The ability of a cow to cope with environmental disturbances, such as pathogens or heat waves, is called resilience. To improve resilience through breeding, we need resilience indicators, which could be based on the fluctuation patterns in milk yield resulting from disturbances. The aim of this study was to explore 3 traits that describe fluctuations in milk yield as indicators for breeding resilient cows: the variance, autocorrelation, and skewness of the deviations from individual lactation curves. We used daily milk yield records of 198,754 first-parity cows, recorded by automatic milking systems. First, we estimated a lactation curve for each cow using 4 different methods: moving average, moving median, quantile regression, and Wilmink curve. We then calculated the log-transformed variance (LnVar), lag-1 autocorrelation (rauto), and skewness (Skew) of the daily deviations from these curves as resilience indicators. A genetic analysis of the resilience indicators was performed, and genetic correlations between resilience indicators and health, longevity, fertility, metabolic, and production traits were estimated. The heritabilities differed between LnVar (0.20 to 0.24), rauto (0.08 to 0.10), and Skew (0.01 to 0.02), and the genetic correlations among the indicators were weak to moderate. For rauto and Skew, genetic correlations with health, longevity, fertility, and metabolic traits were weak or the opposite of what we expected. Therefore, rauto and Skew have limited value as resilience indicators. However, lower LnVar was genetically associated with better udder health (genetic correlations from -0.22 to -0.32), better longevity (-0.28 to -0.34), less ketosis (-0.27 to -0.33), better fertility (-0.06 to -0.17), higher BCS (-0.29 to -0.40), and greater dry matter intake (-0.53 to -0.66) at the same level of milk yield. These correlations support LnVar as an indicator of resilience. Of all 4 curve-fitting methods, LnVar based on quantile regression systematically had the strongest genetic correlations with health, longevity, and fertility traits. Thus, quantile regression is considered the best curve-fitting method. In conclusion, LnVar based on deviations from a quantile regression curve is a promising resilience indicator that can be used to breed cows that are better at coping with disturbances.

In this study, we compared multiple logistic regression, a linear method, to naive Bayes and random forest, 2 nonlinear machine-learning methods. We used all 3 methods to predict individual survival to second lactation in dairy heifers. The data set used for prediction contained 6,847 heifers born between January 2012 and June 2013, and had known survival outcomes. Each animal had 50 genomic estimated breeding values available at birth and up to 65 phenotypic variables that accumulated over time. Survival was predicted at 5 moments in life: at birth, at 18 mo, at first calving, at 6 wk after first calving, and at 200 d after first calving. The data sets were randomly split into 70% training and 30% testing sets to evaluate model performance for 20-fold validation. The methods were compared for accuracy, sensitivity, specificity, area under the curve (AUC) value, contrasts between groups for the prediction outcomes, and increase in surviving animals in a practical scenario. At birth and 18 mo, all methods had overlapping performance; no method significantly outperformed the other. At first calving, 6 wk after first calving, and 200 d after first calving, random forest and naive Bayes had overlapping performance, and both machine-learning methods outperformed multiple logistic regression. Overall, naive Bayes has the highest average AUC at all decision points up to 200 d after first calving. Random forest had the highest AUC at 200 d after first calving. All methods obtained similar increases in survival in the practical scenario. Despite this, the methods appeared to predict the survival of individual heifers differently. All methods improved over time, but the changes in mean model outcomes for surviving and non-surviving animals differed by method. Furthermore, the correlations of individual predictions between methods ranged from r = 0.417 to r = 0.700; the lowest correlations were at first calving for all methods. In short, all 3 methods were able to predict survival at a population level, because all methods improved survival in a practical scenario. However, depending on the method used, predictions for individual animals were quite different between methods.

