Genetic parameters for dry matter intake, energy balance, residual energy intake, and liability to diseases in German Holstein and Fleckvieh dairy cows.

Selection for feed efficiency (FE) is a hot topic in dairy cow breeding. Dry matter intake (DMI) and residual energy intake (REI) are mostly discussed as new selection traits. Selection for lower DMI or REI seems to increase FE if other traits, such as milk yield or health, are not affected negatively. However, genetic relationships with other traits have not been adequately investigated because of the difficulties in recording sufficient feed intake data for genetic evaluations. The aim of this study was to examine the genetic relationships between FE-related traits and liability to diseases throughout lactation. First, heritabilities for all traits are presented. Subsequently, genetic correlations between DMI, energy-corrected milk yield, energy balance (EB), and REI on the one hand and 3 disease categories (mastitis, claw and leg diseases, and all diseases) on the other throughout lactation in German Holstein (GH) dairy cows are illustrated. Production and health data from the projects optiKuh and eMissionCow were used. Data consisted of weekly observations recorded over a 325-wk period in 2,387 GH and over a 300-wk period in 632 Fleckvieh (FV) primiparous and multiparous dairy cows from 13 dairy research farms in Germany. Variance and covariance components were estimated univariately or bivariately with linear random regression models for production data and threshold random regression models for health data. Heritabilities for DMI, EB, and REI were on average 0.17 and 0.15, 0.14 and 0.15, as well as 0.11 and 0.14 in GH and FV, respectively. Heritabilities on the underlying scale for mastitis, claw and leg diseases, and all diseases were on average 0.17 and 0.16, 0.18 and 0.12, as well as 0.15 and 0.11 in GH and FV, respectively. In GH, almost all genetic correlations were negative, especially in early lactation. Within the first 50 d in milk, genetic correlations between DMI and REI on the one hand and disease categories on the other ranged from -0.25 to -0.14 for mastitis, from -0.31 to -0.13 for claw and leg diseases, and from -0.58 to -0.30 for all diseases. Consequently, selection for lower DMI or REI could lead to a higher liability to diseases, especially in early lactation. A possibility to mitigate these undesirable side effects could be lactation stage-specific selection for FE. For FV, further studies with more data are needed to assess genetic relationships.

Residual energy intake, energy balance, and liability to diseases: Genetic parameters and relationships in German Holstein dairy cows.

Residual energy intake (REI) is an often-suggested trait for direct selection of dairy cows for feed efficiency. Cows with lower REI seem to be more efficient but are also in a more severe negative energy balance (EB), especially in early lactation. A negative EB leads to a higher liability to diseases. Due to this fact, this study aims to investigate the genetic relationship between REI and liability to diseases. Health and production data were recorded from 1,370 German Holstein dairy cows from 8 research farms over a period of 2 yr. We calculated 2 phenotypes for REI that considered the following energy sinks: milk energy content, metabolic body weight, body weight change, body condition score, and body condition score change. Genetic parameters were estimated with threshold or linear random regression models from days in milk (DIM) 1 to 305. Heritabilities for REI, EB, and all diseases ranged from 0.12 to 0.39, 0.15 to 0.31, and 0.09 to 0.20, respectively. Genetic correlations between selected DIM for REI and EB were higher for adjacent DIM than for more distant DIM. Pearson correlation coefficients between estimated breeding values (EBV) for REI and EB varied between 0.47 and 0.81; they were highest in mid lactation. Correlations between EBV for all diseases and REI as well as EB were negative, with lowest values in early lactation. Within the first 50 DIM, proportions of diseased days for cows with lowest EBV for REI were almost twice as high as for cows with highest EBV for REI. In conclusion, selecting dairy cows for lower REI should be treated with caution because of an unfavorable relationship with liability to diseases, especially in early lactation.

Liability to diseases and their relation to dry matter intake and energy balance in German Holstein and Fleckvieh dairy cows.

