Pigs in the EU are commonly reared in climate-controlled systems mainly dependent on air ventilation based on a single point (one sensor) temperature set value. These systems assume even spatial distributions of the indoor climate. Since pigs are sensitive to both temperature and other environmental aspects such as humidity and noxious gas concentration, adverse climate conditions can decrease animal welfare and consequently lead to economic losses. In an on-farm study, we measured temperature, relative air humidity, air velocity, ammonia and carbon dioxide concentrations continuously on five different measuring points in one room for a total of three consecutive batches (672 pigs per batch). Fresh air was supplied through ceiling diffuse inlet by a negative pressure ventilation system with two exhaust air ducts which were automatically controlled by the climate control unit The animals were continuously monitored using four network cameras (CCTV) during the whole batches (growing period). Deep learning techniques were developed to automatically detect the activity level of the animals in the recorded video data. Severe spatial differences (horizontal and vertical) in environmental conditions between the measurement points and temporal changes within and across batches were observed, which directly affected the animals’ activity levels. The results demonstrate that environmental control based on single point measurements can have severe shortfalls. Furthermore, environmental information beyond air temperature need to be integrated into climate control to assure appropriate conditions for the animals' health and welfare.