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Profitability Measures as Decision-Making Tools for Québec Dairy Herds
Abstract
A retrospective cohort study was conducted using production and health records from Québec dairy herds which were integrated with the objectives of calculating different lifetime profitability measures and developing analyses of the impact of selected reproduction and health variables on profitability. The dataset for the study consisted of lifetime records for a total of 13 668 Holstein cows from 113 herds and in cohorts of cows calving for the first time between 2000 and 2009. To calculate profit, all the revenues and costs were accumulated on a lifetime basis, and cumulative lifetime profit and cumulative lifetime profit adjusted for the opportunity cost of postponed replacement were selected for analysis. There were statistically significant effects in both profit measures because of age at first calving, cumulative number of days in milk, and cumulative number of days dry as well as the number of health events accumulated by animal lifetime (clinical mastitis, reproductive problems, and feet and legs problems). The results of lifetime profit measures and management variables with an effect on profit results could help dairy producers in the process of monitoring and making decisions regarding profit goals and herd life of their cows.
... Nine articles had access to longitudinal data on AHMs (Table S1). Five studies reported a monetary outcome; Delgado et al. (2018) analyzed lifetime records for 13 668 cows from 113 farms over a 10-year period and estimated the cumulative lifetime profit. The cost of health traits included did not necessarily correspond to the actual cost related to each cow. ...
... Among the 31 articles that used mastitis as an AHM, 14 estimated the average cost of a clinical mastitis case (listed in Table 4). Seven articles used data from North America (Bar et al., 2008;Cha et al., 2011;Rollin et al., 2015;Liang et al., 2017;Aghamohammadi et al., 2018;Dahl et al., 2018;Delgado et al., 2018) and seven used data from Europe (Yalcin, 2000;Wolfová et al., 2006;Pérez-Cabal et al., 2008;Hagnestam-Nielsen and Østergaard, 2009;Hultgren and Svensson, 2009;Heikkilä et al., 2012;Doehring and Sundrum, 2019). The estimated cost per case of clinical mastitis varied widely, as did the input parameters chosen to estimate the cost ( Table 4). ...
... Raboisson et al. (2015) and Mostert et al. (2018) estimated the total average costs of subclinical ketosis to vary between €130 and €257 per case per year. Fertility problems expressed as a negative change in 6-week calving rate, suboptimal estrus detection, and increased age at first calving were all associated with increased cost for farmers Shalloo et al., 2014;Delgado et al., 2018). Pérez-Báez et al. (2021) found that metritis caused large economic losses to dairy herds by decreasing milk production, reproduction, and survival in the herd. ...
Farm animal health is an area of increasing interest to both the public and industry stakeholders. There is an ongoing debate on whether improving animal health, and thereby increasing welfare, is profitable or not. Improving animal health often requires investments in the farm or increases labour costs. As a result, the impact of animal health on farm economy is particularly interesting. This study systematically maps and assesses the available evidence in the published scientific literature regarding the link between farms’ economic outcomes on dairy cow health, with the aim of identifying knowledge gaps in this field of research. In total, 59 peer-reviewed articles were included using a broad range of animal health variables and economic outcomes. We found a heterogeneous body of evidence in terms of both methods, animal health measures (AHMs) and economic outcome measures used. None of the included studies makes explicit causal claims between AHMs and economic outcomes. The results suggest that common production diseases such as clinical mastitis and lameness, which are painful and affect cow health and welfare, are costly for farmers. We found a knowledge gap and lack of evidence on the impact of animal health interventions on farms’ economic outcomes, as well as the long-term effects of such interventions. Future research should aim to investigate the causal links between animal health and economic outcomes.
... Adoption of AMDs is likely to increase as dairy herd size and consolidation continue [14][15][16]. Since clinical mastitis (CM) is a severe concern for dairy farmers, many AMDs have been developed to aid in early diagnosis [17,18]. Given the difficulties in visually analyzing milk quality in herds with automatic milking systems, applying AMDs to detect intramammary infection (IMI) can be critical [1]. ...
We hypothesized that reticuloruminal temperature, pH as well as cow activity can be used as parameters for the early diagnosis of clinical mastitis in dairy cows. Therefore, we aimed to detect the relationship between these factors and the disease. We randomly selected cows with clinical mastitis and clinically healthy cows (HG) out of 600 milking cows. We recorded the following parameters during the experiment: reticulorumen temperature (RR temp.), reticulorumen pH (RR pH), and cow activity. We used smaXtec boluses (smaXtec animal care technology®, Graz, Austria). In this investigation, reticulorumen data obtained seven days before diagnosis were compared to HG data from the same time period. CM cows were observed on the same days as the healthy cows. The healthy group's RR pH was 7.32% higher than that of cows with CM. Reticulorumen temperature was also 1.25% higher in the CM group than in the control group. The healthy group had a higher average value for walking activity, which was 17.37% higher than the CM group. The data of reticulorumen pH changes during 24 h showed that during the day, the pH changed from 5.53 to 5.83 in the CM group. By contrast, pH changed from 6.05 to 6.31 in the control group. The lowest reticulorumen pH in the CM group was detected on the third day before diagnosis, which was 15.76% lower than the highest reticulorumen pH detected on the sixth day before diagnosis. The lowest reticulorumen pH in CM cows was detected at 0 and 1 days before diagnosis and it was 1.45% lower than the highest reticulorumen pH detected on the second day before diagnosis. The lowest walking activity in the CM group was detected 0 days before diagnosis, which was 50.60% lower than on the fifth day before diagnosis. Overall, the results confirmed our hypothesis that reticuloruminal temperature, reticuloruminal pH, and cow activity could be used as parameters for the early diagnosis of clinical mastitis in dairy cows.
... Adoption of AMD is likely to increase as dairy herd size and consolidation continue [12,13,14]. Since clinical mastitis (CM) is a severe concern for dairy farmers, many AMDs have been developed to aid in early diagnosis [15,16]. Given the difficulties in visually analyzing milk quality in herds with automatic milking systems, applying AMDs to detect intramammary infection (IMI) can be critical [1]. ...
