Big Data Analytics for Monitoring Environmental Quality and Animal Welfare in Commercial Livestock Farms

Abstract

The rapid expansion of commercial livestock farming has increased the need for intelligent systems capable of simultaneously monitoring environmental quality and animal welfare while improving operational efficiency. Conventional livestock monitoring methods primarily rely on manual inspections and isolated sensing devices, limiting their ability to detect environmental risks and animal health issues in real time. This study proposes a comprehensive Big Data analytics framework for environmental quality and animal welfare monitoring in commercial livestock farms. The framework integrates IoT-based environmental sensors, wearable animal monitoring devices, multi-source data acquisition, cloud-edge computing, machine learning, and predictive analytics within a unified decision-support architecture. Environmental parameters, including temperature, humidity, ammonia, carbon dioxide, light, and noise, are analyzed together with animal health, behavior, feeding activity, stress, and physiological indicators to enable continuous monitoring and early risk detection. The proposed framework incorporates data preprocessing, feature engineering, predictive modeling, and automated farm management to support proactive decision-making. Comparative evaluation demonstrates significant improvements over conventional livestock monitoring systems, achieving 98.1% environmental monitoring accuracy, 97.3% animal welfare assessment accuracy, 96.8% disease prediction accuracy, and 96.2% environmental risk detection accuracy, resulting in an overall system performance of 97.1%. Additionally, the framework improves resource utilization efficiency to 94.8%, decision-support accuracy to 97.6%, and overall operational efficiency to 95.4%, while reducing response time from 18.7 s to 5.4 s. These results demonstrate that the proposed Big Data analytics framework provides a scalable, intelligent, and sustainable solution for precision livestock farming by enabling real-time monitoring, predictive management, improved animal welfare, and optimized environmental quality.