Disease & heat resistance

Using Longitudinal Data of Somatic Cell Counts and Milk Yield to Identify Mastitis-Resilient Dairy Cows

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Abstract

Bovine mastitis remains a pertinent challenge facing the dairy industry, with significant effects on animal welfare, farm profitability, and milk quality. Although genetic and non-genetic control strategies have lowered its occurrence, mastitis is difficult to eradicate due to its multi-pathogenic nature, highlighting the need for mastitis-resilient cows (i.e., animals minimally affected or that recover quickly after a health challenge). Resilience is not directly measurable and is therefore commonly quantified using resilience indicators (RIs) based on performance deviations from a hypothetical unperturbed trajectory (the expected performance in the absence of a challenge). To date, RIs for dairy cows have been constructed exclusively from longitudinal performance data (e.g., milk yield). However, to breed for resilience to mastitis, it would be more useful to develop mastitis-specific indicators rather than general production-based ones.As a step towards constructing reliable mastitis RIs, we explore the use of daily records of somatic cell counts (SCC) and milk yield to identify and characterise potential perturbations associated with mastitis in individual dairy cows. Specifically, we evaluate alternative statistical approaches for estimating individuals' unperturbed milk yield and SCC trajectories and how consistently they detect periods of systematic deviations associated with mastitis. To achieve this, data from the Swedish University of Agricultural Sciences cattle infrastructure, Gigacow, were used. The dataset includes daily records of milk yield and online SCC from 1,052 dairy cows across up to 7 lactations, from 6 Swedish dairy farms using automatic milking systems with online cell counters.Three methods were applied separately to daily milk yield and SCC data: (i) weighted spline, where data points likely associated with perturbations were given lower weights (e.g., drops in milk yield or spikes in SCC); (ii) quantile regression (targeting the 0.7 quantile for milk yield and 0.3 quantile for SCC); and (iii) iterative polynomial regression, where large negative (milk yield) and large positive residuals (SCC) were removed progressively to minimise the influence of perturbed observations. From these estimated trajectories, deviations were calculated, and potential perturbations characterised. The sensitivity of individual profiles to the choice of target trajectory estimation method was assessed. Preliminary results suggest that quantile regression and weighted spline methods yield relatively similar unperturbed trajectories and identify comparable perturbations in milk yield and SCC. In contrast, the iterative polynomial method yields a different trajectory and detects fewer perturbations, reflecting a more conservative approach. Ongoing analysis will reveal whether certain methods are better suited to detecting perturbations during specific phases of the curve (e.g., early lactation or post-peak decline). Next, perturbations in milk yield and SCC will be analysed jointly to construct mastitis RIs for subsequent genetic analyses.Ultimately, this work lays the foundation for developing novel resilience phenotypes for mastitis and their potential use in genetic selection.

Keywords: 2026

How to Cite:

Ojo, T., Ghaderi-Zefreh, M., Johnsson, M., Klingström, T., Sanchez-Molano, E., Pong-Wong, R. & Doeschl-Wilson, A., (2026) “Using Longitudinal Data of Somatic Cell Counts and Milk Yield to Identify Mastitis-Resilient Dairy Cows”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2283935. doi: https://doi.org/10.31274/wcgalp.23538

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