Dairy cow efficiency is increasingly important for future breeding decisions. The efficiency is determined mostly by dry matter intake (DMI). Reducing DMI seems to increase efficiency if milk yield remains the same, but resulting negative energy balance (EB) may cause health problems, especially in early lactation. Objectives of this study were to examine relationships between DMI and liability to diseases. Therefore, cow effects for DMI and EB were correlated with cow effects for 4 disease categories throughout lactation. Disease categories were mastitis, claw and leg diseases, metabolic diseases, and all diseases. In addition, this study presents relative percentages of diseased cows per days in milk (DIM), repeatability, and cow effect correlations for disease categories across DIM. A total of 1,370 German Holstein (GH) and 287 Fleckvieh (FV) primiparous and multiparous dairy cows from 12 dairy research farms in Germany were observed over a period of 2 yr. Farm staff and veterinarians recorded health data. We modeled health and production data with threshold random regression models and linear random regression models. From DIM 2 to 305 average daily DMI was 22.1 kg/d in GH and 20.2 kg/d in FV. Average weekly EB was 2.8 MJ of NEL/d in GH and 0.6 MJ of NEL/d in FV. Most diseases occurred in the first 20 DIM. Multiparous cows were more susceptible to diseases than primiparous cows. Relative percentages of diseased cows were highest for claw and leg diseases, followed by metabolic diseases and mastitis. Repeatability of disease categories and production traits was moderate to high. Cow effect correlations for disease categories were higher for adjacent lactation stages than for more distant lactation stages. Pearson correlation coefficients between cow effects for DMI, as well as EB, and disease categories were estimated from DIM 2 to 305. Almost all correlations were negative in GH, especially in early lactation. In FV, the course of correlations was similar to GH, but correlations were mostly more negative in early lactation. For the first 20 DIM, correlations ranged from -0.31 to 0.00 in GH and from -0.42 to -0.01 in FV. The results illustrate that future breeding for dairy cow efficiency should focus on DMI and EB in early lactation to avoid health problems.

Estimation of genetic parameters and breeding values for feed intake and energy balance using pedigree relationships or single-step genomic evaluation in Holstein Friesian cows.

At the beginning of lactation, high-performing dairy cows often experience a severe energy deficit, which in turn is associated with metabolic stress. Increasing feed intake (FI) or reducing the energy deficit during this period could improve the metabolic stability and thus the health of the animals. Genomic selection for the first time enables the inclusion of this hard-to-measure trait in breeding programs. The objective of the current study was the estimation of genetic parameters and genomic breeding values for FI and energy balance (EB). For this purpose, 1,374 Holstein Friesian (HF) dairy cows from 8 German research farms were phenotyped with standardized FI data protocols. After data editing, phenotypic data of HF comprised a total of 40,012 average weekly FI records with a mean of 21.8 ± 4.3 kg/d. For EB 33,376 average weekly records were available with a mean of 3.20 ± 29.4 MJ of NEL/d. With the Illumina Bovine SNP50 BeadChip (Illumina Inc., San Diego, CA) 1,128 of phenotyped cows were genotyped. Thirty-five female candidates of the HF population were genotyped but not phenotyped. Pedigree information contained sires and dams 4 generations back. The random regression animal model included the fixed effects of herd test week (alternatively, herd group test week), parity, and stage of lactation, modeled by the function according to Ali and Schaeffer (1987). For both the random permanent environmental effect across lactations and the random additive genetic effect, third-order Legendre polynomials were chosen. Additionally, a random permanent environmental cow effect within lactation was included. Analyses for heritabilities, genetic correlations between different lactation stages, and breeding values were estimated using, respectively, pedigree relationships and single-step genomic evaluation, carried out with the DMU software package (Madsen et al., 2013). This allowed for comparison of conventional reliabilities with genomic-assisted reliabilities based on real data, to evaluate the gain of genotyping. Heritability estimates ranged between 0.12 and 0.50 for FI, and 0.15 and 0.48 for EB, and increased toward the end of lactation. Genetic correlations were weak between early and late lactation, with a value of 0.05 for FI and negative with a value of -0.05 for EB. Reliabilities for genomic values of cows for FI and EB ranged between 0.33 and 0.61, and 0.27 and 0.47, respectively. For the genotyped cows without phenotypes, the inclusion of genomic relationship leads to an increase of the average reliability of the breeding value for FI by nearly 9% and for EB by 4%. The results show the possibility of combining pedigree, genotypes, and phenotypes for increasing FI or EB to reduce health and reproductive problems, especially at the beginning of lactation. Nevertheless, the reference population needs to be extended to reach higher breeding value reliabilities.

Lactation curves and model evaluation for feed intake and energy balance in dairy cows.

