The Effect of Calf Gender on Milk Production in Seasonal Calving Cows and Its Impact on Genetic Evaluations

Gender of the calf whose birth initiates lactation could influence whole lactation milk yield of the dam due to hormonal influences on mammary gland development, or through calf gender effects on gestation length. Fetal gender could influence late lactation yields because cows become pregnant at peak lactation. The effects of calf gender sequences in parities 1–3 were assessed by separately fitting animal models to datasets from New Zealand comprising 274 000 Holstein Friesian and 85 000 Jersey cows, decreasing to 12 000 and 4 000 cows by parity 3. The lactation initiated by the birth of a female rather than a male calf was associated with a 0.33–1.1% (p≤0.05) higher milk yield. Female calf gender had carryover effects associated with higher milk yield in second lactations for Holstein Friesians (0.24%; p = 0.01) and third lactations for Jerseys (1.1%; p = 0.01). Cows giving birth to bull calves have 2 day longer gestations, which reduces lactation length in seasonal calving herds. Adding a covariate for lactation length to the animal model eroded some of these calf gender effects, such that calving a female led to higher milk yield only for second lactation Holstein Friesians (1.6%; p = 0.002). The interval centering method generates lower estimates of whole lactation yield when Wood’s lactation curves are shifted to the right by 2 days for male calves and this explained the higher yield in female calves when differences in lactation length were considered. Correlations of estimated breeding values between models including or excluding calf gender sequence were 1.00 for bulls or cows. Calf gender primarily influences milk yield through increased gestation length of male calves, and bias associated with the interval centering method used to estimate whole lactation milk yields. Including information on calf gender is unlikely to have an effect on selection response in New Zealand dairy cattle.     

Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics

A total of 19 376 test day (TD) milk yield records from the first three lactations of 1618 cows daughters of 162 sires were used to estimate genetic and phenotypic parameters and determine the relationship between daily milk yield and lactation milk yield in the Sahiwal cattle in Kenya. Variance components were estimated using animal models based on a derivative free restricted maximum likelihood procedure. Variance components were estimated using various univariate and multi-trait fixed regression test day models (TDM) that defined contemporary groups either based on the year-season of calving (YSCV) or on the year-season of TD milk sampling (YSTD). Variance components were influenced by CG which resulted in differences in heritability and repeatability estimates between TDM. Models considering YSTD resulted in higher additive genetic variances and lower residual variances compared with models in which YSCV was considered. Heritability estimates for daily yield ranged from 0.28 to 0.46, 0.38 to 0.52 and 0.33 to 0.52 in the first, second and third lactation, respectively. In the first and second lactation, the heritability estimates were highest between TD 2 and TD 4. Genetic correlations among daily milk yields ranged from 0.41 to 0.93, 0.50 to 0.83 and 0.43 to 86 in the first, second and third lactation, respectively. The phenotypic correlations were correspondingly lower. Genetic correlations were different from unit when fitting multi-trait TDM. Therefore, a multiple trait model would be more ideal in determining the genetic merit of dairy sires and bulls based on daily yield records. Genetic and phenotypic correlations between daily yield and lactation yields were high and positive. Genetic correlations ranged from 0.84 to 0.99, 0.94 to 1.00 and 0.94 to 0.97 in the first, second and third lactations, respectively. The corresponding phenotypic correlation estimates ranged from 0.50 to 0.85, 0.50 to 0.83 and 0.53 to 0.87. The high genetic correlation between daily yield and lactation yield imply that both traits are influenced by similar genes. Therefore daily yields records could be used in genetic evaluation in the Sahiwal cattle breeding programme.     

Effects of stage of pregnancy on variance components,daily milk yields and 305-day milk yield in Holstein cows,as estimated by using a test-day model

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.     

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel^R. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein–Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.     

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.     

