1932

Abstract

The evidential basis for disease management decision making is provided by data relating to risk factors. The decision process involves an assessment of the evidence leading to taking (or refraining from) action on the basis of a prediction. The primary objective of the decision process is to identify—at the time the decision is made—the control action that provides the best predicted end-of-season outcome, calculated in terms of revenue or another appropriate metric. Data relating to disease risk factors may take a variety of forms (e.g., continuous, discrete, categorical) on measurement scales in a variety of units. Log-likelihood ratios provide a principled basis for the accumulation of evidence based on such data and allow predictions to be made via Bayesian updating of prior probabilities.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-phyto-080516-035342
2017-08-04
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/phyto/55/1/annurev-phyto-080516-035342.html?itemId=/content/journals/10.1146/annurev-phyto-080516-035342&mimeType=html&fmt=ahah

Literature Cited

  1. Aegerter BJ, Nuñez JJ, Davis RM. 1.  2003. Environmental factors affecting rose downy mildew and development of a forecasting model for a nursery production system. Plant Dis 87:732–38 [Google Scholar]
  2. 2. AHDB (Agric. Hortic. Dev. Board). 2012. Topic Sheet 111: Managing Eyespot in Winter Wheat Stoneleigh Park, UK: Agric. Hortic. Dev. Board 2 [Google Scholar]
  3. 3. AHDB (Agric. Hortic. Dev. Board). 2015. Information Sheet 40: Risk Assessment for Fusarium Mycotoxins in Wheat Stoneleigh Park, UK: Agric. Hortic. Dev. Board2 [Google Scholar]
  4. 4. AHDB (Agric. Hortic. Dev. Board). 2015. Oilseed Rape Guide Stoneleigh Park, UK: Agric. Hortic. Dev. Board32, 2nd ed..
  5. 5. AHDB (Agric. Hortic. Dev. Board). 2016. Barley Disease Management Guide Stoneleigh Park, UK: Agric. Hortic. Dev. Board, 28.
  6. 6. AHDB (Agric. Hortic. Dev. Board). 2016. Wheat Disease Management Guide. Stoneleigh Park, UK: Agric. Hortic. Dev. Board28 [Google Scholar]
  7. Biggerstaff BJ. 7.  2000. Comparing diagnostic tests: a simple graphic using likelihood ratios. Statistics Med 19:649–63 [Google Scholar]
  8. Binns MR, Nyrop JP, van der Werf W. 8.  2000. Sampling and Monitoring in Crop Protection: The Theoretical Basis for Designing Practical Decision Guides Wallingford, UK: CAB Int284 [Google Scholar]
  9. Brown SL, Culbreath AK, Todd JW, Gorbet DW, Baldwin JA, Beasley JP. 9.  2005. Development of a method of risk assessment to facilitate integrated management of spotted wilt of peanut. Plant Dis 89:348–56 [Google Scholar]
  10. Burnett F, Butler-Ellis C, Hughes G, Knight S, Ray R. 10.  2012. Forecasting eyespot development and yield losses in winter wheat Proj. Rep. No. 491, Home-Grown Cereals Auth Stoneleigh Park, UK:89
  11. 11. Cabinet Off. 2008. National Risk Register London, UK: HMSO45
  12. 12. Cabinet Off. 2015. National Risk Register of Civil Emergencies London, UK: HMSO57
  13. Caraguel CG, Vanderstichel R. 13.  2013. The two-step Fagan's nomogram: ad hoc interpretation of a diagnostic test result without calculation. Evid. Based Med 18:125–28 [Google Scholar]
  14. Carisse O, McRoberts N, Brodeur L. 14.  2008. Comparison of monitoring- and weather-based risk indicators of Botrytis leaf blight of onion and determination of action thresholds. Can. J. Plant Pathol. 30:442–56 [Google Scholar]
  15. Chiarappa L. 15.  1981. Crop Loss Assessment Methods - Supplement 3 Slough, UK: Commonw. Agric. Bur123 [Google Scholar]
