Disease & heat resistance

Evaluating dairy cattle resilience through proxies and alternative approaches to lactation curve modeling

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Abstract

Proxies of resilience are being evaluated for their ability to infer underlying resilience from longitudinal milk-yield records. Resilience indicators are promising due to low-to-moderate heritability and genetic correlations with health and functional traits, yet their fidelity to true resilience remains uncertain. Using daily milk yields from 1,400 cows recorded by nine automatic milking systems at a mid-large dairy farm in Northern Italy, resilience indicators were computed from deviations between observed and expected yields, where the latter were defined by lactation curve models representing the unperturbed state. Eighteen models were assessed, including classical parametric models (Ali & Schaeffer, Wilmink), non-parametric models (spline interpolation), and data-driven models (moving median). A simulation study defined true resilience as "the animal's capability to be minimally affected by, or to recover rapidly from, disturbances." Cows without health-related issues served as the resilient reference cohort. Environmental and genetic perturbations were imposed on lactation curves to represent random challenges and genetic merit in coping capacity. Lactation curve models produced divergent cow rankings, with similarity across indicators ranging from 11% to 86%. Closely related models (e.g., fourth-degree polynomials) yielded more concordant rankings, whereas dissimilar models (e.g., spline interpolation versus Wilmink) showed the poorest agreement. Simulations indicated that resilience indicators capture only a portion of the genetic variance for resilience. The correlation between resilience indicators and simulated resilience breeding values was approximately 0.25, below the theoretical upper bound of 0.38 (square root of heritability). Overall, resilience indicators derived from milk-yield deviations show potential for genetic selection, but accuracy is constrained by lactation-curve model choice and limited correlation with true resilience. This work was conducted within the Agritech National Research Center and funded by the European Union Next-GenerationEU (PNRR - Mission 4, Component 2, Investment 1.4 - D.D. 1032 17/06/2022, CN00000022). The views expressed are solely those of the authors and do not necessarily represent those of the European Union or the European Commission.

Keywords: 2026

How to Cite:

Delledonne, A., Maltecca, C., Tiezzi, F., Punturiero, C., Strillacci, M. & Bagnato, A., (2026) “Evaluating dairy cattle resilience through proxies and alternative approaches to lactation curve modeling”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2285612. doi: https://doi.org/10.31274/wcgalp.23753

Rights: 1