We hypothesized that reticuloruminal temperature and pH, as well as cow activity, can be used as biomarkers for the early diagnosis of clinical mastitis in dairy cows. Therefore, we aimed to detect the relationship between reticuloruminal temperature and pH, cow activity, and clinical mastitis in dairy cows. We randomly selected cows with clinical mastitis and clinical healthy cows (HG) out of 600 milking cows. We recorded the following parameters during the experiment: reticulorumen tem-perature (RR temp.), reticulorumen pH (RR pH), and cow activity. We used smaXtec boluses (smaXtec animal care technology®, Graz, Austria). In this investigation, reticulorumen data ob-tained seven days before diagnosis were compared to HG data from the same time period. CM cows were observed on the same days as the healthy cows. The healthy group’s RR pH was 7.32% higher than that of cows with CM. Reticulorumen temperature was also 1.25% higher in the CM group than in the control group. The healthy group had a higher average value for walking activity and was 17.37% higher than the CM group. The data of reticulorumen pH changes during 24 h showed that during the day, the pH changed from 5.53 to 5.83 in the CM group. By contrast, pH changed from 6.05 to 6.31 in the control group. The lowest reticulorumen pH in the CM group was detected on the third day before diagnosis. It was 15.76% lower than the highest reticulorumen pH detected on the sixth day before diagnosis. The lowest reticulorumen pH in CM cows was detected at 0 and 1 days before diagnosis. It was 1.45% lower than the highest reticulorumen pH detected on the second day before diagnosis. The lowest walking activity in the CM group was detected 0 days before diagnosis, 50.60% lower than on the fifth day before diagnosis. The lowest walking activity was detected 0 days before diagnosis, 39.57% lower than on the seventh day before diagnosis. In this study, we found that reticuloruminal temperature, reticuloruminal pH, and cow ac-tivity could be used as biomarkers for the early diagnosis of clinical mastitis in dairy cows.
... Adoption of AMD is expected to grow as the size and consolidation of dairy herds continue to increase (Barkema et al., 2015;Gonzalez-Mejia et al., 2018;Luby et al., 2020). Multiple AMD have been developed to aid in early detection of clinical mastitis (CM) as it represents a major concern for dairy farmers (Delgado et al., 2017;Liang et al., 2017;USDA, 2018). Worldwide, 20 to 40% of lactating dairy cows develop CM throughout lactation and Streptococcus uberis is responsible for about 5 to 17% of those cases (Oliveira et al., 2013;Ruegg et al., 2015;Tomazi et al., 2018). ...
Automated monitoring devices have become increasingly utilized in the dairy industry, especially for monitoring or predicting disease status. While multiple automated monitoring devices have been developed for the prediction of clinical mastitis (CM), limitations in performance or applicability remain. The aims of this study were to (1) detect variations in reticuloruminal temperature (RRT) relative to an experimental intramammary challenge with Streptococcus uberis and (2) evaluate alerts generated automatically based on variation in RRT to predict initial signs of CM in the challenged cows based on severity of clinical signs and the concentration of bacteria (cfu/mL) in the infected quarter separately. Clinically healthy Holstein cows without a history of CM in the 60 d before the experiment (n = 37, parity 1 to 5, ≥120 d in milk) were included if they were microbiologically negative and had a somatic cell count under 200,000 cells/mL based on screening of quarter milk samples 1 wk before challenge. Each cow received an intra-reticuloruminal automated monitoring device before the trial and was challenged with 2,000 cfu of Strep. uberis 0140J in 1 rear quarter. Based on interrupted time series analysis, intramammary challenge with Strep. uberis increased RRT by 0.54°C [95% confidence interval (CI): 0.41, 0.66] at 24 h after the challenge, which remained elevated until the end of the study. Alerts based on RRT correctly classified 78.3% (95% CI: 65.8, 87.9) of first occurrences of CM at least 24 h in advance, with a sensitivity of 70.0% (95% CI: 50.6, 85.3) and a specificity of 86.7% (95% CI: 69.3, 96.2). The accuracy of CM for a given severity score was 90.9% (95% CI: 70.8, 98.9) for mild cases, 85.2% (95% CI: 72.9, 93.4) for moderate cases, and 92.9% (95% CI: 66.1, 99.8) for severe cases. Test characteristics of the RRT alerts to predict initial signs of CM improved substantially after bacterial count in the challenged quarter reached 5.0 log10 cfu/mL, reaching a sensitivity of 73.5% (95% CI: 55.6, 87.1) and a specificity of 87.5% (95% CI: 71.0, 96.5). Overall, the results of this study indicated that RRT was affected by the intramammary challenge with Strep. uberis and the RRT-generated alerts had similar accuracy as reported for other sensors and algorithms. Further research that includes natural infections with other pathogens as well as different variations in RRT to determine CM status is warranted.
... In addition to the reduction of production associated with short dry periods, our results also indicated a negative relationship between long dry periods and milk production and its components, which is similar to what has been previously reported (Teixeira et al., 1999;Bachman and Schairer, 2003;Kuhn et al., 2006). No revenue is generated from milk selling while the animal is dry and an unnecessarily long dry period would have a negative impact on profitability (Delgado et al., 2017). On the other hand, even though shortening the dry period is associated with a reduction in milk production, this might not be reflected in revenue loss. ...
The objective of this retrospective longitudinal study was to evaluate the relationship between dry period length and the production of milk, fat, protein, lactose and total milk solids in the subsequent lactation of Holstein dairy cows under tropical climate. After handling and cleaning of the data provided by the Holstein Cattle Breeders Association of Minas Gerais, data from 32 867 complete lactations of 19 535 Holstein animals that calved between 1993 and 2017 in 122 dairy herds located in Minas Gerais state (Brazil) were analysed. In addition to dry period length, calving age, lactation length, milking frequency, parity, calf status at birth, herd, year, and season of calving were included in the analysis as covariables to account for additional sources of variation. The machine learning algorithms gradient boosting machine, extreme gradient boosting machine, random forest and artificial neural network were used to train models using cross validation. The best model was selected based on four error metrics and used to evaluate the variable importance, the interaction strength between dry period length and the other variables, and to generate partial dependency plots. Random forest was the best model for all production outcomes evaluated. Dry period length was the third most important variable in predicting milk production and its components. No strong interactions were observed between the dry period and the other evaluated variables. The highest milk and lactose productions were observed with a 50-d long dry period, while fat, protein, and total milk solids were the highest with dry period lengths of 38, 38, and 44 d, respectively. Overall, dry period length is associated with the production of milk and its components in the subsequent lactation of Holstein cows under tropical climatic conditions, but the optimum length depends on the production outcome.
... In other words, they will not recover their potential milk production. Hence, the presence of CM has an important negative impact on the expected profit (Delgado et al., 2017). The average cost of a CM case varies between $95 and $426.50 (Bar et al., 2008;Liang, Arnold, Stowe, Harmon, & Bewley, 2017;Rollin, Dhuyvetter, & Overton, 2015). ...
Different classification machine learning techniques (Naïve Bayes, Random Forest and Extreme Gradient Boosting) were evaluated to identify cows positive for clinical mastitis (CM) during their first lactation (1st lactation) and to daily predict the onset of CM (continuous). Integrated data from different software were used to feed the algorithms. In both cases, the best predictions were obtained with the Random Forest algorithm. The algorithms correctly classified 71% and 85% of the CM cows for the 1st lactation and continuous models, respectively. Both analyses had the same accuracy of 72%. Results showed that it is feasible to integrate different data streams to develop predictive and prescriptive decision support tools. Having two different algorithms working concomitantly, one for predicting the imminent risk and the other one for the overall risk during the first lactation, could help in the short, mid-, and long-term decision-making process.
