doi: 10.1094/phyto-98-5-0492.PubMedCrossRefGoogle Scholar
  31. Ficke, A., Gadoury, D. M., Seem, R. C., & Dry, I. B. (2003). Effects of ontogenic resistance upon establishment and growth of Uncinula necator on grape berries. Phytopathology, 93(5), 556–563.PubMedCrossRefGoogle Scholar
  32. Finckh, M. R., Gacek, E. S., Czembor, H. J., & Wolfe, M. S. (1999). Host frequency and density effects on powdery mildew and yield in mixtures of barley cultivars. Plant Pathology, 48(6), 807–816.CrossRefGoogle Scholar
  33. Fournier, C., & Andrieu, B. (1999). ADEL-maize: an L-system based model for the integration of growth processes from the organ to the canopy. Application to regulation of morphogenesis by light availability. Agronomie, 19(3–4), 313–327. doi: 10.1051/agro:19990311.CrossRefGoogle Scholar
  34. Fournier, C., Andrieu, B., Ljutovac, S., & Saint-Jean, S. (2003). ADEL-wheat: A 3D architectural model of wheat development (Plant Growth Modeling and Applications, Proceedings).Google Scholar
  35. Frezal, L., Robert, C., Bancal, M. O., & Lannou, C. (2009). Local dispersal of Puccinia triticina and wheat canopy structure. Phytopathology, 99(10), 1216–1224. doi: 10.1094/phyto-99-10-1216.PubMedCrossRefGoogle Scholar
  36. Gadoury, D., Seem, R., Ficke, A., & Wilcox, W. (2003). Ontogenic resistance to powdery mildew in grape berries. Phytopathology, 93(5), 547–555.PubMedCrossRefGoogle Scholar
  37. Garrett, K. A., & Mundt, C. C. (1999). Epidemiology in mixed host populations. Phytopathology, 89(11), 984–990.PubMedCrossRefGoogle Scholar
  38. Geagea, L., Huber, L., Sache, I., Flura, D., McCartney, H. A., & Fitt, B. D. L. (2000). Influence of simulated rain on dispersal of rust spores from infected wheat seedlings. Agricultural and Forest Meteorology, 101, 53–66.CrossRefGoogle Scholar
  39. Gee, C., Gadoury, D., & Cadle-Davidson, L. (2008). Ontogenic resistance to Uncinula necator varies by genotype and tissue type in a diverse collection of Vitis spp. Plant Disease, 92(7), 1067–1073.CrossRefGoogle Scholar
  40. Gigot, C., Saint-Jean, S., Huber, L., Leconte, M., Maumené, C., & De, V. P. (2012). Using wheat cultivar mixtures to reduce severity of septoria tritici blotch, a rain-borne disease. In Paper presented at the International Conference Epidemiology Canopy Architecture, 1–5 July, Rennes, France, 58.Google Scholar
  41. Gilligan, C. A., & Van den Bosch, F. (2008). Epidemiological models for invasion and persistence of pathogens. Annual Review of Phytopathology, 46, 385–418.PubMedCrossRefGoogle Scholar
  42. Girardin, G., Gigot, C., Robert, C., de Vallavielle-Pope, C., Suffert, F., & Saint-Jean, S. (2012). Effect of wheat canopy architecture and rain characteristics on on septoria splash-borne pcynidiospore. Paper presented at the International Conference Epidemiology Canopy Architecture, 1–5 July, Rennes, France, 25.Google Scholar
  43. Godin, C., & Sinoquet, H. (2005). Functional-structural plant modelling. New Phytologist, 166(3), 705–708. doi: 10.1111/j.1469-8137.2005.01445.x.PubMedCrossRefGoogle Scholar
  44. Guyader, S., & Bussière, F. (2012). Comparing anthracnose dynamics and leaf wetness duration in staked and unstaked plots of water yam. Paper presented at the International Conference Epidemiology Canopy Architecture, 1–5 July, Rennes, France, 23.Google Scholar
