Predicting grass dry matter intake,milk yield and milk fat and protein yield of spring calving grazing dairy cows during the grazing season

Predicting the grass dry matter intake (GDMI), milk yield (MY) or milk fat and protein yield (milk solids yield (MSY)) of the grazing dairy herd is difficult. Decisions with regard to grazing management are based on guesstimates of the GDMI of the herd, yet GDMI is a critical factor influencing MY and MSY. A data set containing animal, sward, grazing management and concentrate supplementation variables recorded during weeks of GDMI measurement was used to develop multiple regression equations to predict GDMI, MY and MSY. The data set contained data from 245 grazing herds from 10 published studies conducted at Teagasc, Moorepark. A forward stepwise multiple regression technique was used to develop the multiple regression equations for each of the dependent variables (GDMI, MY, MSY) for three periods during the grazing season: spring (SP; 5 March to 30 April), summer (SU; 1 May to 31 July) and autumn (AU; 1 August to 31 October). The equations generated highlighted the importance of different variables associated with GDMI, MY and MSY during the grazing season. Peak MY was associated with an increase in GDMI, MY and MSY during the grazing season with the exception of GDMI in SU when BW accounted for more of the variation. A higher body condition score (BCS) at calving was associated with a lower GDMI in SP and SU and a lower MY and MSY in all periods. A higher BCS was associated with a higher GDMI in SP and SU, a higher MY in SU and AU and a higher MSY in all periods. The pre-grazing herbage mass of the sward (PGHM) above 4 cm was associated with a quadratic effect on GDMI in SP, on MY in SP and SU and on MSY in SU. An increase in daily herbage allowance (DHA) above 4 cm was associated with an increase in GDMI in AU, an increase in MY in SU and AU and MSY in AU. Supplementing grazing dairy cows with concentrate reduced GDMI and increased MY and MSY in all periods. The equations generated can be used by the Irish dairy industry during the grazing season to predict the GDMI, MY and MSY of grazing dairy herds.     

Comparison of non-linear models to describe the lactation curves for milk yield and composition in buffaloes (Bubalus bubalis)

In order to describe the lactation curves of milk yield (MY) and composition in buffaloes, seven non-linear mathematical equations (Wood, Dhanoa, Sikka, Nelder, Brody, Dijkstra and Rook) were used. Data were 116 117 test-day records for MY, fat (FP) and protein (PP) percentages of milk from the first three lactations of buffaloes which were collected from 893 herds in the period from 1992 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly production records of dairy buffaloes using the NLIN and MODEL procedures in SAS and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination root means square error (RMSE), Durbin–Watson statistic and Akaike’s information criterion (AIC). The Dijkstra model provided the best fit of MY and PP of milk for the first three parities of buffaloes due to the lower values of RMSE and AIC than other models. For the first-parity buffaloes, Sikka and Brody models provided the best fit of FP, but for the second- and third-parity buffaloes, Sikka model and Brody equation provided the best fit of lactation curve for FP, respectively. The results of this study showed that the Wood and Dhanoa equations were able to estimate the time to the peak MY more accurately than the other equations. In addition, Nelder and Dijkstra equations were able to estimate the peak time at second and third parities more accurately than other equations, respectively. Brody function provided more accurate predictions of peak MY over the first three parities of buffaloes. There was generally a positive relationship between 305-day MY and persistency measures and also between peak yield and 305-day MY, calculated by different models, within each lactation in the current study. Overall, evaluation of the different equations used in the current study indicated the potential of the non-linear models for fitting monthly productive records of buffaloes.     

Exploration of lagged relationships between mastitis and milk yield in dairycows using a Bayesian structural equation Gaussian-threshold model

A Gaussian-threshold model is described under the general framework of structural equation models for inferring simultaneous and recursive relationships between binary and Gaussian characters, and estimating genetic parameters. Relationships between clinical mastitis (CM) and test-day milk yield (MY) in first-lactation Norwegian Red cows were examined using a recursive Gaussian-threshold model. For comparison, the data were also analyzed using a standard Gaussian-threshold, a multivariate linear model, and a recursive multivariate linear model. The first 180 days of lactation were arbitrarily divided into three periods of equal length, in order to investigate how these relationships evolve in the course of lactation. The recursive model showed negative within-period effects from (liability to) CM to test-day MY in all three lactation periods, and positive between-period effects from test-day MY to (liability to) CM in the following period. Estimates of recursive effects and of genetic parameters were time-dependent. The results suggested unfavorable effects of production on liability to mastitis, and dynamic relationships between mastitis and test-dayMYin the course of lactation. Fitting recursive effects had little influence on the estimation of genetic parameters. However, some differences were found in the estimates of heritability, genetic, and residual correlations, using different types of models (Gaussian-threshold vs. multivariate linear).     

