Agronomy for Sustainable Development

, Volume 33, Issue 4, pp 767–776 | Cite as

Fungal infections of rice, wheat, and grape in Europe in 2030–2050

  • Simone Bregaglio
  • Marcello Donatelli
  • Roberto Confalonieri
Research Article

Abstract

Although models to predict climate impact on crop production have been used since the 1980s, spatial and temporal diffusion of plant diseases are poorly known. This lack of knowledge is due to few models of plant epidemics, high biophysical complexity, and difficulty to couple disease models to crop simulators. The first step is the evaluation of disease potential growth in response to climate drivers only. Here, we estimated the evolution of potential infection events of fungal pathogens of wheat, rice, and grape in Europe. A generic process-based infection model driven by air temperature and leaf wetness data was parameterized with the thermal and moisture requirements of the pathogens. The model was run on current climate as baseline, and on two time frames centered on 2030 and 2050. Our results show an overall increase in the number of infection events, with differences among the pathogens, and showing complex geographical patterns. For wheat, Puccinia recondita, or brown rust, is forecasted to increase +20–100 % its pressure on the crop. Puccinia striiformis, or yellow rust, will increase 5–20 % in the cold areas. Rice pathogens Pyricularia oryzae, or blast disease, and Bipolaris oryzae, or brown spot, will be favored all European rice districts, with the most critical situation in Northern Italy (+100 %). For grape, Plasmopara viticola, or downy mildew, will increase +5–20 % throughout Europe. Whereas Botrytis cinerea, or bunch rot, will have heterogeneous impacts ranging from −20 to +100 % infection events. Our findings represents the first attempt to provide extensive estimates on disease pressure on crops under climate change, providing information on possible future challenges European farmers will face with in the coming years.

Keywords

Infection process Plant fungal diseases Potential infection Process-based models Spatialized simulation SRES scenarios 

