A hydrothermal time model was developed to simulate field emergence for three weed species in maize (common lambsquarters, johnsongrass, and velvetleaf). Models predicting weed emergence facilitate well-timed and efficient POST weed control strategies (e.g., chemical and mechanical control methods). The model, called AlertInf, was created by monitoring seedling emergence from 2002 to 2008 in field experiments at three sites located in the Veneto region in northeastern Italy. Hydrothermal time was calculated using threshold parameters of temperature and water potential for germination estimated in previous laboratory studies with seeds of populations collected in Veneto. AlertInf was validated with datasets from independent field experiments conducted in Veneto and in Tuscany (west central Italy). Model validation resulted in both sites in efficiency index values ranging from 0.96 to 0.99. AlertInf, based on parameters estimated in a single region, was able to predict the timing of emergence in several sites located at the two extremes of the Italian maize growing area.
Modeling Weed Emergence in Italian Maize Fields
MASIN, ROBERTA;ZANIN, GIUSEPPE
2012
Abstract
A hydrothermal time model was developed to simulate field emergence for three weed species in maize (common lambsquarters, johnsongrass, and velvetleaf). Models predicting weed emergence facilitate well-timed and efficient POST weed control strategies (e.g., chemical and mechanical control methods). The model, called AlertInf, was created by monitoring seedling emergence from 2002 to 2008 in field experiments at three sites located in the Veneto region in northeastern Italy. Hydrothermal time was calculated using threshold parameters of temperature and water potential for germination estimated in previous laboratory studies with seeds of populations collected in Veneto. AlertInf was validated with datasets from independent field experiments conducted in Veneto and in Tuscany (west central Italy). Model validation resulted in both sites in efficiency index values ranging from 0.96 to 0.99. AlertInf, based on parameters estimated in a single region, was able to predict the timing of emergence in several sites located at the two extremes of the Italian maize growing area.Pubblicazioni consigliate
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