Influence of Production and Prices on Optimum Culling Rates and Annualized Net Revenue

Dynamic programming was used to make optimum insemination and culling decisions for a dairy enterprise. Monthly costs and revenues for cows were calculated from milk and fat yields, calf values, feed costs, veterinary costs, housing and equipment costs, and interest. Cows were described in the dynamic programming model by lactation number, month in lactation, milk production during the present and previous lactations, and time of conception. The model considered variation in milk yield, replacement heifer costs, carcass values, involuntary culling, genetic improvement, conception rates, semen costs, and interest. Prices and parameters were chosen to represent the Holstein population in the US. Optimum average yearly culling rate was about 25% (optimum average herd life was 47.8 mo) and the yearly annuity of net revenue for a replacement heifer over a 15-yr planning horizon was $443 in the base situation. Various average mature equivalent yields, replacement heifer prices, milk prices, and feed prices were used in a sensitivity analysis. The yearly annuity of net revenue was sensitive to changes in all these parameters. Milk yield, milk prices, and feed prices had major effects on yearly annuity. Optimum culling decisions were sensitive to changes in replacement heifer prices. Average mature equivalent milk yield, milk price, and feed price had small effects on culling.     

A stochastic model simulating the feeding-health-production complex in a dairy herd

A dynamic, stochastic, and mechanistic Monte Carlo model, simulating a dairy herd with focus on the feeding-health-production complex is presented. By specifying biological parameters at cow level and a management strategy at herd level, the model can simulate the technical and economic consequences of scenarios at herd level. The representation of the feeding-health-production complex is aimed to be sufficiently detailed, to include relationships likely to cause significant herd effects, and to be sufficiently simple to enable a feasible parameterization of the model and interpretation of the results from the model. Consequently, diseases are defined as four disease types: two metabolic disease types, an udder disease type, and a reproductive disease type. Risk factors for the diseases were defined as parity, yield capacity, disease recurrence, disease interrelationships, lactation stage, and season. Direct effects of the diseases were defined according to milk yield, feed intake, feed utilization, conception, culling, involuntary removal, and death. Scenarios differing in base risks of milk fever and ketosis, heat detection rate, and culling strategy were simulated for describing the model behavior. Annual milk yield per cow was decreased by increased risk of ketosis and by increased risk of milk fever, even though no direct effect of milk fever on milk yield was modeled at the cow level. The indirect effect from milk fever is a consequence of increased replacement rate (relatively lower milk yield from younger cows). By ignoring the history of milk fever in insemination and replacement decisions, a significantly reduced net income per cow was found in some herds. We concluded that important benefits from using such a herd model are the capability of accounting for herd management factors and the advantage of avoiding to double count the indirect effects from disease, such as increased risk of other diseases, poorer reproduction results, and increased risk of culling and death.     

Optimizing model: insemination, replacement, seasonal production, and cash flow.

Dynamic programming to solve the Markov decision process problem of optimal insemination and replacement decisions was adapted to address large dairy herd management decision problems in the US. Expected net present values of cow states (151,200) were used to determine the optimal policy. States were specified by class of parity (n = 12), production level (n = 15), month of calving (n = 12), month of lactation (n = 16), and days open (n = 7). Methodology optimized decisions based on net present value of an individual cow and all replacements over a 20-yr decision horizon. Length of decision horizon was chosen to ensure that optimal policies were determined for an infinite planning horizon. Optimization took 286 s of central processing unit time. The final probability transition matrix was determined, in part, by the optimal policy. It was estimated iteratively to determine post-optimization steady state herd structure, milk production, replacement, feed inputs and costs, and resulting cash flow on a calendar month and annual basis if optimal policies were implemented. Implementation of the model included seasonal effects on lactation curve shapes, estrus detection rates, pregnancy rates, milk prices, replacement costs, cull prices, and genetic progress. Other inputs included calf values, values of dietary TDN and CP per kilogram, and discount rate. Stochastic elements included conception (and, thus, subsequent freshening), cow milk production level within herd, and survival. Validation of optimized solutions was by separate simulation model, which implemented policies on a simulated herd and also described herd dynamics during transition to optimized structure.     

Optimal replacement and insemination policies for Holstein cattle in the southeastern region of Brazil: the effect of selling animals for production.