References

  1. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: Guidelines for computing crop water requirements. Irrig Drain 56, 300 pp. UN-FAO, Rome, ItalyGoogle Scholar
  2. Anderson PK, Cunningham AA, Patel NG, Morales FJ, Epstein PR, Daszak P (2004) Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol Evol 19:535–544. doi:10.1016/j.tree.2004.07.021 PubMedCrossRefGoogle Scholar
  3. Bergot M, Cloppet E, Pérarnaud V, Déqué M, Marcais B, Desprez-Loustau ML (2004) Simulation of potential range expansion of oak disease caused by Phytophthora cinnamomi under climate change. Glob Change Biol 10:1539–1552. doi:10.1111/j.1365-2486.2004.00824.x CrossRefGoogle Scholar
  4. Bregaglio S, Donatelli M, Confalonieri R, Acutis M, Orlandini S (2010) An integrated evaluation of thirteen modelling solutions for the generation of hourly values of air relative humidity. Theor Appl Climatol 102:429–438. doi:10.1007/s00704-010-0274-y Google Scholar
  5. Bregaglio S, Donatelli M, Confalonieri R, Acutis M, Orlandini S (2011) Multi metric evaluation of leaf wetness models for large-area application of plant disease models. Agr Forest Meteorol 151:1163–1172. doi:10.1016/j.agrformet.2011.04.003 CrossRefGoogle Scholar
  6. Bregaglio S, Cappelli G, Donatelli M (2012) Evaluating the suitability of a generic fungal infection model for pest risk assessment studies. Ecol Modell 247:58–63. doi:10.1016/j.ecolmodel.2012.08.004 CrossRefGoogle Scholar
  7. Butterworth MH, Semenov MA, Barnes A, Moran D, West JS, Fitt BDL (2010) North–south divide: contrasting impacts of climate change on crop yields in Scotland and England. J R Soc Interface 7:123–130. doi:10.1098/rsif.2009.0111 PubMedCrossRefGoogle Scholar
  8. Campbell GS (1985) Soil physics with BASIC: transport models for soil-plant systems. Elsevier, AmsterdamGoogle Scholar
  9. Cardoso CAA, Reis EM, Moreira EN (2008) Development of a warning system for wheat blast caused by Pyricularia grisea. Summa Phytopathol 34:216–221. doi:10.1590/S0100-54052008000300002 Google Scholar
  10. Chakraborty S, Datta S (2003) How will plant pathogens adapt to host plant resistance at elevated CO2 under a changing climate? New Phytol 159:733–742. doi:10.1046/j.1469-8137.2003.00842.x CrossRefGoogle Scholar
  11. Chakraborty S, Newton AC (2011) Climate change, plant diseases and food security: an overview. Plant Pathol 60:2–14. doi:10.1111/j.1365-3059.2010.02411.x CrossRefGoogle Scholar
  12. Chakraborty S, Pangga IB, Lupton J, Hart L, Room PM, Yates D (2000) Production and dispersal of Colletotrichum gloeosporioides spores on Stylosanthes scabra under elevated CO2. Environ Pollut 108:381–387. doi:10.1016/S0269-7491(99)00217-1 PubMedCrossRefGoogle Scholar
  13. Challinor AJ, Simelton ES, Fraser EDG, Hemming D, Collins M (2010) Increased crop failure due to climate change: assessing adaptation options using models and socio-economic data for wheat in China. Environ Res Lett 034012. doi:10.1088/1748-9326/5/3/034012
  14. Dale VH, Joyce LA, McNulty S, Neilson RP, Ayres MP, Flannigan MD, Hanson PJ, Irland LC, Lugo AE, Peterson CJ, Simberloff D, Swanson FJ, Stocks BJ, Wotton BM (2001) Climate change and forest disturbances. Bioscience 51:723–734. doi:10.1016/j.gloplacha.2006.07.028 CrossRefGoogle Scholar
  15. Dennis JL (1987) Temperature and wet-period conditions for infection by Puccinia striiformis f. sp. tritici race 104e137a+. Trans Br Mycol Soc 88:119–121. doi:10.1016/S0007-1536(87)80194-8 CrossRefGoogle Scholar
  16. Dinoor A (1974) Role of wild and cultivated plants in the epidemiology of plant diseases in Israel. Annu Rev Phythopathol 12:413–436. doi:10.1146/annurev.py.12.090174.002213 CrossRefGoogle Scholar
  17. Dobson A (2004) Population dynamics of pathogens with multiple host species. Am Nat 164:S64–S78. doi:10.1086/424681 PubMedCrossRefGoogle Scholar
  18. Donatelli M, Fumagalli D, Zucchini A, Duveiller G, Nelson RL, Baruth B (2012a) A EU27 database of daily weather data derived from climate change scenarios for use with crop simulation models. In: Seppelt R, Voinov AA, Lange S, Bankamp D (ed) International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software, Managing resources of a limited planet, Leipzig, GermanyGoogle Scholar