  45. Hau, B. (1990). Analytic models of plant disease in a changing environment. Annual Review of Phytopathology, 28, 221–245.CrossRefGoogle Scholar
  46. Heesterbeek, J. A. P. (2002). A brief history of R0 and a recipe for its calculation. Acta Biotheoretica, 50, 189–204.PubMedCrossRefGoogle Scholar
  47. Huber, L., & Itier, B. (1990). Leaf wetness detection in a field bean canopy. Agricultural and Forestry Meteorology, 51, 281–292.CrossRefGoogle Scholar
  48. Huber, L., & Gillespie, T. J. (1992). Modeling leaf wetness in relation to plant disease epidemiology. Annual Review of Phytopathology, 30, 553–577.CrossRefGoogle Scholar
  49. Hugot, K., Aime, S., Conrod, S., Poupet, A., & Galiana, E. (1999). Developmental regulated mechanisms affect the ability of a fungal pathogen to infect and colonize tobacco leaves. The Plant Journal, 20(2), 163–170. doi: 10.1046/j.1365-313x.1999.00587.x.PubMedCrossRefGoogle Scholar
  50. Ingold, C. T. (1971). Fungal spores: Their liberation and dispersal: Oxford University Press.Google Scholar
  51. Jeger, M. J. (1986). The potential of analytic compared with simulation approaches to modeling in plant disease epidemiology. In K. J. Leonard & W. E. Fry (Eds.), Population dynamics and management. Vol. I: Plant disease epidemiology Vol. 1 (pp. 255–281). New York: Macmillan.Google Scholar
  52. Jeger, M. J., & Vandenbosch, F. (1994). Threshold criteria for model-plant disease epidemics. 2. persistence and endemicity. Phytopathology, 84(1), 28–30.Google Scholar
  53. Kennelly, M. M., Gadoury, D. M., Wilcox, W. F., Magarey, P. A., & Seem, R. C. (2005). Seasonal development of ontogenic resistance to downy mildew in grape berries and rachises. Phytopathology, 95(12), 1445–1452.PubMedCrossRefGoogle Scholar
  54. Kermack, W., & Mc Kendrick, A. (1927). Contributions to the mathemathical theory of epidemics, part 1. Proceedings of the Royal Society of London, 115, 700–721.CrossRefGoogle Scholar
  55. Kora, C., McDonald, M., & Boland, G. (2005). Lateral clipping influences the microclimate and development of apothecia of Sclerotinia sclerotiorum in carrots. Plant Disease, 89, 549–557.CrossRefGoogle Scholar
  56. Le May, C., Ney, B., Lemarchand, E., Schoeny, A., & Tivoli, B. (2008). Effect of pea plant architecture on the spatio-temporal epidemic development of ascochyta blight (Mycosphaerella pinodes) in the field. Plant Pathology, 58(2), 332–343.CrossRefGoogle Scholar
  57. Lebon, V., Gigot, C., Leconte, M., Pelzer, E., de Vallavieille-Pope, C., & Saint-Jean, S. (2012). Cultivar and species mixture effect on wheat septoria tritici blotch spreading. Paper presented at the International Conference on Epidemiology, Canopy, Architecture, 1–5 July, Rennes, France, 44.Google Scholar
  58. Leser, C., & Treutter, D. (2005). Effects of nitrogen supply on growth, contents of phenolic compounds and pathogen (scab) resistance of apple trees. Physiologia Plantarum, 123(1), 49–56. doi: 10.1111/j.1399-3054.2004.00427.x.CrossRefGoogle Scholar
  59. Li, B., & Xu, X. (2002). Infection and development of apple scab (Venturia inaequalis) on old leaves. Journal of Phytopathology-Phytopathologische Zeitschrift, 150(11–12), 687–691. doi: 10.1046/j.1439-0434.2002.00824.x.CrossRefGoogle Scholar
