The Porcine Reproductive and Respiratory Syndrome (PRRS) eradication program in Hungary, implemented between 2014 and 2022, utilized complete herd replacement and the introduction of high-performance breeds to enhance production efficiency and environmental sustainability in the swine sector. As a result, the sow population was reduced by 26.2% while maintaining nearly the same number of slaughter pigs. This led to significant reductions in ammonia emissions (−145,857 kg), slurry production (−153,879 m³), nitrogen emissions (−1,409,951 kg), and overall greenhouse gas emissions (91,768,362 kg CO2eq). Additionally, the feed and water consumption were substantially decreased by 53,237,805 kg and 292,978,094 L, respectively, further lowering the sector’s environmental footprint. The study demonstrates the effectiveness of customized eradication strategies and advanced breeding practices in reducing the environmental impact of animal husbandry. These findings underscore the necessity for ongoing collaboration among scientists, policymakers, and industry stakeholders to develop and implement sustainable livestock production methods. The Hungarian experience provides valuable insights into how targeted interventions can simultaneously improve production outcomes and reduce the environmental burden in the swine industry.

Artificial Intelligence (AI) has become an important tool for optimising breeding processes in several areas of animal production. In this thesis, we have presented examples from the literature, mainly for the identification and counting of cattle. The individual identification of animals, the monitoring of their behaviour and the control of their movements support a number of conclusions from both animal welfare and veterinary point of view. Automation of the processing of captured images has also become essential. This process is supported by Artificial Intelligence. Deep learning and neural networks are excellent tools for segmenting images and processing their content based on different features. Convolutional neural networks are specifically powerful for such tasks and we have seen that further developments of these networks (e.g. Faster R-CNN) allow even more efficient image analysis procedures. Processing animal images can be a major step forward for automatic analysis and identification of livestock. It also allows early intervention in the event of disease. In the context of individual identification, it is important to underline that, when complemented with other measurement options, e.g. sensor measurements, it offers even more complex applications that have not been available so far.