Automatic milking systems (AMS) now play a central role in modern dairy production. Beyond improving labour efficiency, these systems collect detailed data at every milking. This information allows researchers to study udder conformation with a level of precision not possible through traditional visual scoring.
A recent study published in the Journal of Dairy Science used AMS-derived data to investigate the genetics of teat spacing in American Holstein cows. The research focused on front teat distance (FTD) and rear teat distance (RTD), two traits that strongly influence robotic attachment success, milking efficiency, and udder health.
Why teat spacing matters in robotic herds
Teat placement has always influenced milking performance. In robotic systems, it becomes a functional trait with direct economic impact. Closely spaced rear teats can interfere with robotic cup attachment and increase failed milkings. Excessively wide spacing may increase exposure to environmental pathogens and raise the risk of teat-end damage.
AMS technology measures teat position objectively by recording the three-dimensional coordinates of each teat tip at every visit. These measurements remove subjectivity and capture real changes across lactation.
Study design and data
The researchers analyzed more than 4.2 million milking records collected from 36 robotic milking units on a U.S. commercial dairy. After quality control, the dataset included teat spacing records from over 4,100 genotyped Holstein cows.
The team calculated front teat distance as the lateral spacing between the two front teats. They calculated rear teat distance using the same approach for the rear teats. Previous studies have shown that these AMS-derived traits are moderately to highly heritable and strongly correlated with traditional conformation scores.
A highly polygenic genetic architecture
The genome-wide association analysis showed that both FTD and RTD are polygenic traits. Many genes contribute small effects, rather than a few genes driving large changes. No individual genetic marker explained more than 0.5% of the total additive genetic variance.
For rear teat distance, the analysis identified significant genomic regions on chromosomes 8, 9, and 26. Several candidate genes in these regions, including HTR1B, EMX2, and PRLHR, have known roles in mammary development, metabolic regulation, and milk production. Many of the same regions also overlap with quantitative trait loci linked to milk fatty acid composition and structural traits. These overlaps suggest shared biological pathways between udder conformation and lactational physiology.
Front teat distance showed significant associations on chromosomes 2, 8, 18, and 28. Candidate genes in these regions include UBE2R2 and UBAP2, which regulate protein turnover and cellular function, and NLRP12, a gene involved in immune and inflammatory responses. One genomic region associated with front teat spacing also overlapped with previously reported loci for disease susceptibility, length of productive life, and stayability.
Implications for breeding and selection
These findings help explain why genetic progress for teat placement has remained limited in recent years. Breeding programs have reduced emphasis on teat spacing in selection indexes, even as robotic milking has expanded.
AMS-derived teat placement traits offer a new opportunity. They provide objective, repeatable phenotypes collected at scale. Incorporating these traits into multi-trait genomic selection indexes could improve robotic milking efficiency, support udder health, and enhance cow longevity.
Next steps for research
The authors note that future studies should include data from multiple herds and robot systems. Teat spacing also changes across lactation, which simple linear models may not fully capture. More advanced modeling approaches could improve trait definition.
Integrating AMS phenotypes with transcriptomics, higher-density genotyping, and advanced imaging technologies may further strengthen genomic predictions. As robotic milking continues to expand, these tools will become increasingly important for precision breeding strategies.
