[Objective] To tackle the present problem of fertilizer response modeling being generally low in success rate, this paper was devoted to discussions about strategies to optimize modeling and to improve its success rate.[Method] Based on collation and analysis of the following four modeling methods, i.e. nonlinear least-squares (NLS) modeling method for ternary non-structured fertilizer response model (TNFM), and ordinary least squares (OLS) method, principal component regression (PCR) method and feasible generalized least squares regression (FGLS) method for ternary quadratic polynomial fertilizer response mode l(TPFM), for adoptability and 1122 NPK fertilizer field experiments conducted in paddy fields and open vegetable gardens, an optimal modeling technology was designed and brought forth for comprehensive application of ternary fertilizer response models.[Result] Results show that ternary fertilizer response modeling using different functional equations and different modeling methods varied significantly in adoptability. The OLS modeling method for TPFM reached only 19.8% on average in proportion of typical models, while the PCR and FGLS modeling methods that had overcome the impacts of multicollinearity and heteroscedasticity, did up to 34.0% and 27.1%, respectively, and the NLS modeling method for TNFM after overcoming the obstacles of model specification bias and multicollinearity simultaneously rose further up to 41.4%, which improved the success rate of modeling. Using the classical OLS modeling method for TPFMs, the modeling had 28.7% failing the Duncan test, 30.1% having unreasonable model coefficient symbols, 15.2% missing maximum yield points and 6.1% extrapolating fertilization recommendation. However, the PCR modeling method reduced significantly the proportion having unreasonable coefficient symbols and increased that extrapolating fertilization recommendation, and the FGLS modeling method brought down to zero the proportion failing the Duncan test, but increased by a large margin in the proportion having unreasonable coefficient symbols. The ternary non-structural fertilizer response model significantly reduced the proportion having unreasonable coefficient symbols or missing maximum yield points, while increasing the proportion of non-typical models extrapolating fertilizer recommendation. Since agricultural production goes on in conditions of extreme complexity and diversity, it is certain that the curve or curved surface that reflects crops response to fertilization diversifies. Therefore, in the light of the applicability of the models and their modeling methods, a four-step modeling method is brought forth herewith for comprehensive application of the ternary fertilizer response model, which may raise the proportion of typical models up to 57.5%, and minimize the differences between double-cropping rice, single-cropping rice and vegetable crops in relevant proportion.[Conclusion] The four-step modeling method is an effective technical method to improve success rate of the modeling for ternary fertilizer response models.