Dynamic programming was used to determine optimal replacement and insemination policies for Holstein-Friesian cattle in the southeastern region of Brazil. Optimal insemination and replacement decisions were determined for two disposal alternatives: selling all cows exclusively for slaughter (A) or selling the cows either for slaughter or to other farmers for production (B). Disposal alternative B reflects the common practice among dairy farmers to sell some of their cows to other farmers at a higher price than the carcass price. In the model, cows were described in terms of lactation number, stage of lactation, calving interval, and milk produced during present and previous lactation. For disposal alternative A, the optimal average herd life was 54.9 mo, corresponding to annual replacement and voluntary culling rates of 21.8 and 4.1%, respectively. For disposal alternative B, the optimal average herd life was 44.0 mo, which corresponded to annual replacement and voluntary culling rates of 27.3 and 10.0%, respectively. In this case, from the total of voluntarily culled cows, 70% were sold to other farmers for production. Sensitivity analyses showed that changes in the disposal value of cows and replacement heifer prices strongly influenced the optimal insemination and replacement policy.     

Economic trade-offs between genetic improvement and longevity in dairy cattle

Genetic improvement in sires used for artificial insemination (AI) is increasing faster compared with a decade ago. The genetic merit of replacement heifers is also increasing faster and the genetic lag with older cows in the herd increases. This may trigger greater cow culling to capture this genetic improvement. On the other hand, lower culling rates are often viewed favorably because the costs and environmental effects of maintaining herd size are generally lower. Thus, there is an economic trade-off between genetic improvement and longevity in dairy cattle. The objective of this study was to investigate the principles, literature, and magnitude of these trade-offs. Data from the Council on Dairy Cattle Breeding show that the estimated breeding value of the trait productive life has increased for 50 yr but the actual time cows spend in the herd has not increased. The average annual herd cull rate remains at approximately 36% and cow longevity is approximately 59 mo. The annual increase in average estimated breeding value of the economic index lifetime net merit of Holstein sires is accelerating from $40/yr when the sire entered AI around 2002 to $171/yr for sires that entered AI around 2012. The expectation is therefore that heifers born in 2015 are approximately $50 more profitable per lactation than heifers born in 2014. Asset replacement theory shows that assets should be replaced sooner when the challenging asset is technically improved. Few studies have investigated the direct effects of genetic improvement on optimal cull rates. A 35-yr-old study found that the economically optimal cull rates were in the range of 25 to 27%, compared with the lowest possible involuntary cull rate of 20%. Only a small effect was observed of using the best surviving dams to generate the replacement heifer calves. Genetic improvement from sires had little effect on the optimal cull rate. Another study that optimized culling decisions for individual cows also showed that the effect of changes in genetic improvement of milk revenue minus feed cost on herd longevity was relatively small. Reduced involuntary cull rates improved profitability, but also increased optimal voluntary culling. Finally, an economically optimal culling model with prices from 2015 confirmed that optimal annual cull rates were insensitive to heifer prices and therefore insensitive to genetic improvement in heifers. In conclusion, genetic improvement is important but does not warrant short cow longevity. Economic cow longevity continues to depends more on cow depreciation than on accelerated genetic improvements in heifers. This is confirmed by old and new studies.     

Economics of delayed replacement when cow performance is seasonal

An optimal dairy cow culling and replacement model was developed; it included the option to delay entering heifers into the herd after cows were culled. The objective was to investigate whether leaving a slot temporarily vacant, to enter a heifer at a more favorable time of the year, could be economically advantageous when cow performance is seasonal. The goal of the optimization was therefore to maximize net return per slot per year. The model consisted of 3 modules: 1) a bioeconomic module to enter and calculate cow performance data and prices, 2) a replacement policy module based on dynamic programming to calculate optimal culling decisions for individual cows and when to enter heifers, and 3) a herd performance module based on Markov chains to calculate summary results for the herd. Results for the optimal culling policy under typical conditions in Florida showed that immediate replacement was economically advantageous throughout the year. However, for a nonoptimal culling policy, cows culled in May, June, and July would not be replaced by heifers until August. Realistic increases in seasonality or heifer prices, or lower milk prices, showed economic advantages of delayed over immediate replacement for both culling policies. The maximum advantage of delayed replacement of 486 price scenarios was 88 US dollars per slot per year; cows that left the herd in the early summer and spring were not replaced by heifers until the late summer. Delayed replacement was economically advantageous when fixed costs and net returns per slot were low and seasonality was high, which is the case for a portion of Florida dairy producers.     

