| Summary: | The present work describes a mathematical model that simulates growth and biomass production of Star grass in the lowland subtropical areas of Central Mexico. The Star grass model simulates growth of the different plant structures such as the root, stem and shoots, and it has five submodels: growth, photosynthesis, mineral intake, ontogenic and disturbances submodels. The growth submodel simulates nutrient partition and growth of root, stem and leaves. The photosynthesis submodel simulates the transformation of solar energy and carbon into biomass, and it is determined by irradiation, ambient temperature and the leaf area index. The mineral uptake submodel represents the interphase between the soil and the plant, and it simulates a plant’s uptake of nitrogen and phosphorous as well as the different factors that determined their availability, such as soil water content and the mineralization rate. The ontogenic submodel emulates the vegetative phase and the senescence of the plant. The disturbances submodel simulates the effect of factors such as harvest, fire and grazing on the above-ground part of the plant. The original model was developed for C3 plants so it was necessary to modify its parameters for simulating a C4 plant like Star grass. The parameters of soil, drought tolerance, nitrogen content (in the degradable fraction of the plant) and temperatures for maximum and minimum plant growth were modified. The phenological phase of the plant was modified and the reproductive routine was eliminated since Star grass is propagated by stolons and generally remains in the vegetative stage. The re-growth of new shoots begins when soil temperature is above 11o C and soil water content above 0.5. Minimum data set for model development was collected from a sward planted with Star grass and located in the Tejupilco municipality (18º 45´ 30†North and 99º 59´ 07†West) México, and data from literature was also used. Daily climate data was obtained from the local meteorological station. Model predictions were validated with data sets collected from two more swards located in the same region. Results indicated that the model’s predicted dry matter yield during the entire production cycle was very close to observed data (R2=0.92, P
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