Abstract: Radio frequency can be expected to apply to individual pig recognition in intensive breeding environments. However, it is highly susceptible to multipath interference, leading to degraded signal stability and reading accuracy. In this study, a quantitative framework was developed to characterize the time-varying channel disturbances in order to guide the system optimization. A dynamic interference model was established using Rician fading channels. The power ratio between the direct path and the scattered components was continuously estimated rather than assumed to be constant. A dynamic estimation was implemented for the so-called K-factor. The recursive optimization was carried out with sliding time windows. The statistical features were also coupled with the received signal strength indicator and phase fluctuations. The impacts of environmental factors were captured, such as the metallic structures and animal movement. An interference scoring function was then formulated to combine the instantaneous K-factor and the variance of received signal strength. A continuous quantitative index was obtained with the interference intensity from 0 to 100. Both controlled laboratory simulations and on-site pig farm experiments were conducted to validate the optimization. In the laboratory, a custom testbed was constructed with a high-precision spectrum analyzer, directive antennas, and resin pig models mounted on mobile platforms, in order to reproduce the dynamic occlusion and reflection. A series of measurements was achieved at 920 MHz. There were clear transitions from Rayleigh-like fading with the severe envelope fluctuation under strong scattering to near-Gaussian stability under strong direct paths, as the K-factor increased. There was a suitability of the channel representation. Field deployments in the commercial pig houses further confirmed that the different physical settings led to systematic differences. The interference score remained low (45.0 to 49.2) under noise or simple tag-reader interaction scenarios, indicating relatively stable communication. In contrast, the environments with stone walls produced scores 60.0. Dynamic individual pig movement raised values to 65.2, while the dense static groups reached 68.8. Metal railings caused the sharp degradation with the scores 76.0. The most severe condition occurred when the metallic structures coincided with pig groups. The scores were 79.2, indicating the substantial attenuation of the direct path and dominance of scattering. Correspondingly, the average read success rates varied from 98% in the background conditions to only 28% under metal railing interference. The received power levels ranged from approximately -58 decibels-milliwatt in the favorable conditions to -70 decibels-milliwatt in unfavorable cases. Comparative analysis against conventional modeling demonstrated the better performance of the dynamic framework. The log-distance path loss model was 75% read success with the average attenuation. The Rayleigh model reached 80%, but it was lacking in adaptability for the mixed propagation. The static Rician model was improved to 85%, but the temporal variability was less captured. Even the generalized Rician model was effective in the static industrial environments, with about 88% read success. It was computationally heavy and unsuitable for real-time agriculture. In contrast, the dynamic Rician approach was achieved in the 92% read rates. The higher received power was maintained for the best adaptability index of 0.91. Its robustness was also obtained under diverse farm conditions. As such, the time-varying direct-to-scattered power ratio was incorporated into the channel representation. The findings can also provide a realistic and flexible description of the multipath propagation in livestock houses. Great contributions can also be gained for the structural reflection, animal density, and movement. In conclusion, the dynamic interference evaluation model is obtained using Rician channel theory. A reliable quantitative tool can also be used to assess the signal stability and then diagnose the high-risk interference zones, in order to guide the antenna placement and system configuration. Both theoretical support and practical data can be deployed the robust RFID systems in pig breeding environments. Ultimately, the high accuracy of individual animal monitoring can greatly contribute to intelligent livestock.