Solar grazing combines photovoltaic energy production with livestock-based vegetation management, but it also creates a control problem in which animal movement, site infrastructure, and wireless communication reliability vary across the same physical space. This thesis develops a simulation-trained decision-support framework for solar-grazing virtual fencing that links cattle movement risk, LoRa radio-frequency (RF) fragility, adaptive collar querying, and moveable-antenna position ranking. A reconstructed solar-farm environment provides the shared geometry for agent-based cattle movement simulation and Ansys High Frequency Structure Simulator (HFSS) and Shooting and Bouncing Rays Plus (SBR+) electromagnetic analysis. The movement model identifies control-critical states such as boundary approach, keep-out approach, row-edge transition, inter-row movement, and recovery after cue. RF simulation then evaluates collar-to-gateway link behavior at movement-derived states using mean coupling, weak-tail occurrence, fading depth, and local sensitivity rather than average coverage alone.The results show that the virtual-fencing communication problem is spatially uneven. In simulation, the cue-aware movement model reduces geofence escape probability from 0.22 to 0.06 and increases boundary recovery probability from 0.70 to 0.92 relative to a reduced baseline. Calibration against external cattle Global Positioning System (GPS) data improves agreement with observed movement statistics, supporting the use of simulated trajectories as structured RF query states. Receiver-placement analysis shows that gateway-antenna position changes weak-tail occurrence and fading stability, while local sensitivity is higher near row-edge and inter-row states than in open line-of-sight regions. Combining movement criticality with RF fragility produces priority labels for adaptive querying. Compared with a static 40-second schedule, an adaptive 20-second critical-state and 120-second open-state schedule reduces scheduled updates from 22,500 to 18,166 while increasing labeled critical-state coverage from approximately 50% to 100%. A herd-aware RF surrogate further enables repeated candidate antenna-position ranking for changing herd states, reaching RMSE 1.83 dB and R^2 = 0.96 on an unseen 15-cow simulation-labeled test scene. Overall, the thesis shows that adaptable Internet of Things (IoT) support for solar-grazing virtual fencing should be guided by the overlap between animal-control risk and communication risk, not by average RF coverage alone. The present framework supports simulation-trained query prioritization, receiver reassessment, moveable-antenna position ranking, and field-validation planning; field received signal strength indicator (RSSI) calibration, measured packet-reception validation, real collar energy analysis, and antenna mechanism testing remain future work.