Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

Genetic Evaluation of Resilience Indicators in Holstein Cows

The analysis of resilience indicators was based on daily milk yields recorded from 3347 lactations of 3080 Holstein cows located on 10 farms between 2022 and 2024. Six farms used an automatic milking system. A random regression function with a fourth-degree Legendre polynomial was used to predict the lactation curve. The indicators were the natural log-transformed variance (LnVar), lag-1 autocorrelation (r-auto), and skewness (skew) of daily milk yield (DMY) deviations from the predicted lactation curve, as well as the log-transformed variance of DMY (Var). The single-step genomic prediction method (ssGBLUP) was used for genomic evaluation. A total of 9845 genotyped animals and 36,839 SNPs were included. Heritability estimates were low (0.02-0.13). The strongest genetic correlation (0.87) was found between LnVar and Var. The genetic correlation between r-auto and skew was also strong but negative (-0.73). Resilience indicators showed a negative correlation with milk yield per lactation and a positive correlation with fat and protein contents. The negative correlation between fertility and two resilience indicators may be due to the evaluation period (50th-150th day of lactation) being when cows are most often bred after calving, and a decrease in production may accompany a significant oestrus. The associations between resilience indicators and health traits (clinical mastitis, claw health) were weak but mostly favourable.

Toward a resilience selection index with indicator traits in German Holstein dairy cattle

Resilience expresses the ability of an individual to cope with short-term disturbances and to recover quickly by returning to the original level of performance. It can be measured by variance-based parameters and by the autocorrelation of daily milk yields in dairy cows. The design of resilience indicator traits and their heritabilities and genetic correlations have been studied in detail in recent years. There is a need to combine different resilience indicators in an index. The relevance of resilience indicator traits for incorporation into selection indices arises from their correlations with health traits and longevity. The correlations of diverse resilience indicator traits with health traits and longevity were analyzed. The resilience indicator traits were identified that would lead to the highest correlated selection response in the German selection index for health, and appropriate weights of the resilience indicator traits in a selection index for resilience were derived. Certain variance-based indicators were significantly positively correlated with most of the established health and functional traits, whereas the autocorrelation had a negligible correlation with these traits. A resilience selection index composed of 2 different variance-based resilience indicator traits was most likely to be recommended. Its correlation with overall performance was positive but moderately small. Incorporating more than 2 resilience indicator traits into the index improved the correlated response in health traits only slightly.

Selection for Resilience in Livestock Production Systems

Selective breeding for improved animal resilience is becoming critical to increase the sustainability of production systems. Despite the existence of a genetic component for resilience, breeding for improved resilience has been limited by the absence of a consensus on its definition and quantifying method. In this work, we provide a review of (i) the definition of resilience and related concepts such as robustness, resistance, and tolerance; (ii) possible quantifying methods for resilience; (iii) its genetic background; and (iv) insights about its improvement through selective breeding. We suggest that a resilient animal may be defined as an individual that is able to cope with a perturbation(s) and rapidly bounce back to normal functioning if altered. Furthermore, since challenging conditions lead to trade-offs and, consequently, deviations between basic physiological functions, we suggest using these deviations as indicators for resilience. These resilience indicators may also be used as proxies to study the genetic determinism and background of resilience in livestock species. Finally, we discuss possible strategies to improve resilience and review the implementation of associated genetic markers for resilience indicators in selection schemes.

Estimation of Genetic Parameters for Milk Production Rate and Its Stability in Holstein Population

Milk production rate (MPR) refers to the rate of milk secretion per hour (kg/h), calculated from the harvested milk yield and milking interval, and it is considered an appropriate measure to evaluate the production potential of cows. The objective of this study was to estimate the phenotypic and genetic parameters of milk production rate traits. In this study, the milking records of 4760 Holstein cows were collected, and four milk yield traits and six milk production rate traits were defined. The MIXED procedure was used to detect the impacts of non-genetic effects on milk yield and milk production rate traits, including parity, measured season and lactation stage. Variance and covariance components for milk yield and milk production rate traits were estimated using a univariate linear repeatability model. Parity, measurement season and lactation stage had significant effects (p < 0.01) on milk yield, milk production rate and its stability. Milk yield and milk production traits had high heritability, and ranged from 0.25 to 0.39. The stability of milk production rate had low heritability (0.04~0.05). Milk production rate is beneficial for the devolving novel trait in dairy breeding and provides new insights for herd management and genetic selection of production performance of dairy cattle.