A good health status of high-performing dairy cows is essential for successful production. Feed intake affects the metabolic stability of dairy cows and can be used as a measurement for energy balance. By implementing feed intake and energy balance into the breeding goal, these traits provide great potential for an improvement in the health of dairy cows by breeders. In this study, fixed and random regression models were tested to establish appropriate models for a further analysis of this approach. A total of 1,374 Holstein-Friesian cows and 327 Simmental cows (SI) from 12 German research farms participating in a collaboration called optiKuh were phenotyped. Feed intake data recording was standardized across farms, and energy balance was calculated using phenotypic information on milk yield, milk ingredients, live weight, gestation stage, and feed intake. The phenotypic data set consisted of a total of 40,012 Holstein-Friesian and 16,996 SI with average weekly dry matter intakes of 21.8 ± 4.3 and 20.2 ± 3.6 kg/d, respectively. Observations of days in milk 1 to 350 were used to evaluate the best-fitting models and to estimate the repeatability and correlations between cow effects at different stages for feed intake and energy balance. Four parametric functions (Ali and Schaeffer and Legendre polynomials of second, third, and fourth degree) were compared to model the lactation curves. Based on the corrected Akaike information criterion and the Bayesian information criterion, the goodness of fit was evaluated to choose the best-fitting model for the finest description of lactation curves for the traits energy balance and feed intake. Legendre polynomial fourth degree was the best-fitting model for random regression models. In contrast, Ali and Schaeffer was the best choice for fixed regression models. Feed intake and energy balance acted as expected: the feed intake increased slowly at the beginning of lactation and the negative energy balance switched to a positive range around 40 to 80 d of lactation. The repeatabilities of both traits were quite similar and the repeatabilities for SI were the highest for both traits. Additionally, correlations between cow effects were closest between early days in milk. These results emphasize the possibility that the unique optiKuh data set can be used for further genetic analyses to enable genomic selection for feed intake or energy balance.

Short communication: Pro- and antioxidative indicators in serum of dairy cows during late pregnancy and early lactation: Testing the effects of parity, different dietary energy levels, and farm.

Dairy cows face metabolic challenges in the transition from late pregnancy to early lactation. The energy demands for the growing fetus and the onset of milk production are increasing but voluntary feed intake often decreases around parturition and cannot meet these demands. This energy balance, among others, can change the oxidative status. Oxidative stress occurs when antioxidant defense mechanisms are not sufficient to cope with the increasing generation of reactive oxygen species. Our objectives were to investigate (1) the effect of parity on the oxidative status of dairy cows (n = 247) in late pregnancy and early lactation; and (2) the effect of different inclusion rates of concentrate feeding (150 vs. 250 g/kg of energy-corrected milk) during early lactation on 2 farms including 87 cows in total. In addition, we aimed to compare the oxidative status across the 2 farms using equal portions of concentrate feeding. For these purposes, we measured concentrations of the derivatives of reactive oxygen metabolites (dROM) and the ferric reducing ability (FRAP) in serum on d -50, -14, +8, +28, and +100 relative to calving. Furthermore, we calculated the oxidative status index (OSi) as dROM/FRAP × 100. Data were analyzed using a linear mixed model. Cows in the first and second lactations had greater dROM, FRAP, and OSi than cows in their third and greater lactations. Hence, supporting the antioxidative side of the balance might be of particular importance in the first and second lactations. Feeding different amounts of concentrates did not affect dROM, FRAP, or OSi under our experimental conditions, suggesting that the relatively small differences in energy intake were not affecting the oxidative status. Comparing farms, cows from one farm were notable for having greater dROM and lower FRAP, resulting in a greater OSi compared with cows on the other farm. Milk yield showed a time by farm interaction with 7% less milk on d 100 on the farm with the greater OSi. Moreover, cows on that farm had 1.4-fold greater ß-hydroxybutyrate concentrations. Our results emphasize the value of assessing oxidative status with regard to both the pro- and antioxidative sides, and support the association between oxidative and metabolic status. Further investigations are needed to determine the applicability of OSi as a prognostic tool during early lactation and to determine which factors have the greatest influence on oxidative status.

Circulating adiponectin concentrations during the transition from pregnancy to lactation in high-yielding dairy cows: testing the effects of farm, parity, and dietary energy level in large animal numbers.