  19. Donatelli M, Duveiller G, Fumagalli D, Srivastava A, Zucchini A, Angileri V, Fasbender D, Loudjani P, Kay S, Juskevicius P, Toth T, Haastrup P, M’barek R, Espinosa M, Ciaian P, Niemeyer S (2012b) Assessing agriculture vulnerabilities for the design of effective measures for adaptation to climate change (AVEMAC project). Luxembourg: Publications Office of the European Union. doi:10.2788/16181 Google Scholar
  20. Dosio A, Paruolo P (2012) Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: evaluation on the present climate. J Geophys Res 116, D16106. doi:10.1029/2011JD015934 CrossRefGoogle Scholar
  21. European Communities (2008) CGMS version 9.2: User manual and technical documentation. JRC Scientific and Technical Reports. Joint Research Centre, European Commission. Office for Official Publications of the European Communities, LuxembourgGoogle Scholar
  22. Food Chain Evaluation Consortium (2011) Economic damage caused by lack of plant protection products against rice blast (Pyricularia grisea) in rice in Italy. Study on the establishment of a European fund for minor uses in the field of plant protection products: Final report, pp. 159–164. http://ec.europa.eu/food/plant/protection/evaluation/study_establishment_eu_fund.pdf. Accessed 03 October 2012
  23. Garrett KA, Dendy SP, Frank EE, Rouse MN, Travers SE (2006) Climate change effects on plant disease: genomes to ecosystems. Annu Rev Phytopathol 44:489–509. doi:10.1146/annurev.phyto.44.070505.143420 PubMedCrossRefGoogle Scholar
  24. Ghini R, Hamada E, Bettiol W (2008) Climate change and plant diseases. Sci Agr 65:98–107. doi:10.1590/S0103-90162008000700015 CrossRefGoogle Scholar
  25. Glassy JM, Running SW (1994) Validating diurnal climatology logic of the MT-CLIM model across a climatic gradient in Oregon. Ecol Appl 4:248–257CrossRefGoogle Scholar
  26. Goudriaan J, Zadocks JC (1995) Global climate change: modelling the potential responses of agro-ecosystems with special reference to crop protection. Environ Pollut 87:215–224. doi:10.1016/0269-7491(94)P2609-D PubMedCrossRefGoogle Scholar
  27. Hannukkala AO, Kaukoranta T, Lehtinen A, Rahkonen A (2007) Late-blight epidemics on potato in Finland 1933–2002; increased and earlier occurrence of epidemics associated with climate change and lack of rotation. Plant Pathol 56:167–176CrossRefGoogle Scholar
  28. Harvell CD, Mitchell CE, Ward JR, Altizer S, Dobson AP, Ostfeld RS, Samuel MD (2002) Climate warming and disease risks for terrestrial and marine biota. Science 296:2158–2162PubMedCrossRefGoogle Scholar
  29. Hill GN, Beresford RM, Evans KJ (2010) Tools for accurate assessment of botrytis bunch rot (Botrytis cinerea) on wine grapes. N Z Plant Protect-Se 63:174–181Google Scholar
  30. IPCC (2007) Climate Change 2007: The Physical Science Basis, Contribution of working group 1 to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  31. Jørgensen LN, Hovmøller MS, Hansen JG et al (2010) EuroWheat.org: a support to integrated disease management in wheat. Outlooks on Pest Management 21:173–176. doi:10.1564/21aug06 CrossRefGoogle Scholar
  32. Kim CH, Mackenzie DR, Rush MC (1988) Field testing a computerized forecasting systems for rice blast disease. Phytopathology 78:931–934. doi:10.1094/phyto-78-931 CrossRefGoogle Scholar
  33. Kim KS, Taylor SE, Gleason ML, Koehler KJ (2002) Model to enhance site-specific estimation of leaf wetness duration. Plant Dis 86:179–185. doi:10.1094/PDIS.2002.86.2.179 CrossRefGoogle Scholar
  34. Linacre E (1992) Climate data and resources: a reference and guide. Routledge, LondonGoogle Scholar
  35. Magarey RD, Sutton TB (2007) How to create and deploy infection models for plant pathogens. In: Ciancio A, Mukerji KG (eds) General concepts in integrated pest and disease management. Springer, The Netherlands, pp 3–25CrossRefGoogle Scholar
  36. Magarey RD, Sutton TB, Thayer CL (2005) A simple generic infection model for foliar fungal plant pathogens. Phytopathology 95:92–100. doi:10.1094/phyto-95-0092 PubMedCrossRefGoogle Scholar
  37. Mitchell G, Ray H, Griggs VB, Williams J (2000) EPIC documentation. Texas A&M Blackland Research and Extension Center, TempleGoogle Scholar
  38. Mitchell CE, Reich PB, Tilman D, Groth JV (2003) Effects of elevated CO2, nitrogen deposition, and decreased species diversity on foliar fungal plant disease. Glob Change Biol 9:438–451. doi:10.1046/j.1365-2486.2003.00602.x CrossRefGoogle Scholar
  39. Moletti M, Giudici ML, Villa B (2011) Rice Akiochi-brown spot disease in Italy: agronomic and chemical control. CIHEAM—Options Mediterraneennes 15:79–85Google Scholar