  60. Lopez, G., Favreau, R. R., Smith, C., Costes, E., Prusinkiewicz, P., & DeJong, T. M. (2008). Integrating simulation of architectural development and source-sink behaviour of peach trees by incorporating Markov chains and physiological organ function submodels into L-PEACH. Functional Plant Biology, 35(9–10), 761–771. doi: 10.1071/fp08039.CrossRefGoogle Scholar
  61. Lovell, D. J., Parker, S. R., Hunter, T., Royle, D. J., & Coker, R. R. (1997). Influence of crop growth and structure on the risk of epidemics by Mycosphaerella graminicola (Septoria tritici) in winter wheat. Plant Pathology, 46(1), 126–138.CrossRefGoogle Scholar
  62. Lovell, D. J., Parker, S. R., Hunter, T., Welham, S. J., & Nichols, A. R. (2004). Position of inoculum in the canopy affects the risk of septoria tritici blotch epidemics in winter wheat. Plant Pathology, 53(1), 11–21. doi: 10.1046/j.1365-3059.2003.00939.x.CrossRefGoogle Scholar
  63. Madden, L. V., & Boudreau, M. A. (1997). Effect of strawberry density on the spread of anthracnose caused by Colletotrichum acutatum. Phytopathology, 87(8), 828–838. doi: 10.1094/phyto.1997.87.8.828.PubMedCrossRefGoogle Scholar
  64. Madden, L. V., Wilson, L. L., & Ellis, M. A. (1993). Field spread of anthracnose fruit rot of strawberry in relation to ground cover and ambient weather conditions. Plant Disease, 77, 861–866.CrossRefGoogle Scholar
  65. Madden, L. V., Hughes, G., & Bosch, F. v. d. (2007). The study of plant disease epidemics (The study of plant disease epidemics). St. Paul: The American Phytopathology Society.Google Scholar
  66. Magarey, R. D., Russo, J. M., & Seem, R. C. (2006). Simulation of surface wetness with a water budget and energy balance approach. Agricultural and Forest Meteorology, 139(3–4), 373–381. doi: 10.1016/j.agrformet.2006.08.016.CrossRefGoogle Scholar
  67. Meyer, S., Cartelat, A., Moya, I., & Cerovic, Z. G. (2003). UV-induced blue-green and far-red fluorescence along wheat leaves: a potential signature of leaf ageing. Journal of Experimental Botany, 54(383), 757–769. doi: 10.1093/jxb/erg063.PubMedCrossRefGoogle Scholar
  68. Molitor, D., & Berkelmann-Loehnertz, B. (2011). Simulating the susceptibility of clusters to grape black rot infections depending on their phenological development. Crop Protection, 30(12), 1649–1654. doi: 10.1016/j.cropro.2011.07.020.CrossRefGoogle Scholar
  69. Navas-Cortés, J. A., Hau, B., & Jiménez-Díaz, R. M. (1998). Effect of sowing date, host cultivar, and race of Fusarium oxysporum f. sp. ciceris on development of fusarium wilt of chickpea. Phytopathology, 88(12), 1338–1346.PubMedCrossRefGoogle Scholar
  70. Norman, J. M. (1982). Simulation of microclimates. In J. Hatfield, & I. Thomason (Eds.), Biometeorology in integrated pest.Google Scholar
  71. Onstad, D. W. (1992). Evaluation of epidemiologic thresholds and asymptotes with variable plant densities. Phytopathology, 82(10), 1028–1032. doi: 10.1094/Phyto-82-1028.CrossRefGoogle Scholar
  72. Pallas, B., Christophe, A., Cournede, P. H., & Lecoeur, J. (2009). Using a mathematical model to evaluate the trophic and non-trophic determinants of axis development in grapevine. Functional Plant Biology, 36(2), 156–170. doi: 10.1071/fp08178.CrossRefGoogle Scholar
