2.2.1 Impact of environmental and animal-related effects on behavior traits

Analyses of variance for the behavior indicator traits INT and DUR, and the estimation of least squares means within levels of fixed effects were performed using the software package PROC MIXED in SAS (version 9.2; SAS Institute Inc., Cary, NC, USA). The applied linear model was (Eq. 1)

where y was the vector of observations; β was the vector of fixed effects including parities from 1 to 5, the day when the cow entered the AMS, days in milk in classes according to Huth (1995) (< 14 days = 1, 14–77 days = 2, 77–140 days = 3, 140–231 days = 4, and > 231 days = 5), time of day classes (ToD) when the cow visited the AMS (22:00–04:00 LT = 1, 04:00–10:00 LT = 2, 10:00–16:00 LT = 3, and 16:00–22:00 LT = 4), and explicitly for DUR-specific INT classes (< 8 h = 1, 8–10 h = 2, and > 10 h = 3); X was the corresponding incidence matrix for fixed effects; u was the vector of random cow effects for repeated measurements with the corresponding incidence matrix Z; and e was the vector for random residual effects.

2.2.2 Estimation of genetic parameters

A strategy of using an average information (AI) matrix is shown to be computationally convenient and efficient for estimation of variance components by restricted maximum likelihood (REML) in the mixed linear models (Gilmour et al., 1995). In this regard, model I was extended by including the pedigree based on the genetic relationship matrix. Variance components and heritabilities for behavior, health, and production traits were estimated via single-trait animal models using the AI-REML procedure, as implemented in the DMU software package (Madsen and Jensen, 2000).

For the Gaussian distributed traits MY_total, MY_fl, MY_fr, MY_rl, MY_rr, EC_fl, EC_fr, EC_rl, EC_rr AMF, INT, and DUR, a linear animal repeatability model was applied (Eq. 2):

where y was the vector of observations, β was the vector of fixed effects with the corresponding incidence matrix X, u was the vector of random genetic effects with the corresponding incidence matrix Z, and p was the vector of random permanent environmental effects with the corresponding incidence matrix W, and e was the vector of random residual effects. Traits and their corresponding effects as considered in models I and II are given in Table 2.

Table 2Significance of fixed effects and of covariates for the different traits.

^* P < 0.05 significant. ${}^{**}$ P < 0.01: very significant. ${}^{***}$ P < 0.001: highly significant. n.s.: not significant. ¹ MY_fl, _fr, _rl, and _rr: milk yield at a quarter basis: front left, front right, rear left, and rear right. EC_fl, _fr, _rl, and _rr: electrical conductivity at a quarter basis: front left, front right, rear left, and rear right. AMF: average milk flow. MY_total: total milk yield per day. EC: electrical conductivity from all four quarters. INT: interval between two consecutive milkings. DUR: milking time during a visit in the milking robot. VIS3: at least three visits to the milking robot per day. VIS4: at least four visits to the milking robot per day. KO: knock off of the milking device from at least one udder quarter. ² Robot: the AMS the cow used. Consecutively numbered across herds. Date: day the cow entered the AMS. INTcl: interval in classes; < 8 h = 1, 8–10 h = 2, and > 10 h = 3. DIMcl: days in milk (DIM) in classes; < 14 = 1, 14–77 = 2, 77–140 = 3, 140–231 = 4, and > 231 = 5. ToD: time of day when the cow visited the AMS in classes; 22:00–04:00 LT = 1, 04:00–10:00 LT = 2, 10:00–16:00 LT = 3, and 16:00–22:00 LT = 4. ³ CA: calving age in month is (calving date–birth date)∕30.4375.

Download Print Version | Download XLSX

Repeatabilities for the longitudinal traits were calculated as follows (Eq. 3):

where ${\mathit{\sigma }}_{\mathrm{a}}^{\mathrm{2}}$ was the additive genetic variance, ${\mathit{\sigma }}_{\mathrm{pe}}^{\mathrm{2}}$ was the permanent environmental variance, and ${\mathit{\sigma }}_{\mathrm{e}}^{\mathrm{2}}$ was the residual variance.

For the binary traits VIS3, VIS4, and KO, a generalized linear mixed model (GLMM) with a logit link function was applied (Eq. 4):

where η was the vector of logits, β was the vector of fixed effects with the corresponding incidence matrix X, u was the vector of random genetic effects with the corresponding incidence matrix Z, p was the vector of random permanent environmental effects with the corresponding incidence matrix W. The binary traits along with their effects are given in Table 2.

