Agrivoltaic (AV) systems offer a compelling approach to simultaneously address food and energy security by co-locating agricultural production with solar photovoltaics. In countries such as France, Japan, and Germany, national AV policies explicitly require crop yield retention, ensuring that agricultural productivity is not sacrificed for energy generation. In contrast, the United States lacks clear yield-based standards or enforcement mechanisms for AV systems, creating a risk that deployments may prioritize solar output over agricultural outcomes. To address that gap, this study develops a simulation-based optimization framework for single-axis AV tracking that maximizes photovoltaic output subject to crop-performance constraints. At the site scale, crop performance is represented using a simplified radiation-use-efficiency biomass model and evaluated relative to an open-field reference through a biomass-retention metric based on the Yield Realization Ratio (YRR). The framework compares three control strategies: continuous dynamic tracking, a relaxed binary selection between standard and reverse tracking states, and fixed-schedule tracking with predetermined tracking windows. To support broader spatial screening, the framework also includes a second mode that replaces explicit biomass simulation with a minimum Daily Light Integral (DLI) requirement. The site-level analysis uses hourly weather data for Ithaca, New York, over the May-August 2024 growing season, and the regional analysis applies the DLI-based mode to gridded U.S. solar-resource data. The simulations show that feasible tracking schedules can be identified that satisfy prescribed crop constraints while quantifying the associated photovoltaic trade-offs within the modeling framework. The regional analysis further indicates that the power penalty associated with stricter crop-light requirements is geographically heterogeneous. These results should be interpreted as comparative simulation evidence rather than field-validated yield predictions, but they show how agronomic constraints can be incorporated explicitly into AV tracking design and regional screening.