Direct and carryover effect of post-grazing sward height on total lactation dairy cow performance

Grazing pastures to low post-grazing sward heights (PGSH) is a strategy to maximise the quantity of grazed grass in the diet of dairy cows within temperate grass-based systems. Within Irish spring-calving systems, it was hypothesised that grazing swards to very low PGSH would increase herbage availability during early lactation but would reduce dairy cow performance, the effect of which would persist in subsequent lactation performance when compared with cows grazing to a higher PGSH. Seventy-two Holstein–Friesian dairy cows (mean calving date, 12 February) were randomly assigned post-calving across two PGSH treatments (n = 36): 2.7 cm (severe; S1) and 3.5 cm (moderate; M1), which were applied from 10 February to 18 April (period 1; P1). This was followed by a carryover period (period 2; P2) during which cows were randomly reassigned within their P1 treatment across two further PGSH (n = 18): 3.5 cm (severe, SS and MS) and 4.5 cm (moderate, SM and MM) until 30 October. Decreasing PGSH from 3.5 to 2.7 cm significantly decreased milk (−2.3 kg/cow per day), protein (−95 g/day), fat (−143 g/day) and lactose (−109 g/day) yields, milk protein (−1.2 g/kg) and fat (−2.2 g/kg) concentrations and grass dry matter intake (GDMI; −1.7 kg dry matter/cow per day). The severe PGSH was associated with a lower bodyweight (BW) at the end of P1. There was no carryover effect of P1 PGSH on subsequent milk or milk solids yields in P2, but PGSH had a significant carryover effect on milk fat and lactose concentrations. Animals severely restricted at pasture in early spring had a higher BW and slightly higher body condition score in later lactation when compared with M1 animals. During P2, increasing PGSH from 3.5 to 4.5 cm increased milk and milk solids yield as a result of greater GDMI and resulted in higher mean BW and end BW. This study indicates that following a 10-week period of feed restriction, subsequent dairy cow cumulative milk production is unaffected. However, the substantial loss in milk solid yield that occurred during the period of restriction is not recovered.     

Evaluation of production efficiencies at pasture of lactating suckler cows of diverse genetic merit and replacement strategy

Feed costs account for the largest proportion of direct cost within suckler beef production systems. By identifying the cow type with enhanced capability of converting grazed herbage to beef output across lactations, suckler cow systems would become more efficient and sustainable. The objective of this study was to estimate grass DM intake (GDMI) and production efficiency among lactating suckler cows of diverse genetic merit for the national Irish maternal index (Replacement Index) which includes cow efficiency components such as milk yield and feed intake. Data from 131 cows of diverse genetic merit within the Replacement Index, across two different replacement strategies (suckler or dairy sourced), were available over two grazing seasons. Milk yield, GDMI, cow live weight (BW) and body condition score (BCS) were recorded during early, mid and late-lactation, with subsequent measures of production efficiency extrapolated. Genetic merit had no significant effect on any variables investigated, with the exception of low genetic merit (LOW) cows being 22 kg heavier in BW than high genetic merit (HIGH) cows (P < 0.05). Beef cows were 55 kg heavier in BW (P < 0.001), had a 0.31 greater BCS (P < 0.05) and 0.30 Unité Fourragère Lait (UFL) greater energy requirement for maintenance compared to dairy sourced beef × dairy crossbred (BDX) cows (P < 0.001). The BDX had 0.8 kg greater GDMI, produced 1.8 kg more milk (P < 0.001), had a 0.8 UFL greater energy requirement for lactation and produced weanlings that were 17 kg heavier in BW than beef cows (P < 0.05). Subsequent efficiency variables of milk per 100 kg BW (P < 0.001), milk per kg GDMI (P < 0.001) and GDMI per 100 kg BW (P < 0.001) were more favourable for BDX. The correlations examined showed GDMI had moderate positive correlations (P < 0.001) with intake per 100 kg BW, net energy intake per kg milk yield, RFI and intake per 100 kg calf weaning weight but was weakly negatively correlated to milk yield per kg GDMI (P < 0.001). No difference was observed across genetic merit for beef cows for any of the traits investigated. Results from the current study showed that, while contrasting replacement strategies had an effect on GDMI and production efficiency, no main effect was observed on cows diverse in genetic merit for Replacement Index. Nonetheless, utilising genetic indexes in the suckler herd is an important resource for selecting breeding females for the national herd and phenotypic performance generated from this study can be included in future genetic evaluations to improve reliability of genetic values.     