  16. Collett D. 16.  2003. Modelling Binary Data Boca Raton, FL: CRC387, 2nd ed..
  17. Cooke BM. 17.  2006. Disease assessment and yield loss. The Epidemiology of Plant Diseases BM Cooke, DG Jones, B Kaye 43–80 Dordrecht, Neth: Springer576, 2nd ed.. [Google Scholar]
  18. Culbreath AK, Todd JW, Brown SL. 18.  2003. Epidemiology and management of tomato spotted wilt in peanut. Annu. Rev. Phytopathol. 41:53–75 [Google Scholar]
  19. De Wolf ED, Madden LV, Lipps PE. 19.  2003. Risk assessment models for wheat Fusarium head blight epidemics based on within-season weather data. Phytopathology 93:428–35 [Google Scholar]
  20. Duttweiler KB, Gleason ML, Dixon PM, Sutton TB, McManus PS, Monteiro JEBA. 20.  2008. Adaptation of an apple sooty blotch and flyspeck warning system for the Upper Midwest United States. Plant Dis 92:1215–22 [Google Scholar]
  21. Edwards S. 21.  2007. Investigation of Fusarium mycotoxins in UK wheat production Proj. Rep. No. 413, Home-Grown Cereals Auth Stoneleigh Park, UK:86
  22. Edwards S. 22.  2011. Improving risk assessment to minimise Fusarium mycotoxins in harvested wheat grain Proj. Rep. No. 477, Home-Grown Cereals Auth Stoneleigh Park, UK:47
  23. Fabre F, Dedryver CA, Leterrier JL, Plantegenest M. 23.  2003. Aphid abundance on cereals in autumn predicts yield losses caused by Barley yellow dwarf virus. Phytopathology 93:1217–22 [Google Scholar]
  24. Fagan TJ. 24.  1975. Nomogram for Bayes's theorem. N. Engl. J. Med. 293:257 [Google Scholar]
  25. Fischer JE, Bachmann LM, Jaeschke R. 25.  2003. A readers’ guide to the interpretation of diagnostic test properties: clinical example of sepsis. Intensive Care Med 29:1043–51 [Google Scholar]
  26. Fischhoff B. 26.  2015. The realities of risk-cost-benefit analysis. Science 350:aaa6516 [Google Scholar]
  27. Fosgate GT. 27.  2005. Letter to the editor. Statistics Med 24:1287–88 [Google Scholar]
  28. Gent DH, De Wolf ED, Pethybridge SJ. 28.  2011. Perceptions of risk, risk aversion, and barriers to adoption of decision support systems and integrated pest management: an introduction. Phytopathology 101:640–43 [Google Scholar]
  29. Gent DH, Mahaffee WF, McRoberts N, Pfender WF. 29.  2013. The use and role of predictive systems in disease management. Annu. Rev. Phytopathol. 51:267–89 [Google Scholar]
  30. Gent DH, Ocamb CM. 30.  2009. Predicting infection risk of hop by Pseudoperonspora humuli. . Phytopathology 99:1190–98 [Google Scholar]
  31. Gent DH, Turechek WW, Mahaffee WF. 31.  2007. Sequential sampling for estimation and classification of the incidence of hop powdery mildew I: leaf sampling. Plant Dis 91:1002–12 [Google Scholar]
  32. Gent DH, Turechek WW, Mahaffee WF. 32.  2007. Sequential sampling for estimation and classification of the incidence of hop powdery mildew II: cone sampling. Plant Dis 91:1013–20 [Google Scholar]
  33. Giroux M-E, Bourgeois G, Dion Y, Rioux S, Pageau D. 33.  et al. 2016. Evaluation of forecasting models for Fusarium head blight of wheat under growing conditions of Quebec, Canada. Plant Dis 100:1192–201 [Google Scholar]
  34. Good IJ. 34.  1979. Studies in the history of probability and statistics. XXXVII. A. M. Turing's statistical work in World War II. Biometrika 66:393–96 [Google Scholar]
  35. Good IJ. 35.  1985. Weight of evidence: a brief survey. Bayesian Statistics2 JM Bernardo, MH DeGroot, DV Lindley, AFM Smith 249–70 Amsterdam, Neth.: Elsevier Sci. Publ778 [Google Scholar]