... Although significant research has been done about prevention and treatment of CM, the presence of CM still has an important negative effect on dairy farm profitability (Delgado et al., 2017). The average cost of a CM case varies between $325.76 ± 71.12 (±SD; Liang et al., 2017) and $426.50 ± 80.27 (Rollin et al., 2015). ...
We are developing a real-time, data-integrated, data-driven, continuous decision-making engine, The Dairy Brain, by applying precision farming, big data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multidisciplinary scientists, dairy farmers, and industry professionals. Dairy farms have embraced large and diverse technological innovations such as sensors and robotic systems, and procured vast amounts of constant data streams, but they have not been able to integrate all this information effectively to improve whole-farm decision making. Consequently, the effects of all this new smart dairy farming are not being fully realized. It is imperative to develop a system that can collect, integrate, manage, and analyze on- and off-farm data in real time for practical and relevant actions. We are using the state-of-the-art database management system from the University of Wisconsin-Madison Center for High Throughput Computing to develop our Agricultural Data Hub that connects and analyzes cow and herd data on a permanent basis. This involves cleaning and normalizing the data as well as allowing data retrieval on demand. We illustrate our Dairy Brain concept with 3 practical applications: (1) nutritional grouping that provides a more accurate diet to lactating cows by automatically allocating cows to pens according to their nutritional requirements aggregating and analyzing data streams from management, feed, Dairy Herd Improvement (DHI), and milking parlor records; (2) early risk detection of clinical mastitis (CM) that identifies first-lactation cows under risk of developing CM by analyzing integrated data from genetic, management, and DHI records; and (3) predicting CM onset that recognizes cows at higher risk of contracting CM, by continuously integrating and analyzing data from management and the milking parlor. We demonstrate with these applications that it is possible to develop integrated continuous decision-support tools that could potentially reduce diet costs by $99/cow per yr and that it is possible to provide a new dimension for monitoring health events by identifying cows at higher risk of CM and by detecting 90% of CM cases a few milkings before disease onset. We are securely advancing toward our overarching goal of developing our Dairy Brain. This is an ongoing innovative project that is anticipated to transform how dairy farms operate.
Increasing the productive lifespan of dairy cows is important to achieve a sustainable dairy industry, but making strategic culling decisions based on cow profitability is challenging for farmers. The objective of this study was to carry out a lifetime cost-benefit analysis based on production and health records and to explore different culling decisions among farmers. The cost-benefit analysis was conducted for 22 747 dairy cows across 114 herds in Quebec, Canada for which feed costs and the occurrence of diseases were reported. Costs and revenues related to productive lifespan were compared among cohorts of cows that left their respective herd at the end of their last completed lactation or stayed for a complete additional lactation. Hierarchical clustering analysis was carried out based on costs and revenues to explore different culling decisions among farmers. Our results showed that the knowledge of lifetime cumulative costs and revenues was of great importance to identify low-profitable cows at an earlier lactation, while only focusing on current lactation costs and revenues can lead to an erroneous assessment of profitability. While culling decisions were mostly based on current lactation costs and revenues and disregarded the occurrence of costly events on previous lactations, there was variation among farmers as we identified three different culling decision clusters. Monitoring cumulative costs and revenues would help farmers to identify low-profitable cows at an earlier lactation and make the decision to increase herd productive lifespan and farm profitability by keeping the most profitable cows.
A dairy farm's ability to generate positive profit is dependent on the cow's response to management decisions made in conjunction with input cost management. Therefore, farm managers consider a multifaceted set of choices, managing their herd not as a homogeneous group of animals, but justifying the influence of individual cows on the farm's financial performance. We combined cow-level performance records from Minnesota DHIA and farm-level financials from the University of Minnesota Center for Farm Financial Management database FINBIN (https://finbin.umn.edu/) from 2012 to 2018 to evaluate farm- and cow-level profitability. The objective of this study was to evaluate individual cow performance matched with farm-level input expenses allocated to the cow level to measure a dairy farm's ability to be profitable over time, considering input and milk price fluctuations. Conventional Minnesota dairy farms were divided into 2 groups—financially resilient and non-resilient—based on their adjusted net farm income ratio over time. Yearly farm-level expenses and revenues were allocated to cows based on performance measures provided in monthly DHIA test data, and a cumulative lifetime break-even was calculated for all cows with consecutive farm data from 2012 to 2018. Herd-level and cow-level characteristics were analyzed to test for statistical difference between resilient and non-resilient farms as well as cows who achieved their break-even versus those that did not for resilient and non-resilient farms. Results showed that resilient farms had statistically different and lower expenses than non-resilient farms, with lower heifer raising expenses ($1,839.32 vs. $1,886.20), lifetime feed expenses ($4,197.07 vs. $4,975.39), and lifetime non-feed expenses ($2,761.63 vs. $4,502.67). Resilient farms had 38.3% of cows reach break-even, whereas non-resilient farms had 25.2% of cows break even. On average, cows who achieved their break-even remained in the herd for approximately 1 yr longer for both resilient farms (1,011 d for cows who break even and 627 d for those that do not) and non-resilient farms (1,033 d for cows who break even and 683 d for those that do not). Cows on resilient farms who achieved their lifetime break-even had an average lifetime profit of $1,613.48, which was $3,095.10 higher than the lifetime profit of −$1,481.62 of cows who never reach their break-even. Cows who reached their break-even on non-resilient farms had a lifetime profit of $1,270.51, which was $3,854.11 higher than the lifetime profit of −$2,583.60 for those who did not break even. Therefore, financially resilient dairy farms were utilizing a low-input, low-output model that proved to be successful and resulted in maintained profitability across volatile and fluctuating commodity prices.
Lameness is a persistent and underreported health and welfare problem in the dairy industry, resulting in reduced cow performance and profitability as well as early culling. The study objectives were (1) to quantify the impact of the first instance of lameness, at different stages of lactation, on production and economic performance, and (2) to further quantify the impacts of the first instance of lameness when only cows that remain in the herd for at least 100 d in milk (DIM) and those that remain for 305 DIM are included in the analysis. A retrospective longitudinal study was conducted using pre-existing data from animal health records and Dairy Herd Improvement Association records. Data were edited based on selected inclusion criteria, yielding a data set containing records from 15,159 first-lactation Holstein cows from 120 herds with year of first calving between 2003 and 2014. Lame cows were assigned to 1 of 4 groups based on when in the lactation the first event of lameness occurred: transition (1–21 DIM), early lactation (22–100 DIM), mid-lactation (101–200 DIM), or late lactation (201+ DIM). Mid- and late-lactation lame cows were also stratified by cumulative milk yield before the lameness event. Healthy cows (i.e., no recorded lameness event) were randomly assigned for each lactation stage, with mid-lactation healthy and late-lactation healthy cows similarly stratified. Production performance (cumulative milk, fat, and protein yield) and economic performance [milk value, margin over feed cost (MOFC), and gross profit] were analyzed using a mixed model with herd as a random effect. Cumulative milk yields were 811 to 1,290 kg lower for lame cows than for healthy cows, with milk component yields undergoing similar reductions. Reductions in milk yield contributed to losses in milk value (Can$−527 to −1,083; US$−419 to $−862) and MOFC (Can$−510 to −774; US$−406 to $−616). Higher losses were reported using gross profit (Can$−753 to −1,052; US$−599 to $−837), which includes all lameness-related costs. Production and performance losses were smaller when 100 DIM and 305 DIM thresholds were applied (i.e., exclusion of cows culled before 100 and 305 DIM, respectively), however, mid- and late-lactation lame cows maintained high levels of significant losses for all 6 variables analyzed. Lameness also led to higher levels of culling, masking losses for transition and early-lactation lame cows in the 305-DIM analysis. Increasing producer understanding of the costs associated with lameness not only serves to provide insight to producers for more informed culling decisions, but may also help producers weigh the costs of adopting new methods and technologies targeted at reducing on-farm lameness.