Dairy cows experience a negative energy balance due to increasing energy demands and insufficient voluntary feed intake in the transition from late pregnancy to early lactation. For supplying sufficient energy toward the conceptus and the mammary gland, insulin sensitivity in peripheral tissues is reduced leading to adipose tissue mobilization. Adiponectin, an insulin-sensitizing adipokine, is presumably related to energy metabolism and could play an important role in these metabolic adaptations. We hypothesize (1) that primiparous cows would differ from pluriparous cows in their circulating adiponectin concentrations during the transition from late pregnancy to early lactation and (2) that feeding different energy levels would affect the adiponectin concentrations during early lactation in dairy cows. For the first hypothesis, we examined 201 primiparous and 456 pluriparous Holstein dairy cows on three experimental farms. Ante partum, primiparous cows had lower adiponectin and greater NEFA concentrations than pluriparous cows, but vice versa post partum. Hence, adiponectin might be involved in the energy partitioning in primiparous cows (conceptus and lactation vs other still growing body tissues) with changing priorities from pregnancy to lactation. For the second hypothesis, 110 primiparous and 558 pluriparous Holstein and Simmental dairy cows in six experimental farms received either roughage with 6.1 or 6.5 MJ NEl/kg dry matter (adjusted with different amounts of wheat straw) ad libitum, combined with either 150 or 250 g concentrates/kg energy corrected milk. Greater amounts of concentrate lead to greater milk yield, but did not affect the blood variables. The higher energy level in the roughage led to greater glucose and IGF-1 but lower adiponectin in pluriparous cows. Further studies are needed to elucidate the mechanisms behind the roughage effect and its metabolic consequences.

Detecting lameness in sows from ear tag-sampled acceleration data using wavelets.

The objective of this study was to develop an automated monitoring system to detect lameness in group-housed sows early and reliably on the basis of acceleration data sampled from ear tags. To this end, acceleration data from ear tags were acquired from an experimental system deployed at the Futterkamp Agriculture Research Farm from May 2012 until November 2013. The developed method performs a wavelet transform for each individual sow's time series of total acceleration. Feature series are then computed by locally estimating the energy, variation and variance in a small moving window. These feature series are then further decomposed into uniform level sets. From these series of level sets, the highest and lowest levels are monitored for lameness detection. To that end, they are split into a past record to serve as reference data representing a sow's expected behaviour. The deviations between the reference and the remaining detection part of current data, termed feature activated, were then utilised to possibly indicate a lameness condition. The method was applied to a sample of 14 sows, seven of which were diagnosed as lame by a veterinarian on the last day of the sampling period of 14 days each. A prediction part of 3 days was set. Feature activated were clearly separable for the lame and healthy group with means of 8.8 and 0.8, respectively. The day-wise means were 1.93, 9.47 and 15.16 for the lame group and 0.02, 1.13 and 1.44 for the healthy group. A threshold could be set to completely avoid false positives while successfully classifying six lame sows on at least one of the 2 last days. The accuracy values for this threshold were 0.57, 0.71 and 0.78 when restricting to data from the particular day. A threshold that maximised the accuracy achieved values of 0.57, 0.79 and 0.93. Thus, the method presented seems powerful enough to suggest that an individual classification from ear tag-sampled acceleration data into lame and healthy is feasible without previous knowledge of the health status, but has to be validated by using a larger data set.

Automatic detection of lameness in gestating group-housed sows using positioning and acceleration measurements.

Lameness is an important issue in group-housed sows. Automatic detection systems are a beneficial diagnostic tool to support management. The aim of the present study was to evaluate data of a positioning system including acceleration measurements to detect lameness in group-housed sows. Data were acquired at the Futterkamp research farm from May 2012 until April 2013. In the gestation unit, 212 group-housed sows were equipped with an ear sensor to sample position and acceleration per sow and second. Three activity indices were calculated per sow and day: path length walked by a sow during the day (Path), number of squares (25×25 cm) visited during the day (Square) and variance of the acceleration measurement during the day (Acc). In addition, data on lameness treatments of the sows and a weekly lameness score were used as reference systems. To determine the influence of a lameness event, all indices were analysed in a linear random regression model. Test day, parity class and day before treatment had a significant influence on all activity indices (P<0.05). In healthy sows, indices Path and Square increased with increasing parity, whereas variance slightly decreased. The indices Path and Square showed a decreasing trend in a 14-day period before a lameness treatment and to a smaller extent before a lameness score of 2 (severe lameness). For the index acceleration, there was no obvious difference between the lame and non-lame periods. In conclusion, positioning and acceleration measurements with ear sensors can be used to describe the activity pattern of sows. However, improvements in sampling rate and analysis techniques should be made for a practical application as an automatic lameness detection system.

Effect of genetic merit for energy balance on luteal activity and subsequent reproductive performance in primiparous Holstein-Friesian cows.

