By monitoring silage temperature at different locations inside silage stacks, it is possible to detect any significant increases in temperature occurring during silage decomposition. The objectives of this study were: (1) to develop novel noninvasive wireless sensor nodes for measuring the temperature inside silage stacks; (2) to design a suitable sensor protection housing that prevents physical and chemical damage to the sensor: and (3) to mathematically model temperature variations inside a silage stack, using system identification techniques. The designed wireless nodes were used to monitor temperatures in a full-sized silage stack over 53 days. Results showed that the wireless sensor nodes accurately monitored the temperature inside the silage stack at depths of 25 and 50cm and reliably transmitted the measured data through the network; between 98.9% and 99.4% of the packets disseminated from the sensor nodes were successfully delivered to the gateway. The reliable performance of the network confirmed the correct choice of network characteristics (i.e., frequency range of 433 MHz, a handshaking communication protocol, and 10 mW transmission power). The designed sensor housings were capable of withstanding the high loads that occurred during ensiling, storage, and feed-out. Mathematical models estimating the relations between the silage temperatures (at depths of 25 and 50cm) and air and soil temperatures were obtained. Black-box modeling using the prediction error method (PEM) was selected as the identification method. Among different black-box models such as ARX, ARMAX, output-error (OE), and Box-Jenkins (BJ), with different model orders, a third-order Box-Jenkins model structure gave the best performance in terms of prediction accuracy. The success rate of the models proposed for silage temperature prediction ranged between 90.0% and 94.3%. Furthermore, there was no significant autocorrelation remaining in the residuals. The results of this study indicate that the designed wireless sensor nodes could potentially be used for detecting silage decomposition processes and improving the efficacy of silage conservation systems.