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Decision support system for agrotechnology transfer (DSSAT) is increasingly used in agriculture, and the primary task in the localization of DSSAT is to estimate crop cultivar coefficients. Generalized likelihood uncertainty estimation (GLUE) coefficient estimator is a self-contained coefficient estimation tool for DSSAT, but the crop cultivar coefficients estimated by GLUE coefficient estimator are not always effective, and the simulation accuracy of the DSSAT with the estimated coefficents is often not high. Through using the field measured yield data of four cultivars of rice and the comparative analysis method, with the results of running the GLUE coefficient estimator as a reference, treating each particle of particle swarm optimization (PSO) was considered as a group of rice cultivar coefficients, calling DSSAT to simulate rice yield during the operation of the PSO, and modifying the particles according to the yield simulation error and the operation mechanism of PSO, thus verifying the feasibility of PSO to optimize the coefficients of DSSAT rice cultivar coefficients. The results showed that both algorithms can identify the DSSAT rice cultivar coefficients well, but the GLUE coefficient estimator had a higher frequency of estimating invalid coefficients. Compared with the GLUE coefficient estimator, the coefficients identified by the PSO were all efficient, and the accuracy of its optimized parameters for DSSAT simulated rice yield was higher, and the normalized root mean square error (NRMSE) was in the range of 5.98%~8.78%, which was significantly lower than that of the GLUE coefficient estimator, which was ranged from 6.89% to 18.06%, and the simulated rice yield was close to the measured yield.