Littleseed canarygrass is a troublesome grass weed in wheat fields in Iran.Predicting weed emergence dynamics can help farmers more effectively controlweeds. In this work, four nonlinear regression models (beta, three-piecesegmented, two-piece segmented, and modified Malo's exponential sine) werecompared to describe the cardinal temperatures for the germination oflittleseed canarygrass. Two replicated experiments were performed with thesame temperatures. An iterative optimization method was used to calibratethe models and different statistical indices (mean absolute error [MAE],coefficient of determination [R2], intercept and slope of the regression equation of predictedvs. observed hours to germination) were applied to compare theirperformance. The three-piece segmented model was the best model to predictthe germination rate (R2 = 0.99, MAE = 0.20 d, and coefficient of variation 1.01 to4.06%). Based on the model outputs, the base, the lower optimum, the upperoptimum, and the maximum temperatures for the germination of littleseedcanarygrass were estimated to be 4.69, 22.60, 29.62, and 38.13 C,respectively. The thermal time required to reach 10, 50, and 90% germinationwas 31.98, 39.26 and 45.55 degree-days, respectively. The cardinaltemperatures depended on the model used for their estimation. Overall, thethree-piece segmented model was better suited than the other models toestimate the cardinal temperatures for the germination of littleseedcanarygrass.