Analysis of lactation shapes in extended lactations

In order to describe the temporal evolution of milk yield (MY) and composition in extended lactations, 21 658 lactations of Italian Holstein cows were analyzed. Six empirical mathematical models currently used to fit 305 standard lactations (Wood, Wilmink, Legendre, Ali and Schaeffer, quadratic and cubic splines) and one function developed specifically for extended lactations (a modification of the Dijkstra model) were tested to identify a suitable function for describing patterns until 1000 days in milk (DIM). Comparison was performed on individual patterns and on average curves grouped according to parity (primiparous and multiparous) and lactation length (standard ⩽305 days, and extended from 600 to 1000 days). For average patterns, polynomial models showed better fitting performances when compared with the three or four parameters models. However, LEG and spline regression, showed poor prediction ability at the extremes of the lactation trajectory. The Ali and Schaeffer polynomial and Dijkstra function were effective in modelling average curves for MY and protein percentage, whereas a reduced fitting ability was observed for fat percentage and somatic cell score. When individual patterns were fitted, polynomial models outperformed nonlinear functions. No detectable differences were observed between standard and extended patterns in the initial phase of lactation, with similar values of peak production and time at peak. A considerable difference in persistency was observed between 200 and 305 DIM. Such a difference resulted in an estimated difference between standard and extended cycle of about 7 and 9 kg/day for daily yield at 305 DIM and of 463 and 677 kg of cumulated milk production at 305 DIM for the first- and second-parity groups, respectively. For first and later lactation animals, peak yield estimates were nearly 31 and 38 kg, respectively, and occurred at around 65 and 40 days. The asymptotic level of production was around 9 kg for multiparous cows, whereas the estimate was negative for first parity.     

Genetic relationship of discrete-time survival with fertility and production in dairy cattle using bivariate models

Bivariate analyses of functional longevity in dairy cattle measured as survival to next lactation (SURV) with milk yield and fertility traits were carried out. A sequential threshold-linear censored model was implemented for the analyses of SURV. Records on 96 642 lactations from 41 170 cows were used to estimate genetic parameters, using animal models, for longevity, 305 d-standardized milk production (MY305), days open (DO) and number of inseminations to conception (INS) in the Spanish Holstein population; 31% and 30% of lactations were censored for DO and INS, respectively. Heritability estimates for SURV and MY305 were 0.11 and 0.27 respectively; while heritability estimates for fertility traits were lower (0.07 for DO and 0.03 for INS). Antagonist genetic correlations were estimated between SURV and fertility (-0.78 and -0.54 for DO and INS, respectively) or production (-0.53 for MY305), suggesting reduced functional longevity with impaired fertility and increased milk production. Longer days open seems to affect survival more than increased INS. Also, high productive cows were more problematic, less functional and more liable to being culled. The results suggest that the sequential threshold model is a method that might be considered at evaluating genetic relationship between discrete-time survival and other traits, due to its flexibility.     

