AlertInf is a recently developed model to predict the daily emergence ofthree important weed species in maize cropped in northern Italy (commonlambsquarters, johnsongrass, and velvetleaf). Its use can improve theeffectiveness and sustainability of weed control, and there has been growinginterest from farmers and advisors. However, there are two important limitsto its use: the low number of weed species included and its applicabilityonly to maize. Consequently, the aim of this study was to expand theAlertInf weed list and extend its use to soybean. The first objective was toadd another two important weed species for spring-summer crops in Italy,barnyardgrass and large crabgrass. Given that maize and soybean havedifferent canopy architectures that can influence the interrow microclimate,the second objective was to compare weed emergence in maize and soybean sownon the same date. The third objective was to evaluate if AlertInf wastransferable to soybean without recalibration, thus saving time and money.Results showed that predictions made by AlertInf for all five speciessimulated in soybean were satisfactory, as shown by the high efficiencyindex (EF) values, and acceptable from a practical point of view. The factthat the algorithm used for estimating weed emergence in maize was alsoefficient for soybean, at least for crops grown in northeastern Italy withstandard cultural practices, encourages further development of AlertInf andthe spread of its use.