Influence of weather conditions on milk production and rectal temperature of Holsteins fed two levels of concentrate

Twelve lactating Holstein cows in 2nd lactation were allocated randomly, six each, to two feeding treatments: high concentrate (1 kg dairy concentrate to 2 kg milk produced) and low concentrate (1 kg dairy concentrate to 4 kg milk produced) from 7 to 106 days postcalving. Forage and water were provided adalibitum. Milk and butter fat yields and rectal temperatures were examined in relation to 9 weather variables (minimum, maximum and mean temperatures, relative humidity, temperature-humidity index (THI), radiation, wind velocity and mean temperature of the previous day). Averages for milk yield, fat yield and rectal temperature were respectively 20.4 kg, 0.7 kg and 38.9°C for the high concentrate treatment and 18.4 kg, 0.6 kg and 38.6°C for the low concentrate treatment. Weather conditions accounted for 5.6%, 0.8% and 10.8% of the day to day variation in milk yield, fat yield and rectal remperature, respectively, for the high concentrate group and 29.4%, 9.7% and 0.6%, respectively, for the low concentrate group. Only measures of ambient temperature, especially mean temperature, were closely associated with these traits.     

Paratuberculosis: decrease in milk production of German Holstein dairy cows shedding Mycobacterium avium ssp. paratuberculosis depends on within-herd prevalence

Paratuberculosis impairs productivity of infected dairy cows because of reduced milk production and fertility and enhanced risk of culling. The magnitude of the milk yield depression in individual cows is influenced by factors such as parity, the stage of the disease and the choice of test used. The objectives of this case–control study were to substantiate the influence of the different levels of the within-herd prevalence (WHP) on individual milk yield of fecal culture (FC)-positive cows (FC+) compared with FC-negative herd-mates (FC−), and to estimate the magnitude of the deviation of the milk yield, milk components and somatic cell count (SCC) in an FC-based study. Of a total of 31 420 cows from 26 Thuringian dairy herds tested for paratuberculosis by FC, a subset of 1382 FC+ and 3245 FC− with milk recording data were selected as cases and controls, respectively. The FC− cows were matched for the same number and stage of lactation (±10 days in milk) as one FC+ from the same herd. Within a mixed model analysis using the fixed effects of Mycobacterium avium ssp. paratuberculosis (MAP) status, lactation number, days in milk, prevalence class of farm and the random effect of farm on milk yield per day (kg), the amount of fat and protein (mg/dl) and lactose (mg/dl) as well as the SCC (1000/ml) were measured. On the basis of least square means, FC+ cows had a lower test-day milk yield (27.7±0.6 kg) compared with FC− (29.0±0.6 kg), as well as a lower milk protein content and a slightly diminished lactose concentration. FC status was not associated with milk fat percentage or milk SCC. In FC+ cows, reduction in milk yield increased with increasing WHP. An interaction of FC status and farm was found for the test-day milk yield, and milk protein percentage, respectively. We conclude that the reduction in milk yield of FC+ cows compared with FC− herd-mates is significantly influenced by farm effects and depends on WHP class. Owners of MAP-positive dairy herds may benefit from the reduction in WHP not only by reducing number of infected individuals but also by diminishing the individual losses in milk production per infected cow, and therefore should establish control measures.     

Relation of Milk Production Loss to Milk Somatic Cell Count

Milk production loss was studied in relation to increased somatic cell count (SCC). Available data were weekly test-day milk yields and SCC (in 1,000 cells/ml), and mastitis incidences. In total, 18,131 records from 274 cows were used. Production loss was determined for test-day kg milk, kg protein, and kg energy-corrected milk. Least-squares analysis of variance was used to estimate the direct effect of Log10(SCC) on production. The recorded measures of production were first corrected for fixed effects, with adjustment factors estimated from a healthy data-set. The average daily milk yield was 19.7 kg/day in first lactation and 22.0 in later lactations. The geometric mean of SCC was 63.1 in first lactation and 107.2 in later lactations. The incidence of clinical mastitis treated by a veterinarian was 19.8% of the lactations-at-risk. Linear relationships were found between the production parameters and Log10(SCC). Quadratic and cubic effects were evaluated, but were found to contribute little to the overall fit of the models. The individual milk yield loss was 1.29 kg/day for each unit increase in Log10(SCC) for cows in first lactation. Milk yield decreased by 2.04 kg/day per unit Log10(SCC) for older cows. Corresponding values for protein yield were 0.042 and 0.067 kg/day for first and later lactations, respectively.     