Heritabilities were calculated using the variance of the logit link function. This implies a correction of the residual variance by factor π²∕3 (Southey et al., 2003).

The covariance structure of random effects for models II and III was (Eq. 5)

where G was the variance–covariance matrix for genetic effects, A_u was an additive genetic relationship matrix for u animals in the whole pedigree, P was the variance–covariance matrix for permanent environmental effects, I was an identity matrix for n cows and r observations, R was the residual variance–covariance matrix, and ⊗ denotes the Kronecker product.

Because milk yield is an important factor influencing AMF, INT, DUR, VIS3, VIS4, and KO, univariate models with and without milk yield as a covariate were compared.

Furthermore, bivariate animal models were applied to all trait combinations in order to estimate genetic correlations. Bivariate GLMMs (model III) were applied to binary trait combinations (e.g., VIS3 with KO), bivariate GLMM–linear models (models III and II) for the combination of one binary with one Gaussian trait (e.g., VIS3 with MY), and linear–linear models (model II) for Gaussian–Gaussian trait combinations including EC, AMF, MY_total, INT, and DUR.

In some bivariate runs, standard errors (SEs) for genetic correlations were quite large. However, SEs for heritabilities from single-trait animal models were in an acceptable range. Hence, in addition, estimated breeding values (EBVs) from single-trait animal models were correlated, and EBV correlations were compared with genetic correlations. For this purpose, EBVs from the most influential sires with at least five daughters were considered (47 sires).

3.3.1 Variance components and heritabilities for behavior traits

The heritability was 0.25 for AMF, 0.19 for DUR, 0.07 for INT, 0.03 for KO, 0.08 for VIS3, and 0.05 for VIS4 (Table 4). König et al. (2006) estimated a heritability for milking frequency in the range from 0.16 and 0.27, depending on the statistical modeling. Reported heritabilities for INT were in a range from 0.09 to 0.26 (Carlström et al., 2013). The heritability for INT found in the present study was slightly lower (0.07). Also the heritability for AMF (0.25) was slightly lower compared to estimates based on conventional milking technique data (Santos et al., 2015) but still moderate with relatively small standard errors. The moderate heritabilities for the behavior trait indicators DUR and AMF indicate the possibilities for AMS milkability improvements via breeding. Heritabilities estimated with univariate models with and without milk yield as a covariate were comparable, with slightly larger heritabilities for some traits in models with milk yield as a covariate (0.17 for VIS3, 0.08 for VIS4, and 0.18 for INT). Heritabilities and additive genetic variance show that besides environmental components such as food, fetching and machine calibration (e.g., restriction of the INT) there is a genetic component behind the traits. Research showed that there is a possibility to automatically record behavior traits (König et al., 2006; Schwartzkopf-Genswein et al., 2012). The characterization of the traits in the context of animal welfare implies further studies including temperament and behavior tests as validated in animal ethology (e.g., Ebinghaus et al., 2017).

Table 4Heritabilities (h²) with their corresponding SEs, additive genetic variances (σ_a), permanent environmental variances (${\mathit{\sigma }}_{\mathrm{pe}}^{\mathrm{2}}$), residual variances (${\mathit{\sigma }}_{\mathrm{e}}^{\mathrm{2}}$), and repeatabilities (w²) for all traits.

^* See Table 3.

Download Print Version | Download XLSX

3.3.2 Variance components and heritabilities for productivity and health traits for the different udder quarters

Heritabilities for EC from different udder quarters varied between 0.37 and 0.46 (Table 4). For udder health indicators, Juozaitienè et al. (2015) estimated a heritability of 0.51 for EC, reflecting heritabilities for corresponding traits from our study. The moderate to large heritabilities suggest the inclusion of EC into udder health indices. From such a perspective, additional value is generated when treating each quarter separately, in order to identify pathological changes (Umstätter, 2002). The benefit would be in the use of information for monitoring and managing animal health (Kramer et al., 2013).

