Modeling deoxynivalenol contamination of wheat in northwestern Europe for climate change assessments.

Climate change will affect mycotoxin contamination of feed and food. Mathematical models for predicting mycotoxin concentrations in cereal grains are useful for estimating the impact of climate change on these toxins. The objective of the current study was to construct a descriptive model to estimate climate change impacts on deoxynivalenol (DON) contamination of mature wheat grown in northwestern Europe. Observational data from 717 wheat fields in Norway, Sweden, Finland, and The Netherlands were analyzed, including the DON concentrations in mature wheat, agronomical practices, and local weather. Multiple regression analyses were conducted, and the best set of explanatory variables, mainly including weather factors, was selected. The final model included the following variables: flowering date, length of time between flowering and harvest, wheat resistance to Fusarium infection, and several climatic variables related to relative humidity, temperature, and rainfall during critical stages of wheat cultivation. The model accounted for 50 % of the variance, which was sufficient to make this model useful for estimating the trends of climate change on DON contamination of wheat in northwestern Europe. Application of the model in possible climate change scenarios is illustrated.

Statistical analysis of agronomical factors and weather conditions influencing deoxynivalenol levels in oats in Scandinavia.

The relationship between weather data and agronomical factors and deoxynivalenol (DON) levels in oats was examined with the aim of developing a predictive model. Data were collected from a total of 674 fields during periods of up to 10 years in Finland, Norway and Sweden, and included DON levels in the harvested oats crop, agronomical factors and weather data. The results show that there was a large regional variation in DON levels, with higher levels in one region in Norway compared with other regions in Norway, Finland and Sweden. In this region the median DON level was 1000 ng g⁻¹ and the regulatory limit for human consumption (1750 ng g⁻¹) was exceeded in 28% of the samples. In other regions the median DON levels ranged from 75 to 270 ng g⁻¹, and DON levels exceeded 1750 ng g⁻¹ in 3-8% of the samples. Including more variables than region in a multiple regression model only increased the adjusted coefficient of determination from 0.17 to 0.24, indicating that very little of the variation in DON levels could be explained by weather data or agronomical factors. Thus, it was not possible to predict DON levels based on the variables included in this study. Further studies are needed to solve this problem. Apparently the infection and/or growth of DON producing Fusarium species are promoted in certain regions. One possibility may be to study the species distribution of fungal communities and their changes during the oats cultivation period in more detail.

Development of a highly sensitive nested-PCR method using a single closed tube for detection of Fusarium culmorum in cereal samples.

AIMS: The aim of the study was to develop a sensitive detection method of Fusarium culmorum contamination in cereal samples. METHODS AND RESULTS: A nested-PCR method using a single closed tube was developed for the detection of F. culmorum in infected cereal samples. The concentrations of the first primer pair was diluted 10,000 times compared to the concentration used for the second primer pair. Differing annealing temperatures allowed both first and second polymerase chain reaction (PCR) reactions to be performed subsequently in the same closed tube. The detection limit was 5-50 fg of purified target DNA and allowed the detection of 1% infected seeds of wheat in a mixture with uninfected grains. CONCLUSIONS: F. culmorum can be specifically detected in cereal samples by the highly sensitive method of nested-PCR in a single closed tube. SIGNIFICANCE AND IMPACT OF THE STUDY: This work describes the detection of F. culmorum in cereal samples that is approximately 100 times more sensitive than previous PCR methods, involves low risk of cross contaminations between samples, low costs and reduced hands-on time as compared to standard nested-PCR protocols.