Studies were conducted to calibrate and validate a mathematical model previously developed to predict common lambsquarters seedling emergence at different corn seedbed preparation times. The model was calibrated for different types of soil by adjusting the base temperature of common lambsquarters seedling emergence to the soil texture. A relationship was established with the sand mineral fraction of the soil and was integrated into the model. The calibrated model provided a good fit of the field data and was accurate in predicting cumulative weed emergence in different soil types. The validation was done using data collected independently at a site located 80 km from the original experimental area. There were no differences between observed and predicted values. The accuracy of the model is very satisfactory because the emergence of common lambsquarters populations was accurately predicted at the 95% probability level. This model is one of the first to take into consideration seedbed preparation time and soil texture. This common lambsquarters emergence model could be adapted to model other weed species whose emergence is limited by low spring temperature.

A mathematical model was developed to predict common lambsquarters seedling emergence in southwestern Quebec. The model was based on the thermal-time concept, using air temperatures in the double-sine calculation method. The model was built using data from five experiment-years for corn naturally infested with weed populations. Once developed, the model was calibrated using different crop seedbed preparation times. The base temperature was then adjusted for each time of seedbed preparation. A power regression function was used to relate adjusted base temperatures and the accumulated thermal units at seedbed preparation time. A modified Weibull function was then fitted to the field emergence data, expressed as the cumulative proportion of the total seedling emergence over the growing season as a function of cumulative thermal units. The simplicity and accuracy of this model would make it an excellent tool to predict common lambsquarters seedling emergence in field situations, facilitating the determination of the timing of scouting in integrated weed management systems.