The heritability for milk yield per day (MY_total) was 0.18. For the different udder quarters, heritabilities ranged from 0.05 for MY_fr to 0.19 for MY_fl (Table 4). Tančin et al. (2006) suggested productivity improvements via consideration of specific udder quarter information. Also in the present study, identical traits from different udder quarters seem to have a different genetic background in terms of genetic (co)variance components, suggesting udder-quarter-specific selection strategies. As one example, sires will have udder-quarter-specific breeding values, allowing specific udder quarter improvements from a mating perspective. Information at an udder quarter level might be valuable information in future precision dairy farming applications. Eastwood et al. (2004) defined precision dairy farming as “the use of information technologies for assessment of fine-scale animal and physical resource variability aimed at improved management strategies for optimizing economic, social, and environmental farm performance.” In this regard, electrical devices used in precision dairy farming analyzed different milk characteristics and produced indicators based on temperature, conductivity, milk quantity, production balance across quarters, or milk flow (Boichard and Brochard, 2012).

3.3.3 Correlations among behavior indicator traits

Genetic correlations (r_g) as well as EBV correlations (r_EBV) between AMF and DUR were negative (−0.88 and −0.63, respectively) (Table 5), indicating that fast milking cows, i.e., cows with a good temperament, spent less time milking in the milking robot. Hence, genetic associations between AMF and DUR confirm farmers' observations at a phenotypic scale. Both temperament indicators (milking time and milk flow) are economically important, because they determine AMS capacity and efficiency. Explanations for the strong genetic relationship between AMF and DUR with acceptable standard errors can be as follows. The combative behavior of cows causes physiological changes, i.e., affecting the oxytocin hormone level and norepinephrine concentrations (Kondo and Hurnik, 1988), and in ongoing lactation, reduced milk flow. Szentléleki et al. (2015) reported a significantly higher milking speed if cows were calm in the pre-milking process compared to nervous cows. Another explanation is due to the fact that cows with faster AMF need less time to be milked and therefore spend less time in the milking box.

Table 5Genetic correlations among behavior indicator traits (above the diagonal with standard errors in brackets) and corresponding correlations among breeding values from bulls with at least five daughters (below the diagonal).

^* See Table 1.

Download Print Version | Download XLSX

Table 6Genetic correlations (r_g) with standard errors and breeding value correlations (r_EBV) considering bulls with at least five daughters between milk yield and behavior indicator traits.

^* See Table 1. MY_total: total milk yield per day from the AMS.

Download Print Version | Download XLSX

Table 7Genetic correlations (r_g) with standard errors (in brackets) and breeding value correlations (r_EBV) considering bulls with at least five daughters between behavior indicator traits and electrical conductivity of the udder (EC).

^* See Table 1.

Download Print Version | Download XLSX

The genetic correlation between DUR and KO was also negative (r_g= −0.25), with less knock offs of the milking device being associated with longer milk ejection. A hypothesis would be that nervous or fearful cows show more knock offs, leading to a shorter milk ejection because of negative impact on oxytocin release. As outlined by Rousing et al. (2004), kicking and stepping during milking resulted in knock off of the milking device, incomplete milking, and reduced milk yield. Stressed animals not only require more handling during milking (Rushen et al., 1999) but they may kick off the milking device during milking. Fearfulness in cows can result in slower milk let down and milk retention (Munksgaard et al., 2001). EBV correlations between DUR and KO were close to zero. EBV correlations were only based on EBV from influential sires, but due to EBV accuracies lower than 1, r_EBV values are “less extreme”, i.e., an underestimation of genetic correlations (Calo et al., 1973).

Low positive correlations were estimated between VIS3 and DUR (r_g= 0.28, r_EBV= 0.11), and between VIS4 and DUR (r_g= 0.37, r_EBV= 0.12), indicating that an increase of visiting frequencies was associated with a longer milking duration per visit. Extremely long DUR may reflect an inhibited milk flow, being supported on the genetic scale with r_g= −0.88 between DUR and AMF. The genetic correlation between AMF and INT was positive, but close to zero (r_g= 0.14, r_EBV= 0.02), and estimated with quite large standard errors. Accordingly, Hogeveen et al. (2001) found that a longer milking interval was associated with an increase in the milk flow rate (on a phenotypic level), independent of the milk performance level. An increase in milking speed (e.g., AMF) will lead to a reduction of total milking time per cow, with a positive impact on AMS capacity and AMS efficiency (Gäde et al., 2007).

