title: Descriptive modelling to predict deoxynivalenol in winter wheat in the Netherlands.
abstract: Predictions of deoxynivalenol (DON) content in wheat at harvest can be useful for decision-making by stakeholders of the wheat feed and food supply chain. The objective of the current research was to develop quantitative predictive models for DON in mature winter wheat in the Netherlands for two specific groups of end-users. One model was developed for use by farmers in underpinning Fusarium spp. disease management, specifically the application of fungicides around wheat flowering (model A). The second model was developed for industry and food safety authorities, and considered the entire wheat cultivation period (model B). Model development was based on observational data collected from 425 fields throughout the Netherlands between 2001 and 2008. For each field, agronomical information, climatic data and DON levels in mature wheat were collected. Using multiple regression analyses, the set of biological relevant variables that provided the highest statistical performance was selected. The two final models include the following variables: region, wheat resistance level, spraying, flowering date, several climatic variables in the different stages of wheat growing, and length of the period between flowering and harvesting (model B only). The percentages of variance accounted for were 64.4% and 65.6% for models A and B, respectively. Model validation showed high correlation between the predicted and observed DON levels. The two models may be applied by various groups of end-users to reduce DON contamination in wheat-derived feed and food products and, ultimately, reduce animal and consumer health risks.
authors.affiliation: RIKILT-Institute of Food Safety, Wageningen, the Netherlands. ine.vanderfels@wur.nl
authors.forename: H J
authors.initials: HJ
authors.lastname: Van Der Fels-Klerx
authors.forename: S L G E
authors.initials: SL
authors.lastname: Burgers
authors.forename: C J H
authors.initials: CJ
authors.lastname: Booij
mesh_terms: D000383:Agriculture / Q000379:methods; D000821:Animal Feed / Q000032:analysis; D000822:Animal Husbandry / Q000379:methods; D018556:Crops, Agricultural / Q000737:chemistry* / Q000254:growth & development; D004198:Disease Susceptibility; D005103:Expert Systems; D005506:Food Contamination / Q000517:prevention & control; D019649:Food Industry / Q000379:methods; D005659:Fungicides, Industrial / Q000008:administration & dosage; D005670:Fusarium / Q000187:drug effects / Q000254:growth & development / Q000378:metabolism; D008954:Models, Biological; D009426:Netherlands; D010935:Plant Diseases / Q000382:microbiology / Q000517:prevention & control; D018570:Risk Assessment / Q000379:methods*; D012639:Seeds / Q000737:chemistry / Q000254:growth & development; D013997:Time Factors; D014255:Trichothecenes / Q000032:analysis* / Q000096:biosynthesis; D014908:Triticum / Q000737:chemistry* / Q000254:growth & development; D014887:Weather
chemical_list: D005659:Fungicides, Industrial; D014255:Trichothecenes; C007262:deoxynivalenol