1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Phytopathology
  4. Volume 53, 2015
  5. Article

Annual Review of Phytopathology

Volume 53, 2015

Landscape-Scale Disease Risk Quantification and Prediction

Abstract

The study of plant disease epidemics at a landscape scale can be extended to allow for predictions about disease occurrence at this scale. Examined within the context of the disease triangle, systems developed to incorporate information primarily about the pathogen and conditions conducive to the infection process. Parametric methods can be used to relate environmental conditions to disease, and specifically relate environment to the inoculum production, the resulting infection process, or both. Aspects relating to the presence or absence of the host plant within the landscape, or patterns of the host within the landscape, are much rarer in disease prediction, although analyses incorporating these factors have been conducted. Predictive systems at the landscape scale may concentrate only on the conditions for infection or possible migratory paths of pathogen propagules. Incorporation of all components of the disease triangle may be one way to improve these systems.

Keyword(s): Bayesian decision theory, disease triangle, epidemiology, modeling
Loading

Article metrics loading...

/content/journals/10.1146/annurev-phyto-080614-120406
2015-08-04
2024-04-26
Loading full text...

Full text loading...

/deliver/fulltext/phyto/53/1/annurev-phyto-080614-120406.html?itemId=/content/journals/10.1146/annurev-phyto-080614-120406&mimeType=html&fmt=ahah

