Prediction of Italian ryegrass (Lolium multiflorum L.) emergence using soil thermal time

Abstract

Italian ryegrass (Lolium multiflorum L.) is a highly competitive weed widely disseminated worldwide that affects both summer and winter crops. The development of predictive emergence models can contribute to optimizing weed management. The aim of this study was to develop and validate an empirical emergence model of Italian ryegrass based on soil thermal time. For model development, cumulative emergence in two locations was obtained, and the model was validated with data collected in an experiment conducted independently. Three commonly used emergence models were compared (Gompertz, Logistic, and Weibull). The relationship between emergence and soil thermal time was described best by the Gompertz model. The Gompertz model predicted Italian ryegrass emergence start at 300 thermal time (TT), reaching 50% emergence at 444 TT, and 90% at 590 TT. Model validation performed well in predicting Italian ryegrass emergence and proved to be efficient at describing its emergence. This is a potential predictive tool for assisting farmers with Italian ryegrass management.