Volume 17, Issue 2 (6-2026)
Res Anim Prod 2026, 17(2): 1-2 |
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Rahimi Mianji G A. (2026). The Editor's note: Livestock and Poultry Industry in the Era of Artificial Intelligence: Challenges, Opportunities and Future Perspectives. Res Anim Prod. 17(2), 1-2. doi:10.61882/rap.2026.1647
URL: http://rap.sanru.ac.ir/article-1-1647-en.html
URL: http://rap.sanru.ac.ir/article-1-1647-en.html
Sari Agricultural Sciences and Natural Resources University, Sari, Iran
Abstract: (20 Views)
Over the past decades, animal science has progressed rapidly in genetics and breeding, nutrition, physiology, reproduction, health, welfare, and management, contributing to higher productivity and better quality of animal products. However, livestock and poultry production now faces challenges that differ fundamentally from those of the past, including growing demand, limited water and feed resources, climate changes, rising production costs, increasing disease and environmental stresses and greater public concern about animal health and welfare. These challenges require production systems that balance productivity with health, welfare, resource-use efficiency, resilience, and sustainability. Achieving this goal requires a more precise understanding of animals, their environments, and the complex interactions between them. In this context, artificial intelligence (AI) and data-driven technologies have opened new horizons for animal science. Their applications are expanding from traditional machine learning toward deep learning, machine vision, the Internet of Things, and precision livestock farming. AI is therefore becoming an important component of the digital transformation of animal science. One area with considerable potential is animal breeding and genetics. Although milk, meat, and egg production remain important objectives, greater attention is increasingly being given to disease resistance, adaptation to environmental stresses, fertility, longevity, feed efficiency, health, and welfare. AI can support these objectives by extracting information from images, videos, and sensor data and by contributing to predictive genetic models. At the same time, rigorous model evaluation remains essential before such approaches can be widely adopted. In animal and poultry nutrition, future systems will increasingly move from generalized feeding strategies toward precision nutrition based on the actual requirements of individual animals or groups. Nutrient requirements vary with age, production stage, health, environmental conditions, and individual characteristics. Integrating animal and environmental data with mathematical models and AI may improve estimation of nutritional requirements, prediction of performance, and development of dynamic feeding strategies. AI can also transform approaches to animal health and welfare. Cameras, sensors, and intelligent systems can continuously record and analyze changes in behavior, physiology, and health, supporting a shift from delayed diagnosis toward proactive monitoring. However, the value of these technologies depends on the quality of the data and on the ability to convert complex information into reliable knowledge and practical decisions. Large, standardized, and trustworthy datasets, together with evaluation under independent and real-world conditions, are therefore as important as algorithm development. AI should not be regarded as a replacement for scientific expertise in animal science. Algorithms cannot replace specialized knowledge in genetics and breeding, nutrition, physiology, health, or management. Their real value will emerge through integration with this expertise. The future belongs not to competition between animal scientists and AI, but to the convergence of animal science, data science, and AI. Combining genetic information with feed intake, activity, health, physiological and productive performance, and environmental data can provide a more comprehensive understanding of an animal’s potential, requirements, and responses. Consequently, future animal scientists will need strong disciplinary knowledge together with familiarity with data management, advanced statistical methods, and the fundamentals of AI. Close collaboration between animal scientists and data and AI specialists will be essential for developing valid and practically relevant models. The editorial board of the Journal of Research on Animal Production therefore considers high-quality research in genetics and breeding, nutrition, physiology, reproduction, health and welfare, management, and emerging technologies particularly important. Future research should respond increasingly to real-world challenges such as resource sustainability, climate adaptation, animal health and welfare, and production efficiency. In this context, the intelligent and responsible use of AI can bring research closer to the real needs of production.
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