Determining the optimum replacement policy for Holstein dairy herds in Iran

The objective of this study was to determine the optimum replacement policy for Holstein dairy herds in Iran using a dynamic programming model. Cows were described in terms of state variables that included milk production class, parity, pregnancy status, and month in milk with a 1-mo stage length. The objective function maximized the net present value of cows over a 15-yr planning horizon. Markov simulation was used to estimate expected herd dynamics under the optimal decision plan determined by dynamic programming. Stochastic elements included probabilities of pregnancy and abortion, production level, and involuntary culling. The optimum annual culling rate was estimated to be 31.4%, and cows had an expected herd life (time from first calving until culling) of 3.18 yr. High replacement cost and low carcass value resulted in only 2.87% voluntary culling (i.e., optimal model-based replacement). Assuming a heat detection rate of 0.4, cows averaged 2.8 services per lactation under the optimal policy. Sensitivity analyses were carried out to evaluate the effect of milk price, herd-average production, feed cost, heifer price, and carcass value on optimum replacement decisions. Herd-average production, replacement cost, and risk of involuntary culling were important factors affecting the optimal culling policy. Changes in the price of feed, calves, and milk and the probability of pregnancy had no considerable effect on the optimal policy considering the market situation in Iran during 2008.     

Optimal replacement policies for dairy cows based on daily yield measurements

Markov decision processes (MDP) with finite state and action space have often been used to model sequential decision making over time in dairy herds. However, the length of each stage has been at least 1 mo, resulting in models that do not support decisions on a daily basis. The present paper describes the first step of developing an MDP model that can be integrated into a modern herd management system. A hierarchical MDP was formulated for the dairy cow replacement problem with stage lengths of 1 d. It can be used to assist the farmer in replacement decisions on a daily basis and is based on daily milk yield measurements that are available in modern milking systems. Bayesian updating was used to predict the performance of each cow in the herd and economic decisions were based on the prediction. Moreover, parameters in the model were estimated using data records of the specific herd under consideration. This includes herd-specific lactation curves.     

Associations between feed efficiency and aspects of lactation curves in primiparous Holstein dairy cattle

Genetic selection for improved feed efficiency in dairy cattle has received renewed attention over the last decade to address the needs of a growing global population. As milk yield is a critical component of feed efficiency metrics in dairy animals, our objective was to evaluate the associations between feed efficiency in primiparous Holstein cattle and parameters of a mathematical model describing individual lactation curves. The Dijkstra lactation curve model was fit to individual lactation records from 34 Holstein heifers with previously estimated measures of feed efficiency. We found that the optimal fit of the Dijkstra model was achieved using daily milk yield records up to 21 d in milk to capture the rise to peak milk yield and using monthly dairy herd improvement records for the remainder of lactation to accurately characterize lactation persistency. In the period of lactation before peak milk yield, improved feed efficiency was associated with a faster increase in daily milk yield over a shorter period of time at the expense of increased mobilization of body reserves; this serves to reinforce the concept that dairy cattle are primarily capital breeders versus income breeders. Feed efficiency in the period following peak lactation, as measured by gross feed efficiency, return over feed costs, and net energy efficiency of lactation, was positively associated with higher peak milk yield. The findings in early lactation suggest that estimates of feed efficiency could be improved by evaluating feed efficiency relative to conception, rather than parturition and lactation, to better account for the energy stored and released from body reserves in capital breeding.     

Cow and herd variation in milk urea nitrogen concentrations in lactating dairy cattle

Milk urea nitrogen (MUN) is correlated with N balance, N intake, and dietary N content, and thus is a good indicator of proper feeding management with respect to protein. It is commonly used to monitor feeding programs to achieve environmental goals; however, genetic diversity also exists among cows. It was hypothesized that phenotypic diversity among cows could bias feed management decisions when monitoring tools do not consider genetic diversity associated with MUN. The objective of the work was to evaluate the effect of cow and herd variation on MUN. Data from 2 previously published research trials and a field trial were subjected to multivariate regression analyses using a mixed model. Analyses of the research trial data showed that MUN concentrations could be predicted equally well from diet composition, milk yield, and milk components regardless of whether dry matter intake was included in the regression model. This indicated that cow and herd variation could be accurately estimated from field trial data when feed intake was not known. Milk urea N was correlated with dietary protein and neutral detergent fiber content, milk yield, milk protein content, and days in milk for both data sets. Cow was a highly significant determinant of MUN regardless of the data set used, and herd trended to significance for the field trial data. When all other variables were held constant, a percentage unit change in dietary protein concentration resulted in a 1.1 mg/dL change in MUN. Least squares means estimates of MUN concentrations across herds ranged from a low of 13.6 mg/dL to a high of 17.3 mg/dL. If the observed MUN for the high herd were caused solely by high crude protein feeding, then the herd would have to reduce dietary protein to a concentration of 12.8% of dry matter to achieve a MUN concentration of 12 mg/dL, likely resulting in lost milk production. If the observed phenotypic variation is due to genetic differences among cows, genetic choices could result in herds that exceed target values for MUN when adhering to best management practices, which is consistent with the trend for differences in MUN among herds.     

