Fusarium graminearum is the predominant component of the Fusarium head blight complex of wheat. F. graminearum ascospores, which initiate head infection, mature in perithecia on crop residues and become airborne. The effects of temperature ( T ) and moisture on perithecium production and maturation and on ascospore production on maize stalk residues were determined. In the laboratory, perithecia were produced at temperatures between 5 and 30°C (the optimum was 21.7°C) but matured only at 20 and 25°C. Perithecia were produced when relative humidity (RH) was ≥75% but matured only when RH was ≥85%; perithecium production and maturation increased with RH. Equations describing perithecium production and maturation over time as a function of T and RH ( R ² > 0.96) were developed. Maize stalks were also placed outdoors on three substrates: a grass lawn exposed to rain; a constantly wet, spongelike foam exposed to rain; and a grass lawn protected from rain. No perithecia were produced on stalks protected from rain. Perithecium production and maturation were significantly higher on the constantly wet foam than on the intermittently wet lawn (both exposed to rain). Ascospore numbers but not their dispersal patterns were also affected by the substrate.

Forecasting models for mycotoxins in cereal grains during cultivation are useful for pre-harvest and post-harvest mycotoxin management. Some of such models for deoxynivalenol (DON) in wheat, using two different modelling techniques, have been published. This study aimed to compare and cross-validate three different modelling approaches for predicting DON in winter wheat using data from the Netherlands as a case study. To this end, a published empirical model was updated with a new mixed effect logistic regression method. A mechanistic model for wheat in Italy was adapted to the Dutch situation. A new Bayesian network model was developed to predict DON in wheat. In developing the three models, the same dataset was used, including agronomic and weather data, as well as DON concentrations of individual samples in the Netherlands over the years 2001–2013 (625 records). Similar data from 2015 and 2016 (86 records) were used for external independent validation. The results showed that all three modelling approaches provided good accuracy in predicting DON in wheat in the Netherlands. The empirical model showed the highest accuracy (88%). However, this model is highly location and data-dependent, and can only be run if all of the input data are available. The mechanistic model provided 80% accuracy. This model is easier to implement in new areas given similar mycotoxin-producing fungal populations. The Bayesian network model provided 86% accuracy. Compared with the other two models, this model is easier to implement when input data are incomplete. In future research, the three modelling approaches could be integrated to even better support decision-making in mycotoxin management.

Fusarium graminearum is a predominant component of the Fusarium head blight (FHB) complex of small grain cereals. Ascosporic infection plays a relevant role in the spread of the disease. A 3-year study was conducted on ascospore discharge. To separate the effect of weather on discharge from the effect of weather on the production and maturation of ascospores in perithecia, discharge was quantified with a volumetric spore sampler placed near maize stalk residues bearing perithecia with mature ascospores; the residues therefore served as a continuous source of ascospores. Ascospores were discharged from perithecia on 70% of 154 days. Rain (R) and vapor pressure deficit (VPD) were the variables that most affected ascospore discharge, with 84% of total discharges occurring on days with R≥0.2 mm or VPD≤11 hPa, and with 70% of total ascospore discharge peaks (≥ 30 ascospores/m³ air per day) occurring on days with R≥0.2 mm and VPD≤6.35 hPa. An ROC analysis using these criteria for R and VPD provided True Positive Proportion (TPP) = 0.84 and True Negative Proportion (TNP) = 0.63 for occurrence of ascospore discharge, and TPP = 0.70 and TNP = 0.89 for occurrence of peaks. Globally, 68 ascospores (2.5% of the total ascospores sampled) were trapped on the 17 days when no ascospores were erroneously predicted. When a discharge occurred, the numbers of F. graminearum ascospores sampled were predicted by a multiple regression model with R² = 0.68. This model, which includes average and maximum temperature and VPD as predicting variables, slightly underestimated the real data and especially ascospore peaks. Numbers of ascospores in peaks were best predicted by wetness duration of the previous day, minimum temperature, and VPD, with R² = 0.71. These results will help refine the epidemiological models used as decision aids in FHB management programs.