Mastitis is a highly prevalent disease, which negatively affects cow performance, profitability, welfare, and longevity. The objectives of this study were (1) to quantify the impact of the first instance of mastitis, at different stages of lactation, on production and economic performance, and (2) to further quantify the impact of the first instance of mastitis when only cows that remain in the herd for at least 100 d in milk (DIM) and those that remain for 305 DIM are included in the analysis. A retrospective longitudinal study was conducted using data from existing animal health record files and Dairy Herd Improvement records. After editing based on selected inclusion criteria and completeness of health records, data consisted of records from first-lactation Holstein cows, from 120 herds, that calved for the first time between 2003 and 2014, inclusive. Mastitic cows were assigned to 1 of 4 groups based on when in the lactation the first event of mastitis occurred: transition (1–21 DIM), early lactation (22–100 DIM), mid lactation (101–200 DIM), or late lactation (201+ DIM). Mid-lactation and late-lactation mastitic cows were also stratified by cumulative milk yield before the mastitis event. Healthy cows (i.e., no recorded mastitis event) were randomly assigned for each lactation stage, with mid-lactation healthy and late-lactation healthy cows similarly stratified. Production performance (cumulative milk, fat, and protein yield) and economic performance [milk value, margin over feed cost (MOFC), and gross profit] were analyzed using a mixed model with herd as a random effect. Significant losses in cumulative milk yield (−382 to −989 kg) and correspondingly lower fat and protein yields were found in mastitic cows, with transition and late-lactation mastitic cows having the highest losses. Drops in production translated to significant reductions in cumulative milk value (Can$−287 to Can$−591; US$−228 to US$−470), MOFC (Can$−243 to Can$−540; US$−193 to US$−429), and gross profit (Can$−649 to Can$−908; US$−516 to US$−722) for mastitic cows at all stages. Differences between mastitic and healthy cows in the early lactation and transition stages remained for all variables in the 100-DIM analysis, but, aside from gross profit, were nonsignificant in the 305-DIM analysis. Gross profit accounted for all costs associated with mastitis and thus continued to be lower for mastitic cows at all stages, even in the 305-DIM analysis in which culled cows were omitted (Can$−485 to Can$−979; US$−386 to US$−779). The research reflects the performance implications of mastitis, providing more information upon which the producer can make informed culling decisions and maximize both herd profitability and cow longevity.
Lactating cows and nulliparous heifers are in distinctive and unique physiological conditions when they are approaching the planned time of breeding, at approximately 60 days in milk and 13–15 months of age, respectively. This study aimed to profile the metabolic milieu in heifers (N = 14) and lactating cows (N = 15) in the weeks leading up to planned time of breeding. All cows were followed for a period of 15 weeks, from 3 weeks pre-calving to 12 weeks post-calving, while heifers were monitored for a period of 4 weeks leading up to the tentative week of breeding (pre-breeding period). For data analysis, we further divided cows into primiparous (N = 8) and multiparous (N = 7) cows owing to the significant difference in their milk yield. Assessment of reproductive performance showed that primiparous and multiparous cows tended to have lower pregnancy rates compared to heifers (P < 0.1). Plasma concentrations of β-hydroxybutyric acid were about 2-fold higher in multiparous cows than those of heifers in the week leading up to planned time of breeding (P < 0.05). Total bile acid levels during the pre-breeding period were higher in all lactating cows compared to heifers (P < 0.05) and glucose levels were lower in lactating cows (P < 0.05). Triglyceride concentrations were lowest in multiparous cows compared to both primiparous cows and nulliparous heifers (P < 0.05). In addition, lactating cows had higher concentrations of total-cholesterol and the high-density lipoprotein and low-density lipoprotein compared to heifers (P < 0.05). Conversely, concentrations of very low-density lipoprotein were lower in multiparous cows than primiparous cows and nulliparous heifers (P < 0.05). There were no differences in plasma glutathione levels, as measured by liquid chromatography-tandem mass spectrometry, between the groups, but the ferric reducing ability of plasma was higher in lactating cows compared to heifers (P < 0.05). These data establish the differences in the profile of metabolic and oxidative markers during the period approaching planned time of breeding in lactating cows compared to nulliparous heifers. As certain metabolites in the plasma have been shown to be represented in the ovarian follicular microenvironment, the unique profiles may influence reproductive performance in dairy cattle in different physiological stages.
Lameness is a severe welfare problem and a production-limiting disease in dairy farming. The objectives of this study were to determine prevalence of lameness and investigate cow- and herd-level factors associated with lameness in dairy cows housed in freestall barns in 3 Canadian provinces. A purposive sample of 40 Holstein-Friesian cows was selected from each of 141 dairy farms in Québec, Ontario, and Alberta. In total, 5,637 cows were scored once for lameness (presence of limping when walking). Data collected included information on individual cows (hock lesions, claw length, body condition score, parity, days in milk, and milk production), management practices (floor and stall cleaning routine, bedding routine, and footbath practices), and facility design (stall dimensions, stall base and bedding type, width of feed alley, flooring type, and slipperiness) hypothesized to be risk factors for lameness. Multilevel mixed logistic regression models were constructed (including farm as a random effect and province as a fixed effect). Herd-level lameness prevalence ranged from 0 to 69% (mean = 21%). Lameness prevalence increased with increasing parity; compared with first parity, cows in parity 2, 3, and ≥4 had 1.6, 3.3, and 4 times, respectively, higher odds of being lame. Furthermore, the odds of lameness were 1.6 times greater in cows with low body condition score (≤2.5) than in cows with a higher body condition score. In addition, injured hocks and overgrown claws were associated with 1.4- and 1.7-fold increased odds of being lame, respectively, whereas every 1 kg increase in daily milk production was associated with a 3% decrease in the odds of being lame. Lameness prevalence was higher in herds with ≤100 cows, but lower in barns with a sand or dirt stall base, or with bedding ≥2 cm deep. Cows exposed to very slippery floors had 2 times the odds of being lame compared with cows exposed to nonslippery floors. We attributed the wide range of lameness prevalence to the great variability in facilities and management practices among farms. Finally, we inferred that the prevalence of lameness could be decreased by improving management of multiparous, thin, or injured cows and by adopting management practices intended to improve cow comfort, namely the floor's slip resistance and the stall's lying surface.