A stochastic model simulating pathogen-specific mastitis control in a dairy herd

The objective of this study was to develop a model simulating mastitis control in dairy herds and to investigate how sensitive the model is when varying the effect parameters according to the uncertainty. The model simulates 9 pathogen-specific mastitis types, each of which can be subclinical or clinical. The clinical cases can be 1 of 4 severities defined according to the effect of the mastitis case: mild, moderate, severe, and permanent effect. The risk factors include lactation stage, parity, yield level, previous diseases, season, and contagious spread of the infection from herd mates. Occurrence of mastitis is modeled to have direct effects on feed intake, body weight, milk yield, somatic cell count in the milk, subsequent mastitis cases within the cow and in herd mates, voluntary and involuntary culling, mortality, and milk withdrawal. Thirty-five scenarios were simulated to study model behavior and model sensitivity. The consequences per cow/yr of mastitis in the default simulated herd included 0.42 clinical mastitis occurrences, 0.56 subclinical mastitis occurrences, loss of 385-kg milk yield, a 1.3% reduced feed intake, 61-kg milk withdrawal and €146 in reduced economic net return. Based on scenarios demonstrating model behavior and sensitivity analysis, the model appears to produce valid consequences of mastitis control strategies. Representation of the effect of subclinical mastitis and of variation in mastitis severity was concluded in this study to be important when modeling mastitis economics in a dairy herd. The model offers the opportunity to study the long-term herd specific effects of a wide range of control strategies against mastitis.     

Management Guides for Insemination and Replacement Decisions

Decisions to inseminate and replace cows should be based on expected future income. Two management guides are described to help dairy farmers make decisions 1) profit after conception for decisions on insemination and 2) future profitability for decisions on immediate replacement and veterinary treatment. Management guides are obtained by comparing expected cash flow of a cow currently in the herd with that of a replacement heifer entering the herd at different times. Besides optimum decisions, management guides indicate expected loss when making an nonoptimum decision. This allows farmers to weigh additional information on cow or herd and to rank cows on expected profitability.Dynamic programming can be used to calculate management guides for all possible states of a cow for a given price and production situation. Results can be used to derive management guides for individual cows in a herd, characterizing each cow by state variables. Most information needed on cows can be taken from milk recording data and breeding records. Lactation number, milk production, and stage of lactation clearly affect the management guides.     

Associations between routinely collected Dairy Herd Improvement data and insemination outcome in UK dairy herds

Milk constituent concentrations in samples taken during early lactation are often used to generate proxy measures for energy balance in dairy herds. This study aimed to explore associations between these and other measures routinely recorded by Dairy Herd Improvement schemes and insemination outcome, with an emphasis on the likely predictiveness of such measures for conception risk (the proportion of inseminations that are successful) at herd level. Data from 312 UK dairy herds were restructured so that each unit of data represented an insemination at less than 100 d in milk (DIM). Milk constituent concentrations from the first and second test days (corrected for the effects of season and DIM at sampling) were used as potential predictors of insemination outcome in a logistic regression model. Other predictors included representations of milk yield and other information routinely collected by Dairy Herd Improvement Associations; random effects were used to account for clustering at cow and herd levels. The final model included a large number of predictors, with several interaction and nonlinear terms. The relative effect sizes of the measures of early lactation milk constituent concentrations were small. The full model predicted just under 64% of observed variation in herd-year conception risk (i.e., the proportion of inseminations that were successful in each herd in each calendar year); however, around 40% was accounted for by the herd-level random effect. The predictors based on early lactation milk constituent concentrations accounted for less than 0.5% of observed variation, and representations of milk yield (both overall level of yield and early lactation curve shape) accounted for around 7%; DIM at insemination, parity, interservice interval, year, and month accounted for the remaining 15%. These results suggest that early lactation milk constituent information is unlikely to predict herd conception risk to a useful extent. The large proportion of observed variation explained by the herd-level random effect suggests that there are unmeasured (in this study) or unmeasurable factors that are consistent within a herd and are highly influential in determining herd conception risk.     