Postpartum energy status is critically important to fertility. However, studies dealing with the relationship between both traits are rare and most refer only to the phenotypic level. In this study, random regression models were used to generate cow-specific lactation curves for daily breeding values (BV) of energy balance (EB) to assess the effect of genetic merit for energy status on different traits derived from progesterone profiles and on subsequent reproductive performance of high-producing dairy cows. Individual feed intake, milk yield, and live weight were recorded for lactation d 11 to 180, and EB was estimated on a daily basis. The results provided the basis for the estimation of BV for 824 primiparous Holstein-Friesian cows. For a subset of these cows (n = 334), progesterone profiles for the resumption of ovarian activity were available. Four different traits describing the genetic merit for EB were defined to evaluate their relationship with fertility. Two EB traits referred to the period in which the average daily EB across all cows was negative (d 11 to 55 postpartum), and 2 parameters were designed considering only daily BV for d 11 to 180 in lactation that were negative. We found that cows with a high genetic merit for EB had a significantly earlier resumption of ovarian activity postpartum. Thus, an EB (indicator) trait should be included in future breeding programs to reduce the currently prolonged anovulatory intervals after parturition.

Genetic parameters for lameness and claw and leg diseases in dairy cows.

Lameness in dairy cows is a serious welfare and economic problem in dairy production. The majority of all lameness cases seem to stem from claw and leg diseases. Indirect selection on claw health potentially might be feasible with lameness as indicator trait. Therefore, the genetic parameters for the 2 traits were estimated by applying both linear and threshold models. In addition, the impact of environmental effects, parity, and stage of lactation was analyzed. In total, 8,299 locomotion scores (1-5) of 326 dairy cows and 708 claw and leg disease diagnoses or treatments of 335 dairy cows from the dairy research farm Karkendamm (Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Kiel, Germany) were analyzed. Lameness was defined by a locomotion score of ≥ 3. Days in milk were limited to the range of 10 to 350 d. To quantify the effect of the claw disease digital dermatitis, a second data set without this disease was built; 52.8 and 36.4% (without digital dermatitis) of the cows were treated at least once; 47.2% of the cows were clinically lame at least at one time. Genetic parameters were estimated bivariately using the average information restricted maximum likelihood procedure as implemented in the DMU software package. The heritability estimates derived from the threshold model were about twice as large as the values based on the linear model. For lameness, the threshold heritability increased from 0.15 to 0.22 and decreased for the diseases from 0.24 to 0.22 after exclusion of digital dermatitis. The genetic correlations were high and even increased from 0.60 to 0.72 after the exclusion of digital dermatitis, which suggests that lameness (locomotion score) seems to be a good indicator for claw and leg diseases. Digital dermatitis seems to affect the mobility of the dairy cow less strongly than other claw and leg diseases.

Genetic parameters for energy balance, fat /protein ratio, body condition score and disease traits in German Holstein cows.

Various health problems in dairy cows have been related to the magnitude and duration of the energy deficit post partum. Energy balance indicator traits like fat/protein ratio in milk and body condition score could be used in selection programmes to help predicting breeding values for health traits, but currently there is a lack of appropriate genetic parameters. Therefore, genetic correlations among energy balance, fat/protein ratio, and body condition score, and mastitis, claw and leg diseases, and metabolic disorders were estimated using linear and threshold models on data from 1693 primiparous cows recorded within the first 180 days in milk. Average daily energy balance, milk fat/protein ratio and body condition score were 8 MJ NEL, 1.13 and 2.94, respectively. Disease frequencies (% cows with at least one case) were 24.6% for mastitis, 9.7% for metabolic disorders and 28.2% for claw and leg diseases. Heritability estimates were 0.06, 0.30 and 0.34 for energy balance, fat/protein ratio and body condition score, respectively. For the disease traits, heritabilities ranged between 0.04 and 0.15. The genetic correlations were, in general, associated with large standard errors, but, although not significant, the results suggest that an improvement of overall health can be expected if energy balance traits are included into future breeding programmes. A low fat/protein ratio might serve as an indicator for metabolic stability and health of claw and legs. Between body condition and mastitis, a significant negative correlation of -0.40 was estimated. The study provides a new insight into the role energy balance traits can play as auxiliary traits for robustness of dairy cows. It was concluded that both, fat/protein ratio and body condition score, are potential variables to describe how well cows can adapt to the challenge of early lactation. However, the genetic parameters should be re-estimated on a more comprehensive data set.