  36. Gustafson DH, Shukla RK, Delbecq A, Walster GW. 36.  1973. A comparative study of differences in subjective likelihood estimates made by individuals, interacting groups, Delphi groups, and nominal groups. Organ. Behav. Hum. Perform. 9:280–91 [Google Scholar]
  37. Gustafson LL, Ellis SK, Bartlett CA. 37.  2005. Using expert opinion to identify risk factors important to infectious salmon-anemia (ISA) outbreaks on salmon farms in Maine, USA and New Brunswick, Canada. Prev. Vet. Med. 70:17–28 [Google Scholar]
  38. Gustafson L, Klotins K, Tomlinson S, Karreman G, Cameron A. 38.  et al. 2010. Combining surveillance and expert evidence of viral hemorrhagic septicemia freedom: a decision science approach. Prev. Vet. Med. 94:140–53 [Google Scholar]
  39. Hand DJ, Yu K. 39.  2001. Idiot's Bayes—not so stupid after all? Int. Stat. Rev. 69:385–98 [Google Scholar]
  40. Harikrishnan R, del Río LE. 40.  2008. A logistic regression model for predicting risk of white mold incidence on dry bean in North Dakota. Plant Dis 92:42–46 [Google Scholar]
  41. Harremöes P. 41.  2009. Entropy—new editor-in-chief and outlook. Entropy 11:1–3 [Google Scholar]
  42. Hellmich M, Lehmacher W. 42.  2005. A ruler for interpreting diagnostic test results. Methods Inf. Med. 44:124–26 [Google Scholar]
  43. Herich L, Lehmacher W, Hellmich M. 43.  2015. Drop the likelihood ratio: a novel non-electronic tool for interpreting diagnostic test results. Methods Inf. Med. 54:283–87 [Google Scholar]
  44. Hughes G. 44.  1999. Sampling for decision making in crop loss assessment and pest management: introduction. Phytopathology 89:1080–83 [Google Scholar]
  45. Hughes G. 45.  2012. Applications of Information Theory to Epidemiology St. Paul, MN: APS Press158
  46. Hughes G, Burnett FJ. 46.  2015. Integrating experience, evidence and expertise in the crop protection decision process. Plant Dis 99:1197–203 [Google Scholar]
  47. Hughes G, Burnett FJ, Havis ND. 47.  2013. Disease risk curves. Phytopathology 103:1108–14 [Google Scholar]
  48. Hughes G, Gottwald TR. 48.  1998. Survey methods for assessment of citrus tristeza virus incidence. Phytopathology 88:715–23 [Google Scholar]
  49. Hughes G, Gottwald TR. 49.  1999. Survey methods for assessment of citrus tristeza virus incidence when Toxoptera citricida is the predominant vector. Phytopathology 89:487–94 [Google Scholar]
  50. James WC. 50.  1974. Assessment of plant diseases and losses. Annu. Rev. Phytopathol. 12:27–48 [Google Scholar]
  51. Jeffreys H. 51.  1939. Theory of Probability Oxford, UK: Clarendon Press380
  52. Johnson DA, Alldredge JR, Hamm PB. 52.  1998. Expansion of potato late blight forecasting models for the Columbia Basin of Washington and Oregon. Plant Dis 82:642–45 [Google Scholar]
  53. Johnson DA, Alldredge JR, Vackoch DL. 53.  1996. Potato late blight forecasting models for the semi-arid environment of south-central Washington. Phytopathology 86:480–84 [Google Scholar]
  54. Johnson NP. 54.  2004. Advantages to transforming the receiver operating characteristic (ROC) curve into likelihood ratio co-ordinates. Statistics Med 23:2257–66 [Google Scholar]
  55. Kannel WB, Dawber TR, Kagan A, Revotskie N, Stokes J III. 55.  1961. Factors of risk in the development of coronary heart disease—six-year follow-up experience. Ann. Intern. Med. 55:33–50 [Google Scholar]