A total of 144 006 weight (calculated from tape girth measurements) and height data records from Québec dairy heifers were analyzed using random regression to estimate growth curve parameters of Ayrshires, Brown Swiss and Holstein animals to permit prediction of individual heifer growth from 0 to 32 mo. There were, on average, 5.15 records per heifer (minimum 3 records, maximum 25 records). The body weight data were analyzed using linear and quadratic fixed and random regressions, with a powerof-the-mean (POM) function to model the residual variance. The POM was 1.2 for Holstein and Ayrshire and slightly less than 1 for Brown Swiss. Estimated body weight at 24 mo was 507, 564, 624 kg, for Ayrshires, Brown Swiss and Holstein, respectively. The height data were analyzed with a Brody, monomolecular non-linear growth curve model. Mature height was estimated to be 148 cm in both Holstein and Ayrshires, and 150 cm in Brown Swiss. Random regression models were shown to be able to predict individual growth, and can be incorporated in decision-support tools to help producers reducing the average age at first calving.
Selection in dairy cattle has been mainly on production traits for many years, but in fact lifetime profitability is the breeding goal. In this study lifetime profitability was calculated based on test day records. Prediction formulas for feed costs were determined taking into account relationships between milk yield, fat percentage, protein percentage, body weight, parity and stage of lactation. All costs and revenues were discounted to the birth date of the cow. Further, opportunity costs of postponed replacement were calculated per herd and subtracted from the discounted lifetime profit resulting in discounted lifetime profit adjusted for opportunity costs of postponed replacement (DLPOC). DLPOC was regressed on the estimated breeding values of the sire. The economic values per standard deviation were much larger for production traits and herd life than for conformation traits. Sensitivity analysis on quota opportunity costs (quota market value times interest rate on savings) showed that these costs have a large impact on the economic values of fat (110%) and protein yield (-15%). Only the economic value of herd life was sensitive (+/-6%) to differences (+/-20%) in overhead costs (all variable or fixed costs other than feed costs or rearing cost). Further it could be concluded that large, tall, deep cows were less profitable.
The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21 farms had dRI < -1 SD, 32 farms were between -1 SD and 0, 67 farms were between 0 and +1 SD, and 15 farms had dRI > +1 SD. The top 10% of the farms had a dRI greater than 0.170 €/L, whereas the bottom 10% farms had a dRI lower than 0.116 €/L. This stochastic approach allowed us to understand the relationships among the inputs of the studied dairy farms and to develop a ranking index for comparison purposes. The developed methodology may be improved by using more inputs at the dairy farm level and considering the actual cost to measure profitability.
Milk yield per cow has more than doubled in the previous 40 years and many cows now produce more than 20,000 kg of milk per lactation. The increase in production should be viewed with concern because: i) the increase in milk yield has been accompa-nied by declining fertility, increasing leg and metabolic problems and declining longevity; ii) there are unfavourable genetic corre-lations between milk yield and fertility, mastitis and other production diseases, indicating that deterioration in fertility and health is largely a consequence of selection for increased milk yield; and iii) high disease incidence, reduced fertility, decreased longevity and modification of normal behaviour are indicative of substantial decline in cow welfare. Improving welfare is important as good welfare is regarded by the public as indicative of sustainable systems and good product quality and may also be economically beneficial. Expansion of the Profitable Lifetime Index used in the UK to include mastitis resistance and fertility could increase economic response to selection by up to 80%, compared with selection for milk production alone. In the last 10 years, several breeding organisations in Europe and North America followed the example of Nordic Countries and have included improving fertility and reducing incidence of mastitis in their breeding objectives, but these efforts are still timid. A multi-trait selection programme in which improving health, fertility and other welfare traits are included in the breeding objective, and appropriately weighted relative to production traits, should be adopted by all breeding organisations motivated in their goal of improving welfare.
Computerized information systems can potentially help the dairy producer to deal with the increased complexity of decision making and availability of information in dairy farming. These systems, however, should be fully integrated to ensure a coordinated execution of dairy farming activities. A framework was developed, therefore, to support the creation of computerized management and control systems in dairy farming. Within this framework, a management and control system was defined as a network consisting of the management and control activities and the information flows that are involved in dairy farming. The management and control activities consist of a cycle of decision making, implementation, and assessment. These activities were classified according to level (strategic, tactical, operational, or regulatory) and sphere (breeding, health, nutrition, environment, milk production, fixed assets, labor, or finance). These activities can be performed by human beings or automated systems that are located on or off the farm. A large amount of information exchange exists among these management and control activities, between the overall management and control system, and between the physical farm environment and external agents. The interdependence among decisions at the various levels and spheres necessitates computerized management and control systems that are integrated and that support the exchange of information. The developed framework should facilitate the creation of such systems and could also act as a reference base for the analysis and improvement of existing dairy farm information systems.
Bovine mastitis is an economic and a welfare problem on dairy farms. The objective of this study was to estimate the costs of clinical mastitis (CM), having a special focus on the cost variation related to culling decisions. A dynamic optimization model was developed to determine an optimal replacement time of a mastitic cow and to estimate the costs of CM, taking into account the risk of premature culling and the uncertainty in CM prevalence. Six lactations were analyzed at monthly periods for Ayrshire and Holstein-Friesian breeds. The estimates reflect Finnish production conditions where mastitis is treated only by veterinarians. Biological parameters of the model were adapted from the literature and the Finnish dairy herd health recording system. Field data were used to produce the risk parameters of culling due to mastitis on commercial dairy farms. The model recommended treating the cows with CM and keeping them in most cases until their fifth lactation. A cheaper (-20%) heifer transferred the optimum to the previous lactation and a more expensive (+20%) heifer to the following lactation. Conditional on optimal replacements, the average cost of CM of an Ayrshire (Holstein-Friesian costs in parentheses) cow was €485 (€458), varying from €209 (€112) to €1,006 (€946). The costs were at the highest when the occurrence of CM was at a top yield phase. In the scenario where the risk of culling due to mastitis was included in the model, the average cost of CM was €596 (€623). Disposing of a young cow at the end of her first lactation month caused the highest costs. The costs converted to figures per cow-year were €121 (€147) with optimal cullings and €155 (€191) in the current Finnish conditions. Thus, the increase in the costs of CM due to premature cullings was 28% (30%.) The main cost sources were long-term production losses regardless of the culling decisions. Premature culling formed 20% (23%) of the total costs. To decrease the costs of CM, more emphasis should be given to hidden costs, especially the high cost of premature culling should be underlined.
