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The effect of the uniformity of sprinkler irrigation and plant density on the variability of maize grain yield under semi-arid conditions was evaluated, and the relevance of the spatial variability of these two variables on the simulation of maize grain yield was tested with the DSSAT-CERES-Maize model (V 4.5). Experimental field data from three maize growing seasons (26, 29 and 21) with nighttime or daytime sprinkler irrigation were used to test the model performance. Yield, irrigation depths and plant density distribution were measured in 18m×18m plots divided in 25 sub-plots. Regression analysis showed that the variability of plant density and seasonal irrigation depth (due to irrigation non-uniformity) was able to explain from 28 to 77% of the variability in maize grain yield for the experiments with a relatively high coefficient of uniformity (CU) (73–84%) and high plant density (more than 74,844plantsha−1). Taking into account irrigation depth distribution improved maize yield simulations compared to simulations with the average irrigation water applied. The root mean square error (RMSE) decreased from 637 to 328kgha−1. Maize yield was over-predicted by 3% when irrigation depth distribution was not considered. Including plant density distribution in the simulations did not improve maize yield simulations. The simulated decrease in maize yield with decreasing CU of irrigation from 1 to 7% varied from year to year and caused reductions in yield ranging from .75 to 2.5Mgha−1. The ability of the model to simulate CU effects on maize yield is shown. Including irrigation non-uniformity improved maize yield simulation. Including plant density non-uniform distribution did not improve maize yield simulation. The simulated decrease in maize yield with decreasing coefficient of uniformity (CU) of irrigation varied from year to year.