Background factors such as vagaries in monsoon, unsuitable soil, inappropriate sowing time, non-adoption of recommended technologies, especially plant geometry and fertilizer use, are limiting cotton production at farmers' fields. The yield gaps can be reduced with better crop management, such as optimum date of sowing, plant spacing, and nitrogen. Against this background, the current investigation was carried out to test and validate the model in the Raichur area of Karnataka, India for the dynamic simulation of cotton development, growth, and seed cotton yield under varied sowing times, plant densities, and nitrogen levels. The model was calibrated using observed data on phenology and yield components from the experiments conducted at the Main Agricultural Research Station, Raichur, during the kharif periods 2022-23 to 2023-24. The CSM-CROPGRO-Cotton model performed well under different dates of sowing, plant densities, and nitrogen levels for the simulation of phenology; the model performance was fair for the simulation of seed cotton yield, biomass, and nitrogen uptake for cultivar US7067. The model application through seasonal analysis was also used to confirm the results of the CROPGRO-Cotton model validation using the past 30 years of weather data. Optimum sowing time for predicting higher seed cotton yield was at the second fortnight of June under semi-arid conditions. In the case of plant population from 12,345 plants ha-1 (90 cm x 90 cm) to plant density of 74,074 plants ha-1 (90 cm x 15 cm), an increased seed cotton yield was predicted. The incremental increase in nitrogen level from 100 to 250 kg N ha-1 did not show much influence on predicted mean seed cotton yield. However, a higher mean seed cotton yield (1,682 kg ha-1) was predicted with higher levels of nitrogen application, i.e., 250 and 300 kg N ha-1. The CROPGRO-Cotton model applicability for the research area was evident from its calibration and validation in the Karnataka semi-arid environment. Using a seasonal analysis tool, the CROPGRO-Cotton model results demonstrated a clear path to increased seed cotton yield.