Literature Cited

  1. Andrade-Piedra JL, Hijmans RJ, Juárez HS, Forbes GA, Shtienberg D, Fry WE. 1.  2005. Simulation of potato late blight in the Andes. II: Validation of the LATEBLIGHT model. Phytopathology 95:1200–8 [Google Scholar]
  2. Barnes JM, Trinidad-Correa R, Orum TV, Felix-Gastelum R, Nelson MR. 2.  1999. Landscape ecology as a new infrastructure for improved management of plant viruses and their insect vectors in agroecosystems. Ecosyst. Health 5:26–35 [Google Scholar]
  3. Beaumont A. 3.  1947. The dependence on the weather of the dates of outbreak of potato blight epidemics. Trans. Br. Mycol. Soc. 31:1–245–53 [Google Scholar]
  4. Bruhn JA, Fry WE. 4.  1981. Analysis of potato late blight epidemiology by simulation modeling. Phytopathology 71:612–16 [Google Scholar]
  5. Bruhn JA, Fry WE. 5.  1982. A mathematical model of the spatial and temporal dynamics of chlorothalonil residues on potato foliage. Phytopathology 72:1306–12 [Google Scholar]
  6. Bruhn JA, Fry WE. 6.  1982. A statistical model of fungicide deposition on potato foliage. Phytopathology 72:1301–5 [Google Scholar]
  7. Byamukama E, Eggenberger SK, Coelho-Netto RA, Robertson AE, Nutter FW Jr. 7.  2014. Geospatial and temporal analyses of bean pod mottle virus epidemics in soybean at three spatial scales. Phytopathology 104:365–78 [Google Scholar]
  8. De Wolf ED, Isard SA. 8.  2007. Disease cycle approach to plant disease prediction. Annu. Rev. Phytopathol. 45:203–20 [Google Scholar]
  9. De Wolf ED, Madden LV, Lipps PE. 9.  2003. Risk assessment models for wheat Fusarium head blight epidemics based on within-season weather data. Phytopathology 93:4428–35 [Google Scholar]
  10. Draxler RR, Hess GD. 10.  1998. An overview of the HYSPLIT_4 modelling system for trajectories. Aust. Meteorol. Mag. 47:295–308 [Google Scholar]
  11. Fabre F, Plantegenest M, Mieuzet L, Dedryver CA, Leterrier J-L, Jacquot E. 11.  2005. Effects of climate and land use on the occurrence of viruliferous aphids and the epidemiology of barley yellow dwarf disease. Agric. Ecosyst. Environ. 106:149–55 [Google Scholar]
  12. Fahrmeir L, Kneib T. 12.  2011. Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data. Oxford, UK: Oxford Univ. Press
  13. Försund E, Flaatten HK. 13.  1958. Experiments of forecasting late blight epiphytotics in Norway. Eur. Potato J. 1:5–13 [Google Scholar]
  14. Fry WE, Apple AE, Bruhn JA. 14.  1983. Evaluation of potato late blight forecasts modified to incorporate host resistance and fungicide weathering. Phytopathology 73:1054–59 [Google Scholar]
  15. Fry WE, Goodwin SB, Dyer AT, Matuszak JM, Drenth A. 15.  et al. 1993. Historical and recent migrations of Phytophthora infestans: chronology, pathways, and implications. Plant Dis. 77:653–61 [Google Scholar]
  16. Garrett KA, Mundt CC. 16.  2000. Effects of planting density and the composition of wheat cultivar mixtures on stripe rust: an analysis taking into account limits to the replication of controls. Phytopathology 90:1313–21 [Google Scholar]
  17. Grünwald NJ, Romero Montes G, Lozoya Saldaña H, Rubio Covarrubias OA, Fry WE. 17.  2002. Potato late blight management in the Toluca Valley: field validation of SIMCAST modified for cultivars with high field resistance. Plant Dis. 86:1163–68 [Google Scholar]
  18. Haas SE, Hooten MB, Rizzo DM, Meentemeyer RK. 18.  2011. Forest species diversity reduces disease risk in a generalist plant pathogen invasion. Ecol. Lett. 14:111108–16 [Google Scholar]
  19. Hadders J. 19.  2008. An example of integrated forecasting system for Phytophthora infestans on potato. Integrated Management of Diseases Caused by Fungi, Phytoplasma and Bacteria A Ciancio, KG Mukerji 179–89 Dordrecht, Neth.: Springer [Google Scholar]
  20. Han W, Yang Z, Di L, Mueller R. 20.  2012. CropScape: a web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Comput. Electron. Agric. 84:111–23 [Google Scholar]
  21. Hermansen A, Nærstad R, Glorvigen B, Alm H, Sørensen K. 21.  2009. Forecasting potato late blight in Norway. PPO-Spec. Rep. No. 13301 Appl. Plant Res., AGV Res. Unit, Leylstad, Neth.
  22. Hess GR. 22.  1994. Conservation corridors and contagious disease: a cautionary note. Conserv. Biol. 8:1256–62 [Google Scholar]
  23. Hijmans RJ, Guarino L, Cruz M, Rojas E. 23.  2001. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genet. Resour. Newsl. 127:15–19 [Google Scholar]
  24. Hosmer DW Jr, Lemeshow S, Sturdivant RX. 24.  2013. Applied Logistic Regression 398 Hoboken, NJ: Wiley & Sons
  25. Hyre RA. 25.  1955. Three methods of forecasting late blight of potato and tomato in northeastern United States. Am. Potato J. 32:10362–71 [Google Scholar]
