- Artificial intelligence is becoming an important technology in modern animal breeding, offering new opportunities to analyze genetic information, predict animal performance, and support breeding decisions. Animal breeding aims to improve inherited characteristics such as growth rate, milk production, fertility, disease resistance, feed efficiency, longevity, and adaptability to environmental conditions. Traditionally, genetic improvement has relied on pedigree records, performance measurements, statistical models, and the careful selection of breeding animals. Artificial intelligence can complement these established methods by processing large and complex datasets, identifying patterns, and supporting more informed decisions. Its growing role reflects the increasing availability of genomic information, automated monitoring systems, digital farm records, and advanced computational tools.
- Artificial intelligence refers to computer systems designed to perform tasks that typically require aspects of human intelligence, such as recognizing patterns, making predictions, classifying information, and learning from data. In animal breeding, AI methods include machine learning, deep learning, computer vision, natural language processing, and automated decision-support systems. These methods can analyze information from multiple sources, including DNA markers, pedigree records, growth measurements, milk yield, reproductive performance, health records, and environmental data. By combining these sources, AI systems may help breeders better understand differences among animals and identify individuals with desirable genetic potential.
- One of the most important applications of artificial intelligence is the prediction of genetic merit. Breeding programmes need to estimate how much of an animal’s observed performance is attributable to inherited genetic factors rather than environmental influences. An animal that produces more milk or grows faster than its peers may have superior genetics, but it may also benefit from better nutrition, housing, health, or management. Genetic evaluation methods account for these influences using statistical models. AI-based approaches can complement such methods by identifying complex relationships in large datasets and improving predictions when the information contains useful patterns that conventional models do not capture easily.
- Genomic selection has transformed modern animal breeding by using DNA marker information across the genome to estimate breeding values. These estimates help breeders select young animals before all their production or reproductive characteristics have been observed. Artificial intelligence can support genomic prediction by identifying relationships between genetic markers and economically important traits. Machine learning algorithms can process high-dimensional genomic data, examine nonlinear patterns, and incorporate information from multiple sources. However, more complex algorithms do not automatically provide more accurate predictions. Their performance depends on the quality and representativeness of the training data, the genetic structure of the population, and the methods used to validate results. Established statistical approaches such as genomic best linear unbiased prediction remain important benchmarks and may perform very well for many breeding objectives.
- Artificial intelligence can also contribute to the selection of breeding animals by integrating information about multiple traits. Modern breeding programmes rarely focus on a single characteristic. Dairy cattle, for example, may be evaluated for milk production, fertility, udder health, longevity, calving performance, and feed efficiency. Meat-producing animals may be assessed for growth, carcass quality, disease resistance, and reproductive performance. Poultry breeding programmes may consider egg production, egg quality, growth, survival, and feed conversion. AI systems can help analyze these interconnected traits and identify patterns that support balanced selection decisions. Nevertheless, breeding objectives must be defined by people and breeding organizations, because an algorithm cannot independently determine which characteristics are most valuable from scientific, economic, ethical, or welfare perspectives.
- Disease resistance is another important area in which artificial intelligence may support animal breeding. Livestock diseases can reduce productivity, increase veterinary costs, affect animal welfare, and create serious economic losses. AI systems can analyze health records, genetic data, clinical observations, and environmental conditions to identify animals or families with a lower risk of particular diseases. Machine learning may also help researchers investigate relationships between genetic variants and disease outcomes. These predictions can support the identification of breeding candidates with desirable health characteristics. However, disease resistance is often complex, and the results may be influenced by pathogen exposure, vaccination, management, and differences in recording practices. Genetic predictions therefore need careful validation before they are used in breeding decisions.