A model to calculate the economic losses of mastitis on an average Dutch dairy farm was developed and used as base for a tool for farmers and advisors to calculate farm-specific economic losses of mastitis. The economic losses of a clinical case in a default situation were calculated as ¿210, varying from ¿164 to ¿235 depending on the month of lactation. The total economic losses of mastitis (subclinical and clinical) per cow present in a default situation varied between ¿65 and ¿182/cow per year depending on the bulk tank somatic cell count. The tool was used to measure perception of the total economic losses of mastitis on the farm and the farmers' assessment of the cost factors of mastitis on 78 dairy farms, of which 64 were used for further analyses. Most farmers (72%) expected their economic losses to be lower than those revealed by our calculation made with their farm information. Underestimating the economic losses of mastitis can be regarded as a general problem in the dairy sector. The average economic losses assessed by the farmers were ¿78/cow per year, but a large variation was given, ¿17¿198/cow per year. Although the average assessment of the farmers of the different cost factors is close to the default value, there is much variation. To improve the adoption rate of advice and lower the incidence of mastitis, it is important to show the farmers the economic losses of mastitis on their farm. The tool described in this paper can play a role in that process.
Lifetime records of 122,679 cows from 7557 herds, obtained from Mid States Dairy Records Processing Center (Ames, IA), were used to determine net income and net income for the planning horizon. With a planning horizon of five lactations for each cow, the estimated profit from the replacements was credited to each cow not surviving until fifth calving. Net income was defined as lifetime income minus costs. Net income for the planning horizon was defined as net income plus profit from replacements within the planning horizon. Income was from the sale of milk, calves, and culled cows. Costs were included for heifer rearing, feed, labor, and breeding. Longer herd life yielded greater profit for net income and net income for the planning horizon. The rate of increase in profit for longer herd life was reduced for net income for the planning horizon, which accounts for profit from cows replacing a culled cow compared with profit from net income. The relative economic value (phenotypic standard deviation basis) of production to herd life was 0.18:1 for net income and 0.46:1 for net income for the planning horizon. The relative value for herd life was overestimated by about 2.5 times when profit from replacements was not considered. Values for production relative to herd life increased for high milk prices and low feed prices. Lower prices for culled cows in combination with high prices for milk and feed increased the relative economic value of production.
The objectives of this presentation are to review results of our previous and on-going research with respect to the risk factors and consequences of poor reproductive performance in dairy cows, and to develop an economic framework to optimize decisions related to dairy cow reproductive performance. To make profitable breeding and replacement decisions, the farmer must account for factors including age, production level, lactation stage, pregnancy status, and disease history of the cows in the herd. Establishing the interrelationships among disease, milk yield, reproduction, and herd management is necessary for developing a decision model for disease treatment, insemination, and replacement.
Mastitis is the most prevalent production disease in dairy herds world-wide and is responsible for several production effects. Milk yield and composition can be affected by a more or less severe short-term depression and, in case of no cure, by a long-acting effect, and, sometimes, an overlapping effect to the next lactation. Summary values in the literature for losses of milk production were proposed at 375 kg for a clinical case (5% at the lactation level) and at 0.5 kg per 2-fold increase of crude SCC of a cow. Due to the withdrawal period after treatment, composition changes in milk can almost be neglected in economic calculations. Lethality rate for clinical mastitis is very low on the average, while anticipated culling occurs more frequently after clinical and subclinical mastitis (relative risk between 1.5 and 5.0). The economics of mastitis needs to be addressed at the farm level and, per se, depends on local and regional epidemiological, managerial and economic conditions. To assess the direct economic impact of mastitis, costs (i.e. extra resource use) and losses (i.e. reduced revenues) have to be aggregated. To support decision making for udder health control, it is necessary to use a marginal approach, based on the comparison of the losses avoided and the additional costs of modified plans, compared to the existing ones.
Genetic parameters for lifetime profit and some productive traits were estimated from records of 42,401 Holstein cows with first calving before May 1996 from Navarra and Basque Autonomous Regions of Spain. Profit from the first, first two, and first three lactations were tested as early measures of profitability. Profit prediction was tested for another population of 2127 cows using selection indexes (Type-Production and economic indexes) and multitrait analysis for directly predicting profit from first-lactation records. High genetic correlations of actual profit with estimated profit from the first two or first three lactation records, (0.97 and 0.99, respectively) suggest that lifetime profit can be accurately estimated from data in second lactation. Profit was positively correlated to production traits (0.79 to 0.83), functional herd life (0.38), mature body weight (0.25), and days in milk (0.35), but genetic correlation was found to be close to zero with calving interval. Complicated relationships among profit and economic traits (i.e., calving interval, days in milk, and functional herd life) were found. Although the correlation between calving interval and profit was near zero, calving interval was the most important trait after production in prediction of sire profit by a stepwise regression analysis. Profit breeding values from multitrait analysis obtained higher correlation (0.48) with actual profit than Spanish official Type-Production index ICO (0.44) and economic index MEG (0.46). A correlation of 0.49 between profit breeding values and the economic index MEG2002, where stature and calving interval were included as new traits, was obtained.
A proportional hazards model was used to investigate the phenotypic effect of traits other than production (TOP) on true and functional longevity across purebred and crossbred Holstein-Friesian and Jersey dairy cattle in registered and commercial herds in New Zealand. The hazard function was described as the product of a baseline hazard function and the time-independent effects of age at first calving, heterosis, proportion of breed, period of last calving relative to herdmates, and TOP; a time-dependent effect of herd-year was also included. The influence of TOP on functional longevity was assessed by adjusting true longevity for the time-independent effects of production values as well as the time-dependent effects of deviation of milk, fat, and protein yield within contemporary group. All analyses were stratified by breed, and separate analyses were carried out for registered or commercial herds. All TOP were significantly related to true and functional longevity. Obvious differences existed in the relative influence of individual TOP on longevity in registered or commercial herds. Of the individual TOP describing the physical characteristics of the cow, the udder-related TOP exhibited the largest influence on functional longevity. Farmer opinion explained the largest proportion of variation in true and functional longevity among cows. In commercial herds, the risk of culling in cows with very low farmer opinion was 1.5 to 2.0 times that in cows with average or high farmer opinion.
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.
The objective of this study was to compute economic values of traits using an empirical approach. The data set consisted of 193 257 lifetime records of Holstein and Ayrshire cows. Different profitability measurements were used as the dependent variables in covariance models to compute different sets of economic values. A kilogram genetic increase in fat production had economic values between $25 and $31 in Holstein herds and between $34 and $36 in Ayrshire herds using lifetime profit as the dependent variable. A unit genetic increase in conformation had the highest positive impact on profit ($176 in Holstein herds and $300 in Ayrshire herds) while a similar increase in capacity had a negative impact on profit (between $-30 and $-102 in Holstein herds and $-92 in Ayrshire herds). Using lifetime profit adjusted for the opportunity cost of postponed replacement reduced the influence of type traits on profit. Finally, profits of first lactations were used to study the consequences of changes in pricing systems that occurred in Quebec in August 1992. A kilogram genetic increase in protein production had negative economic values in the 1980s ($-3.70$ in Holstein herds and $-8.33 in Ayrshire herds) and positive economic values after August 1992 ($7.50 in Holstein herds and $12.83 in Ayrshire herds).