Using post-grazing sward height to impose dietary restrictions of varying duration in early lactation: its effects on spring-calving dairy cow production

The objective of this study was to investigate the immediate and carryover effects of imposing two post-grazing sward heights (PGSH) for varying duration during early lactation on sward characteristics and dairy cow production. The experiment was a randomised block design with a 2×2 factorial arrangement of treatments. A total of 80 spring-calving (mean calving date – 6 February) dairy cows were randomly assigned, pre-calving, to one of the two (n=40) PGSH treatments – S (2.7 cm) and M (3.5 cm) – from 13 February to 18 March, 2012 (P1). For the subsequent 5-week period (P2: 19 March to 22 April, 2012), half the animals from each P1 treatment remained on their treatment, whereas the other half of the animals switched to the opposing treatment. Following P2, all cows were managed similarly for the remainder of the lactation (P3: 23 April to 4 November, 2012) to measure the carryover effect. Milk production, BW and body condition score were measured weekly, and grass dry matter intake (GDMI) was measured on four occasions – approximately weeks 5, 10, 15 and 20 of lactation. Sward utilisation (above 2.7 cm; P1 and P2) was significantly improved by reducing the PGSH from 3.5 (0.83) to 2.7 cm (0.96). There was no effect of PGSH on cumulative annual grass dry matter (DM) production (15.3 t DM/ha). Grazing to 2.7 cm reduced GDMI by 1.7 and 0.8 kg DM/cow in P1 and P2, respectively, when compared with 3.5 cm (13.3 and 14.0 kg/cow per day, respectively). Cows grazing to 2.7 cm for both P1 and P2 (SS) tended to have reduced cumulative 10-week milk yield (−105 kg) and milk solids yield (−9 kg) when compared with cows grazing to 3.5 cm for both periods (MM; 1608 and 128 kg/cow, respectively). Treatments that alternated PGSH at the end of P1, SM and MS had intermediate results. There was no interaction between P1 and P2 treatments. There was also no carryover effect of early lactation grazing regime on milk and milk solids production in P3, given the reduction in early lactation milk yield. The results indicate that the diet of dairy cows should not be restricted by imposing a severe PGSH for all of the first 10 weeks of lactation, cows should graze to 3.5 cm for at least 5 of these weeks.     

The effect of diet, parity, year and number of kids on milk yield and milk composition in Maltese goat

The aim of the present study was to study the effects of forage/concentrate ratio, year, parity and number of kids on milk yield (MY), lactation length (LL) and milk composition (fat, protein and lactose) on 180 Maltese goats analysing 530 milk samples collected from 2000 to 2002.

The main average results were: MY = 288.2 kg, LL = 254 d with 3.5% fat, 3.4% protein and 4.6% lactose.

Forage/concentrate ratio significantly affected MY and fat being highest in goats receiving a ratio of 65/35. Milk yield in goats fed with a ratio of 35/65 was richer in fat (3.6%). The protein and lactose content was not affected by the different ratios. The effect of diet on fat content was small but significant. Parity influenced all the factors considered, in particular goats in ≥4th parity, had longer LL (257 d) and consequently a higher milk yield (302.1 kg). Goats kidding twins yielded more milk and had longer lactation (P < 0.001).     

Towards a better understanding of the respective effects of milk yield and body condition dynamics on reproduction in Holstein dairy cows

The overall reproductive performance has decreased over the last decades, involving changes in cyclicity, oestrous behaviour and fertility. High milk yield (MY), low body condition score (BCS) and large body condition (BC) loss have been identified as risk factors. However, these effects are often confounded, as high MY and body lipid reserve mobilization are correlated. The aim of this study was to evaluate the respective effects of MY and BC on post-partum ovarian cyclicity, oestrus and fertility of Holstein cows. This study provides novel information, as MY and BC change were uncoupled in the overall dataset that included 98 lactations and milk progesterone profiles. Cows were assigned to two feeding-level groups: high feed, which achieved high MY and moderate BC loss throughout lactation (8410 kg, -1.17 unit from calving to nadir BCS), and low feed, which limited MY and triggered a large BC loss (5719 kg, -1.54 unit). MY and BC had different effects at different stages of the reproductive process. Cyclicity as well as non-fertilization and early embryo mortality were mainly driven by body lipid reserves, whereas oestrous behaviour and late embryo mortality were related to MY. The results point to possible uncoupling between cyclicity, oestrus and early and late embryo survival allowing compensation along the reproductive process and leading to similar final reproductive performance. In compact calving systems, which require high pregnancy rates within a short period, higher MY strategies appear unsuitable even where BCS is maintained, owing to depressed oestrous behaviour and probably increased late embryo mortality, which delays rebreeding. Similarly, strategies that compromise cyclicity and fertility by excessively low BCS are unsuitable.     

