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Abstract: With increasing interest in retaining crop residues on the soil surface, there is a need to evaluate their short- and long-term effects on crop yield and water and fertilizer requirements. Therefore, research on the interactions between residue and irrigation management on wheat crop performance and water use was initiated, using the dual approach of field experiments and crop modelling. This paper presents the results of a comprehensive evaluation of the APSIM model for its ability to simulate the effects of mulch and water management, and their interactions, for wheat in Punjab, India. The model was evaluated for its ability to predict crop development, grain yield, biomass production over time, soil water dynamics, daily soil evaporation (Es), total evapotranspiration (ET) and water productivity (WPET kgha−1 mm−1), using two years of data from field experiments at Ludhiana, Punjab. The model predicted grain yield adequately, with coefficients of determination (r 2) of 0.91 and 0.81 with and without mulch, respectively, and prediction of total biomass was even better, with r 2 of 0.99 and 0.92. The corresponding absolute RMSE values were 433 and 550kgha−1 for grain yield (means 4100 and 3800kg ha−1) and 300 and 800kgha−1 for total biomass (means 10,200 and 9300kgha−1). However, grain yield was underpredicted (by 600–1000kgha−1) in treatments where the crop was subjected to water deficit stress, even though simulation of soil water dynamics, and the effect mulch on soil water content, was generally very good. The model accurately predicted total crop seasonal evaporation and the effect of mulch; however, daily Es was poorly simulated. APSIM does not attempt to capture the soil temperature driven effect of mulch on crop phenology. The evaluation shows that APSIM is suitable to use for wheat under the conditions of north-west India. However, additional model processes that capture the effects of mulch on crop development and growth, as driven by soil temperature, are needed to help design intensive cropping systems to optimise land and water productivity. The ability to better simulate crop performance under conditions of water deficit is also needed to help determine irrigation management strategies that minimise irrigation input while maintaining yield. [Copyright &y& Elsevier]