The objective was to provide an overview of heifer growth in Quebec and analyze its relationship with age at first calving. Heifer body weights, wither heights, body condition scores, and breeding events collected from 1993 to 2003 were obtained and combined with first lactation data, mature body weights, type classification final score and frame/capacity, and estimated breeding values for milk, conformation, frame/capacity, and Lifetime Profit Index. Analyses were restricted to 39 animal-level variables. The resulting data set consisted of 44 989 Holsteins and 2294 Ayrshires with data available for at least one variable. The average age at first calving was 26.5 and 27.1 mo for Holsteins and Ayrshires, respectively. For Holsteins, average daily gains in body weights were 0.79, 0.89, 0.87, and 0.70 kg d-1 for 0 to 4 mo (birth to weaning), 4 to 10 mo (to target onset of puberty), 10 to 14 mo (to first breeding), and 14 mo to pre-calving periods of growth, respectively. Body weights at 14 mo, conception, and post calving were 398, 464, and 601 kg, respectively, and relative to mature body weight were 59, 68 and 89%, respectively. Correlations with age at first calving, corrected for herd and year of birth effects, were positive for body weight at conception (0.69), body weight at first calving (0.31), and number of breedings (0.37), and negative for the average daily gain from conception to post calving (-0.38) and body weight at 14 mo relative to mature body weight (-0.20). For Ayrshires, body weights and average daily gains were lower than for Holsteins, but body weights relative to mature body weight and correlations were generally similar. Holsteins and Ayrshires calved substantially earlier and at a heavier body weight compared with Quebec data from the 1980s. The results of this study suggest that heifer growth may not be a limiting factor in reducing the average age at first calving in Québec.
The objectives were to investigate the effects of various environmental factors that may affect herd-life of Israeli Holsteins, including first-calving age and season, calving ease, number of progeny born, and service sire for first calving in complete and truncated records; and to estimate heritabilities and genetic correlations between herd-life and the other traits included in the Israeli breeding index. The basic data set consisted of 590,869 cows in milk recording herds with first freshening day between 1985 and at least 8 yr before the cut-off date of September 15, 2013. Herd-life was measured as days from first calving to culling. The phenotypic and genetic trends for herd-life were 5.7 and 16.8 d/yr. The genetic trend was almost linear, whereas the phenotypic trend showed 4 peaks and 3 valleys. Cows born in February and March had the shortest herd-life, whereas cows born in September had the longest herd-life. Herd-life was maximal with calving age of 23 mo, which is 1 mo less than the mean calving age, and minimal at 19 and 31 mo of calving age. Dystocia and twinning on first-parity calving reduced herd-life by approximately180 and 120 d, but the interaction effect increased herd-life by 140 d. Heritability for herd-life was 0.14. Despite the fact that the service sire effect was significant in the fixed model analysis, service sire effect accounted for <0.05% of the total variance. In the analysis of 1,431,938 truncated records, the effects of dystocia and twinning rate were very similar but less than 50% of the effects found in the analysis of complete records. Pregnancy at the truncation date increased expected herd-life by 432 d. The correlation between actual herd-life and predicted herd-life based on truncated records was 0.44. Genetic correlations between the truncated records and actual herd-life were 0.75 for records truncated after 6 mo but approached unity for records truncated after 3 yr. The genetic correlations of herd-life with first-parity milk, fat, and protein production, somatic cell score (SCS), and female fertility were all positive, except for SCS, in which negative values are economically favorable. The highest correlations with herd-life in absolute value were with female fertility and SCS.
Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Profit equations or functions that reflect the realized profitability of cows have been used in the literature to determine the relative importance of different variables such as milk yield and herd life. In all profit equations, the opportunity cost of postponed replacement, which reflects the profit sacrificed on an average replacement cow by keeping the cow, has been omitted.In this study, three profit equations were compared: total lifetime profit; total lifetime profit accounting for opportunity costs, and profit per day of herd life. Linear regression was used to determine the relative value of first lactation production and herd life on profitability in simulated data.When accounting for opportunity costs, the value of an additional day of herd life was equal to that of a 4.0 kg higher first lactation milk production. The relative value of herd life was overestimated by 260% when opportunity costs of postponed replacement were not accounted for. The need to account for the opportunity cost of postponed replacement in calculating economic weights is clearly demonstrated.
This investigation was to define profit functions from cow performance; to establish relationships among profit func- tions; and to determine effects of prices on characteristics of the functions and on rank of cows. Data were one to three lactations of 182 cows in the Beltsville Holstein herd. Three subsets of the data were used: 1) complete data set (182 cows), 2) cows with an opportunity for three lactations (92 cows), and 3) cows that completed three lactations (47 cows). Four profit functions were com- puted from income and expense for each cow. The correlation between profit per day and income/expense was .98, while the correlation between profit per day and total profit ranged from .76 to .87 for the two largest data sets. The correla- tions among profit per day for 1970, 1980, and 1985 price estimates ranged from .98 to 1.00. Correlations among estimates of total profit based on these prices were similarly high. Conclusions are: 1) Defining economic efficiency for dairy cattle as the linear
The objectives of this study were to evaluate and compare alternative measures of individual cow lifetime profitability and to determine what lifetime traits are significantly related to profitability of dairy cattle. Profitability measures considered were: 1) lifetime milk revenue minus lifetime feed costs (MMF); 2) lifetime profit (LP); 3) discounted lifetime profit (DLP); 4) annualized DLP per year of total life (ADLPLTL); 5) annualized DLP per year of productive life (ADLPLPL); 6) DLP adjusted for opportunity cost of postponed replacement (DLPOC), and 7) economic efficiency (EF). Data for this study consisted of 1112 lifetime performance records of Holstein cows from the National Cooperative Dairy Cattle Breeding Project, which was implemented by Agriculture Canada in 1972. Correlations were highest among MMF, LP, and DLP. EF had slightly lower correlations with MMF, LP, and DLP, but higher with ADLPLTL and ADLPLPL. ADLPLPL and DLPOC had low correlations with all other measures. DLPOC was recommended as the best because it considered the opportunity costs of postponed replacement. For DLPOC, average milk revenue per lactation was found to be the most important income trait, followed by length of productive life. Days dry (average over completed lactations) had the greatest negative impact on profitability. Age at first calving and average number of veterinary treatments for reproductive diseases over lactations were both negative contributions to profit. Lifetime traits accounted for 65% of variation in DLPOC.