  56. Langlotz CP. 56.  2003. Fundamental measures of diagnostic examination performance: usefulness for clinical decision making and research. Radiology 228:3–9 [Google Scholar]
  57. Lindblad M, Waern P. 57.  2002. Correlation of wheat dwarf incidence to winter wheat cultivation practices. Agric. Ecosyst. Environ. 92:115–22 [Google Scholar]
  58. Looseley ME, Newton AC. 58.  2014. Assessing the consequences of microbial infection in field trials: seen, unseen, beneficial, parasitic and pathogenic. Agronomy 4:302–21 [Google Scholar]
  59. Madden LV. 59.  2006. Botanical epidemiology: some key advances and its continuing role in disease management. Eur. J. Plant Pathol. 115:3–23 [Google Scholar]
  60. Madden LV, Hughes G. 60.  1999. Sampling for plant disease incidence. Phytopathology 89:1088–103 [Google Scholar]
  61. Madden LV, Hughes G, van den Bosch F. 61.  2007. The Study of Plant Disease Epidemics St. Paul, MN: APS Press421
  62. McRoberts N, Hughes G, Madden LV. 62.  2003. The theoretical basis and practical application of relationships between different disease intensity measurements in plants. Ann. Appl. Biol. 142:191–211 [Google Scholar]
  63. Mila AL, Driever GF, Morgan DP, Michailides TJ. 63.  2005. Effects of latent infection, temperature, precipitation, and irrigation on panicle and shoot blight of pistachio in California. Phytopathology 95:926–32 [Google Scholar]
  64. Montgomery DC. 64.  1991. Introduction to Statistical Quality Control New York: Wiley & Sons674, 2nd ed..
  65. Nyrop JP, Binns MR, van der Werf W. 65.  1999. Sampling for IPM decision making: Where should we invest time and resources?. Phytopathology 89:1104–11 [Google Scholar]
  66. Oxley SJP, Hunter EA. 66.  2005. Appropriate fungicide doses on winter barley: producing dose-response data for a decision guide Proj. Rep. No. 366, Home-Grown Cereals Auth Stoneleigh Park, UK:85
  67. Oxley SJP, Hunter EA. 67.  2009. Appropriate fungicide doses on winter barley: producing dose-response data for a decision guide Proj. Rep. No. 455, Home-Grown Cereals Auth Stoneleigh Park, UK:93
  68. Paul PA, Munkvold GP. 68.  2004. A model-based approach to preplanting risk assessment for gray leaf spot of maize. Phytopathology 94:1350–57 [Google Scholar]
  69. Paveley ND, Lockley KD, Sylvester-Bradley R, Thomas J. 69.  1997. Determinants of fungicide spray decisions for wheat. Pestic. Sci. 49:379–88 [Google Scholar]
  70. Pethybridge SJ, Gent DH, Esker PD, Turechek WW, Hay FS, Nutter FW Jr. 70.  2009. Site-specific risk factors for ray blight in Tasmanian pyrethrum fields. Plant Dis 93:229–37 [Google Scholar]
  71. Pethybridge SJ, Gent DH, Hay FS. 71.  2011. Epidemics of ray blight on pyrethrum are linked to seed contamination and overwintering inoculum of Phoma ligulicola var. inoxydabilis. Phytopathology 101:1112–21 [Google Scholar]
  72. Plant RE. 72.  1986. Uncertainty and the economic threshold. J. Econ. Entomol. 79:1–6 [Google Scholar]
  73. Prandini A, Sigolo S, Filippi L, Battilani P, Piva G. 73.  2009. Review of predictive models for Fusarium head blight and related mycotoxin contamination in wheat. Food Chem. Toxicol. 47:927–31 [Google Scholar]
  74. Seem RC. 74.  1984. Plant disease incidence and severity relationships. Annu. Rev. Phytopathol. 22:137–50 [Google Scholar]
