A statistical model for determining zearalenone contamination in rice (Oryza sativa L.) at harvest and its prediction under different climate change scenarios in South Korea

• Published in: Applied biological chemistry Vol. 62; no. 1; pp. 1 - 9
• Main Authors: Joo, Yongsung, Ok, Hyun Ee, Kim, Jihyun, Lee, Sang Yoo, Jang, Su Kyung, Park, Ki Hwan, Chun, Hyang Sook
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.12.2019; Springer Nature B.V; 한국응용생명화학회
• Subjects: Applied Microbiology; Biological Techniques; Bioorganic Chemistry; Chemistry; Chemistry and Materials Science; Climate change; climatic zones; Contamination; Environmental impact; Flowering; Food; Food contamination; food grains; Food safety; Grain; harvest date; Harvesting; humidity; Mathematical models; Meteorological data; milling; Mycotoxins; observational studies; Oryza; Oryza sativa; paddies; prediction; Predictions; Regression analysis; Regression models; Rice; Rice fields; South Korea; Statistical analysis; Statistical models; Statistical prediction; temperature; Weather; Zearalenone; 농학; South Korea; Climate change; Zearalenone; Statistical model; Predictive map; Rice
• ISSN: 2468-0834; 2468-0842; 2468-0842
• DOI: 10.1186/s13765-019-0447-z

Mycotoxin contamination of food grains is a food safety hazard, and zearalenone (ZEN) is one such mycotoxin affecting rice grains ( Oryza sativa L.). A statistical model for estimating the impacts of climate change on ZEN contamination of rice grains in South Korea was constructed. Observational data on ZEN concentrations in rice grains at harvest and local weather information from 241 rice fields in South Korea were collected. To estimate the impact of weather variables on ZEN concentrations, multiple regression analyses were conducted along with variable selection procedure. The final model included the following variables: average temperature and humidity over the flowering period, daily (between days) change in temperature over the harvest period, degree of milling, and the climate region. On the basis of this regression model, maps showing ZEN contamination were produced for South Korea in the present day, the 2030s, and the 2050s, using the representative concentration pathway (RCP) emission scenarios RCP 2.6, 4.5, and 8.5. The predictive maps project that in the 2030s and 2050s, ZEN contamination in rice grains will increase nationwide, particularly more so on the western side of South Korea. Our research results might be helpful in developing effective control measures against ZEN contamination due to climate change.