Soil water is an important variable in agricultural environments as it contributes to yield response as well as areas of environmental concern including erosion, runoff, and nitrogen leaching (through deep drainage). Crop models have been established as a method for simulating agricultural production and examining ecosystem responses. However, because all crop models are based on limited system information, models contain errors which increase uncertainty around their predictions. Data assimilation provides the opportunity to merge both model and observational data in order to obtain a better representation of the true physical system. The objectives of our experiments are to (1) evaluate the efficacy and feasibility of implementing a simple data assimilation algorithm for near-surface soil moisture in the DSSAT (Decision Support System for Agrotechnology Transfer) Model and (2) examine changes in yield from different data assimilation cases. In this paper we use direct insertion, a simple data assimilation method, to examine how assimilation of near-surface (0 – 5 cm) soil water content observations impacts maize yields. Three synthetic experiments were performed using 20 years of simulated climate data, two common Iowa soil types, and two nitrogen rates. The CERES-Maize component of the DSSAT Model was used for simulations. The first experiment consists of simple perturbations of model observations, the second experiment uses incorrect model soil parameters, and the third experiment examines a model bias. The results of the experiments performed here show that it is possible to implement a direct insertion algorithm for near-surface soil water content into the DSSAT model. Yield differences varied according to year, soil type, and nitrogen rate. The results of all three experiments showed that yield differences can occur between scenarios which use the original model generated values and assimilated values even when a simple assimilation method (direct insertion) is used. This information provides preliminary insights into the feasibility and impact of using data assimilation with agricultural systems.