  73. Pangga, I. B., Hanan, J., & Chakraborty, S. (2011). Pathogen dynamics in a crop canopy and their evolution under changing climate. Plant Pathology, 60, 70–81.CrossRefGoogle Scholar
  74. Pariaud, B., Ravigné, V., Halkett, F., Goyeau, H., Carlier, J., & Lannou, C. (2009). Aggressiveness and its role in the adaptation of plant pathogens. Plant Pathology, 58, 409–424.CrossRefGoogle Scholar
  75. Payne, A. F., & Smith, D. L. (2012). Development and evaluation of two pecan scab prediction models. Plant Disease, 96, 117–123.CrossRefGoogle Scholar
  76. Rapilly, F. (1991). L’épidémiologie en pathologie végétale: Mycoses aériennes. Paris: Institut National de la Recherche Agronomique.Google Scholar
  77. Rapilly, F., Fournet, F., & Skajennikoff, M. (1970). Études sur l’épidémiologie et la biologie de la rouille jaune du blé Puccinia striiformis Westendorp. Annales de Phytopathologie, 2, 5–31.Google Scholar
  78. Reinhardt, D., & Kuhlemeier, C. (2002). Plant architecture. EMBO Reports, 3(9), 846–851.PubMedCrossRefGoogle Scholar
  79. Richard, B., Jumel, S., Rouault, F., & Tivoli, B. (2012). Influence of plant stage and organ age on the receptivity of Pisum sativum to Mycosphaerella pinodes. European Journal of Plant Pathology, 132(3), 367–379. doi: 10.1007/s10658-011-9882-3.CrossRefGoogle Scholar
  80. Ripoche, A., Metay, A., Celette, F., & Gary, C. (2011). Changing the soil surface management in vineyards: immediate and delayed effects on the growth and yield of grapevine. Plant and Soil, 339(1–2), 259–271. doi: 10.1007/s11104-010-0573-1.CrossRefGoogle Scholar
  81. Robert, C., Fournier, C., Andrieu, B., & Ney, B. (2008). Coupling a 3D virtual wheat plant model with a Septoria tritici epidemic model (Septo3D): a new approach to investigate plant-pathogen interactions linked to canopy architecture. Functional Plant Biology, 35(9–10), 997–1013.CrossRefGoogle Scholar
  82. Sache, I. (2000). Short-distance dispersal of wheat rust spores by wind and rain. Agronomie, 20, 757–767.CrossRefGoogle Scholar
  83. Sahile, S., Ahmed, S., Fininsa, C., Abang, M. M., & Sakhuja, P. K. (2008). Survey of chocolate spot (Botrytis fabae) disease of faba bean (Vicia faba L.) and assessment of factors influencing disease epidemics in northern Ethiopia. Crop Protection, 27(11), 1457–1463. doi: 10.1016/j.cropro.2008.07.011.CrossRefGoogle Scholar
  84. Saint-Jean, S., Chelle, M., & Huber, L. (2004). Modelling water transfer by rain-splash in a 3D canopy using Monte Carlo integration. Agricultural and Forest Meteorology, 121(3/4), 183–196.CrossRefGoogle Scholar
  85. Saint-Jean, S., Testa, A., Madden, L. V., & Huber, L. (2006). Relationship between pathogen splash dispersal gradient and Weber number of impacting drops. Agricultural and Forest Meteorology, 141(2/4), 257–262.CrossRefGoogle Scholar
  86. Saint-Jean, S., Kerhornou, B., Derbali, F., Leconte, M., de Vallavieille-Pope, C., & Huber, L. (2008). Role of rain-splash in the progress of Septoria leaf blotch within a winter wheat variety mixture. Aspects of Applied Biology, 89, 49–54.Google Scholar
  87. Saudreau, M., Marquier, A., Adam, B., Monney, P., & Sinoquet, H. (2009). Experimental study of fruit temperature dynamics within apple tree crowns. Agricultural and Forest Meteorology, 149, 362–372.CrossRefGoogle Scholar
