Abstract
Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains. Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. Thus, it would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. We conducted a functional data analysis of weather-based time series data linked to DON content in order to identify weather patterns associated with increased DON levels. Since flowering date was not recorded in our dataset, a mathematical model was developed to predict phenological growth stages in Norwegian spring oats. Through functional data analysis, weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Oat fields with elevated DON levels generally had warmer weather around sowing, and lower temperatures and higher relative humidity or rain prior to flowering onwards, compared to fields with low DON levels. Our results are in line with results from similar studies presented for FHB epidemics in wheat. Functional data analysis was found to be a useful tool to reveal weather patterns of importance for DON development in oats.
Highlights
Academic Editors: Vittorio Rossi, Elisa Gonzalez-Dominguez and Division of Food Production and Society, Norwegian Institute of Bioeconomy Research (NIBIO), Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research (NIBIO), Department of Plant Sciences, Norwegian University of Life Sciences (NMBU), 1432 Ås, Norway; Centre for Integrated Pest Management, Harper Adams University, Newport TF 10 8NB, UK; Abstract: Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains
Of the 18 phenological models developed in this study, model OPM1.1 was identified to give the best estimate of phenological growth stages of oat varieties in Norway according to our dataset
Functional data analysis was used with smoothing functions, and in order to identify weather patterns of main significance, we presented our data in three different ways: i: Weather patterns reflecting either daily output or cumulative weather variables presented from 90 days prior, to 30 days posterior to estimated flowering, ii: Weather patterns for cumulative time-series of selected weather variables presented from 30 days prior, to 30 days posterior to estimated flowering, iii: Weather patterns for cumulative time-series of selected weather variables presented from sowing according to crop growth resolution instead of on a daily time step
Summary
Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. It would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. Weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Despite the effort put into breeding for increased resistance to Fusarium graminearum, an important member of the Fusarium Head Blight (FHB) disease complex and regarded as the main producer of the mycotoxin deoxynivalenol (DON) in Norwegian oats [1], the varieties are still only moderately resistant [3]. FHB epidemics in wheat are highly associated with weather conditions in the growing season, weather conditions are the main drivers of statistical and process based models published maps and institutional affiliations
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