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

Abstract

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.

Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

“…Our results suggest that (i) rh-type predictors are better than other moisture-based summaries, considered here as representing the association between major FHB epidemics and underlying wetness requirements, and (ii) moisture is more of a limiting factor than t for major FHB epidemics in the United States. The greater relative importance of moisture-related predictors over t-related ones was also a qualitative observation made in previous empirical models (12,29,41,50,71). One may be tempted to relate weather-based predictors to specific processes within the FHB cycle but these empirical predictors only show correlation or association with the disease, and no proof of causation or mechanism should be made.…”

“…However, the modeling results are consistent with known aspects of the disease cycle. Given that wheat is most susceptible to infection at anthesis (47,63), one may presume that the pre-anthesis weather-based predictors capture conditions associated with inoculum production and dispersal up to anthesis (50). The post-anthesis predictors would likewise represent additional favorable conditions for infection and colonization of the spikes occurring within 15 days post-anthesis (9).…”

“…The remainder were downloaded from weather data archives maintained by Wolfram Research via Mathematica scripts we wrote (version 7.0; Wolfram Research, Champaign, IL). Some weather stations collected other variables, but w = {t,rh,r} were common to all weather data sets; moreover, predictors associated with FHB epidemics in the United States (12,29,40,50) are related in one form or another to the elements of the vector w. All w j , j = 1,…,150, were checked for out-of-range (e.g., rh > 100) and missing data (5). Time series plots identified values that were within expected ranges but, nevertheless, were erroneous due to either instrument failure or transcriptional error (e.g., consecutive 12 h for which t = 15°C).…”

“…10 . Fixed-length windows of 5, 7, 10, 14, and 15 days pre-and post-anthesis were created, the decision being influenced by previous research (6,7,12,28,29,40,41,50,71). The windows were discrete, a subset of all possible windows, and were nested on either side of fdate.…”

“…Previous research (7,12,28,29,40,41,50,71) guided the creation of 380 weather-based predictors from w pre.5 , w pre.7 , w pre.10 , w pre.14 , w pre.15 , w post.5 , w post.7 , w post.10 , w post.14 , and w post. 15 (Table 1).…”