Body condition score and live-weight effects on milk production in Irish Holstein-Friesian dairy cows

The objective of the present study was to quantify the relationships among body condition score (BCS; scale 1 to 5), live weight (WT) and milk production in Irish Holstein-Friesian spring calving dairy cows. Data were from 66 commercial dairy herds during the years 1999 and 2000. The data consisted of up to 9886 lactations with records for BCS or WT at least once pre-calving, or at calving, nadir or 60 days post-calving. Change in BCS and WT was also calculated between time periods. Mixed models with cow included as a random effect were used to quantify the effect of BCS and WT, as well as change in each trait, on milk yield, milk fat concentration and milk protein concentration. Significant and sometimes curvilinear associations were observed among BCS at calving or nadir and milk production. Total 305-day milk yield was greatest in cows calving at a BCS of 4.25 units. However, cows calving at a BCS of 3.50 units produced only 68 kg less milk than cows calving at a BCS of 4.25 units while cows calving at 3.25 or 3.00 BCS units produced a further 50 and 114 kg less, respectively. Cows that lost more condition in early lactation produced more milk of greater fat and protein concentration, although the trend reversed in cows that lost large amounts of condition post-calving. Milk yield increased with WT although the marginal effect decreased as cows got heavier. Milk fat and protein concentration in early lactation also increased with WT pre-calving, calving and nadir, although WT did not significantly affect average lactation milk fat concentration.     

A comparison of energy balance and metabolic profiles of the New Zealand and North American strains of Holstein Friesian dairy cow

The milk production, energy balance (EB), endocrine and metabolite profiles of 10 New Zealand Holstein Friesian (NZ) cows and 10 North American Holstein Friesian (NA) cows were compared. The NA cows had greater peak milk yields and total lactation milk yields (7387 v. 6208 kg; s.e.d. = 359), lower milk fat and similar protein concentrations compared with the NZ cows. Body weight (BW) was greater for NA cows compared with NZ cows throughout lactation (596 v. 544 kg; s.e.d. = 15.5), while body condition score (BCS) tended to be lower. The NA strain tended to have greater dry matter intake (DMI) (17.2 v. 15.7 kg/day; s.e.d. = 0.78) for week 1 to 20 of lactation, though DMI as a proportion of metabolic BW was similar for both strains. No differences were observed between the strains in the timing and magnitude of the EB nadir, interval to neutral EB, or mean daily EB for week 1 to 20 of lactation. Plasma concentrations of glucose and insulin were greater for NA cows during the transition period (day 14 pre partum to day 28 post partum). Plasma IGF-I concentrations were similar for the strains at this time, but NZ cows had greater plasma IGF-I concentration from day 29 to day 100 of lactation, despite similar calculated EB. In conclusion, the results of this study do not support the premise that the NZ strain has a more favourable metabolic status during the transition period. The results, however, indicate that NZ cows begin to partition nutrients towards body reserves during mid-lactation, whereas NA cows continue to partition nutrients to milk production.     

Modeling homeorhetic trajectories of milk component yields,body composition and dry-matter intake in dairy cows: Influence of parity,milk production potential and breed

