Canola (Brassica napus L.) has recently been adopted as a dual-purpose crop (graze and grain) in the higher rainfall areas (>550 mm) of eastern Australia, but the feasibility in drier inland areas with a shorter growing season is uncertain. We modified the APSIM-Canola model by using observations from an irrigated grazing experiment, with the aim of using a simulation approach to investigate various aspects of dual-purpose canola production. Sowing opportunities, forage production for grazing and grain production were considered in the simulations, and effects of variables such as sowing date, cultivar type, plant density and nitrogen supply were investigated in simulations for 109 years of climate data from Wagga Wagga, NSW. APSIM-Canola predictions of vegetative growth and grain yield for recent varieties were inadequate when using existing parameters, but were improved by increasing the maximum leaf area parameter to reflect those of modern hybrid types. For grazed crops, APSIM-Canola overestimated the initial rate of regrowth, but accurately simulated biomass at flowering. Simulations of a range of management options to generate different pre-grazing biomass predicted that sowing before 15 May, using vigorous (hybrid) cultivars, high plant density (60–80 plants m–2) and adequate soil nitrogen, maximised biomass production. Assuming a rainfall-based sowing opportunity of 25 mm over 3 days and a minimum pre-grazing biomass of 1000 kg ha–1, grazing was possible in 53% of years, with 50% of those years providing grazing opportunities before 7 June at Wagga Wagga. Depending on stocking rate, crops could be grazed until early to mid-July, providing 400–1000 dry sheep equivalent days ha–1 of grazing, and allow regrowth to achieve a target biomass of 5000 kg ha–1 at flowering, which was required to maximise potential yield. The simulation analysis confirms significant opportunities to achieve valuable livestock grazing from canola crops sown in an early window (before May) without compromising potential yield, and the simulation framework developed can be readily applied to other regions.