Genetics of heat tolerance for milk yield and quality in Holsteins

Tropical and sub-tropical climates are characterized by high temperature and humidity, during at least part of the year. Consequently, heat stress is common in Holstein cattle and productive and reproductive losses are frequent. Our objectives were as follows: (1) to quantify losses in production and quality of milk due to heat stress; (2) to estimate genetic correlations within and between milk yield (MY) and milk quality traits; and (3) to evaluate the trends of genetic components of tolerance to heat stress in multiple lactations of Brazilian Holstein cows. Thus, nine analyses using two-trait random regression animal models were carried out to estimate variance components and genetic parameters over temperature–humidity index (THI) values for MY and milk quality traits (three lactations: MY×fat percentage (F%), MY×protein percentage (P%) and MY×somatic cell score (SCS)) of Brazilian Holstein cattle. It was demonstrated that the effects of heat stress can be harmful for traits related to milk production and milk quality of Holstein cattle even though most herds were maintained in a modified environment, for example, with fans and sprinklers. For MY, the effect of heat stress was more detrimental in advanced lactations (−0.22 to −0.52 kg/day per increase of 1 THI unit). In general, the mean heritability estimates were higher for lower THI values and longer days in milk for all traits. In contrast, the heritability estimates for SCS increased with increasing THI values in the second and third lactation. For each trait studied, lower genetic correlations (different from unity) were observed between opposite extremes of THI (THI 47 v. THI 80) and in advanced lactations. The genetic correlations between MY and milk quality trait varied across the THI scale and lactations. The genotype×environment interaction due to heat stress was more important for MY and SCS, particularly in advanced lactations, and can affect the genetic relationship between MY and milk quality traits. Selection for higher MY, F% or P% may result in a poor response of the animals to heat stress, as a genetic antagonism was observed between the general production level and specific ability to respond to heat stress for these traits. Genetic trends confirm the adverse responses in the genetic components of heat stress over the years for milk production and quality. Consequently, the selection of Holstein cattle raised in modified environments in both tropical and sub-tropical regions should take into consideration the genetic variation in heat stress.     

An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows

To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reproduction model sensitive to both MY and body condition score (BCS). A dynamic and stochastic individual reproduction model was built mainly from data of a single recent long-term experiment. This model covers the whole reproductive process and is composed of a succession of discrete stochastic events, mainly calving, ovulations, conception and embryonic loss. Each reproductive step is sensitive to MY or BCS levels or changes. The model takes into account recent evolutions of reproductive performance, particularly concerning calving-to-first ovulation interval, cyclicity (normal cycle length, prevalence of prolonged luteal phase), oestrus expression and pregnancy (conception, early and late embryonic loss). A sensitivity analysis of the model to MY and BCS at calving was performed. The simulated performance was compared with observed data from the database used to build the model and from the bibliography to validate the model. Despite comprising a whole series of reproductive steps, the model made it possible to simulate realistic global reproduction outputs. It was able to well simulate the overall reproductive performance observed in farms in terms of both success rate (recalving rate) and reproduction delays (calving interval). This model has the purpose to be integrated in herd simulation models to usefully test the impact of management strategies on herd reproductive performance, and thus on calving patterns and culling rates.     

Production and composition of Iberian sow's milk and use of milk nutrients by the suckling Iberian piglet

