Transmission of digital dermatitis in dairy cattle

This project aims to study the transmission of the infectious claw disease Digital Dermatitis (“Mortellaro’ s disease”) in dairy cattle and to investigate the genetic and environmental factors contributing to the transmission of the infection. Knowledge of those factors allows genomic selection for better claw health.

Digital Dermatitis (DD) is an important claw infection in dairy cattle, leading to poor health and welfare. Therefore, to better breed against DD, it is vital to understand the transmission of DD better, and in particular, to estimate the genetic variation in the propensity of individuals to infect others, the so-called Indirect Genetic Effect.

This involves the large-scale collection of individual infection status, knowledge of the cows’ interaction history, particularly (indirect) interactions between infected individuals with their susceptible counterparts via the floor (the “whereabouts” to determine environment sharing), and a mechanistic model to identify and quantify the genetic factors underlying the transmission of DD.

The researchers focus on automated detection of DD status, tracking of animals in time and space and modelling. They build on our previous research, which has demonstrated considerable genetic variation in traits underlying DD prevalence and has provided proof of the principle that DD can be detected visually while cows are standing (for example, in the milking parlour).

Progress (September 2022)

To automatically detect claw infections, the researchers have set up a camera system in the milking parlour, to examine the claws and link the image to the individual cow. The data from this system will be combined with the cows’ hoof trimming data (the ground truth for claw health) and the cow tracking system that has recently been installed at Dairy Campus in collaboration with Noldus IT. Using the hoof trimming data, the researchers will annotate claw images and subsequently train AI algorithms to automatically detect infection status from images. In the next stage, they will use the trained algorithms to collect larger-scale data on individual infection status and then link these data to transmission models of DD and cow genotypes.