Validating statistical properties of resilience indicators derived from simulated longitudinal performance measures of farmed animals

Resilience is commonly defined as the ability of an individual to be minimally affected or to quickly recover from a challenge. Improvement of animals' resilience is a vital component of sustainable livestock production but has so far been hampered by the lack of established quantitative resilience measures. Several studies proposed that summary statistics of the deviations of an animal's observed performance from its target performance trajectory (i.e., performance in the absence of challenge) may constitute suitable quantitative resilience indicators. However, these statistical indicators require further validation. The aim of this study was to obtain a better understanding of these resilience indicators in their ability to discriminate between different response types and their dependence on different response characteristics of animals, and data recording features. To this purpose, milk-yield trajectories of individual dairy cattle differing in resilience, without and when exposed to a short-term challenge, were simulated. Individuals were categorised into three broad response types (with individual variation within each type): Fully Resilient animals, which experience no systematic perturbation in milk yield after challenge, Non-Resilient animals whose milk yield permanently deviates from the target trajectory after challenge and Partially Resilient animals that experience temporary perturbations but recover. The following statistical resilience indicators previously suggested in the literature were validated with respect to their ability to discriminate between response types and their sensitivity to various response features and data characteristics: logarithm of mean of squares (LMS), logarithm of variance (LV), skewness (S), lag-1 autocorrelation (AC1), and area under the curve (AUC) of deviations. Furthermore, different methods for estimating unknown target trajectories were evaluated. All of the considered resilience indicators could distinguish between the Fully Resilient response type and either of the other two types when target trajectories were known or estimated using a parametric method. When the comparison was between Partially Resilient and Non-Resilient, only LMS, LV, and AUC could correctly rank the response types, provided that the observation period was at least twice as long as the perturbation period. Skewness was in general the least reliable indicator, although all indicators showed correct dependency on the amplitude and duration of the perturbations. In addition, all resilience indicators except for AC1 were robust to lower frequency of measurements. In general, parametric methods (quantile or repeated regression) combined with three resilience indicators (LMS, LV and AUC) were found the most reliable techniques for ranking animals in terms of their resilience.

Resilience indicator traits in 3 dairy cattle breeds in Baden-Württemberg

In recent years, research in animal breeding has increasingly focused on the topic of resilience, which is expected to continue in the future due to the need for high-yielding, healthy, and robust animals. In this context, an established approach is the calculation of resilience indicator traits with time series analyses. Examples are the variance and autocorrelation of daily milk yield in dairy cows. We applied this methodology to the German dairy cow population. Data from the 3 breeds (German Holstein, German Fleckvieh, and German Brown Swiss) were obtained, which included 13,949 lactations from 36 farms from the state Baden-Württemberg in Germany working with automatic milking systems. Using the milk yield data, the daily absolute milk yields, deviations between observed and expected daily milk yields, and relative proportions of daily milk yields in relation to lactation performance were calculated. We used the variance and autocorrelation of these data as phenotypes in our statistical analyses. We estimated a heritability of 0.047 for autocorrelation and heritabilities between 0.026 and 0.183 for variance-based indicator traits. Furthermore, significant breed differences could be observed, with a tendency of better resilience in Brown Swiss. The breed differences can be due to both genetic and environmental factors. A high value of a variance-based indicator trait indicates a low resilience. Performance traits were positively correlated with variance-based indicator traits calculated from absolute daily milk yields, but they were negatively correlated with variance-based indicators calculated from relative daily milk yields. Thus, they can be considered as different traits. Although variance-based indicators based on absolute daily milk yields were affected by the performance level, variance-based indicators based on relative daily milk yields were corrected for the performance level and also showed higher heritabilities. Thus, they seem to be more suitable for practical use. Further studies need to be conducted to calculate the correlations between resilience indicators, functional traits, and health traits.

Phenotypic and genomic modeling of lactation curves: A longitudinal perspective

Lactation curves, which describe the production pattern of milk-related traits over time, provide insightful information about individual cow health, resilience, and milk production efficiency. Key functional traits can be derived through lactation curve modeling, such as lactation peak and persistency. Furthermore, novel traits such as resilience indicators can be derived based on the variability of the deviations of observed milk yield from the expected lactation curve fitted for each animal. Lactation curve parameters are heritable, indicating that one can modify the average lactation curve of a population through selective breeding. Various statistical methods can be used for modeling longitudinal traits. Among them, the use of random regression models enables a more flexible and robust modeling of lactation curves compared with traditional models used to evaluate accumulated milk 305-d yield, as they enable the estimation of both genetic and environmental effects affecting milk production traits over time. In this symposium review, we discuss the importance of evaluating lactation curves from a longitudinal perspective and various statistical and mathematical models used to analyze longitudinal data. We also highlighted the key factors that influence milk production over time, and the potential applications of longitudinal analyses of lactation curves in improving animal health, resilience, and milk production efficiency. Overall, analyzing the longitudinal nature of milk yield will continue to play a crucial role in improving the production efficiency and sustainability of the dairy industry, and the methods and models developed can be easily translated to other longitudinal traits.

