A predictive model on deoxynivalenol in harvested wheat in China: Revealing the impact of the environment and agronomic practicing

Sen Li, Ningjing Liu, Di Cai, Cheng Liu, Jin Ye*, Bingjie Li, Yu Wu, Li Li, Songxue Wang, H.J. van der Fels-Klerx

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

Deoxynivalenol (DON) in wheat is one of the major food safety concerns worldwide. In this study, 70 characteristic precursive factors associated with environment and 6 agronomic practicing factors were explored, using historical data of 479 wheat fields in the Huang-Huai-hai, China. Results showed that DON concentrations influenced by air temperature, relative humidity, precipitation, and sunshine duration in the period from 17 days before flowering to 10 days before harvest. Rice crop rotation, straw returning, larger density of sowing, and lower latitude planting increased DON risk. Furthermore, an empirical model of DON prediction was established. The classification accuracy of internal and external validation were 87.73% (R2 = 0.62) and 80.21% (R2 = 0.60), respectively. This model is the first large-scale prediction of mycotoxin contamination in grain at harvest in China. It can be used to predict the risk of DON contamination for nearly 14 % of the global wheat supply.

Original languageEnglish
Article number134727
JournalFood Chemistry
Volume405
DOIs
Publication statusPublished - 30 Mar 2023

Keywords

  • Agronomy
  • Climate
  • Grain
  • Mycotoxin
  • Predictive model

Fingerprint

Dive into the research topics of 'A predictive model on deoxynivalenol in harvested wheat in China: Revealing the impact of the environment and agronomic practicing'. Together they form a unique fingerprint.

Cite this