Sixteen purebred Iberian (IB) sows were used in two consecutive trials to determine the efficiency of conversion of sow's milk into piglet body weight (BW) gain and the relationship between milk protein and body protein retention and between milk energy yield and body energy retention in the nursing IB piglet. In each trial, four sows were selected in order to evaluate their milk production, litter growth and nutrient balance measurements, together with four additional sows for milk sampling. Litter size was equalized to six piglets. Daily milk yield (MY) was determined weekly by the weigh-suckle-weigh technique over a 34-day lactation period. Piglets were weighed individually at birth and then weekly from day 5 of lactation. Milk samples were collected on days 5, 12, 19, 26 and 34 post partum. The comparative slaughter procedure was used to determine piglet nutrient and energy retention. One piglet from each litter was slaughtered at birth and four on the morning of day 35. Total MY was on average 5.175 ± 0.157 kg/day. The average chemical composition (g/kg) of the milk was 179 ± 4 dry matter, 53.4 ± 1.0 CP, 58.5 ± 3.8 fat, 10.4 ± 0.3 ash and 56.9 ± 2.3 lactose. Milk gross energy (GE) was 4.626 ± 0.145 MJ/kg. Milk intake per piglet tended to increase in trial 2 (832 v. 893 g/day; P = 0.066). Piglet BW gain contained (g/kg) 172.1 ± 1.3 protein, 151.5 ± 3.5 fat, 41.4 ± 0.6 ash and 635 ± 3 water and 10.127 ± 0.126 MJ GE/kg. Throughout the 34-day nursing period, the piglets grew at an average rate of 168 ± 3 g/day. The ratio of daily piglet BW gain to daily MY was 0.195 ± 0.002 g/g and the gain per MJ milk GE intake was 41.9 ± 0.5 g/MJ. The overall efficiency of protein accretion (g CP gain/g CP milk intake) was low and declined in trial 2 (0.619 v. 0.571; P = 0.016). Nutrient and energy deposition between birth and weaning were 27.4 ± 0.5 g/day protein, 24.2 ± 0.8 g/day fat and 1615 ± 40 kJ/day energy. Piglet energy requirements for maintenance were 404 kJ metabolizable energy (ME)/kg BW0.75. ME was used for growth with a net efficiency of 0.584. These results suggest that poor efficiency in the use of sow's milk nutrients rather than a shortage in milk nutrient supply might explain the low growth rate of the suckling IB piglet.     

Acute physiological and perceptual responses to moderate intensity cycling with different levels of blood flow restriction

The aim of this study was to compare: i) the physiological and perceptual responses of low-load exercise [(moderate intensity exercise (MI)] with different levels of blood flow restriction (BFR), and ii) MI with BFR on the bike with high intensity (HI) exercise without BFR. The protocol involved large muscle mass exercise at different levels of BFR, and this differentiates our study from others. Twenty-one moderately trained males (age: 24.6 ± 2.4 years; VO_2peak: 47.2 ± 7.0 ml^.kg^-1.min^-1, mean ± sd) performed one maximal graded exercise test and seven 5-min constant-load cycling bouts. Six bouts were at MI [40% _peak power (P_peak), 60%VO_2peak], one without BFR and five with different levels of BFR (40%, 50%, 60%, 70%, 80% of estimated arterial occlusion pressure). The HI bout (70%P_peak, 90%VO_2peak) was without BFR. Oxygen uptake (VO₂), heart rate (HR), blood lactate (BLa), rate of perceived exertion (RPE), and tissue oxygen saturation (TSI) were recorded. Regardless of pressure, HR, BLa and RPE during MI-BFR were higher compared to MI (p < 0.05, ES: moderate to very large), and TSI reduction was greater in MI-BFR than MI (p < 0.05, ES: moderate to large). The responses of VO₂, HR, BLa, RPE and TSI induced by the different levels of BFR in MI-BFR were similar. Regardless of pressure, the responses of VO₂, HR, BLa and RPE induced by MI-BFR were lower than HI (p < 0.05), except for TSI. TSI change was similar between MI-BFR and HI. It appears that BFR equal to 40% of arterial occlusion pressure is sufficient to reduce TSI when exercising with a large muscle mass.     

Confirmation and refinement of a QTL on BTA5 affecting milk production traits in the Fleckvieh dual purpose cattle breed

We analysed a QTL affecting milk yield (MY), milk protein yield (PY) and milk fat yield (FY) in the dual purpose cattle breed Fleckvieh on BTA5. Twenty-six microsatellite markers covering 135 cM were selected to analyse nine half-sib families containing 605 sons in a granddaughter design. We thereby assigned two new markers to the public linkage map using the CRI-MAP program. Phenotypic records were daughter yield deviations (DYD) originating from the routinely performed genetic evaluations of breeding animals. To determine the position of the QTL, three different approaches were applied: interval mapping (IM), linkage analysis by variance component analysis (LAVC), and combined linkage disequilibrium (LD) and linkage (LDL) analysis. All three methods mapped the QTL in the same marker interval ( BM2830-ETH152 ) with the greatest test-statistic value at 118, 119.33 and 119.33 cM respectively. The positive QTL allele simultaneously increases DYD in the first lactation by 272 kg milk, 7.1 kg milk protein and 7.0 kg milk fat. Although the mapping accuracy and the significance of a QTL effect increased from IM over LAVC to LDL, the confidence interval was large (13, 20 and 24 cM for FY, MY and PY respectively) for the positional cloning of the causal gene. The estimated averages of pair wise marker LD with a distance <5 cM were low (0.107) and reflect the large effective population size of the Fleckvieh subpopulation analysed. This low level of LD suggests a need for increase in marker density in following fine mapping steps.     