A comprehensive meta-analysis of genetic parameters for resilience and productivity indicator traits in Holstein cattle

Selection for resilience indicator (RIND) traits in Holstein cattle is becoming an important breeding objective as the worldwide population is expected to be exposed to increased environmental stressors due to both climate change and changing industry standards. However, genetic correlations between RIND and productivity indicator (PIND) traits, which are already being selected for and have the most economic value, are often unfavorable. As a result, it is necessary to fully understand these genetic relationships when incorporating novel traits into selection indices, so that informed decisions can be made to fully optimize selection for both groups of traits. In the past 2 decades, there have been many estimates of RIND traits published in the literature, albeit in small populations. To provide valuable pooled summary estimates, a random-effects meta-analysis was conducted for heritability and genetic correlation estimates for PIND and RIND traits in worldwide Holstein cattle. In total, 926 heritability estimates for 9 PIND and 27 RIND traits, along with 362 estimates of genetic correlation (PIND × RIND traits) were collected. Resilience indicator traits were grouped into the following subgroups: Metabolic Diseases, Hoof Health, Udder Health, Fertility, Heat Tolerance, Longevity, and Other. Pooled estimates of heritability for PIND traits ranged from 0.201 ± 0.05 (energy-corrected milk) to 0.377 ± 0.06 (protein content), while pooled estimates of heritability for RIND traits ranged from 0.032 ± 0.02 (incidence of lameness, incidence of milk fever) to 0.497 ± 0.05 (measures of body weight). Pooled estimates of genetic correlations ranged from -0.360 ± 0.25 (protein content vs. milk acetone concentration) to 0.535 ± 0.72 (measures of fat-to-protein ratio vs. milk acetone concentration). Additionally, out of 243 potential genetic correlations between PIND and RIND traits that could have been reported, only 40 had enough published estimates to implement the meta-analysis model. Our results confirmed that the interactions between PIND and RIND traits are complex, and all relationships should be evaluated when incorporating novel traits into selection indices. This study provides a valuable reference for breeders looking to incorporate RIND traits for Holstein cattle into selection indices.

Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves

Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

Genetic analysis of lactation consistency in US Holsteins using temporal variation in daily milk weights

The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience. Consistency, defined as the quality of performing as expected each day of the lactation, could be highly associated with resilience, defined as animal's ability to maintain health and performance in the presence of environmental challenges, including pathogens, heat waves, and nutritional changes. A total of 51,415,022 daily milk weights collected from 2018 to 2023 were provided for 255,191 multiparous Holstein cows milked 3 times daily in conventional parlor systems on farms in 32 states. The temporal variance (TEMPVAR) of milk yield from 5 to 305 d postpartum was computed as the log-transformed variance of daily deviations between observed and expected individual milk weights. Lower values of TEMPVAR imply smaller day-to-day deviations from expectations, indicating consistent performance, whereas larger values indicate inconsistent performance. Expected daily milk weights were computed using 3 nonparametric and parametric regression models: (1) loceally estimated scatterplot smoothing regression with a 0.75 span; (2) polynomial quantile regression using the median (0.5 quantile), and (3) polynomial quantile regression using a 0.7 quantile. The univariate statistical model included age at first calving and herd-year-season as fixed effects and cow as a random effect. Heritability estimates (standard errors) of TEMPVAR phenotypes calculated over the entire lactation ranged between 0.227 (0.011) and 0.237 (0.011), demonstrating that cows are genetically predisposed to display consistent or inconsistent performance. Estimated genetic correlations calculated using a multiple-trait model between TEMPVAR traits and between lactations were high (>0.95), indicating TEMPVAR is repeatable across lactations and robust to the model used to compute expected daily milk yield. Higher TEMPVAR phenotypes reflect more variation in performance, hence greater inconsistency, which is undesirable. Therefore, correlations between predicted transmitting abilities (PTA) for TEMPVAR and milk yield of 0.57 indicate that high-producing cows exhibit more day-to-day variation in performance. Correlations with productive life and livability were -0.38 and -0.48, respectively. Correlations between PTA for TEMPVAR and those of postpartum health traits were also negative, ranging from -0.41 to -0.08. Given that health traits are derived from disease resistance measurements, and higher health trait PTA are preferred, our results indicate that more consistent cows tend to have fewer health problems and greater longevity. Overall, our findings suggest that temporal variation in daily milk weights can be used to identify consistent animals that maintain expected performance throughout the lactation, which will enable selection for greater resilience to management and environmental perturbations.