The control of nutrient partitioning is complex and affected by many factors, among them physiological state and production potential. Therefore, the current model aims to provide for dairy cows a dynamic framework to predict a consistent set of reference performance patterns (milk component yields, body composition change, dry-matter intake) sensitive to physiological status across a range of milk production potentials (within and between breeds). Flows and partition of net energy toward maintenance, growth, gestation, body reserves and milk components are described in the model. The structure of the model is characterized by two sub-models, a regulating sub-model of homeorhetic control which sets dynamic partitioning rules along the lactation, and an operating sub-model that translates this into animal performance. The regulating sub-model describes lactation as the result of three driving forces: (1) use of previously acquired resources through mobilization, (2) acquisition of new resources with a priority of partition towards milk and (3) subsequent use of resources towards body reserves gain. The dynamics of these three driving forces were adjusted separately for fat (milk and body), protein (milk and body) and lactose (milk). Milk yield is predicted from lactose and protein yields with an empirical equation developed from literature data. The model predicts desired dry-matter intake as an outcome of net energy requirements for a given dietary net energy content. The parameters controlling milk component yields and body composition changes were calibrated using two data sets in which the diet was the same for all animals. Weekly data from Holstein dairy cows was used to calibrate the model within-breed across milk production potentials. A second data set was used to evaluate the model and to calibrate it for breed differences (Holstein, Danish Red and Jersey) on the mobilization/reconstitution of body composition and on the yield of individual milk components. These calibrations showed that the model framework was able to adequately simulate milk yield, milk component yields, body composition changes and dry-matter intake throughout lactation for primiparous and multiparous cows differing in their production level.     

Production,partial cash flows and greenhouse gas emissions of simulated dairy herds with extended lactations

The transition period is the most critical period in the lactation cycle of dairy cows. Extended lactations reduce the frequency of transition periods, the number of calves and the related labour for farmers. This study aimed to assess the impact of 2 and 4 months extended lactations on milk yield and net partial cash flow (NPCF) at herd level, and on greenhouse gas (GHG) emissions per unit of fat- and protein-corrected milk (FPCM), using a stochastic simulation model. The model simulated individual lactations for 100 herds of 100 cows with a baseline lactation length (BL), and for 100 herds with lactations extended by 2 or 4 months for all cows (All+2 and All+4), or for heifers only (H+2 and H+4). Baseline lactation length herds produced 887 t (SD: 13) milk/year. The NPCF, based on revenues for milk, surplus calves and culled cows, and costs for feed, artificial insemination, calving management and rearing of youngstock, was k€174 (SD: 4)/BL herd per year. Extended lactations reduced milk yield of the herd by 4.1% for All+2, 6.9% for All+4, 1.1% for H+2 and 2.2% for H+4, and reduced the NPCF per herd per year by k€7 for All+2, k€12 for All+4, k€2 for H+2 and k€4 for H+4 compared with BL herds. Extended lactations increased GHG emissions in CO₂-equivalents per t FPCM by 1.0% for All+2, by 1.7% for All+4, by 0.2% for H+2 and by 0.4% for H+4, but this could be compensated by an increase in lifespan of dairy cows. Subsequently, production level and lactation persistency were increased to assess the importance of these aspects for the impact of extended lactations. The increase in production level and lactation persistency increased milk production of BL herds by 30%. Moreover, reductions in milk yield for All+2 and All+4 compared with BL herds were only 0.7% and 1.1% per year, and milk yield in H+2 and H+4 herds was similar to BL herds. The resulting NPCF was equal to BL for All+2 and All+4 and increased by k€1 for H+2 and H+4 due to lower costs for insemination and calving management. Moreover, GHG emissions per t FPCM were equal to BL herds or reduced (0% to −0.3%) when lactations were extended. We concluded that, depending on lactation persistency, extending lactations of dairy cows can have a positive or negative impact on the NPCF and GHG emissions of milk production.     

Variation in enteric methane emissions among cows on commercial dairy farms

Methane (CH₄) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH₄ emissions can be substantial. The extent of variation in CH₄ emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH₄ emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH₄ during milking on commercial dairy farms. Enteric CH₄ emissions from 1964 individual cows across 21 farms were measured for at least 7 days/cow using CH₄ analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH₄ emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH₄ emissions varied among farms. Considerable variation in CH₄ emissions was observed among cows after adjusting for fixed and random effects, with the CV ranging from 22% to 67% within farms. This study confirms that enteric CH₄ emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.     