The effect of temperate or tropical pasture grazing state and grain-based concentrate allocation on dairy cattle production and behavior

Grain-based concentrate (GBC) supplement is of high cost to dairy farmers as a feed source as opposed to grazed pasture. Milk production response to GBC is affected by the composition and nutritive value of the remainder of the diet, animal factors, and interactions between forage type and level of GBC. In grazing systems, dairy cattle encounter contrasting pasture states, primarily because the social structure of the herd affects the timing of when each animal accesses a paddock after milking as a result of a relatively consistent cow milking order. However, the effect of feed management, namely pasture state and GBC allocation, on dairy cattle production and behavior is unknown. We examined the effect of varying GBC allocation for dairy cattle grazing differing states of kikuyu grass (Pennisetum clandestinum, a tropical pasture species; experiment 1) and annual ryegrass (Lolium multiflorum L., a temperate pasture species; experiment 2) on dry matter intake, milk production and composition, and grazing behavior. For each experiment, 90 lactating dairy cattle were randomly allocated to 2 consistent (fresh–fresh and depleted–depleted) and 2 inconsistent (fresh–depleted and depleted–fresh pasture state treatments (defined as sequences of pasture state allocation for the morning and afternoon grazing events) and 3 GBC treatments [2.7, 5.4, and 8.1 kg of dry matter (DM)/cow per day], giving 12 treatment combinations for each experiment. The duration of each experiment was 14 d, with the first 7 d used as adaptation to treatment. In each experiment, 3 cattle were selected from each of the 12 pasture type × GBC treatment groups within the experimental herd to determine herbage intake and total DM digestibility using the n-alkanes method (n = 36). There was no interaction between kikuyu grass or ryegrass pasture state and GBC level for intake, digestibility, or milk yield or components. Dairy cattle offered fresh–fresh and depleted–fresh ryegrass produced 9% more milk yield, in line with greater pasture intakes, compared with fresh–depleted and depleted–depleted pasture states. Dairy cattle offered fresh–fresh kikuyu grass had 8% more milk yield and 14% more milk protein yield than other pastures states, but there was no effect of pasture state on milk composition. Milk yield increased with GBC level for both pasture species (～0.7–0.8 kg of milk/kg of DM GBC) as GBC level increased from 2.5 to 5.4 kg of DM/cow per day. There was a poor response (0.3 kg of milk/kg of DM GBC), and no response, when GBC levels increased from 5.4 to 8.1 kg of DM/cow per day for kikuyu grass and ryegrass, respectively, in line with pasture DMD. Time spent grazing, lying, and ruminating were not associated with kikuyu grass pasture state, GBC, or their interaction. Despite this, there was a linear increase in grazing time in the afternoon coinciding with a linear decrease in lying and rumination time for both kikuyu grass and ryegrass pasture. Together these findings reveal the effect of pasture state and GBC allocation on dairy cattle production and behavior. Tailoring GBC allocation to the state of pasture accessed by cattle appears unwarranted, but there is an opportunity to alter the timing of pasture access to increase herd-level milk production efficiency.     

Effects of sire fertility and daughter stayability on profitability of sire selection.

Holstein sires (n = 340) with milk, milk fat, semen unit fertility, daughter stayability evaluations, and semen price for 1986 were studied. Effects of variation in sire fertility and daughter stayability on profitability of sire selection using the net present value criterion were estimated. The model estimated expected profit from a cow bred to pregnancy from future production and from cattle disposal and replacement after discounting costs and returns to the time of insemination. Effects of semen sexing and semen unit dilution on profitability to determine optimal breeding strategies for dairy herds were examined. Sire profitability increased with herd average conception rate and sire selection intensity. Daughter stayability had a greater impact on profitability than semen unit fertility when profit maximization was computed under the criterion of breeding a cow to pregnancy. Genetic progress for production was compromised when selecting to maximize profit. Dilution of semen units seems profitable only when semen availability is limited for high demand sires. The use of sexed female semen may only be appropriate when it can generate additional income from the sale of surplus heifers.     

