Dairy Cow’s Resilience
The word resilience is taken over from the Latin resilio and expresses resisting a disturbing effect [1]. Stability, robustness, vulnerability, and resilience are concepts characterised by having a highly multidimensional nature and have been used in various papers related to agricultural systems [1]. Interest in resilience in the breeding of animals is increasing, particularly in 20 years about processing climate change [2]. Resilience is generally defined as the ability of animals to resist short-term disturbing effects or to return quickly to their pre-exposure state after they have subsided [3] as short-term disturbing effects are considered diseases, especially ketosis clinical mastitis and increased somatic cell count.
Daily milk production, and fluctuations therein, can provide information on the health and resilience of dairy cows [4]. They assessed whether variance and autocorrelation of deviations in daily milk yield were associated with clinical mastitis in dairy cows. Results indicate that variance and autocorrelation are suitable for evaluating udder health. Fluctuations in milk yield are expected to be smaller for more resilient cows that are less affected by disturbances or recover more quickly [3]. If healthy but more vulnerable cows (i.e. less resilient) would have larger fluctuations in daily milk yield throughout their lactation due to a larger impact of minor day-to-day disturbances [3], deviations in the absence of disease may be informative of future disease risk. The genetic correlation between resilience and udder health is −0.36 [3]. In other words, higher resilience is genetically correlated with better udder health [3]. Researchers also focused on fluctuations in milk yield levels for defining resilience indicators [5]. However, fluctuations in milk components may also be related to resilience. For example, fluctuations in fat content may indicate resilience to ketosis or rumen acidosis, and fluctuations in SCS may indicate resilience to mastitis [6];. Poppe et al. (2021) estimated genetic correlations with health traits. The resilience indicators were the natural log-transformed variance (LnVar). For LnVar, genetic correlations with resilience-related traits, such as udder health, ketosis, and longevity, adjusted for correlations with milk yield, were almost always favourable (−0.59 to 0.02) [7].
Usually, these genetic correlations were stronger based on full lactations than on lactation periods. Genetic correlations were similar across full lactations, but the correlation with udder health increased substantially from −0.31 in lactation 1 to −0.51 in lactation 3. Poppe et al. (2022) studied dairy cows’ activity expressed as a step count level, mean of step count and fluctuation of step count level [8]. They found that low values of indicators were associated with lower incidence of illnesses, better health, fitness and longevity. Describing resilience and implementing it as a breeding target and identifying resilient genotypes based on the identified genetic correlations. They would result in better dairy cows’ health, welfare, and quality of life. Identifying resilient genotypes can extend cow production time, reduce cost and carbon footprint on farms and increase the sustainability and profitability of dairy cow breeding.
Costing of general resilience is a big concern, and it is often included in health indicators [9]. The closest indicator of resilience is the longevity of dairy cows. Dairy cows’ resilience is affected by many components of the environment that are still a big challenge, and exploring the net of relationships remains secret. The study used Legendre polynomials to describe lactation curves and monitor dairy cows’ resilience. Daily milk yield observations (DMY) were analysed from 510 purebred Holstein and 165 Czech purebred Fleckvieh cows. There were monitored via Affimilk software on a farm from 1998 to 2020. The original database included 767 577 observations from 946 dairy cows and 2 795 lactations. After the data edit, it contained 527 966 DMY and 1 839 lactations from cows with days in milk (DIM) from 1–305. The highest completed lactation was the eighth. Individual cows’ Persistence of Milk Production (PMP) was calculated and then computed to the average PMP by breed and lactation. The average PMP were 97.5 % in Holstein primiparous cows, 88.31 % in older Holstein cows, 93.68 % in Fleckvieh primiparous cows and 81.81 % in older Fleckvieh cows. Average lactations curves were modelled for each lactation by random regression models with Legendre polynomials. Average DMY (according to 305 DIM) and individual DMY were compared with predicted values. Both were assessed for the first and second & further lactations. The average DMY deviated by 8.82 % in Holstein primiparous cows, 9.55 % in Fleckvieh primiparous cows, 19.31 % in Holstein older cows and 22.85 % in Fleckvieh older cows from the modelled lactation curves. Individual DMY deviated 14.29 % in Holstein primiparous cows, 13.96 % in Fleckvieh primiparous cows, 23.54 % in Holstein older cows, and 28.12 % in Fleckvieh older cows. Primiparous cows reported more persistent lactations and less deviated milk yields from modelled lactation curves than older cows, which had inconsistent DMY.
1. Urruty, N.; Tailliez-Lefebvre, D.; Huyghe, C. Stability, Robustness, Vulnerability and Resilience of Agricultural Systems. A Review. Agronomy for Sustainable Development 2016, 36, 1–15, doi:10.1007/S13593–015–0347–5/FIGURES/1.
2. Colditz, I.G.; Hine, B.C. Resilience in Farm Animals: Biology, Management, Breeding and Implications for Animal Welfare. Animal Production Science 2016, 56, 1961–1983, doi:10.1071/AN15297.
3. Berghof, T.V.L.; Poppe, M.; Mulder, H.A. Opportunities to Improve Resilience in Animal Breeding Programs. Frontiers in Genetics 2019, 10, doi:10.3389/fgene.2018.00692.
4. Kok, A.; Tsousis, G.; Niozas, G.; Kemp, B.; Kaske, M.; van Knegsel, A.T.M. Short Communication: Variance and Autocorrelation of Deviations in Daily Milk Yield Are Related with Clinical Mastitis in Dairy Cows. Animal 2021, 15, doi:10.1016/j.animal.2021.100363.
5. Poppe, M.; Veerkamp, R.F.; van Pelt, M.L.; Mulder, H.A. Exploration of Variance, Autocorrelation, and Skewness of Deviations from Lactation Curves as Resilience Indicators for Breeding. Journal of Dairy Science 2020, 103, 1667–1684, doi:10.3168/jds.2019–17290.
6. De Haas, Y.; Ouweltjes, W.; Ten Napel, J.; Windig, J.J.; De Jong, G. Alternative Somatic Cell Count Traits as Mastitis Indicators for Genetic Selection. Journal of Dairy Science 2008, 91, 2501–2511, doi:10.3168/jds.2007–0459.
7. Poppe, M.; Bonekamp, G.; van Pelt, M.L.; Mulder, H.A. Genetic Analysis of Resilience Indicators Based on Milk Yield Records in Different Lactations and at Different Lactation Stages. Journal of Dairy Science 2021, 104, 1967–1981, doi:10.3168/jds.2020–19245.
8. Poppe, M.; Mulder, H.A.; van Pelt, M.L.; Mullaart, E.; Hogeveen, H.; Veerkamp, R.F. Development of Resilience Indicator Traits Based on Daily Step Count Data for Dairy Cattle Breeding. Genetics Selection Evolution 2022, 54, doi:10.1186/s12711–022–00713-x.
9. König, S.; May, K. Invited Review: Phenotyping Strategies and Quantitative-Genetic Background of Resistance, Tolerance and Resilience Associated Traits in Dairy Cattle. Animal 2019, 13, 897–908, doi:10.1017/S1751731118003208.
