Abstract
A general method is presented for analyzing how climatic conditions affect plant disease severity. An example of its application is given for the analysis of stripe rust (caused by Puccinia striiformis) data on winter wheat cultivar Gaines and climatic data collected at Pullman, WA. for 1968–1986. A computer program WINDOW was written to identify the climatic factors most highly correlated with disease. This program is designed to utilize meteorological data for an entire growing season of a crop as well as to include climatic conditions preceding planting. This program uses an iterative process to examine variable-length segments of meteorological data in a more exhaustive analysis than previously possible. Climatic factors considered include: mean maximum, minimum, and average temperature; total and frequency of precipitation; consecutive days with and without precipitation; accumulation of negative and positive degree days; and number of days with extreme temperature events. Variables that were highly correlated with disease were the basis for regression models that were developed to predict disease severity index for each of the three cultivars. Two- and three-variable models explained, respectively, 75 and 76% of the variation in disease from year to year. Predictions (which could be made early enough in the growing season to allow application of chemical control) were evaluated on the basis of whether years with severe disease were accurately predicted. Models were validated using Allen's PRESS statistic and by application to new data. The method is potentially applicable to studies of how climatic conditions affect the populations or productivity of other types of organisms.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Armitage, P.: 1971, Statistical Methods in Medical Research, Blackwell Scientific Publ., Oxford, 504 pp.
Coakley, S. M.: 1978, ‘The Effect of Climate Variability on Stripe Rust of Wheat in the Pacific Northwest’, Phytopathology 68, 207–212.
Coakley, S. M.: 1979, ‘Climate Variability in the Pacific Northwest and its Effect on Stripe Rust Disease of Winter Wheat’, Climatic Change 2, 33–51.
Coakley, S. M.: 1985, ‘Describing and Quantifying the Environment’, Plant Dis. 69, 461–466.
Coakley, S. M.: 1987, ‘Historical Weather Data: Its Use in Epidemiology’, in K. Leonard and W. Fry, (eds.), Plant Disease Epidemiology, Vol. II, Macmillan Publishing Co. (in press).
Coakley, S. M. and Line, R. F.: 1981a, ‘Climatic Variables that Control Development of Stripe Rust Disease on Winter Wheat’, Climatic Change 3, 303–315.
Coakley, S. M. and Line, R. F.: 1981b, ‘Quantitative Relationships Between Climatic Variables and Stripe Rust Epidemics of Winter Wheat’, Phytopathology 71, 461–467.
Coakley, S. M. and Line, R. F.: 1984, ‘Validation of Regional Models for Predicting Stripe Rust on Winter Wheat’, Phytopathology 74, 871–872 (abstr.).
Coakley, S. M., Boyd, W. S., and Line, R. F.: 1982, ‘Statistical Models for Prediction of Stripe Rust on Winter Wheat in the Pacific Northwest’, Phytopathology 72, 1539–1542.
Coakley, S. M., Boyd, W. S., and Line, R. F.: 1984, ‘Development of Regional Models that Use Meteorological Variables for Predicting Stripe Rust Disease on Winter Wheat’, J. Climate and Appl. Meteor. 23, 1234–1240.
Coakley, S. M., Line, R. F., and Boyd, W. S.: 1983, ‘Regional Models for Predicting Stripe Rust on Winter Wheat in the Pacific Northwest’, Phytopathology 73, 1382–1385.
Coakley, S. M., Line, R. F., and McDaniel, L. R.: 1988, ‘Predicting Stripe Rust Severity on Winter Wheat Using an Improved Method for Analyzing Meteorological and Rust Data’, Phytopathology (in press).
Coakley, S. M., McDaniel, L. R., and Shaner, G.: 1985, ‘Model for Prediction of Septoria tritici Blotch Severity on Winter Wheat’, Phytopathology 75, 1245–1251.
Daniel, C. and Wood, F. S.: 1980, Fitting Equations To Data: Computer Analysis of Multifactor Data, John Wiley & Sons, N.Y., 458 pp.
Draper, N. R. and Smith, H.: 1981, Applied Regression Analysis, John Wiley & Sons, New York.
Jones, A. L., Fisher, P. D., Seem, R. C., Kroon, J. C., and Van DeMotter, P. J.: 1984, ‘Development and Commercialization of an In-field Microcomputer Delivery System for Weather-driven Predictive Models’, Plant Dis. 68, 458–463.
Montgomery, D. C. and Peck, E. A.: 1982, Introduction to Linear Regression Analysis, John Wiley & Sons, New York.
Rotem, J.: 1978, ‘Climatic and Weather Influences on Epidemics’, in J. G. Horsfall and E. B. Cowling (eds.), Plant Disease, How Disease Develops in Populations, Academic Press, New York, Vol. 2, pp. 317–337.
SAS Institutes Inc.: 1985, SAS User's Guide: Statistics Version 5, 1985 Edition, Cary, NC.
Shaner, G. and Finney, R. E.: 1976, ‘Weather and Epidemics of Septoria Leaf Blotch of Wheat’, Phytopathology 66, 781–785.
Sharp, E. L.: 1965, ‘Prepenetration and Postpenetration Environment and Development of Puccinia striiformis on wheat’, Phytopathology 55, 198–203.
Snee, R. D.: 1977, ‘Validation of Regression Models: Methods and Examples’, Technometrics 19, 415–428.
Stone, J. F.: 1983, ‘On Julian Day Notation for Meteorological Conditions’, Agric. Meteor. 29, 137–140.
Sutton, J. C., Gillespie, T. J., and Hildebrand, P. D.: 1984, ‘Monitoring Weather Factors in Relation to Plant Disease’, Plant Dis. 68, 78–84.
Teng, P. S.: 1981, ‘Validation of Computer Models of Plant Disease Epidemics: A Review of Philosophy and Methodology’, Journal of Plant Diseases and Protection 88, 49–63.
Teng, P. S.: 1985, ‘Construction of Predictive Models: II. Forecasting Crop Losses’, in C. A. Gilligan (ed.), Advances in Plant Pathology, Mathematical Modelling of Crop Disease, Academic Press Inc. New York, Vol. 3, pp. 179–206.
Teng, P. S. and Rouse, D. I.: 1984, ‘Understanding Computers: Applications in Plant Pathology’, Plant Dis. 68, 539–543.
Zadoks, J. C. and Konzak, C. F.: 1974, ‘A Decimal Code for the Growth Stages of Cereals’, Eucarpia Bull. 7, 12 pp.
Author information
Authors and Affiliations
Additional information
This research was supported by a National Science Foundation Grant (ATM 85-03115), Climate Dynamics Program, Division of Atmospheric Sciences.
Rights and permissions
About this article
Cite this article
Coakley, S.M., McDaniel, L.R. & Line, R.F. Quantifying how climatic factors affect variation in plant disease severity: A general method using a new way to analyze meteorological data. Climatic Change 12, 57–75 (1988). https://doi.org/10.1007/BF00140264
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF00140264