The effect of lactation length on greenhouse gas emissions from the national dairy herd

Many governments have signed up to greenhouse gas emission (GHGE) reduction programmes under their national climate change obligations. Recently, it has been suggested that the use of extended lactations in dairy herds could result in reduced GHGE. Dairy GHGE were modelled on a national basis and the model was used to compare emissions from lactations of three different lengths (305, 370 and 440 days), and a current ‘base’ scenario on the basis of maintaining current milk production levels. In addition to comparing GHGE from the average ‘National Herd’ under these scenarios, results were used to investigate how accounting for lactations of different lengths might alter the estimation of emissions calculated from the National Inventory methodology currently recommended by Intergovernmental Panel on Climate Change. Data for the three lactation length scenarios were derived from nationally recorded dairy performance information and used in the GHGE model. Long lactations required fewer milking cows and replacements to maintain current milk yield levels than short ones, but GHGEs were found to rise from 1214 t of CO₂ equivalent (CE)/farm per year for lactations of 305 days to 1371 t CE/farm per year for 440-day lactations. This apparent anomaly can be explained by the less efficient milk production (kg milk produced per kg cow weight) found in later lactation, a more pronounced effect in longer lactations. The sensitivity of the model to changes in replacement rate, persistency and level of milk yield was investigated. Changes in the replacement rate from 25% to 20% and in persistency by −10% to +20% resulted in very small changes in GHGE. Differences in GHGE due to the level of milk yield were much more dramatic with animals in the top 10% for yield, producing about 25% less GHGE/year than the average animal. National Inventory results were investigated using a more realistic spread of lactation lengths than recommended for such calculations using emissions calculated in the first part of the study. Current UK emission calculations based on the National Inventory were 329 Gg of methane per year from the dairy herd. Using the national distribution of lactation lengths, this was found to be an underestimate by about 10%. This work showed that the current rise in lactation length or a move towards calving every 18 months would increase GHGE by 7% to 14% compared with the current scenario, assuming the same milk yield in all models. Increased milk yield would have a much greater effect on reducing GHGE than changes to lactation length, replacement rate or persistency. National Inventory methodology appears to underestimate GHGE when the distribution of lactation lengths is considered and may need revising to provide more realistic figures.     

The effect of dairy heifer pre-breeding growth rate on first lactation milk yield in spring-calving,pasture-based herds

Optimising heifer growth rate may offer an opportunity to improve lifetime milk yield per cow, enhancing the environmental and economic efficiency of dairy farming operations. The effect of dairy heifer pre-breeding average daily weight gain (ADG_PB) on first lactation milk yield was investigated. This observational study employed a data set comprising 265 Holstein-Friesian, or Holstein-Friesian-cross-Jersey heifers from seven commercial, spring-calving, pasture-based dairy herds, where the major component of the diet was grazed grass. These were weighed at birth and prior to breeding and ADG_PB was calculated. Milk recordings were performed throughout the heifers' first lactation and 305-day yield figures calculated from these records. Yields were corrected to 4% fat and 3.1% protein to create standardised 305-day milk yield (SMY), which was the outcome of interest. Median ADG_PB was 0.72 kg/day. Median 305-day yield was 5 967 kg. Linear regression was used to investigate the effect of weight and genetic, age and first calving factors on SMY. Pre-breeding average daily weight gain, age at first calving and predicted transmitting abilities for milk protein production and calving interval were all significant in the final model, which also included the random effects of farm and month of calving within year. ADG_PB was quadratically related to first lactation SMY, with an ADG_PB of 0.82 kg/day corresponding to the maximum predicted SMY. The model predicted that a heifer growing at 0.82 kg/day would produce 1 120 kg more SMY than a heifer growing at 0.55 kg/day, 218 kg more than a heifer growing at 0.7 kg/day and 103 kg more than a heifer growing at 0.90 kg/day. Manipulation of heifer growth rate may offer a viable method of increasing first lactation milk yield.     

