Forecasting site-specific leaf wetness duration for input to disease-warning systems

Plant Disease

Volumn 90, Issue 5, 2006, Pages 650-656

  Kim, K S ^a   Gleason, M L ^b   Taylor, S E ^b  

^a THE NEW ZEALAND INSTITUTE FOR PLANT AND FOOD RESEARCH LIMITED   (New Zealand)

^b IOWA STATE UNIVERSITY   (United States)

Author keywords

Melon; Tomato

Indexed keywords

FUZZY SETS; MEASUREMENT ERRORS; REGRESSION ANALYSIS; TREES (MATHEMATICS);

CLASSIFICATION AND REGRESSION TREE ANALYSIS (CART MODELS); MEAN ERRORS (ME); MELON; TOMATO;

DISEASE CONTROL;

DISEASES; FUZZY LOGIC; MEASUREMENT; REGRESSION ANALYSIS; TREES;

CUCUMIS; LYCOPERSICON ESCULENTUM;

EID: 33646379347     PISSN: 01912917     EISSN: None     Source Type: Journal    
DOI: 10.1094/PD-90-0650     Document Type: Article

References (29)

1. Chtioui, Y., Francl, L. J., and Panigrahi, S. 1999. Moisture prediction from simple micrometeorological data. Phytopathology 89:668-672.
2. Davis, D. R., and Hughes, J. E. 1970. A new approach to recording the wetting parameter by the use of electrical resistance sensors. Plant. Dis. Rep. 54:474-479.
3. Francl, L. J., and Panigrahi, S. 1997. Artificial neural network models of wheat leaf wetness. Agric. For. Meteorol. 88:57-65.
4. Gillespie, T. J., and Barr, A. 1984. Adaptation of a dew estimation scheme to a new crop and site. Agric. For. Meteorol. 31:289-295.
5. Gillespie, T. J., and Kidd, G. E. 1978. Sensing duration of leaf moisture retention using electrical impedance grids. Can. J. Plant. Sci. 58:179-187.
6. Gleason, M. L. 2001. Disease-warning systems. Pages 367-370 In: Encyclopedia of Plant Pathology, Vol. 1. John Wiley & Sons, New York.
7. Gleason, M. L., Parker, S. K., Pitblado, R. E., Latin, R. X., Speranzini, D., Hazzard, R. V., Maletta, M. J., Cowgill, W. P., Jr., and Biederstedt, D. L. 1997. Validation of a commercial system for remote estimation of wetness duration. Plant Dis. 81:825-829.
8. Gleason, M. L., Taylor, S. E., Loughin, T. M., and Koehler, K. J. 1994. Development and validation of an empirical model to estimate the duration of dew periods. Plant Dis. 78:1011-1016.
9. Huber, L., and Gillespie, T. J. 1992. Modeling leaf wetness in relation to plant disease epidemiology. Annu. Rev. Phytopathology 30:553-577.
10. Johnson, K. B., Stockwell, V. O., and Sawyer, T. L. 2004. Adaptation of fire blight forecasting to optimize the use of biological controls. Plant Dis. 88:41-48.
11. Kaplan, M. L., Zack, J. W., Wong, V. C., and Tucillo, J. J. 1982. Initial results from an atmospheric simulation system and comparison with an AVE-SESAME 1 data set. Mon. Weather. Rev. 110:1564-1590.
12. Kelley, J. G. W., Russo, J. M., Eyton, J. R., and Carlson, T. N. 1988. Mesoscale forecasts generated from operational numerical weather-prediction model output. Bull. Am. Meteorol. Soc. 69:7-15.
13. Kim, K. S., Taylor, S. E., and Gleason, M. L. 2004. Development and validation of a leaf wetness model using a fuzzy logic system. Agric. For. Meteorol. 127:53-64.
14. Kim, K. S., Taylor, S. E., Gleason, M. L., and Koehler, K. J. 2002. Model to enhance site-specific estimation of leaf wetness duration. Plant Dis. 86:179-185.
15. Kim, K. S., Taylor, S. E., Gleason, M. L., Villalobos, R., and Arauz, L. F. 2005. Estimation of leaf wetness duration using empirical models in northwestern Costa Rica. Agric. For. Meteorol. 129:53-67.
16. Latin, R., and Egel, D. 2001. Melcast melon disease forecaster. Bulletin BP-64-W, Purdue University, West Lafayette, IN.
17. Lau, Y. F., Gleason, M. L., Zriba, N., Taylor, S. E., and Hinz, P. N. 2000. Effects of coating, deployment angle, and compass orientation on performance of electronic wetness sensors during dew periods. Plant Dis. 84:192-197.
18. Llorente, I., Vilardell, P., Bugiani, R. Gherardi, I., and Montesinos, E. 2000. Evaluation of BSPcast disease-warning system in reduced fungicide use programs for management of brown spot of pear. Plant Dis. 84:631-637.
19. Madden, L. V., Ellis, M. A., Lalancette, N., Hughes, G., and Wilson, L. L. 2000. Evaluation of a disease-warning system for downy mildew of grapes. Plant Dis. 84:549-554.
20. Madeira, A. C., Kim, K. S., Taylor, S. E., and Gleason, M. L. 2002. A simple cloud-based energy balance model to estimate dew. Agric. For. Meteorol. 111:55-63.
21. Magarey, R. D. 1999. A theoretical standard for estimation of surface wetness duration in grape. Ph.D. dissertation, Cornell University, Ithaca, NY.
22. Pedro, M. J., and Gillespie, T. J, 1982. Estimating dew duration. II. Utilizing standard weather station data. Agric. Meteorol. 25:297-310.
23. Pitblado, R. E. 1988. The development of a weather-timed fungicide spray program for field tomatoes. Can. J. Plant Pathol. 10:371.
24. Potratz, K. J., Gleason, M. L., Hockmuth, M. L., Parker, S. K., and Pearston, G. A. 1994. Testing the accuracy and precision of wetness sensors in a tomato field and on turfgrass. J. Iowa Acad. Sci. 101:56-60.
25. Poysa, V., Brammall, R. A., and Pitblado, R. E. 1993. Effects of foliar fungicide sprays on disease and yield of processing tomatoes in Ontario. Can. J. Plant Sci. 73:1209-1215.
26. Rao, P. S., Gillespie, T. J., and Schaafsma, A. W. 1998. Estimating wetness duration on maize ears from meteorological observations. Can. J. Soil. Sci. 78:149-154.
27. Sentelhas, P. C., Gillespie, T. J., Batzer, J. C., Gleason, M. L., Monteiro, J. E. B. A., Pezzopane, J. R. M., and Pedro, M. J., Jr. 2005. Spatial variability of leaf wetness duration in different crop canopies. Int. J. Biometeorol. 49:363-370.
28. Shtienberg, D., and Elad, Y. 1997. Incorporation of weather forecasting in integrated biological-chemical management of Botrytis cinerea. Phytopathology 87:332-340.
29. Truxall, D. L., and Travis, J. W 1994. Analysis of the accuracy of predicted weather data on the impact of apple disease management. (Abstr.) Phytopathology 84:1125.