  40. Nair NG, Allen RN (1993) Infection of grape flowers and berries by Botrytis cinerea as a function of time and temperature. Mycol Res 97:1012–1014. doi:10.1016/S0953-7562(09)80871-X CrossRefGoogle Scholar
  41. Nair NG, Hill GK (1992) Bunch rot of grapes caused by Botrytis cinerea. In: Kumar J, Chaube HS, Singh US, Mukhopadhyay AN (eds) Plant diseases of international importance. v. III: diseases of fruit crops. Prentice-Hall, New Jersey, pp 147–169Google Scholar
  42. Oerke EC (2006) Crop losses to pests. J Agr Sci 144:31–43. doi:10.1017/S0021859605005708 CrossRefGoogle Scholar
  43. Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Ann Rev Ecol Evol Syst 37:637–669. doi:10.1146/annurev.ecolsys.37.091305.110100 CrossRefGoogle Scholar
  44. Percich JA, Nyvall RF, Malvick DK, Kohls CL (1997) Interaction of temperature and moisture on infection of wild rice by Bipolaris oryzae in the growth chamber. Plant Dis 81:1193–1195. doi:10.1094/PDIS.1997.81.10.1193 CrossRefGoogle Scholar
  45. Rosenzweig C, Parry ML (1994) Potential impact of climate change on world supply. Nature 367:133–138. doi:10.1038/367133a0 CrossRefGoogle Scholar
  46. Rosslenbroich HJ, Stuebler D (2000) Botrytis cinerea-history of chemical control and novel fungicides for its management. Crop Prot 19:557–561. doi:10.1016/S0261-2194(00)00072-7 CrossRefGoogle Scholar
  47. Roy BA, Gusewell S, Harte J (2004) Response of plant pathogens and herbivores to a warming experiment. Ecology 85:2570–2581. doi:10.1890/03-0182 CrossRefGoogle Scholar
  48. Running SW, Nemani RR, Hungerford RD (1987) Extrapolation of synoptic meteorological data in mountainous terrain, and its use for simulating forest evapotranspiration. Can J Forest Res 17:472–483. doi:10.1139/x87-081 CrossRefGoogle Scholar
  49. Schröder G, Gabriele E (2001) Observation on the appearance of fungal diseases on winter triticales (Triticosecale Wittmack) in field population of Brandenburg County. Gesunden Pflanzen 53:185–190Google Scholar
  50. Sharma BR, Kapoor AS (2003) Some epidemiological aspects of rice blast. Plant Disease Research Ludhiana 18:106–109Google Scholar
  51. Stockle CO, Campbell GS, Nelson R (1999) ClimGen manual. Biological Systems Engineering Department, Washington State University, PullmanGoogle Scholar
  52. Suzuki H (1975) Meteorological factors in the epidemiology of rice blast. Annu Rev Phytopathol 13:239–256. doi:10.1146/annurev.py.13.090175.001323 CrossRefGoogle Scholar
  53. Tian S, Weinert G, Wolf GA (2004) Infection of triticale cultivars by Puccinia striiformis: first report on disease severity and yield loss. J Plant Dis Protect 111:461–464Google Scholar
  54. Travers SE, Tang Z, Caragea D et al (2010) Spatial and temporal variation of big bluestem (Andropogon gerardii) transcription profiles with climate change. J Ecol 98:374–383. doi:10.1111/j.1365-2745.2009.01618.x CrossRefGoogle Scholar
  55. Tubiello NF, Donatelli M, Rosenzweig C, Stöckle CO (2000) Effects of climate change and elevated CO2 on cropping systems: model predictions at two Italian locations. Eur J Agron 13:179–189. doi:10.1016/S1161-0301(00)00073-3 CrossRefGoogle Scholar
  56. Tylianakis JM, Didham RK, Bascompte J, Wardle DA (2008) Global change and species interactions in terrestrial ecosystems. Ecol Lett 11:1351–1363. doi:10.1111/j.1461-0248.2008.01250.x PubMedCrossRefGoogle Scholar
  57. Vallavieille Pope C, Huber L, Leconte M, Goyeau H (1995) Comparative effects of temperature and interrupted wet periods on germination, penetration and infection of Puccinia recondita f. sp. tritici and P. striiformis on wheat seedlings. Phytopathology 85:409–415CrossRefGoogle Scholar
  58. van der Linden P, Mitchell JFB (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, Fitzroy Road, UKGoogle Scholar
  59. Yan W, Hunt LA (1999) An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann Bot-London 84:607–614. doi:10.1006/anbo.1999.0955 CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France 2013

Authors and Affiliations

  • Simone Bregaglio
    • 1
  • Marcello Donatelli
    • 2
  • Roberto Confalonieri
    • 1
  1. 1.Department of Agricultural and Environmental Sciences—Production, Landscape, Agroenergy, CASSANDRAUniversity of MilanMilanItaly
  2. 2.Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Consiglio per la Ricerca e la Sperimentazione in AgricolturaBolognaItaly

Personalised recommendations