Short communication: genetic relationships among daily energy balance, feed intake, body condition score, and fat to protein ratio of milk in dairy cows.

Postpartum energy status is critically important to health and fertility, and it remains a major task to find suitable indicator traits for energy balance. Therefore, genetic parameters for daily energy balance (EB) and dry matter intake (DMI), weekly milk fat to protein ratio (FPR), and monthly body condition score (BCS) were estimated using random regression on data collected from 682 Holstein-Friesian primiparous cows recorded between lactation d 11 to 180. Average energy-corrected milk (ECM), EB, DMI, BCS, and FPR were 32.0 kg, 9.6 MJ of NE(L), 20.3 kg, 2.95, and 1.12, respectively. Heritability estimates for EB, DMI, BCS, and FPR ranged from 0.03 to 0.13, 0.04 to 0.19, 0.34 to 0.59, and 0.20 to 0.54. Fat to protein ratio was a more valid measure for EB in early lactation than DMI, BCS, or single milk components. Correlations between FPR and EB were highest at the beginning of lactation [genetic correlation (r(g)) = -0.62 at days in milk (DIM) 15] and decreased toward zero. Dry matter intake was the trait most closely correlated with EB in mid lactation (r(g) = 0.73 at DIM 120 and 150). Energy balance in early lactation was negatively correlated to EB in mid lactation. The same applied to DMI. Genetic correlations between FPR across lactation stages were all positive; the lowest genetic correlation (0.55) was estimated between the beginning of lactation and early mid lactation. Hence, to improve EB at the beginning of lactation, EB and indicator traits need to be recorded in early lactation. We concluded that FPR is an adequate indicator for EB during the energy deficit phase. Genetic correlations of FPR with ECM, fat percentage, and protein percentage showed that a reduction of FPR in early lactation would have a slightly negative effect on ECM, whereas milk composition would change in a desirable manner.

Genetic analysis of mastitis data with different models.

The aim of this study was to analyze different mastitis data sets with different statistical models and compare results. Data recording took place on 3 commercial milk farms with an average herd size of 3,200 German Holstein cows. Recording started in February 1998 and was completed in December 2005. During this period, 63,540 treatments for clinical mastitis were recorded. Five different data sets were analyzed and the number of cows varied between 12,972 and 13,618, depending on the data set. Data collection periods contained either the first 50 or the first 300 d of lactation. When the data-recording period ended after 50 d of lactation, data sets were analyzed with a lactation threshold model (LTM), a multiple threshold lactation model (MTLM), and a test-day threshold model (TDTM). In the LTM analysis, mastitis was treated as a binary trait coded as 0 (no mastitis) or 1 (mastitis), whereas in MTLM mastitis, codes were between 0 and 4, depending on the number of estimated days with mastitis. The TDTM treated each day as a single observation coded similarly to that of the LTM. When the data collection period included the first 300 d of lactation, data sets were analyzed with the LTM or MTLM only, because the TDTM was computationally infeasible. Mastitis frequencies in LTM data sets were 25.8 and 39.2%, and 26.9 and 39.2% in MTLM data sets, when data recording ended after 50 and 300 d of lactation, respectively. The mastitis frequency in the TDTM data set was 5.2%. Respective heritability estimates of liability to clinical mastitis were 0.08 and 0.09 using the LTM, and 0.08 and 0.11 using the MTLM. When the TDTM was used, the estimated heritability was 0.15. Rank correlation between breeding values of the different data sets ranged between 0.40 and 0.97. Rank correlation between the LTM and MTLM were higher (0.78 to 0.97) than those between these 2 models and the TDTM (0.40 to 0.59).The MTLM combined the positive effects of both the LTM, with respect to the size of the data sets, and the TDTM, with respect to the lack of information.

Evaluation of five lactation curve models fitted for fat:protein ratio of milk and daily energy balance.