  88. Saudreau, M., Marquier, A., Adam, B., & Sinoquet, H. (2011). Modelling fruit temperature dynamics within apple tree crowns using virtual plants. Annals of Botany, 108, 1111–1120.PubMedCrossRefGoogle Scholar
  89. Schnee, S., Jolivet, J., & Calonnec, A. (2011). Consideration of dynamical plant-pathogen interactions for an improved management of powdery mildew epidemics in grapevine. IOBC/wprs Bulletin, 67, 131–138.Google Scholar
  90. Schoeny, A., Menat, J., Darsonval, A., Rouault, F., Jumel, S., & Tivoli, B. (2008). Effect of pea canopy architecture on splash dispersal of Mycosphaerella pinodes conidia. Plant Pathology, 57, 1073–1085.CrossRefGoogle Scholar
  91. Schoeny, A., Jumel, S., Rouault, F., Lemarchand, E., & Tivoli, B. (2010). Effect and underlying mechanisms of pea-cereal intercropping on the epidemic development of ascochyta blight. European Journal of Plant Pathology, 126(3), 317–331. doi: 10.1007/s10658-009-9548-6.CrossRefGoogle Scholar
  92. Schwartz, H., Steadman, J., & Coyne, D. (1978). Influens of Phaseolus vulgaris blossoming characteristics and canopy structure upon reaction to Sclerotinia sclerotiorum. Phytopathology, 68, 465–470.CrossRefGoogle Scholar
  93. Segarra, J., Jeger, M. J., & van den Bosch, F. (2001). Epidemic dynamics and patterns of plant diseases. Phytopathology, 91(10), 1001–1010. doi: 10.1094/phyto.2001.91.10.1001.PubMedCrossRefGoogle Scholar
  94. Sentelhas, P., Gillespie, T., Batzer, J., Gleason, M., Monteiro, J., Pezzopane, J., et al. (2005). Spatial variability of leaf wetness duration in different crop canopies. International Journal of Biometeorology, 49, 363–370.PubMedCrossRefGoogle Scholar
  95. Shafia, A., Sutton, J. C., Yu, H., & Fletcher, R. A. (2001). Influence of preinoculation light intensity on development and interactions of Botrytis cinerea and Clonostachys rosea in tomato leaves. Canadian Journal of Plant Pathology-Revue Canadienne De Phytopathologie, 23(4), 346–357.CrossRefGoogle Scholar
  96. Shaw, M. W. (1990). Effects of temperature, leaf wetness and cultivar on the latent period of Mycosphaerella graminicola on winter wheat. Plant Pathology, 39(2), 255–268.CrossRefGoogle Scholar
  97. Simon, S., Lauri, P. E., Brun, L., Defrance, H., & Sauphanor, B. (2006). Does manipulation of fruit-tree architecture affect the development of pests and pathogens? A case study in an organic apple orchard. The Journal of Horticultural Science and Biotechnology, 81(4), 765–773.Google Scholar
  98. Suffert, F., & Sache, I. (2011). Relative importance of different types of inoculum to the establishment of Mycosphaerella graminicola in wheat crops in north-west Europe. Plant Pathology, 60, 878–889.CrossRefGoogle Scholar
  99. Tenenhaus, M., Esposito Vinzi, V., Chatelinc, Y., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205.CrossRefGoogle Scholar
  100. Tivoli, B., Calonnec, A., Richard, B., Ney, B., & Andrivon, D. (2012). How do plant architectural traits modify the expression and development of epidemics? Consequences for reducing epidemic progress. European Journal of Plant Pathology. doi: 10.1007/s10658-012-0066-6.
  101. Tremblay, N., Wang, Z., & Cerovic, Z. G. (2011). Sensing crop nitrogen status with fluorescence indicators. Agronomy for Sustainable Development. doi: 10.1007/s13593-011-0041-1.
