Background and Objectives
Fusarium head blight (FHB) is a serious disease affecting wheat. FHB has occurred sporadically in Iran for many years and ranks among the most important fungal diseases of wheat in Mazandaran, Golestan, Zanjan, Fars, and Ardabil provinces, including the Moghan Plain. Several factors influence FHB epidemics, including pathogen persistence in crop residues, wheat cultivar resistance, initial inoculum dynamics, a microclimate favorable for infection, and host phenology. Disease-risk prediction models based on weather data can identify periods favorable for disease development and spread. However, without direct measurements of pathogen inoculum, these models may not achieve the desired level of accuracy. Aerobiological pathogen monitoring using volumetric spore traps has provided valuable data on inoculum levels in wheat fields. Researchers have used these data to improve disease predictions across various cereal production systems.
Materials and Methods
A field investigation on the Moghan Plain, Ardabil Province, monitored 35 fields over three cropping seasons, 2020-2021, 2021-2022, and 2022-2023, to determine disease incidence and severity. Concurrently, continuous aerobiological sampling was conducted for 30 days, from March 29 to April 29, 2023, using a single spore trap installed in one field to examine the population structure and spatial distribution of spores (macroconidia). Disease incidence was recorded over the three-year period, while disease severity was recorded during the 2023 cropping season. Both parameters were calculated at the grain-filling stage. Pathogen species were identified using morphological and molecular methods. A generalized linear model (GLM) was used to analyze the relationship between the number of spores released within each 24-hour period as the dependent variable, based on 30 observations, and nine meteorological variables.
Results
The disease occurred in 77–85% of the surveyed fields, and symptom incidence within infected clusters ranged from 5 to 45%. Aerobiological trapping detected pathogen spores on 62.5% of the sampling days. Median 24-hour spore counts during the pre-flowering and flowering stages were 2.8 and 10.2, respectively, with peak densities reaching 25 spores per 24 hours. Morphological and PCR-based identification of 31 isolates showed that Fusarium graminearum predominated, accounting for 83% of the fungal isolates, while F. culmorum was the second most frequent species, accounting for 17%. Statistical analyses identified daily rainfall and minimum relative humidity as the strongest predictors of 24-hour spore counts. A bivariate regression model incorporating these two variables explained approximately 78% of the variation in spore numbers (R² ≈ 0.783; adjusted R² ≈ 0.675). Variance inflation factors (VIF ≈ 1.09) indicated no substantial multicollinearity. However, the residuals showed possible temporal autocorrelation, suggesting that the model should account for temporal dependence.
Discussion
These results indicate that aerobiological monitoring during flowering and the use of lower intervention thresholds in risk-prediction models could help reduce epidemic risk. This study included 30 observations in the regression analysis and 35 farms in the field survey. Although the results were significant, future studies should increase the sample size to improve estimate stability and allow broader regional or national generalization. Future research should also collect larger longitudinal datasets and validate the models against independent datasets. Given the Durbin–Watson statistic, time-series or mixed-effects models may better account for temporal dependence and variance heterogeneity. Combining aerobiological data with climate models and farm management data, such as residue cover, crop rotation, planting date, and cultivar type, could improve predictions of FHB risk. This study showed that FHB was highly prevalent on the Moghan Plain and that moisture and precipitation influenced airborne spore dispersal. F. graminearum was the dominant species, and the regression models identified precipitation and minimum relative humidity as the most important predictors of spore counts. To improve the validity and applicability of these findings, future studies should expand the spatial and temporal scope, assess the statistical assumptions more precisely, and examine the direct relationships among spore counts, disease incidence, and mycotoxin risk.