- Reproductive performance is central to the efficiency and sustainability of animal production. Traits such as conception rate, age at first calving, litter size, calving interval, semen quality, and embryo survival influence the number of offspring produced and the overall profitability of breeding programmes. Artificial intelligence can analyze reproductive histories, sensor measurements, and genetic records to help predict reproductive outcomes and identify animals with favourable characteristics. In dairy cattle, for example, automated activity monitoring may help identify changes associated with oestrus, while predictive models may assist in interpreting reproductive records. These tools can support management and genetic evaluation, but they must distinguish between inherited fertility and temporary influences such as nutrition, disease, age, or management conditions.
- Feed efficiency is another trait that attracts considerable attention in animal breeding. Efficient animals convert feed into milk, meat, eggs, or other useful products while requiring fewer resources for maintenance and production. Improving feed efficiency can reduce feed costs and may lower environmental impacts per unit of output. However, feed efficiency is influenced by numerous genetic, physiological, and environmental factors. AI systems can combine feed intake measurements, body weight, growth rates, milk yield, and genomic information to identify patterns associated with efficient resource use. They may also help predict feed efficiency in animals for which direct measurements are unavailable. The reliability of these predictions depends on the quality of the data and whether the animals used to develop the model are representative of the target population.
- Computer vision is expanding the possibilities for collecting animal phenotype data. Traditional breeding programmes often depend on measurements recorded manually by farmers, technicians, or researchers. These measurements can be expensive to collect, and some characteristics may be recorded infrequently. Computer vision uses images and video to estimate traits such as body size, body condition, posture, gait, conformation, and certain behavioural patterns. In some production systems, image analysis may also help measure growth or identify physical abnormalities. When these measurements are combined with pedigree and genomic data, they may provide additional information for genetic evaluation. However, image-based estimates can be affected by lighting, camera angle, animal movement, breed differences, and housing conditions. Reliable systems need to be validated under the conditions in which they will be used.
- Wearable sensors and automated farm monitoring systems generate another valuable source of information. Devices that record movement, rumination, body temperature, or other physiological indicators can provide frequent observations of animal activity and health. AI algorithms can analyze these data streams to identify unusual patterns or predict changes in performance. For breeding programmes, long-term monitoring may help researchers identify animals that maintain good health, fertility, or productivity under different conditions. These records can also support the development of more detailed phenotypes for genetic analysis. Nevertheless, a sensor signal does not directly reveal whether a trait is inherited. Researchers must combine monitoring data with appropriate genetic models and account for environmental and management effects.
- Artificial intelligence can support the development of breeding strategies for climate resilience. Climate change is increasing the importance of traits such as heat tolerance, resistance to environmental stress, and the ability to maintain production under variable feed and water availability. AI systems can analyze genetic information alongside temperature, humidity, nutrition, and performance records to identify patterns associated with adaptation. These findings may help breeders select animals that perform consistently across different environments. However, predictions developed in one climate or production system may not transfer reliably to another. Breeding programmes need data from relevant environments and should evaluate adaptability alongside fertility, health, production, and animal welfare.
- The integration of AI with precision livestock farming may further improve the availability of useful breeding data. Precision livestock farming uses digital technologies to monitor individual animals and their environments. Cameras, sensors, automated weighing systems, robotic milking equipment, and electronic identification devices can generate large quantities of observations. Artificial intelligence can help organize these records and identify patterns that would be difficult to detect through manual analysis alone. When linked to genetic and pedigree information, such data may improve the evaluation of traits that are otherwise expensive or difficult to measure. The value of this integration depends on reliable identification of animals, consistent data collection, and appropriate protection of farm and breeding records.
- Despite its potential, artificial intelligence presents important scientific challenges. One major concern is data quality. Incorrect pedigree information, inconsistent measurements, missing records, and systematic recording errors can weaken model performance. If a model is trained primarily on animals from one breed, farm, or production environment, it may produce less accurate predictions for other populations. This problem is particularly important in animal breeding because genetic relationships and trait distributions can differ substantially among breeds and countries. Researchers should evaluate AI models using independent datasets, monitor performance across relevant groups, and update models when populations or production conditions change.