  26. Isard SA, Gage SH, Comtois P, Russo JM. 26.  2005. Principles of the atmospheric pathway for invasive species applied to soybean rust. Bioscience 55:10851–61 [Google Scholar]
  27. Isard SA, Russo JM, DeWolf ED. 27.  2006. The establishment of a national pest information platform for extension and education. Plant Health Prog. 2006:1–4 [Google Scholar]
  28. Jørgensen LN, Hovmøller MS, Hansen JG, Lassen P, Clark B. 28.  et al. 2010. Eurowheat.org: a support to integrated disease management in wheat. Outlooks Pest Manag. 21:4173–76 [Google Scholar]
  29. Jules ES, Kauffman MJ, Ritts WD, Carroll AL. 29.  2002. Spread of an invasive pathogen over a variable landscape: a nonnative root rot on Port Orford cedar. Ecology 83:113167–81 [Google Scholar]
  30. Laine A-L, Hanski I. 30.  2006. Large-scale spatial dynamics of a specialist plant pathogen in a fragmented landscape. J. Ecol. 94:1217–26 [Google Scholar]
  31. Lu X, Robertson AE, Byamukama E, Nutter FW Jr. 31.  2010. Prevalence, incidence, and spatial dependence of Soybean mosaic virus in Iowa. Phytopathology 100:9931–40 [Google Scholar]
  32. MacKenzie DR. 32.  1981. Scheduling fungicide applications for potato late blight with blitecast. Plant Dis. 65:394–99 [Google Scholar]
  33. Main CE, Keever T, Holmes GJ, Davis JM. 33.  2001. Forecasting long-range transport of downy mildew spores and plant disease epidemics. APSnet Featur. doi: 10.1094/APSnetFeature-2001-0501
  34. Madden LV. 34.  2006. Botanical epidemiology: some key advances and its continuing role in disease management. Eur. J. Plant Pathol. 115:3–23 [Google Scholar]
  35. Madden LV, Hughes G, Van den Bosch F. 35.  2007. The Study of Plant Disease Epidemics St Paul, MN: Am. Phytopathol. Soc.
  36. Marasas CN, Smale M, Singh RP. 36.  2003. The economic impact of productivity maintenance research: breeding for leaf rust resistance in modern wheat. Agric. Econ. 29:253–63 [Google Scholar]
  37. McMullen M, Bergstrom G, De Wolf E, Dill-Macky R, Hershman D. 37.  et al. 2012. A unified effort to fight an enemy of wheat and barley: Fusarium head blight. Plant Dis. 96:1712–28 [Google Scholar]
  38. McMullen M, Jones R, Gallenberg D. 38.  1997. Scab of wheat and barley: a re-emerging disease of devastating impact. Plant Dis. 81:1340–48 [Google Scholar]
  39. Meentemeyer RK, Haas SE, Václavík T. 39.  2012. Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems. Annu. Rev. Phytopathol. 50:379–402 [Google Scholar]
  40. Mila AL, Michailides TJ. 40.  2006. Use of Bayesian methods to improve prediction of panicle and shoot blight severity of pistachio in California. Phytopathology 96:101142–47 [Google Scholar]
  41. Mila AL, Ngugi H. 41.  2011. A Bayesian approach to meta-analysis of plant pathology studies. Phytopathology 101:142–51 [Google Scholar]
  42. Mila AL, Yang XB, Carriquiry AL. 42.  2003. Bayesian logistic regression of soybean Sclerotinia stem rot prevalence in the US north-central region: accounting for uncertainty in parameter estimation. Phytopathology 93:6758–64 [Google Scholar]
  43. Mills WD. 43.  1944. Efficient Use of Sulfur Dusts and Sprays During Rain to Control Apple Scab Ithaca, NY: N.Y. State Coll. Agric.
  44. Molineros JE, De Wolf ED, Madden LV, Paul P, Lipps PE. 44.  2005. Incorporation of host reaction and crop residue level into prediction models for Fusarium head blight. Proc. Natl. Fusarium Head Blight Forum, 2005, Milwaukee, Dec. 11–13 119–21 East Lansing, MI: Michigan State Univ. [Google Scholar]
  45. Nagarajan S, Kogel HJ, Zadoks JC. 45.  2012. Epidemiology of Puccinia graminis f.sp. tritici-Ug99 in the Rift valley “flyway” from Uganda-Kenya to Yemen. Plant Health Prog. doi:10.1094/PHP-2012-1114-01-RV
  46. Nagarajan S, Singh DV. 46.  1990. Long-distance dispersion of rust pathogens. Annu. Rev. Phytopathol. 28:139–53 [Google Scholar]
  47. Ojiambo PS, Kang EL. 47.  2013. Modeling spatial frailties in survival analysis of cucurbit downy mildew epidemics. Phytopathology 103:3216–27 [Google Scholar]
  48. Orum TV, Bigelow DM, Cotty PJ, Nelson MR. 48.  1999. Using predictions based on geostatistics to monitor trends in Aspergillus flavus strain composition. Phytopathology 89:9761–69 [Google Scholar]
  49. Park R, Fetch T, Hodson D, Jin Y, Nazari K. 49.  et al. 2011. International surveillance of wheat rust pathogens: progress and challenges. Euphytica 179:1109–17 [Google Scholar]
  50. Rosso PH, Hansen EM. 50.  2003. Predicting Swiss needle cast disease distribution and severity in young Douglas-fir plantations in coastal Oregon. Phytopathology 93:7790–98 [Google Scholar]
  51. Shah DA, De Wolf ED, Paul PA, Madden LV. 51.  2014. Predicting Fusarium head blight epidemics with boosted regression trees. Phytopathology 104:7702–14 [Google Scholar]
