AI to Upgrade Russia’s Thoroughbred Gene Pool
Russian racetracks could be poised for new records as breeders begin deploying artificial intelligence to produce faster, tougher racehorses.
Breeding the Best
Horse breeding remains an important branch of Russian livestock production, even if the role of horses in the broader economy has shifted. In small-scale farms they are still used as draft animals. Another segment is meat and dairy horse breeding. And, perhaps the most high-profile sphere, is sport. Flat racing and other equestrian disciplines demand horses that are ever faster, more resilient and more athletically built.
Selective breeding in horses is complex and time-consuming. Developing a bloodline in a specific direction, strengthening desirable traits while eliminating weaknesses, requires deep expertise, long-term commitment and considerable resources.
Selection for breed improvement is based on a combination of characteristics. The ultimate benchmark of any breeding program is quality offspring.
Within that framework, pairing stallions and mares to consolidate desired traits in progeny is especially intricate. Breeders must consider not only conformation and performance of individual horses, but also the characteristics of their bloodlines and broader genealogical complexes. They need to understand which combinations are likely to click, which attributes should be amplified in progeny and which should be diluted. That is how mating plans are drawn up. Until now, traditional breeding methods in Russian stud farms have, to a significant extent, relied on accumulated experience and, at times, professional intuition.
A Smarter Selection Process
Artificial intelligence is significantly enhancing that process. Russian breeders in the sport-horse segment are using AI and big data analytics to forecast breeding value and identify individuals most likely to produce elite athletic offspring.
To do this, AI systems process race results from racetracks for prospective sires and dams. The platform analyses performance databases, seeking patterns. At the first stage, the key task is to detect markers that indicate a high probability of transmitting championship-level qualities to the next generation. From there, potentially superior parents are identified.
According to the analysis, horses that appear most promising for producing future champions consistently post strong results over middle distances – from 1,800 to 2,000 metres. That genetic profile, researchers say, tends to yield offspring capable of competing effectively both in shorter contests and over extended trips.
Russia counts about 5,000 Thoroughbreds in its population, along with roughly 1,500 Arabian racehorses. The neural network draws on publicly available race data. It also tracks results in the United States, where, as in Russia, racing is conducted on dirt tracks. That parallel allows breeders to source suitable stallions or mares from overseas with greater precision.
Human Judgment Plus AI
Even so, industry professionals stress that human expertise remains central. AI is a tool – not a replacement. A genetically strong horse and a potentially valuable sire or broodmare may not, on paper, appear to be an outstanding champion.
“Let us say a horse shows certain results or clear potential. The next question is whose hands it ends up in. It may have the makings of a super champion, but circumstances intervene – someone misses a detail, the horse sustains an injury, it never performs to the limit of its ability. Statistics are statistics, but so much depends on the specialist,” said Alena Akimova, head of the production department at Pyatigorsk Racetrack.
“One could ask AI to select the most productive sires and analyse bloodlines. That would be valuable support for breeders. Combined with human intelligence, it would be beneficial, but relying on artificial intelligence alone would be a mistake. It will almost certainly suggest breeding the best to the best. Yet racing aptitude, jumping ability and locomotion are complex traits. They are difficult to isolate, and achieving them requires human talent,” added Yegor Melentyev, CEO of the Rosplemkonzavod Association.
A New Phase for the Industry
Digital technologies could push Russian sport horse breeding into a new stage of development. Beyond identifying genetically superior racehorses, AI can support the industry more broadly. Integration with other IT systems – covering not only genetics but also veterinary monitoring, care regimes and training programs – may lead to a “smart breeding” ecosystem.
Such systems could find demand abroad, including in CIS markets and especially across parts of the Global South, where horse racing remains a prestigious and capital-intensive sector.
Introducing AI into breeding programs is likely to benefit more than just the racing segment. Russia’s equine industry encompasses stud farms, breeding reproducers, sport stables, riding schools, racetracks, farms, hunting estates, educational institutions and research centres. Across the country there are more than 70 stud farms and over 500 horse-breeding operations. AI-driven selection and “smart breeding” ecosystems could help lift profitability and professional standards across the board.