  102. Ulevicius, V., Peciulyte, D., Lugauskas, A., & Andriejauskiene, J. (2004). Field study on changes in viability of airborne fungal propagules exposed to UV radiation. Environmental Toxicology, 19, 437–441.PubMedCrossRefGoogle Scholar
  103. Valdes-Gomez, H., Fermaud, M., Roudet, J., Calonnec, A., & Gary, C. (2008). Grey mould incidence is reduced on grapevines with lower vegetative and reproductive growth. Crop Protection, 27(8), 1174–1186.CrossRefGoogle Scholar
  104. Valdes-Gomez, H., Gary, C., Cartolaro, P., Lolas-Caneo, M., & Calonnec, A. (2011). Powdery mildew development is positively influenced by grapevine vegetative growth induced by different soil management strategies. Crop Protection, 30, 1168–1177.CrossRefGoogle Scholar
  105. Van den Bosch, F., McRoberts, N., Van den Berg, F., & Madden, L. V. (2008). The basic reproduction number of plant pathogens: Matrix approaches to complex dynamics. Phytopathology, 98, 239–249.PubMedCrossRefGoogle Scholar
  106. Van der Plank, J. (1963). Plant diseases: Epidemic and control. New-York: Academic.Google Scholar
  107. Verhulst, P. F. (1845). Recherches mathématiques sur la loi d’accroissement de la population. Nouveaux Mémoires de l’Académie Royale des Sciences et Belles-Lettres de Bruxelles, 18, 1–42.Google Scholar
  108. Willocquet, L., Colombet, D., Rougier, M., Fargues, J., & Clerjeau, M. (1996). Effects of radiation, especially ultraviolet B, on conidial germination and mycelial growth of grape powdery mildew. European Journal of Plant Pathology, 102(5), 441–449. doi: 10.1007/bf01877138.CrossRefGoogle Scholar
  109. Wilson, P. A., & Chakraborty, S. (1998). The virtual plant: a new tool for the study and management of plant diseases. Crop Protection, 17(3), 231–239.CrossRefGoogle Scholar
  110. Wolfe, M. S. (1985). The current status and prospects of multiline cultivars and variety mixtures for disease resistance. Annual Review of Phytopathology, 23(1), 251–273.CrossRefGoogle Scholar
  111. Yang, X., & Te Beest, D. (1991). Rain dispersal o f Colletotrichum gloeosporioides under simulated rice field conditions. Phytopathology, 81 :815 (Abstr.).Google Scholar
  112. Yang, X., Madden, L. V., Wilson, L. L., & Ellis, M. A. (1990). Effects of surface topography and rain intensity on splash dispersal of Colletotrichum acutatum. Phytopathology, 80, 1115–1120.CrossRefGoogle Scholar
  113. Zahavi, T., & Reuveni, M. (2012). Effect of grapevine training systems on susceptibility of berries to infection by Erysiphe necator. European Journal of Plant Pathology, 133(3), 511–515.CrossRefGoogle Scholar
  114. Zahavi, T., Reuveni, M., Scheglov, D., & Lavee, S. (2001). Effect of grapevine training systems on development of powdery mildew. European Journal of Plant Pathology, 107, 495–501.CrossRefGoogle Scholar

Copyright information

© KNPV 2012

Authors and Affiliations

  • A. Calonnec
    • 1
    • 2
    Email author
  • J-B. Burie
    • 3
    • 4
  • M. Langlais
    • 3
    • 4
  • S. Guyader
    • 5
  • S. Saint-Jean
    • 6
    • 7
  • I. Sache
    • 8
    • 6
  • B. Tivoli
    • 9
  1. 1.INRA, UMR1065 SAVE Santé et Agroécologie du VignobleVillenave d’OrnonFrance
  2. 2.Université de Bordeaux, ISVV, UMR SAVEVillenave d’OrnonFrance
  3. 3.Université de Bordeaux, IMB, UMR 5251BordeauxFrance
  4. 4.CNRS, IMB, UMR 5251TalenceFrance
  5. 5.INRA, UR1321 ASTRO Agrosystèmes tropicauxPetit-BourgFrance
  6. 6.AgroParisTech, UMR Environnement et Grandes CulturesThiverval-GrignonFrance
  7. 7.INRA, UMR1091 EGCThiverval-GrignonFrance
  8. 8.INRA, UR BIOGER-CCPThiverval-GrignonFrance
  9. 9.INRA, UMR1349 IGEPPLe RheuFrance

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