- Another challenge is interpretability. Some machine learning methods can produce accurate predictions while making it difficult to explain which variables influenced a particular result. In breeding programmes, understanding why an animal receives a high genetic prediction can be important for scientific evaluation and practical decision-making. Breeders may need to know whether a prediction reflects genetic merit, environmental conditions, population structure, or biases in the training data. Methods that examine feature importance, prediction uncertainty, and model behaviour can help improve transparency, although these techniques do not always provide a complete explanation of complex algorithms. AI predictions should therefore be interpreted alongside established genetic principles and independent scientific evidence.
- Artificial intelligence should not be treated as a replacement for quantitative genetics. Traditional statistical methods, including linear mixed models and best linear unbiased prediction, have a strong scientific foundation and remain central to many genetic evaluation systems. These approaches can account for pedigree relationships, genetic relationships, environmental effects, and the structure of breeding populations. AI methods may be particularly useful when data are extremely large, relationships are nonlinear, or complex image and sensor data need to be analyzed. In many cases, the most effective approach is to combine statistical genetics with machine learning rather than relying on a single method. Model choice should be guided by predictive accuracy, interpretability, computational requirements, and the biological question being addressed.
- The use of artificial intelligence also raises questions about genetic diversity. If AI systems repeatedly select animals from a narrow group of high-ranking individuals, breeding programmes may increase inbreeding and reduce the effective population size. This could limit the genetic variation available for future improvement and increase vulnerability to emerging diseases or environmental changes. Breeding algorithms should therefore consider relatedness, inbreeding, and genetic diversity alongside predicted production and health traits. Selection decisions should aim to achieve sustained genetic gain while preserving the diversity needed for long-term resilience.
- Animal welfare must remain a central consideration in AI-supported breeding. Algorithms may help identify genetic patterns associated with disease resistance, fertility, longevity, or physical soundness, but the outcome depends on which traits are included in the breeding objective. If a model emphasizes production without adequately accounting for health and welfare, it may encourage decisions that improve output while increasing biological stress or other adverse effects. Breeding organizations should therefore include appropriate welfare-related traits and evaluate the consequences of selection over time. Human oversight is essential to ensure that AI-generated recommendations support responsible breeding goals rather than simply maximizing a numerical prediction.
- Economic and practical barriers can also limit the use of AI in animal breeding. Developing reliable systems requires computing resources, high-quality data, technical expertise, and long-term validation. Smaller farms and breeding organizations may lack the infrastructure needed to adopt advanced tools. Data-sharing arrangements can help expand the size and diversity of training datasets, but they must address privacy, ownership, commercial confidentiality, and fair access. Collaboration among universities, breeding companies, farmers, veterinarians, and technology providers can support more reliable and accessible systems. Training and technical support are also necessary so that users understand both the capabilities and limitations of AI-based recommendations.
- The future of artificial intelligence in animal breeding will depend on continued progress in genomics, computational biology, sensor technology, and data management. New approaches may combine genomic predictions with images, health records, environmental measurements, and detailed production histories. Such systems could help identify animals that perform well across several traits and conditions, supporting more balanced and precise breeding decisions. However, progress should be measured by improvements in prediction quality, animal health, genetic progress, and practical outcomes rather than by the complexity of the algorithms themselves. Independent validation and ongoing monitoring will remain essential as new methods are introduced.
- In conclusion, artificial intelligence offers valuable opportunities to strengthen animal breeding by improving the analysis of genomic data, predicting genetic merit, monitoring health, evaluating complex traits, and supporting selection decisions. Its integration with genomic selection and precision livestock farming may help breeders develop healthier, more productive, and more adaptable animals. However, the effectiveness of AI depends on reliable data, appropriate models, transparent evaluation, and careful consideration of genetic diversity and animal welfare. AI should complement established quantitative genetics and professional breeding expertise, not replace them. When used responsibly, it can become an important tool for sustainable genetic improvement and the long-term development of resilient livestock populations.