Relationships between conception rate in Holstein heifers and cows and milk yield at various stages of lactation

We investigated the relationships between conception rates (CRs) at first service in Japanese Holstein heifers (i.e. animals that had not yet had their first calf) and cows and their test-day (TD) milk yields. Data included records of artificial insemination (AI) for heifers and cows that had calved for the first time between 2000 and 2008 and their TD milk yields at 6 through 305 days in milk (DIM) from first through third lactations. CR was defined as a binary trait for which first AI was a failure or success. A threshold-linear animal model was applied to estimate genetic correlations between CRs of heifers or cows and TD milk yield at various lactation stages. Two-trait genetic analyses were performed for every combination of CR and TD milk yield by using the Bayesian method with Gibbs sampling. The posterior means of the heritabilities of CR were 0.031 for heifers, 0.034 for first-lactation cows and 0.028 for second-lactation cows. Heritabilities for TD milk yield increased from 0.324 to 0.433 with increasing DIM but decreased slightly after 210 DIM during first lactation. These heritabilities from the second and third lactations were higher during late stages of lactation than during early stages. Posterior means of the genetic correlations between heifer CR and all TD yields were positive (range, 0.082 to 0.287), but those between CR of cows and milk yields during first or second lactation were negative (range, −0.121 to −0.250). Therefore, during every stage of lactation, selection in the direction of increasing milk yield may reduce CR in cows. The genetic relationships between CR and lactation curve shape were quite weak, because the genetic correlations between CR and TD milk yield were constant during the lactation period.     

Daily rumination time of lactating dairy cows under heat stress conditions

The dairy industry in regions with moderate climates, such as Central Europe, will be increasingly challenged in the future by climate change. The problem of heat stress will especially affect dairy husbandry in naturally ventilated barns (NVB). The approach of the study was to determine a heat stress threshold of the average daily temperature-humidity index (THI) that results in changes in the daily rumination time (RT) of lactating, high-yielding cows. The data set was composed of a high sample size of 183 cows and long-duration measurements of 21240 daily observations over two years from June 2015 to May 2017, which were collected in an NVB in Groβ Kreutz, Germany. The THI was calculated in 5-min intervals by data from several sensors in different positions inside the barn. Additionally, every cow from the herd of an average of 53 cows in the experimental procedure was wearing a neck collar with a Lely Qwes HR system that provided the RT 24 h a day (12 2-h recordings were summarized). The study showed that heat stress also negatively influenced RT in moderate climates. The heat stress threshold of 52 THI was determined by broken-stick regression and indicated changes of RT of lactating dairy cows in Germany. During the experimental period, the heat stress threshold for RT was reached from April to September for up to 720 h per month. The changes in RT to the heat stress threshold will be affected by cows' characteristics. Therefore, we considered several cow-related factors, such as milk yield (MY), lactation number (LN), lactation stage (days in milk, or DIM) and pregnancy stage (P) to better understand cows’ individual reactions to heat stress. Multiparous, high-yielding cows in later lactation stages are potentially more strongly affected than other cows.     

Lactation curve model with explicit representation of perturbations as a phenotyping tool for dairy livestock precision farming

In the context of dairy farming, ruminant females often face challenges inducing perturbations that affect their performance and welfare. A key issue is how to assess the effect of perturbations and provide metrics to quantify how animals cope with their environment. Milk production dynamics are good candidates to address this issue: i) they are easily accessible, ii) overall dynamics throughout lactation process are well described and iii) perturbations are visible through milk losses. In this study, a perturbed lactation model (PLM) with explicit representation of perturbations was developed. The model combines two components: i) the unperturbed lactation model that describes a theoretical lactation curve, assumed to reflect female production potential and ii) the perturbation model that describes all the deviations from the unperturbed lactation model with four parameters: starting date, intensity and shape (collapse and recovery). To illustrate the use of the PLM as a phenotyping tool, it was fitted on a data set of 319 complete lactations from 181 individual dairy goats. A total of 2 354 perturbations were detected, with an average of 7.40 perturbations per lactation. Loss of milk production for the whole lactation due to perturbations varied between 2 and 19% of the milk production predicted by the unperturbed lactation model. The number of perturbations was not the major factor explaining the loss of milk production, suggesting that there are different types of animal response to challenges. By incorporating explicit representation of perturbations in a lactation model, it was possible to determine for each female the potential milk production, characteristics of each perturbation and milk losses induced by perturbations. Further, it was possible to compare animals and analyze individual variability. The indicators produced by the PLM are likely to be useful to move from raw data to decision support tools in dairy production.     

