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Climatic variations, continuously increasing population pressure and market infrastructures are driven forces to reduce agricultural productivity. New management options and appropriate genotypes are need of the day to be considered for sustainable production. Crop simulation models are complementary tools in field experiments to develop innovative crop management systems. In this perspective, two crop growth models viz., APSIM (Agricultural Production System Simulator) and DSSAT (Decision Support System for Agro Technology Transfer) were calibrated and validated to predict growth and yield of wheat under rainfed conditions for Pothwar region of Pakistan. Five wheat cultivars; Tatara, NARC-2009, Sehar-2006, SKD-1 and F-Sarhad were planted in plot size of 5 m x 3 m with row spacing of 25 cm arranged in Randomized Complete Block Design (RCBD) replicated thrice during 2008-09 and 2009-10 at Islamabad. Both the models were parameterized using different agronomic parameters (phenological development, drymatter accumulation, leaf area index, physiological indices and grain yield) and climatic data. Efficiency of both models was tested using model validation skill scores including d-stat, RMSE and R-2. RMSE between observed and predicted value by APSIM for drymatter for Tatara genotype was 0.34 t ha(-1) as compared to DSSAT with RMSE 1.40 t ha(-1). Similarly, results observed for grain yield and other agronomic parameters generated by APSIM were more precise while less accurate results were depicted by DSSAT. Poor simulations were observed for Sehar-2006 by both models. While, predicted results for NARC-2009 were close to Tatara, whereas, SKD-1 and F-Sarhad showed similar behaviour. Findings of the study highlighted that crop models as a decision support tool to design agricultural production and crop management system, select suitable crop and genotypes under changing climatic scenario, fertilizer management, weeds management and appropriate sowing time. Similarly, these models are helpful in the managements of biotic and abiotic stresses. Meanwhile, this study will introduce both models under rainfed conditions of Pakistan which will further help to assess the constraints that influenced the model parameterization and validation. A set of validation skill scores were used to evaluate the models to examine their usefulness, representativeness and robustness. Similarly, the focus of the models comparison was based upon crop, soil and climatic data, technique and model outcomes. The results generated by the models will help in evaluating strengths and weakness of each of the modeling approaches, and based on these outcomes recommendations about managements could be made.

The results led to the conclusion that APSIM model was more efficient and suitable to simulate wheat growth and development for arid area of Pakistan. Therefore, it can further be used for decision making to mitigate climatic risk, selection of suitable genotypes and management options for agricultural sustainability. Similarly, climatic scenarios analysis might be performed in the APSIM model to mitigate future problems related to climatic extremes, therefore, it's important to design future strategies based upon models outputs.