INT and KO were negatively correlated (r_g= −0.19, r_EBV= −0.13), meaning that shorter intervals were genetically associated with more knock offs. This might be associated with udder deformations, resulting in improper attachment of the milking device. Also, the correlation between DUR and INT was slightly negative (r_g= −0.15, r_EBV= −0.09), indicating that shorter intervals were associated with a longer milking duration. Milking duration depends not only on the amount of milk, but also on the temperament of the cow. Kaihilahti et al. (2006) stated that 19 % of the cows kicked off the teat cups during milking, with an impact on milk duration and milking time independent from the milk yield level. In most cases, EBV correlations were in agreement with genetic correlations.

3.3.4 Correlations between behavior and production traits

As indicated in Table 6, MY_total was positively correlated with DUR (r_g= 0.87; r_EBV= 0.25), with VIS3 (r_g= 0.49; r_EBV= 0.52), and with VIS4 (r_g= 0.80; r_EBV= 0.47). These findings suggest that sires with high EBV for milk yield tend to have daughters with longer milking duration but also daughters that come easily or voluntary into the milking box. These findings were also reported by König et al. (2006) when defining milking frequencies as a behavior indicator trait. In their models, milk yield was used as covariate in statistical models, showing that milk yield was not the only force determining voluntary visits. Kazlauckas et al. (2005) compared two groups of mice (with high and low exploration of an object) and concluded that although both groups were driven by food motivation, high exploratory mice showed increased locomotion. Results from such research, as conducted in other species, support our hypothesis that there are additional motivations to visit the AMS apart from the offered food. In accordance with Nixon et al. (2009), selection for increased milk production implies an increase of milking frequencies. Associations between calm temperament and high milk production were shown by Lawstuen et al. (1988) and Breuer et al. (2000). Also across species, temperament and behavior were favorably correlated with productivity. For example, in pigs, selection for calm temperament simultaneously improved carcass and meat quality traits (Reverter et al., 2003). Nevertheless, the definition of temperament differed, complicating the comparison of correlation coefficients from different studies. Accordingly, Hoppe et al. (2010) found that selection towards improved temperament in beef cattle simultaneously improved production traits. In the present study, milk yield was positively correlated with AMF (r_g= 0.40; r_EBV= 0.47), indicating that higher productivity was associated with a faster milk flow, supporting previous results from conventional milking systems (Santos et al., 2015). Selection towards desired temperament had a positive effect on the number of visits and on the duration in the milking box, as well as on milk production and milk efficiency (i. e., AMF). The correlation between MY_total and KO (r_g= 0.21; r_EBV= 0.05) was moderately positive, indicating antagonistic genetic relationships from a breeding perspective. Knocking off the milking device has different reasons, e.g., udder morphology, mastitis, or the temperament of the cow. More aggressive cows produce more knock offs of the milking device due to the agitation in the milking box. The behavior indicator INT was negatively correlated with MY_total (r_g= −0.51; r_EBV= −0.52). Shorter milking intervals, indicating a good individual behavior or temperament, were also associated with higher milk production per cow and hour (Hogeveen et al., 2001). Extended milking intervals cause practical problems in AMS, and so far, direct selection tools are missing. Nevertheless, some genetic correlation standard errors were quite large, encouraging further analyses in this regard.

3.3.5 Correlations between behavior and health traits

Genetic correlations among all behavioral traits and EC were positive, but also close to zero (Table 7). The strongest positive genetic correlation was found between INT and EC (0.19), indicating that longer milking intervals had a negative impact on animal health, or that animals having health problems have longer milking intervals. The number of AMS visits (VIS3) was positively correlated with EC (r_g= 0.15). The risk of bacterial invasion into the mammary gland increased with the number of milkings per day (Hogeveen et al., 2001). The genetic correlation between AMF and EC was low, but again positive (r_g= 0.11), confirming the unfavorable relationships between temperament and health traits (Santos et al., 2015). Increased AMF is also due to decreased tension of the teat sphincter. A weak sphincter decreases resistance to infection of the udder by specific major pathogens, causing clinical mastitis (Boettcher et al., 1998). Hence, from an animal breeding perspective, AMF should be treated as a trait with an intermediate optimum, as already suggested by Santos et al. (2015). Generally weak genetic correlations suggest that breeding on behavior indicator traits does not impair health indicator traits. Regarding the correlations between AMS behavior indicator traits with EC, all EBV correlations were close to zero, and partly differed in sign from genetic correlations. The genetic correlations between AMS behavior traits with other functional traits were estimated with quite large standard errors, suggesting ongoing studies with additional data.