Wireless sensor networks (WSNs) are widely deployed in logistics and cold chain monitoring systems to ensure product quality. Wireless devices of multiple stakeholders operate on the same premises during logistic operations sharing the same radio frequency spectrum. In large scale deployments, strict compliance with regulations and limited use of airtime is a crucial design element for planning wireless food chain monitoring. Furthermore, the limited battery lifetime of sensor nodes restricts the number of total available radio transmissions. This paper presents prediction-based adaptive transmission methods for LoRa-based WSNs that reduce the number of radio transmissions while maintaining data accuracy and robustness. The approach uses lightweight polynomial prediction models executed in parallel on both the sensor node and the server side. Measurements are transmitted only when the prediction error exceeds a defined threshold, while periodic ground-truth packets ensure long-term synchronization. Two prediction models are evaluated: a fixed-window linear least-squares fit and an adaptive scheme switching between linear and quadratic fits depending on signal dynamics. Experiments with real cold room datasets demonstrate that transmissions can be reduced by up to 70% for individual variables and around 60% under dual-variable thresholds, significantly extending battery lifetime. The adaptive approach further improves reconstruction accuracy during non-linear cooling cycles. These results confirm that prediction-based transmission reduction is an effective strategy for energy-efficient, scalable, and regulation-compliant monitoring in logistics applications.