Effective environmental monitoring within livestock facilities is fundamental for ensuring optimal animal welfare, promoting farm worker safety, and demonstrating environmental stewardship. Airborne pollutants, such as ammonia, particulate matter, and carbon dioxide can negatively impact animal health, welfare, productivity, the working conditions of staff, and the environmental sustainability of the farming operation. In addition, the precise and continuous evaluation of thermal conditions in livestock buildings is becoming an essential management duty, as climate change-related impacts can severely compromise whole-farm efficiency.

Traditionally, the comprehensive assessment of in-barn environmental conditions has required the deployment of multiple, often non-integrated devices, complicating data collection and analysis. As part of three EU-funded projects, such as LivestockSense (https://livestocksense.eu/), aWISH (https://www.awish-project.eu/), and ET4D (https://www.et4d.eu/), a number of integrated environmental monitoring systems or Enviro-Detect units (ED; PLF Agritech, Brisbane, Australia) were deployed in commercial pig, poultry, and cattle buildings in Estonia, Israel, Germany, Hungary, Poland, Serbia, Spain, and Sweden to evaluate (1) the capacity of a single platform to continuously track a suite of critical environmental parameters under commercial conditions, (2) the practical feasibility of such technology in real-world farm settings, and (3) the utility of these measurements for assessing animal health, welfare, and production efficiency.

The ED systems were installed in the above-mentioned countries between 2021 and 2026, with weekly environmental reports delivered to farm management. Comparative analysis with data from these pig, poultry, and dairy buildings were undertaken and the results will be presnted during this conference. The findings showcased the ED system’s ability to deliver actionable, real-time data on barn climate and pollutant levels, empowering farm managers to rapidly respond to suboptimal conditions and optimize production efficiency.