Preweaning milk replacer intake and effects on long-term productivity of dairy calves

The preweaning management of dairy calves over the last 30 yr has focused on mortality, early weaning, and rumen development. Recent studies suggest that nutrient intake from milk or milk replacer during the preweaning period alters the phenotypic expression for milk yield. The objective of this study was to investigate the relationship between nutrient intake from milk replacer and pre- and postweaning growth rate with lactation performance in the Cornell dairy herd and a commercial dairy farm. The analysis was conducted using traditional 305-d first-lactation milk yield and residual lactation yield estimates from a test-day model (TDM) to analyze the lactation records over multiple lactations. The overall objective of the calf nutrition program in both herds was to double the birth weight of calves by weaning through increased milk replacer and starter intake. First-lactation 305-d milk yield and residuals from the TDM were generated from 1,244 and 624 heifers from the Cornell herd and from the commercial farm, respectively. The TDM was used to generate lactation residuals after accounting for the effects of test day, calving season, days in milk, days pregnant, lactation number, and year. In addition, lactation residuals were generated for cattle with multiple lactations to determine if the effect of preweaning nutrition could be associated with lifetime milk yield. Factors such as preweaning average daily gain (ADG), energy intake from milk replacer as a multiple of maintenance, and other growth outcomes and management variables were regressed on TDM milk yield data. In the Cornell herd, preweaning ADG, ranged from 0.10 to 1.58 kg, and was significantly correlated with first-lactation yield; for every 1 kg of preweaning ADG, heifers, on average, produced 850 kg more milk during their first lactation and 235 kg more milk for every Mcal of metabolizable energy intake above maintenance. In the commercial herd, for every 1 kg of preweaning ADG, milk yield increased by 1,113 kg in the first lactation and further, every 1 kg of prepubertal ADG was associated with a 3,281 kg increase in first-lactation milk yield. Among the 2 herds, preweaning ADG accounted for 22% of the variation in first-lactation milk yield as analyzed with the TDM. These results indicate that increased growth rate before weaning results in some form of epigenetic programming that is yet to be understood, but has positive effects on lactation milk yield. This analysis identifies nutrition and management of the preweaned calf as major environmental factors influencing the expression of the genetic capacity of the animal for milk yield.     

Short communication: Prediction of retention pay-off using a machine learning algorithm

Replacement decisions have a major effect on dairy farm profitability. Dynamic programming (DP) has been widely studied to find the optimal replacement policies in dairy cattle. However, DP models are computationally intensive and might not be practical for daily decision making. Hence, the ability of applying machine learning on a prerun DP model to provide fast and accurate predictions of nonlinear and intercorrelated variables makes it an ideal methodology. Milk class (1 to 5), lactation number (1 to 9), month in milk (1 to 20), and month of pregnancy (0 to 9) were used to describe all cows in a herd in a DP model. Twenty-seven scenarios based on all combinations of 3 levels (base, 20% above, and 20% below) of milk production, milk price, and replacement cost were solved with the DP model, resulting in a data set of 122,716 records, each with a calculated retention pay-off (RPO). Then, a machine learning model tree algorithm was used to mimic the evaluated RPO with DP. The correlation coefficient factor was used to observe the concordance of RPO evaluated by DP and RPO predicted by the model tree. The obtained correlation coefficient was 0.991, with a corresponding value of 0.11 for relative absolute error. At least 100 instances were required per model constraint, resulting in 204 total equations (models). When these models were used for binary classification of positive and negative RPO, error rates were 1% false negatives and 9% false positives. Applying this trained model from simulated data for prediction of RPO for 102 actual replacement records from the University of Wisconsin-Madison dairy herd resulted in a 0.994 correlation with 0.10 relative absolute error rate. Overall results showed that model tree has a potential to be used in conjunction with DP to assist farmers in their replacement decisions.     

Realized genetic selection differentials in Canadian Ayrshire,Jersey, and Brown Swiss dairy cattle populations

