To allow development of a dynamic plant growth model for the simulation of dry matter (DM) production in the field based on meteorological data, in the absence of detailed information on phenology and growth, a model was developed solely using data from plants grown in large pots under glasshouse conditions. The little studied Australian native perennial legume Cullen australasicum, which has potential as a pasture species, was used. Phenological development (e.g. rooting depth, leaf appearance, leaf growth and shedding) and physiological characteristics (e.g. photosynthetic capacity) were measured in the glasshouse for model development and parameterisation. Whenever possible and appropriate, established models were used to explain individual processes (e.g. canopy photosynthesis and evapotranspiration); if unavailable, models were developed. A diverse independent data set was used to validate the model, including published and unpublished results from the Western Australian wheatbelt under non-irrigated conditions and different plant densities (1, 2, 4, 8 and 16 plants m−2), and from other locations as well. Main stem node appearance was simulated fairly well, with a root mean square of deviations (RMSD) of 1.86 nodes day−1 (r = 0.996, n = 11). The model predicts the DM production well for non-irrigated conditions in the Western Australian wheatbelt under densities of 1, 2, 4 and 8 plants m−2, and at Wagga Wagga and Barmedman, New South Wales, but not for a density of 16 plants m−2 in the Western Australian wheatbelt and not under irrigated, fertilised conditions. Overall, the model explained over 78% of the observed variation in DM with a RMSD of 54 g m−2 (r = 0.89, n = 31). The high prediction accuracy suggests that this generic approach to model development offers promise for the simulation of the growth of C. australasicum for the tested locations, particularly under non-fertilised and non-irrigated conditions. This approach should prove valuable for quick evaluation of the diverse array of novel crop and pasture species now under evaluation for low-rainfall areas, as it requires only limited information and minimal parameterisation.