Selection for milk yield increases the metabolic load of dairy cows. The fat:protein ratio of milk (FPR) could serve as a measure of the energy balance status and might be used as a selection criterion to improve metabolic stability. The fit of different fixed and random regression models describing FPR and daily energy balance was tested to establish appropriate models for further genetic analyses. In addition, the relationship between both traits was evaluated for the best fitting model. Data were collected on a dairy research farm running a bull dam performance test. Energy balance was calculated using information on milk yield, feed intake per day, and live weight. Weekly FPR measurements were available. Three data sets were created containing records of 577 primiparous cows with observations from lactation d 11 to 180 as well as records of 613 primiparous cows and 96 multiparous cows with observations from lactation d 11 to 305. Five well-established parametric functions of days in milk (Ali and Schaeffer, Guo and Swalve, Wilmink, Legendre polynomials of third and fourth degree) were chosen for modeling the lactation curves. Evaluation of goodness of fit was based on the corrected Akaike information criterion, the Bayesian information criterion, correlation between the real observation and the estimated value, and on inspection of the residuals plotted against days in milk. The best model was chosen for estimation of correlations between both traits at different lactation stages. Random regression models were superior compared with the fixed regression models. In general, the Ali and Schaeffer function appeared most suitable for modeling both the fixed and the random regression part of the mixed model. The FPR is greatest in the initial lactation period when energy deficit is most pronounced. Energy balance stabilizes at the same point as the decrease in FPR stops. The inverted patterns indicate a causal relationship between the 2 traits. A common pattern was also observed for repeatabilities of both traits, with repeatabilities being largest at the beginning of lactation. Additionally, correlations between cow effects were closest at the beginning of lactation (r(c)=-0.43). The results support the hypothesis that FPR can serve as a suitable indicator for energy status, at least during the most metabolically stressful stage of lactation.

Analysis of water intake and dry matter intake using different lactation curve models.

The objective was to evaluate 6 different lactation curve models for daily water and dry matter intake. Data originated from the Futterkamp dairy research farm of the Chamber of Agriculture of Schleswig-Holstein in Germany. A data set of about 23,000 observations from 193 Holstein cows was used. Average daily water and dry matter intake were 82.3 and 19.8 kg, respectively. The basic linear mixed model included the fixed effects of parity and test-day within feeding group. Additionally, 6 different functions were tested for the fixed effect of lactation curve and the individual (random) effect of cow-lactation curve. Furthermore, the autocorrelation between repeated measures was modeled with the spatial (power) covariance structure. Model fit was evaluated by the likelihood ratio test, Akaike's and Bayesian information criteria, and the analysis of mean residual at different days in milk. The Ali and Schaeffer function was best suited for modeling the fixed lactation curve for both traits. A Legendre polynomial of order 4 delivered the best model fit for the random effect of cow-lactation. Applying the error covariance structure led to a significantly better model fit and indicated that repeated measures were autocorrelated. Generally, the best information criteria values were yielded by the most complex model using the Ali and Schaeffer function and Legendre polynomial of order 4 to model the average lactation and cow-specific lactation curves, respectively, with inclusion of the spatial (power) error covariance structure. This model is recommended for the analysis of water and dry matter intake including missing observations to obtain estimation of correct statistical inference and valid variance components.

Analysis of feed intake and energy balance of high-yielding first lactating Holstein cows with fixed and random regression models.

At the dairy research farm Karkendamm, the individual roughage intake was measured since 1 September 2005 using a computerised scale system to estimate daily energy balances as the difference between energy intake and calculated energy requirements for lactation and maintenance. Data of 289 heifers with observations between the 11th and 180th day of lactation over a period of 487 days were analysed. Average energy-corrected milk yield, feed intake, live weight and energy balance were 31.8kg, 20.6kg, 584 kg and 13.6 MJ NEL (net energy lactation), respectively, per day. Fixed and random regression models were used to estimate repeatabilities, correlations between cow effects and genetic parameters. The resulting genetic correlations in different lactation stages demonstrate that feed intake and energy balance at the beginning and the middle of lactation are genetically different traits. Heritability of feed intake is low with h2=0.06 during the first days after parturition and increases in the middle of lactation, whereas the energy balance shows the highest heritability with h2=0.34 in the first 30 days of lactation. Genetic correlations between energy balance and feed intake and milk yield, respectively, illustrate that energy balance depends more on feed intake than on milk yield. Genetic correlation between body condition score and energy balance decreases rapidly within the first 100 days of lactation. Hence, to avoid negative effects on health and reproduction as consequences of strong energy deficits at the beginning of lactation, the energy balance itself should be measured and used as a selection criterion in this lactation stage. Since the number of animals is rather small for a genetic analysis, the genetic parameters have to be evaluated on a more comprehensive dataset.

Analysis of water intake, dry matter intake and daily milk yield using different error covariance structures.

