Impact on cattle welfare with IoT technology and statistical data analytics

The study analyzed the impact of implementing IoT technology combined with statistical analysis to improve the welfare of cattle on the Ecuadorian coast. A quasi-experimental methodology was adopted, validated in a controlled environment using computer simulation, in order to analyze the functionality of the system before its application in real conditions. To this end, five virtual devices were configured in Arduino Cloud to replicate the variables of heart rate, body temperature, and feeding patterns, generating data processed using Python and Django, with alerts sent via WhatsApp when values fell outside the permitted range. The results showed stability in temperature (37.9 °C average, minimum variance), consistency in feeding (mode = 1.0 in most observations), and slight variability in heart rate (79.8 BPM with a non-significant upward trend, p = 0.08). The analysis was performed using descriptive statistics (mean, median, mode, standard deviation) and mixed-effects models, the results of which were represented in box plots showing the homogeneity and variability among the simulated animals. In conclusion, the system demonstrated robustness and scalability for intelligent livestock monitoring, promoting more sustainable and technologically advanced livestock practices.