Investigation of major gene for milk yield, milking speed, dry matter intake, and body weight in dairy cattle

The main aim of this study was to determine if there exist any major gene for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) recorded at various stages of lactation in first-lactation dairy cows (2543 observations from 320 cows) kept at the research farm of the Swiss Federal Institute of Technology between April 1994 and April 2004. Data were modelled based a simple repeatability covariance structure and analysed by using Bayesian segregation analyses. Gibbs sampling was used to make statistical inferences on posterior distributions; inferences were based on a single run of the Markov chain for each trait with 500,000 samples, with each 10th sample collected because of the high correlation among the samples. The posterior mean (+/-SD) of major gene variance was 2.61 (+/-2.46) for MY, 0.83 (+/-1.26) for MS, 4.37 (+/-2.34) for DMI, and 2056.43 (+/-665.67) for BW. Highest posterior density regions for 3 of the 4 traits did not include 0 (except MS), which supported the evidence for major gene. With additional tests for agreement with Mendelian transmission probabilities, we could only confirm the existence of a major gene for MY, but not for MS, DMI, and BW. Expected Mendelian transmission probabilities and their model fits were also compared.     

Pseudopregnancy and aseasonal breeding in dairy goats: genetic basis of fertility and impact on lifetime productivity

Until recently, the main selection focus in UK dairy goats has been on milk yield. To develop a selection index suitably weighted for a variety of traits, it is important to understand the genetic relationships between production, health and fertility traits. This study focussed on three aspects of reproduction that are of interest to goat breeders. (1) Out of season (OOS) kidding ability: goats are highly seasonal breeders so achieving consistent, year-round dairy production presents a challenge. It may be possible to select for extended or shifted breeding cycles, however, there are no published studies on the genetic basis of seasonal kidding ability, and a genetic correlation with milk production in dairy goats; (2) age at first kidding (AFK): a reduced AFK offers the opportunity for more rapid genetic improvement, as well as reducing the amount of time and resources required to raise the animals to producing age; (3) pseudopregnancy (PPG): as it is difficult to diagnose pregnancy within 30 days of mating, high herd levels of PPG could add a significant delay in breeding replacement animals, or commencing a new lactation. Using records from 9546 goats, the objective of this study was to investigate the genetic relationships between the reproductive traits described above, and the production traits 520-day milk yield (MY520), lifetime milk yield (MYLife) and lifetime number of days in milk (DIMLife). The ‘out of season’ phenotype was defined as week of kidding relative to the 4 weeks of the year where the highest average number of births occur. Incidences of PPG that occurred during the first lactation were used as cases, while goats with none were assigned as controls. Relevant fixed and random effects were fitted in the models. In line with other reproduction traits, heritability estimates were low ranging from 0.08 to 0.11. A negative genetic correlation was found between AFK and MY520 (−0.22±0.10), whereas a positive genetic correlation was found between PPG and DIMLife (0.58±0.11). Pseudopregnancy and OOS were positively genetically correlated (0.36±0.15). All other genetic correlations were very low. The results of this study indicate that selection for the reproductive traits analysed is feasible, without adversely affecting MYLife.     

