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Modeling weed emergence as a function of meteorological records

Published online by Cambridge University Press:  20 January 2017

Andrew Mead
Biometrics Department, Horticulture Research International, Wellesbourne, Warwick CV35 9EF, Great Britain
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Unique long-term historical emergence records were used to assess the association between weed seedling emergence and various elements of meteorological data. These elements included both temperature-based and rainfall-related variables in the 7-d periods before and during which emergence occurred. Five weed species (Stellaria media, Chenopodium album, Capsella bursa-pastoris, Matricaria perforata, and Veronica hederifolia) with contrasting emergence patterns were studied in disturbed soil. Logistic regression analysis was used to identify meteorological variables of interest and allowed their relative importance to be assessed and ranked. Logistic regression was further used to associate probabilities of emergence with observed levels of important individual meteorological elements. This approach enabled prediction of the probability of emergence following given meteorological conditions and hence an assessment of the risk of omitting weed control measures. Predictions were made based on single meteorological variables and compared with observed data. Results indicated that temperature was the dominant factor in predicting emergence. Soil moisture, while also important, was a secondary factor only becoming important once the species-specific temperature requirement had been satisfied. The potential for further development of the model is discussed.

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Copyright © Weed Science Society of America 

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