  52. Shah DA, Molineros JE, Paul PA, Willyerd KT, Madden LV, De Wolf ED. 52.  2013. Predicting Fusarium head blight epidemics with weather-driven pre-and post-anthesis logistic regression models. Phytopathology 103:9906–19 [Google Scholar]
  53. Sikora E, Allen T, Wise KA, Bergstrom GC, Bradley CA. 53.  et al. 2014. A coordinated effort to manage soybean rust in North America: a success story in soybean disease monitoring. Plant Dis. 98:864–75 [Google Scholar]
  54. Singh R. 54.  2006. Current status, likely migration and strategies to mitigate the threat to wheat production from race UG99 (TTKS) of stem rust pathogen. CAB Rev. Perspect. Agric. Vet. Sci. Nutr. Nat. Resour. 1:541–13 [Google Scholar]
  55. Singh RP, Hodson DP, Huerta-Espino J, Jin Y, Bhavani S. 55.  et al. 2011. The emergence of UG99 races of the stem rust fungus is a threat to world wheat production. Annu. Rev. Phytopathol. 49:465–81 [Google Scholar]
  56. Skelsey P, Rossing WA, Kessel GJ, van der Werf W. 56.  2010. Invasion of Phytophthora infestans at the landscape level: How do spatial scale and weather modulate the consequences of spatial heterogeneity in host resistance?. Phytopathology 100:111146–61 [Google Scholar]
  57. Smith EK, Resler LM, Vance EA, Carstensen LW, Kolivras KN. 57.  2011. Blister rust incidence in treeline whitebark pine, Glacier National Park, USA: environmental and topographic influences. Arct. Antarct. Alp. Res. 43:1107–17 [Google Scholar]
  58. Sparks AH, Forbes GA, Hijmans RJ, Garrett KA. 58.  2011. A metamodeling framework for extending the application domain of process-based ecological models. Ecosphere 2:8art90 [Google Scholar]
  59. Sparks AH, Forbes GA, Hijmans RJ, Garrett KA. 59.  2014. Climate change may have limited effect on global risk of potato late blight. Glob. Change Biol. 20:123621–31 [Google Scholar]
  60. Stakman EC. 60.  1934. Epidemiology of cereal rusts. Proc. Pac. Sci. Congr., 5th, Victoria, BC June 1–15 3177–84 Toronto, Ont.: Univ. Tor. Press [Google Scholar]
  61. Taylor MC, Hardwick NV, Bradshaw NJ, Hall AM. 61.  2003. Relative performance of five forecasting schemes for potato late blight (Phytophthora infestans) I. Accuracy of infection warnings and reduction of unnecessary, theoretical, fungicide applications. Crop Prot. 22:275–83 [Google Scholar]
  62. Tischendorf L, Fahrig L. 62.  2000. On the usage and measurement of landscape connectivity. Oikos 90:17–19 [Google Scholar]
  63. Turechek WW, McRoberts N. 63.  2013. Considerations of scale in the analysis of spatial pattern of plant disease epidemics. Annu. Rev. Phytopathol. 51:453–72 [Google Scholar]
  64. Turechek WW, Wilcox WF. 64.  2005. Evaluating predictors of apple scab with receiver operating characteristic curve analysis. Phytopathology 95:679–91 [Google Scholar]
  65. Ullrich J, Schrödter H. 65.  1966. Das problem der vorhersage des auftretens der kartoffelkrautfäule (Phytophthora infestans) und die möglichkeit seiner lösung durch eine “negativprognose.”. Nachrichtenblatt Dtsch. Pflanzenschutzd. Braunschw. 18:33–40 [Google Scholar]
  66. Wallin JR. 66.  1962. Summary of recent progress in predicting late blight epidemics in United States and Canada. Am. Potato J. 39:8306–12 [Google Scholar]
  67. Wang H, Yang XB, Ma Z. 67.  2010. Long-distance spore transport of wheat stripe rust pathogen from Sichuan, Yunnan, and Guizhou in southwestern China. Plant Dis. 94:7873–80 [Google Scholar]
  68. Wu BM, Van Bruggen AHC, Subbarao KV, Pennings GGH. 68.  2001. Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems. Phytopathology 91:2134–42 [Google Scholar]
  69. Yuen J, Twengström E, Sigvald R. 69.  1996. Calibration and verification of risk algorithms using logistic regression. Eur. J. Plant Pathol. 102:9847–54 [Google Scholar]
  70. Yuen JE. 70.  2012. Modelling pathogen competition and displacement: Phytophthora infestans in Scandinavia. Eur. J. Plant Pathol. 133:125–32 [Google Scholar]
  71. Yuen JE, Andersson B. 71.  2013. What is the evidence for sexual reproduction of Phytophthora infestans in Europe?. Plant Pathol. 62:485–91 [Google Scholar]
  72. Yuen JE, Hughes G. 72.  2002. Bayesian analysis of plant disease prediction. Plant Pathol. 51:4407–12 [Google Scholar]
  73. Zadoks JC. 73.  1965. Epidemiology of wheat rusts in Europe. FAO Plant Prot. Bull. 13:597–108 [Google Scholar]
/content/journals/10.1146/annurev-phyto-080614-120406
Loading
Landscape-Scale Disease Risk Quantification and Prediction
Annual Review of Phytopathology 53, 471 (2015); https://doi.org/10.1146/annurev-phyto-080614-120406
/content/journals/10.1146/annurev-phyto-080614-120406
/content/journals/10.1146/annurev-phyto-080614-120406
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error