Development and evaluation of the herd dynamic milk model with focus on the individual cow component

The herd dynamic milk (HDM) model is a dynamic model capable of simulating the performance of individual dairy animals (from birth to death), with a daily time step. Within this study, the HDM model is described and evaluated in relation to milk production, body condition score (BCS) and BCS change throughout lactation by comparing model simulations against data from published experimental studies. The model’s response to variation in genetic potential, herbage allowance and concentrate supplementation was tested in a sensitivity analysis. Data from experiments in Ireland and France over a 3-year period (2009–11) were used to complete the evaluation. The aim of the Irish experiment was to determine the impact of different stocking rates (SRs) (SR1: 3.28 cow/ha, SR2: 2.51 cow/ha) on key physical, biological and economic performance. The aim of the French experiment was to evaluate over a prolonged time period, the ability of two breeds of dairy cows (Holstein and Normande) to produce and to reproduce under two feeding strategies (high level and low level) in the context of compact calving. The model evaluation was conducted at the herd level with separate evaluations for the primiparous and multiparous cows. The evaluation included the two extreme SRs for the Irish experiment, and an evaluation at the overall herd and individual animal level for the different breeds and feeding levels for the French data. The comparison of simulation and experimental data for all scenarios resulted in a relative prediction error, which was consistently <15% across experiments for weekly milk production and BCS. In relation to BCS, the highest root mean square error was 0.27 points of BCS, which arose for Holstein cows in the low feeding group in late lactation. The model responded in a realistic fashion to variation in genetic potential for milk production, herbage allowance and concentrate supplementation.     

Effect of lactation trimester and parity on eating behavior,milk production and efficiency traits of dairy cows

There is absence knowledge about the effects of lactation trimester and parity on eating behavior, production and efficiency of dairy cows. Objective of this study was to identify and characterize in 340 dairy cows, the 20% high efficient (HE), 20% low efficient (LE) and 60% mid efficient (ME) cows according to their individual residual feed intake (RFI) values, within and between lactation trimesters and between 1st and 2nd parities. Efficiency effect within each lactation trimester, was exhibited in daily dry matter intake (DMI), eating rate and meal size, that were the highest in LE cows, moderate in the ME cows and lowest in the HE group. Daily eating time, meal frequency, yields of milk and energy-corrected milk (ECM) and BW were similar in the three efficiency groups within each trimester. The lower efficiency of the LE cows in each trimester attributes to their larger metabolic energy intake, heat production and energy losses. In subgroup of 52 multiparous cows examined along their 1st and 2nd trimesters, milk and ECM production, DMI, eating behavior and efficiency traits were similar with high Pearson’s correlation (r=0.78 to 0.89) between trimesters. In another subgroup of 42 multiparous cows measured at their 2nd and 3rd trimesters, milk and ECM yield, DMI and eating time were reduced (P<0.01) at the 3rd trimester, but eating rate, meal frequency and meal size remained similar with high Pearson’s correlation (r=0.74 to 0.88) between trimesters. In subgroup of 26 cows measured in 1st and 2nd parities, DMI, BW, milk and ECM yield, and ECM/DMI increased in the 2nd lactation, but eating behavior and RFI traits were similar in both parities. These findings encourage accurate prediction of DMI based on a model that includes eating behavior parameters, together with individual measurement of ECM production. This can be further used to identify HE cows in commercial herd, a step necessary for potential genetic selection program aimed to improve herd efficiency.     

Prediction of first test day milk yield using historical records in dairy cows

The transition between two lactations remains one of the most critical periods during the productive life of dairy cows. In this study, we aimed to develop a model that predicts the milk yield of dairy cows from test day milk yield data collected in the previous lactation. In the past, data routinely collected in the context of herd improvement programmes on dairy farms have been used to provide insights in the health status of animals or for genetic evaluations. Typically, only data from the current lactation is used, comparing expected (i.e., unperturbed) with realised milk yields. This approach cannot be used to monitor the transition period due to the lack of unperturbed milk yields at the start of a lactation. For multiparous cows, an opportunity lies in the use of data from the previous lactation to predict the expected production of the next one. We developed a methodology to predict the first test day milk yield after calving using information from the previous lactation. To this end, three random forest models (nextMILK_FULL, nextMILK_PH, and nextMILK_P) were trained with three different feature sets to forecast the milk yield on the first test day of the next lactation. To evaluate the added value of using a machine-learning approach against simple models based on contemporary animals or production in the previous lactation, we compared the nextMILK models with four benchmark models. The nextMILK models had an RMSE ranging from 6.08 to 6.24 kg of milk. In conclusion, the nextMILK models had a better prediction performance compared to the benchmark models. Application-wise, the proposed methodology could be part of a monitoring tool tailored towards the transition period. Future research should focus on validation of the developed methodology within such tool.