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A Distributed Lag Analysis of the Relationship Between Gibberella zeae Inoculum Density on Wheat Spikes and Weather Variables

First, second, and tenth authors: Department of Plant Pathology, The Ohio State University, Ohio Agricultural Research and Development Center, Wooster 44691; third author: Department of Plant Pathology, Kansas State University, Manhattan 66506; fourth and fifth authors: Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907; sixth and seventh authors: Department of Plant Pathology, North Dakota State University, Fargo 58105; and eighth and ninth authors: Plant Science Department, South Dakota State University, Brookings 57007.

In an effort to characterize the association between weather variables and inoculum of Gibberella zeae in wheat canopies, spikes were sampled and assayed for pathogen propagules from plots established in Indiana, North Dakota, Ohio, Pennsylvania, South Dakota, and Manitoba between 1999 and 2005. Inoculum abundance was quantified as the daily number of colony forming units per spike (CFU/spike). A total of 49 individual weather variables for 24-h periods were generated from measurements of ambient weather data. Polynomial distributed lag regression analysis, followed by linear mixed model analysis, was used to (i) identify weather variables significantly related to log-transformed CFU/spike (the response variable; Y), (ii) determine the time window (i.e., lag length) over which each weather variable affected Y, (iii) determine the form of the relationship between each weather variable and Y (defined in terms of the polynomial degree for the relationship between the parameter weights for the weather variables and the time lag involved), and (iv) account for location-specific effects and random effects of years within locations on the response variable. Both location and year within location affected the magnitude of Y, but there was no consistent trend in Y over time. Y on each day was significantly and simultaneously related to weather variables on the day of sampling and on the 8 days prior to sampling (giving a 9-day time window). The structural relationship corresponded to polynomial degrees of 0, 1, or 2, generally showing a smooth change in the parameter weights and time lag. Moisture- (e.g., relative humidity-) related variables had the strongest relationship with Y, but air temperature- and rainfall-related variables also significantly affected Y. The overall marginal effect of each weather variable on Y was positive. Thus, local weather conditions can be utilized to improve estimates of spore density on wheat spikes around the time of flowering.

Additional keywords:Fusarium graminearum, Fusarium head blight, wheat scab.