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Dryland farming strategies in the High Plains must make efficient use of limited and variable precipitation and stored water in the soil profile for stable and sustainable farm productivity. Current research efforts focus on replacing summer fallow in the region with more profitable and environmentally sustainable spring and summer crops. In the absence of reliable precipitation forecasts for the crop growing season, farmers rely mainly upon knowledge of plant available water (PAW) in the soil profile at planting for making crop choice decisions. To develop a decision support strategy for crop selection based on initial PAW, experiments were conducted with spring triticale (X Titicosecale Wittmack), proso millet (Panicum miliaceum L.), and foxtail millet (Setaria italica L. Beauv.) under artificially controlled Low, Medium, and High initial PAW levels during 2004 and 2005 at Akron, Colorado, and Sidney, Nebraska. The objectives of this study were to adapt an existing cropping systems model for the simulation of triticale and millet and to evaluate simulations from the adapted model by comparing results with field data collected under varying initial PAW conditions. The Root Zone Water Quality Model with DSSAT v4.0 crop growth modules (RZWQM2) was used. Specifically, the Cropping System Model (CSM)–CERES–Wheat module was adapted for simulating triticale, and CSM–CERES– Sorghum (v4.0) module was adapted for simulating proso millet and foxtail millet. Soil water, leaf area index, grain yield, and biomass data for the highest PAW treatment from one crop season for each of the three crops were used to adapt and calibrate the crop modules. The models were then evaluated with data from the remaining PAW treatments. The proso milletmodule was further tested with four years of data from a crop rotation experiment at Akron from 2003 to 2006. Simulation results indicated that the adapted and calibrated crop modules have the potential to simulate these new crops under a range of varying water availability conditions. Consequently, these models can aid in the development of decision support tools for the season-to-season management of these summer fallow replacement crops under dryland conditions in semi-arid environments.