The aim of the present study was to investigate the daily measured traits milk yield, water intake and dry matter intake with fixed and random regression models added with different error covariance structures. It was analysed whether these models deliver better model fitting in contrast to conventional fixed and random regression models. Furthermore, possible autocorrelation between repeated measures was investigated. The effect of model choice on statistical inference was also tested. Data recording was performed on the Futterkamp dairy research farm of the Chamber of Agriculture of Schleswig-Holstein. A dataset of about 21 000 observations from 178 Holstein cows was used. Average milk yield, water intake and dry matter intake were 34.9, 82.4 and 19.8 kg, respectively. Statistical analysis was performed using different linear mixed models. Lactation number, test day and the parameters to model the function of lactation day were included as fixed effects. Different structures were tested for the residuals; they were compared for their ability to fit the model using the likelihood ratio test, and Akaike's and Bayesian's information criteria. Different autocorrelation patterns were found. Adjacent repeated measures of daily milk yield were highest correlated (p1 = 0.32) in contrast to measures further apart, while for water intake and dry matter intake, the measurements with a lag of two units had the highest correlations with p2 = 0.11 and 0.12. The covariance structure of TOEPLITZ was most suitable to indicate the dependencies of the repeated measures for all traits. Generally, the most complex model, random regression with the additional covariance structure TOEPLITZ(4), provided the lowest information criteria. Furthermore, the model choice influenced the significance values of one fixed effect and therefore the general inference of the data analysis. Thus, the random regression + TOEPLITZ(4) model is recommended for use for the analysis of equally spaced datasets of milk yield, water intake and dry matter intake.

Genetic parameters for serial, automatically recorded milkability and its relationship to udder health in dairy cattle.

Serial measurements of three milkability traits from two commercial dairy farms in Germany were used to estimate heritabilities and breeding values (BVs). Overall, 6352 cows in first, second and third lactations supplied 2 188 810 records based on daily values recorded from 1998 to 2003. Only the records between day 8 and day 305 after calving were considered. The estimated genetic correlations between different parities within the three milkability traits ranged from rg = 0.88 to 0.98, i.e. they were sufficiently high to warrant a repeatability model. The resulting estimated heritability coefficients were h2 = 0.42 for average milk flow, h2 = 0.56 for maximum milk flow and h2 = 0.38 for milking time. We analysed the genetic correlation between milkability and somatic cell score (SCS) and between milkability and the liability to mastitis, respectively, as the optimum milk flow for udder health is not well defined. There were 66 146 records with information on somatic cell count. Furthermore, 23 488 days of medical treatment for udder diseases were available, resulting in 2 600 302 days of observation in total. Heritabilities for the liability to mastitis, estimated with a test-day threshold model, were h2 = 0.19 and h2 = 0.13, depending on the data-recording period (first 50 days of lactation and first 305 days of lactation, respectively). With respect to the relationship between milkability and udder health, the results indicated a slight and linear correlation insofar as one can assume: the higher the milk flow, the worse the udder health. For this reason, bulls and cows with high BVs for milk flow should be excluded from breeding to avoid a deterioration of udder health. The establishment of a special data-recording scheme for functional traits such as milkability and mastitis on commercial dairy farms may be possible according to these results.

Genetic analyses of mastitis data using animal threshold models and genetic correlation with production traits.

In the present study, 6 different mastitis data sets of 3 dairy herds with an overall herd size of 3200 German Holstein cows were analyzed. Data collection periods included the first 50, 100, or 300 d of lactation. The 3 data collection periods were analyzed with a lactation model and a test-day model. All models were animal threshold models. Mastitis frequencies in the lactation model data sets varied between 29 and 45%, and varied between 3 and 6% in the test-day model data sets. Depending on the period of data collection, heritabilities of liability to mastitis in the lactation models were 0.05 (50 d), 0.06 (100 d), and 0.07 (300 d). In the test-day models, heritabilities were slightly higher with values of 0.09 (50 and 100 d), and 0.06 (300 d). Between lactation models, the rank correlations between the relative breeding values were high and varied between 0.86 and 0.94. Rank correlations between the relative breeding values of the test-day models ranged from 0.68 to 0.87. The rank correlations between the relative breeding values of lactation models and test-day models varied from 0.51 and 0.80. Genetic correlations between mastitis and milk production traits were estimated with a linear animal test-day model. The correlations with mastitis were 0.29 (milk yield), 0.30 (fat yield), 0.20 (fat content), 0.34 (protein yield), and 0.20 (protein content). The estimated genetic correlation between mastitis and somatic cell score was 0.84.