Accurate mathematical models to describe the lactation curve of Lacaune dairy sheep under intensive management

Although the intensive production system of Lacaune dairy sheep is the only profitable method for producers outside of the French Roquefort area, little is known about this type of systems. This study evaluated yield records of 3677 Lacaune sheep under intensive management between 2005 and 2010 in order to describe the lactation curve of this breed and to investigate the suitability of different mathematical functions for modeling this curve. A total of 7873 complete lactations during a 40-week lactation period corresponding to 201 281 pieces of weekly yield data were used. First, five mathematical functions were evaluated on the basis of the residual mean square, determination coefficient, Durbin Watson and Runs Test values. The two better models were found to be Pollott Additive and fractional polynomial (FP). In the second part of the study, the milk yield, peak of milk yield, day of peak and persistency of the lactations were calculated with Pollot Additive and FP models and compared with the real data. The results indicate that both models gave an extremely accurate fit to Lacaune lactation curves in order to predict milk yields (P = 0.871), with the FP model being the best choice to provide a good fit to an extensive amount of real data and applicable on farm without specific statistical software. On the other hand, the interpretation of the parameters of the Pollott Additive function helps to understand the biology of the udder of the Lacaune sheep. The characteristics of the Lacaune lactation curve and milk yield are affected by lactation number and length. The lactation curves obtained in the present study allow the early identification of ewes with low milk yield potential, which will help to optimize farm profitability.     

Milk fatty acids as indicators of negative energy balance of dairy cows in early lactation

Most dairy cows experience negative energy balance (NEB) in early lactation because energy demand for milk synthesis is not met by energy intake. Excessive NEB may lead to metabolic disorders and impaired fertility. To optimize herd management, it is useful to detect cows in NEB in early lactation, but direct calculation of NEB is not feasible in commercial herds. Alternative methods rely on fat-to-protein ratio in milk or on concentrations of non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) in blood. Here, we considered methods to assess energy balance (EB) of dairy cows based on the fatty acid (FA) composition in milk. Short- and medium-chain FAs (primarily, C14:0) are typically synthesized de novo in the mammary gland and their proportions in milk fat decrease during NEB. Long-chain FAs C18:0 and C18:1 cis-9 are typically released from body fat depots during NEB, and their proportions increase. In this study, these FAs were routinely determined by Fourier-transform infrared spectroscopy (FTIR) of individual milk samples. We performed an experiment on 85 dairy cows in early lactation, fed the same concentrate ration of up to 5 kg per day and forage ad libitum. Daily milk yield and feed intake were automatically recorded. During lactation weeks 2, 4, and 6 after calving, two milk samples were collected for FTIR spectroscopy, Tuesday evening and Wednesday morning, blood plasma samples were collected Thursday morning. Net energy content in feed and net energy required for maintenance and lactation were estimated to derive EB, which was used to compare alternative indicators of severe NEB. Linear univariate models for EB based on NEFA concentration (deviance explained = 0.13) and other metabolites in blood plasma were outperformed by models based on concentrations of metabolites in milk: fat (0.27), fat-to-protein ratio (0.18), BHB (0.20), and especially C18:0 (0.28) and C18:1 cis-9 (0.39). Analysis of generalized additive models (GAM) revealed that models based on milk variables performed better than those based on blood plasma (deviance explained 0.46 vs. 0.21). C18:0 and C18:1 cis-9 also performed better in severe NEB prediction for EB cut-off values ranging from −50 to 0 MJ NEL/d. Overall, concentrations of C18:0 and C18:1 cis-9 in milk, milk fat, and milk BHB were the best variables for early detection of cows in severe NEB. Thus, milk FA concentrations in whole milk can be useful to identify NEB in early-lactation cows.