Estimated breeding values of a selection index, production, durability, health, and fertility traits from Canadian Ayrshire, Jersey, and Brown Swiss bulls and cows were used to study genetic selection differentials (GSD). The bulls and cows were born from 1950 and 1960, respectively. The GSD for the 3 Canadian dairy populations were studied along the 4-path selection model: sire-to-bull (SB), dam-to-bull (DB), sire-to-cow (SC), and dam-to-cow (DC) pathways. We also determined the variations in realized GSD due to herd and herd × year of conception in addition to the effects of some environmental factors on realized GSD of the SC and DC paths. The mean realized GSD of the DB were higher than those of other paths and were increasing for lifetime performance index, 305-d milk yield, 305-d fat yield, and 305-d protein yield in all 3 dairy cattle populations. We observed no clear trends in realized GSD for type traits in all 3 dairy cattle breeds except for the apparent increasing trends in realized GSD of mammary system, dairy strength, and feet and legs in the DB and SC paths of the Ayrshire breed. No clear patterns were observed in the realized GSD of daughter fertility in the SB, DB, and SC paths of all dairy cattle breeds. Realized GSD for somatic cell score showed increasing and favorable trends in the 3 most influential selection paths (SB, DB, and SC). Year of conception influenced realized GSD of artificial insemination bulls in Ayrshire, Jersey, and Brown Swiss dairy populations. Selection emphases for the SC path generally increased with time. There was considerable variation among herds in selection pressures applied in the SC and DC pathways but no clear association with housing system or region. This study demonstrates that variations exist among herds of minor dairy cattle breeds in their selection for economically important traits. These variations offer opportunities for further improvements in these populations.     

Economic value of pregnancy in dairy cattle

The objective of this study was to estimate the value of pregnancy for dairy cows. Effects of the stage of gestation, stage of lactation, lactation number, milk yield, milk price, replacement heifer cost, probability of pregnancy, probability of involuntary culling, and breeding decisions were studied. A bioeconomic model was used, and breeding and replacement decisions were optimized. A general Holstein herd in the United States was modeled. The average value of a new pregnancy was $278. The value of a new pregnancy increased with days in milk early in lactation but typically decreased later in lactation. Relatively high-producing cows and first-lactation cows reached greater values, and their values peaked later in lactation. The average cost of a pregnancy loss (abortion) was $555. The cost of a pregnancy loss typically increased with gestation length. Sensitivity analyses showed that an increased probability of pregnancy, an increased persistency of milk yield, and a smaller replacement heifer cost greatly reduced the average value of a pregnancy. The value of a new pregnancy was negative for relatively high-producing first-lactation cows when persistency of lactation and the probability of pregnancy were increased. Breeding was delayed when the value of pregnancy was negative. Changes in milk price, absolute milk yield, and probability of involuntary culling had less effect on the value of pregnancy. The value of pregnancy and optimal breeding decisions for individual cows were greatly dependent on the predicted daily milk yield for the remaining period of lactation. An improved understanding of the value of pregnancy may support decision making in reproductive management when resources are limited.     

Evaluation of different feed intake models for dairy cows

The objective of the current study was to evaluate feed intake prediction models of varying complexity using individual observations of lactating cows subjected to experimental dietary treatments in periodic sequences (i.e., change-over trials). Observed or previous period animal data were combined with the current period feed data in the evaluations of the different feed intake prediction models. This would illustrate the situation and amount of available data when formulating rations for dairy cows in practice and test the robustness of the models when milk yield is used in feed intake predictions. The models to be evaluated in the current study were chosen based on the input data required in the models and the applicability to Nordic conditions. A data set comprising 2,161 total individual observations was constructed from 24 trials conducted at research barns in Denmark, Finland, Norway, and Sweden. Prediction models were evaluated by residual analysis using mixed and simple model regression. Great variation in animal and feed factors was observed in the data set, with ranges in total dry matter intake (DMI) from 10.4 to 30.8 kg/d, forage DMI from 4.1 to 23.0 kg/d, and milk yield from 8.4 to 51.1 kg/d. The mean biases of DMI predictions for the National Research Council, the Cornell Net Carbohydrate and Protein System, the British, Finnish, and Scandinavian models were −1.71, 0.67, 2.80, 0.83, −0.60 kg/d with prediction errors of 2.33, 1.71, 3.19, 1.62, and 2.03 kg/d, respectively, when observed milk yield was used in the predictions. The performance of the models were ranked the same, using either mixed or simple model regression analysis, but generally the random contribution to the prediction error increased with simple rather than mixed model regression analysis. The prediction error of all models was generally greater when using previous period data compared with the observed milk yield. When the average milk yield over all periods was used in the predictions of feed intake, the increase in prediction error of all models was generally less than when compared with previous period animal data combined with current feed data. Milk yield as a model input in intake predictions can be substantially affected by current dietary factors. Milk yield can be used as model input when formulating rations aiming to sustain a given milk yield, but can generate large errors in estimates of future feed intake and milk production if the economically optimal diet deviates from the current diet.