  75. Selvin S. 75.  1996. Statistical Analysis of Epidemiologic Data New York: Oxford Univ. Press510, 3rd ed..
  76. Sherman J, Gent DH. 76.  2014. Concepts of sustainability, motivations for pest management approaches, and implications for communicating change. Plant Dis 98:1024–35 [Google Scholar]
  77. Stern VM, Smith RF, van den Bosch R, Hagen KS. 77.  1959. The integrated control concept. Hilgardia 29:81–101 [Google Scholar]
  78. Swets JA. 78.  1973. The relative operating characteristic curve in psychology. Science 182:990–1000 [Google Scholar]
  79. Swets JA, Dawes RM, Monahan J. 79.  2000. Better decisions through science. Sci. Am. 283:70–75 [Google Scholar]
  80. Turechek WW, Ellis MA, Madden LV. 80.  2001. Sequential sampling for incidence of Phomopsis leaf blight of strawberry. Phytopathology 91:336–47 [Google Scholar]
  81. Turechek WW, Wilcox WF. 81.  2005. Evaluating predictors of apple scab with receiver operating characteristic curve analysis. Phytopathology 95:679–91 [Google Scholar]
  82. Twengström E, Sigvald R, Svensson C, Yuen J. 82.  1998. Forecasting Sclerotinia stem rot in spring sown oilseed rape. Crop Prot 17:405–11 [Google Scholar]
  83. Van den Ende J, Bisoffi Z, Van Puymbroek H, Van Gompel A, Derese A. 83.  et al. 2007. Bridging the gap between clinical practice and diagnostic clinical epidemiology: pilot experiences with a didactic model based on a logarithmic scale. J. Eval. Clin. Pract. 13:374–80 [Google Scholar]
  84. Van den Ende J, Moreira J, Basinga P, Bisoffi Z. 84.  2005. The trouble with likelihood ratios. Lancet 366:548 [Google Scholar]
  85. 85. VHSV Expert Panel Work. Group. 2010. Viral hemorrhagic septicemia virus (VHSV IVb) risk factors and association measures derived by expert panel. Prev. Vet. Med. 94:128–39 [Google Scholar]
  86. Yuen JE, Hughes G. 86.  2002. Bayesian analysis of plant disease prediction. Plant Pathol 51:407–12 [Google Scholar]
  87. Yuen J, Mila A. 87.  2004. Are Bayesian approaches useful in plant pathology?. Bayesian Statistics and Quality Modeling in the Agro-Food Production Chain 3 MAJS van Boekel, A Stein, AHC van Bruggen 95–103 Dordrecht, Neth.: Kluwer Acad. Publ165 [Google Scholar]
  88. Yuen J, Twengström E, Sigvald R. 88.  1996. Calibration and verification of risk algorithms using logistic regression. Eur. J. Plant Pathol. 102:847–54 [Google Scholar]
  89. Zadoks JC. 89.  1985. On the conceptual basis of crop loss assessment: the threshold theory. Annu. Rev. Phytopathol. 23:455–73 [Google Scholar]
  90. Zweig MH, Campbell G. 90.  1993. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem 39:561–77 [Google Scholar]
/content/journals/10.1146/annurev-phyto-080516-035342
Loading
/content/journals/10.1146/annurev-phyto-080516-035342
Loading

Data & Media loading...

  • Article Type: Review Article
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