Modeling weed emergence as a function of meteorological records

Unique long-term historical emergence records were used to assess the assoc iation between weed seedling emergence and various elements of meteorologic al data. These elements included both temperature-based and rainfall-relate d variables in the 7-d periods before and during which emergence occurred. Five weed species (Stellaria media, Chenopodium album, Capsella bursa-pasto ris, Matricaria perforata, and Veronica hederifolia) with contrasting emerg ence 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 fur ther used to associate probabilities of emergence with observed revels of I mportant individual meteorological elements. This approach enabled predicti on of the probability of emergence following given meteorological condition s and hence an assessment. of the risk of omitting weed control measures. P redictions were made based on single meteorological variables and compared with observed data. Results indicated that temperature was the dominant fac tor in predicting emergence. Soil moisture, while also important, was a sec ondary factor only becoming important once the species-specific temperature requirement had been satisfied. The potential for further development of t he model is discussed.