Investigating the relationship between fluctuations in daily milk yield as resilience indicators and health traits in Holstein cattle

Disease-related milk losses directly affect dairy herds' profitability and the production efficiency of the dairy industry. Therefore, this study aimed to quantify phenotypic variability in milk fluctuation periods related to diseases and to explore milk fluctuation traits as indicators of disease resilience. By combining high-frequency daily milk yield data with disease records of cows that were treated and recovered from the disease, we estimated milk variability trends within a fixed period around the treatment day of each record for 5 diseases: udder health, reproductive disorders, metabolic disorders, digestive disorders, and hoof health. The average milk yield decreased rapidly from 6 to 8 d before the treatment day for all diseases, with the largest milk reduction observed on the treatment day. Additionally, we assessed the significance of milk fluctuation periods highly related to diseases by defining milk fluctuations as a period of at least 10 consecutive days in which milk yield fell below 90% of the expected milk production values at least once. We defined the development and recovery phases of milk fluctuations using 3,847 milk fluctuation periods related to disease incidences, and estimated genetic parameters of milk fluctuation traits, including milk losses, duration of the fluctuation, variation rate in daily milk yield, and standard deviation of milk deviations for each phase and their genetic correlation with several important traits. In general, the disease-related milk fluctuation periods lasted 21.19 ± 10.36 d with a milk loss of 115.54 ± 92.49 kg per lactation. Compared with the development phase, the recovery phase lasted an average of 3.3 d longer, in which cows produced 11.04 kg less milk and exhibited a slower variation rate in daily milk yield of 0.35 kg/d. There were notable differences in milk fluctuation traits depending on the disease, and greater milk losses were observed when multiple diseases occurred simultaneously. All milk fluctuation traits evaluated were heritable with heritability estimates ranging from 0.01 to 0.10, and moderate to high genetic correlations with milk yield (0.34 to 0.64), milk loss throughout the lactation (0.22 to 0.97), and resilience indicator (0.39 to 0.95). These results indicate that cows with lower milk losses and higher resilience tend to have more stable milk fluctuations, which supports the potential for breeding for more disease-resilient cows based on milk fluctuation traits. Overall, this study confirms the high effect of diseases on milk yield variability and provides insightful information about their relationship with relevant traits in Holstein cattle. Furthermore, this study shows the potential of using high-frequency automatic monitoring of milk yield to assist on breeding practices and health management in dairy cows.

Unraveling the genomic background of resilience based on variability in milk yield and milk production levels in North American Holstein cattle through genome-wide association study and Mendelian randomization analyses

Breeding more resilient animals will benefit the dairy cattle industry in the long term, especially as global climate changes become more severe. Previous studies have reported genetic parameters for various milk yield-based resilience indicators, but the underlying genomic background of these traits remain unknown. In this study, we conducted GWAS of 62,029 SNPs with 4 milk yield-based resilience indicators, including the weighted occurrence frequency (wfPert) and accumulated milk losses (dPert) of milk yield perturbations, and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. These variables were previously derived from 5.6 million daily milk yield records from 21,350 lactations (parities 1-3) of 11,787 North American Holstein cows. The average daily milk yield (ADMY) throughout lactation was also included to compare the shared genetic background of resilience indicators with milk yield. The differential genetic background of these indicators was first revealed by the significant genomic regions identified and significantly enriched biological pathways of positional candidate genes, which confirmed the genetic difference among resilience indicators. Interestingly, the functional analyses of candidate genes suggested that the regulation of intestinal homeostasis is most likely affecting resilience derived based on variability in milk yield. Based on Mendelian randomization analyses of multiple instrumental SNPs, we further found an unfavorable causal association of ADMY with LnVar. In conclusion, the resilience indicators evaluated are genetically different traits, and there are causal associations of milk yield with some of the resilience indicators evaluated. In addition to providing biological insights into the molecular regulation mechanisms of resilience derived based on variability in milk yield, this study also indicates the need for developing selection indexes combining multiple indicator traits and taking into account their genetic relationship for breeding more resilient dairy cattle.

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs

Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (>0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3 d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).

Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands

Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time-series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008-2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)12 time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.

Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (r_auto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01-0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88-0.96) than between lactations 1 and 2 or 3 (0.34-0.88) for dPert, LnVar, and r_auto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and r_auto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.