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Validation of crop models is often neglected due to limitations in available measured data. In this work, a cross validation approach was used to validate the CERES-Maize crop growth model in spite of limited measured data. Simulations were run for an Iowa cornfield divided into 100 grids. Five years of yield information were available for calibration of two parameters, tile drainage rate and saturated hydraulic conductivity, in each grid. Cross validation requires that the model be calibrated five times in each grid by alternatively leaving out one season yield information. The model is then evaluated using the fitted parameters to simulate yield for the growing season left out of the calibration. Results indicated that the model performed most poorly when using the wettest or driest growing season to validate the model. Model parameters fitted under moderate weather conditions were less flexible for simulating yield in growing seasons with more extreme weather conditions. Spatial variability in model performance across grids indicated that topography or soil type may influence the ability of the model to simulate yield.