Croplands are important in land-atmosphere interactions and in the modification of local and regional weather and climate, however, they are poorly represented in the current version of the coupled Weather Research and Forecasting (WRF)/ Noah-MP land-surface modeling system. This study introduced dynamic corn (Zea mays) and soybean (Glycine max) growth simulations and field management (e.g., planting date, cultivar selection) into Noah-MP and evaluated the enhanced model (Noah-MP-Crop) at both field and regional scales using crop biomass datasets, surface heat fluxes, and soil moisture observations. Compared to the generic dynamic vegetation and prescribed-LAI driven methods in Noah-MP, the Noah-MP-Crop showed improved performance in simulating leaf area index (LAI) and crop biomass. This model is able to capture the seasonal and annual variability of LAI, and to differentiate corn and soybean in peak values of LAI as well as the length of growing seasons. Improved simulations of crop phenology in Noah-MP-Crop led to better surface heat flux simulations, especially in the early period of growing season where current Noah-MP significantly overestimated LAI. The addition of crop yields and Growing Degree Days (GDD) as model outputs expands the application of Noah-MP-Crop to regional agriculture studies. This presentation will also share our initial results of coupled WRF-Crop in seasonal and short-term forecasting. The capability introduced in Noah-MP allows further crop related studies and development.