Resilience is the ability of cows to cope with disturbances, such as pathogens or heat waves. To breed for improved resilience, it is important to know whether resilience genetically changes throughout life. Therefore, the aim was to perform a genetic analysis on 2 resilience indicators based on data from 3 periods of the first lactation (d 11-110, 111-210, and 211-340) and the first 3 full lactations, and to estimate genetic correlations with health traits. The resilience indicators were the natural log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily deviations in milk yield from an expected lactation curve. Low LnVar and rauto indicate low variability in daily milk yield and quick recovery, and were expected to indicate good resilience. Data of 200,084 first, 155,784 second, and 89,990 third lactations were used. Heritabilities were similar based on different lactation periods (0.12-0.15 for LnVar, 0.05-0.06 for rauto). However, the heritabilities of the resilience indicators based on full first lactation were higher than those based on lactation periods (0.20 for LnVar, 0.08 for rauto), due to lower residual variances. Heritabilities decreased from 0.20 in full lactation 1 to 0.19 in full lactation 3 for LnVar and from 0.08 to 0.06 for rauto. For LnVar, as well as for rauto, the strongest genetic correlation between lactation periods was between period 2 and 3 (0.97 for LnVar, 0.96 for rauto) and the weakest between period 1 and 3 (0.81 for LnVar, 0.65 for rauto). Similarly, for both traits the genetic correlation between full lactations was strongest between lactations 2 and 3 (0.99 for LnVar, 0.95 for rauto) and weakest between lactations 1 and 3 (0.91 for LnVar, 0.71 for rauto). For LnVar, genetic correlations with resilience-related traits, such as udder health, ketosis, and longevity, adjusted for correlations with milk yield, were almost always favorable (-0.59 to 0.02). In most cases 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. For rauto, genetic correlations with resilience-related traits were always favorable in lactation period 1 and in most full lactations, but not in the other lactation periods. However, correlations were weak (-0.27 to 0.15). Therefore, as a resilience indicator for breeding, LnVar is preferred over rauto. A multitrait index based on estimated breeding values for LnVar in lactations 1, 2, and 3 is recommended to improve resilience throughout the lifetime of a cow.

This study aimed to use temperature-humidity index (THI) as an indicator for assessing heat stress conditions for fertility traits in the Holstein dairy cattle breed in the Netherlands. Data from AI and calving events of 416,814 first-parity cows from the Netherlands were used, considering different THI definitions based on different numbers of days before and after artificial insemination events. To achieve our aim, we investigated first, at population level, the relationship between different THI definitions and 4 fertility traits: conception rate, interval calving to first insemination, interval first to last insemination, and calving interval. Second, to investigate individual variation in the relationship between THI and fertility, variance components were estimated for each trait using the so-called broken stick model. This model assumes that breeding values are dependent on THI above but not below a THI threshold identified at the population level and explores the presence of genetic variation associated with fertility decline during heat stress. This study revealed considerable changes in fertility traits during periods of heat stress, with a THI threshold of 60 for conception rate and interval first to last insemination and 50 for interval calving to first insemination and calving interval. Interestingly, as THI levels increased, genetic variance and heritability also increased, indicating that at higher THI levels associated with reduced fertility, the genetic variation of fertility traits is greater. Furthermore, significant genotype-by-environment interactions were observed for all 4 fertility traits, suggesting changes in sire rankings between THI levels below and above the threshold. This study provides insights that may help breeding programs and farmers breed animals resilient to heat stress conditions.

Survival during the first year after first calving was investigated over the last 25 years, 1989–2013, as well as how the association of survival with season of calving, age at first calving (AFC) and within-herd production level has changed over that period. The data set contained 1 108 745 Dutch black-and-white cows in 2185 herds. Linear models were used to estimate (1) effect of year and season and their interaction and (2) effect of AFC, within-herd production level, and 5-year intervals and their two-way interactions, and the genetic trend. All models contained AFC and percentage of Holstein Friesian as a fixed effect, and herd-year-season, sire and maternal grandsire as random effects. Survival and functional survival were analysed. Functional survival was defined as survival adjusted for within-herd production level. Survival rate increased by 8% up to 92% in the last 25 years. When accounting for pedigree, survival showed no improvement up to 1999, but improved since then. Genetically, survival increased 3% to 4% but functional survival did not increase over the 25 years. We found an interesting difference between the genetic trends for survival and functional survival for bulls born between 1985 and 1999, where the trend for survival was still increasing, but was negative for functional survival. Since 1999, genetic trend picked up again for both survival and functional survival. AFC, season of calving and within-herd production level affected survival. Survival rate decreased 0.6%/month for survival and 1.5% for functional survival between AFC of 24 and 32 months. Calving in summer resulted in 2.0% higher survival than calving in winter. Within herd, low-producing cows had a lower survival rate than high-producing cows. However, these effects became less important during the recent years. Based on survival optimum AFC is around 24 months, but based on functional survival it is better to have an AFC<24 months. Overall, survival rate of heifers has improved considerably in the past 25 years, initially due to the focus on a high milk production. More recently, the importance of a high milk production has been reduced towards attention for functional survival.