Opportunity cost of postponed replacement is the income forfeited by keeping a cow an additional lactation and is estimated by the income produced by an average replacement. We studied the effect of refining estimated net income by calculation of an opportunity cost for each lactation. After edits, the data consisted of records for 2,982,001 Holstein cows from herds classified between 1983 and 1992.Prediction factors were developed for net income and herd life so that opportunity cost could be estimated from cows with shorter herd-life opportunities. Within-herd correlations of net income estimated from 84 mo of opportunity for herd life compared with that predicted from cows alive at 36, 48, 60, and 72 mo were .46, .59, .72, and .76, respectively. Corresponding correlations for predicted herd life totals at the same ages were .28, .36, .41. and .47.Opportunity costs that were specific to lactation-herd-year were $35 higher than opportunity cost from first freshening to all lactations for 84 mo of opportunity. Higher costs reflect phenotypic trends of $31 more net income over 13 fewer d of herd life for cows with 84 mo of herd-life opportunity. Accurate adjustment of net income totals for opportunity cost is necessary when economic weights are developed for traits in the aggregate genotype.
Relationships between production, linear type traits, relative net income (with opportunity for calving through 60 mo of age), relative net income adjusted for opportunity cost, and days of productive life were examined in Holsteins. Records were available for 7479 grade and 64,245 registered cows. Correlations between relative net income adjusted for opportunity cost and first lactation product value were .55 and .59 for grade or registered cows, .21 and .23 between relative net income adjusted for opportunity cost and dairy form, and .16 and .20 between relative net income adjusted for opportunity cost and final score. Partial correlations (adjusted for first lactation product value) were lower than simple correlations for dairy form and final score but were higher for fore udder attachment and udder depth in both grade and registered cows. Prediction of relative net income, relative net income adjusted for opportunity cost, and days of productive life with first lactation product value and an additional type trait showed very little increase in R2 over use of first lactation product value alone. The increase in R2 was greater for days of productive life than for the two net income functions. The most profitable cows were high producers. Physical characteristics of the cow did not enable prediction of lifetime profit with appreciably greater accuracy than production alone.
Direct genetic evaluation of profit was investigated as an alternative to a selection index. PROFk was defined as (net income)/(food requirement) until the start of the kth lactation, for k = 2 to 6. Genetic parameters such as heritabilities and genetic correlations were estimated for profit traits for Australian Holstein-Friesian and jersey dairy cattle. Heritabilities for profit until the start of a given lactation were moderate, ranging from 0·12 (for profit until the start of the second lactation in Holsteins) to 0·31 (profit until the start of the third lactation in Jerseys). Genetic correlations between profit traits were very high, and approached unity for most pairs of traits, so that profit early and late in herd life were nearly the same trait. Genetic correlations between profit traits and stayabilities until a given lactation were high, ranging from 0·71 to 0·97. Genetic correlations between profit traits and first lactation milk yield traits were approximately 0·80 for Holsteins and 0·90 for Jerseys. A single analysis urns carried out for lifetime profit using all data, including cows that were still in the herd at the time of data collection. Heritabilities were 0·13 for Holsteins and 0·19 for Jerseys. Genetic correlations between lifetime profit and first lactation yields were high. For the selection of dairy bulls, a multivariate analysis on a milk yield trait (e.g. protein yield) and profit until the last known lactation of bulls' progeny was suggested.
This study contributes to the research literature by providing a new formulation for the cow replacement problem, and it also contributes to the Extension deliverables by providing a user-friendly decision support system tool that would more likely be adopted and applied for practical decision making. The cow value, its related values of a new pregnancy and a pregnancy loss, and their associated replacement policies determine profitability in dairy farming. One objective of this study was to present a simple, interactive, dynamic, and robust formulation of the cow value and the replacement problem, including expectancy of the future production of the cow and the genetic gain of the replacement. The proven hypothesis of this study was that all the above requirements could be achieved by using a Markov chain algorithm. The Markov chain model allowed (1) calculation of a forward expected value of a studied cow and its replacement; (2) use of a single model (the Markov chain) to calculate both the replacement policies and the herd statistics; (3) use of a predefined, preestablished farm reproductive replacement policy; (4) inclusion of a farmer's assessment of the expected future performance of a cow; (5) inclusion of a farmer's assessment of genetic gain with a replacement; and (6) use of a simple spreadsheet or an online system to implement the decision support system. Results clearly demonstrated that the decision policies found with the Markov chain model were consistent with more complex dynamic programming models. The final user-friendly decision support tool is available at http://dairymgt.info/ → Tools → The Economic Value of a Dairy Cow. This tool calculates the cow value instantaneously and is highly interactive, dynamic, and robust. When a Wisconsin dairy farm was studied using the model, the solution policy called for replacing nonpregnant cows 11 mo after calving or months in milk (MIM) if in the first lactation and 9 MIM if in later lactations. The cow value for an average second-lactation cow was as follows: (1) when nonpregnant, (a) $897 in MIM = 1 and (b) $68 in MIM = 8; (2) when the cow just became pregnant,(a) $889 for a pregnancy in MIM = 3 and (b) $298 for a pregnancy in MIM = 8; and (3) the value of a pregnancy loss when a cow became pregnant in MIM = 5 was (a) $221 when the loss was in the first month of pregnancy and (b) $897 when the loss was in the ninth month of pregnancy. The cow value indicated pregnant cows should be kept. The expected future production of a cow with respect to a similar average cow was an important determinant in the cow replacement decision. The expected production in the rest of the lactation was more important for nonpregnant cows, and the expected production in successive lactations was more important for pregnant cows. A 120% expected milk production for a cow with MIM = 16 and 6 mo pregnant in the present lactation or in successive lactations determined between 1.52 and 6.48 times the cow value, respectively, of an average production cow. The cow value decreased by $211 for every 1 percentage point of expected genetic gain of the replacement. A break-even analysis of the cow value with respect to expected milk production of an average second-parity cow indicated that (1) nonpregnant cows in MIM = 1 and 8 could still remain in the herd if they produced at least 84 and 98% in the present lactation or if they produced at least 78 and 97% in future lactations, respectively; and (2) cows becoming pregnant in MIM = 5 would require at least 64% of milk production in the rest of the lactation or 93% in successive lactations to remain in the herd.
Some years ago, systematic research was initiated to improve quantitative insight into the economic impact of diseases and disease control in livestock. This paper deals with the financial loss at farm level caused by reproductive failure in dairy cattle. Economic calculations concerning the calving interval of cows which differ in age, relative production level and persistence of milk production during lactation were made. On average, an optimal interval of 1 year or less was established, while the loss per day lengthening of the interval (norm-loss) amounted to 1–2 Dutch guilders (Dfl.). Also, 676 norm-losses due to forced replacement because of reproductive failure were determined, dependent on age and relative production level of the culled cow and average herd life of the farm. On average, this norm-loss was determined to be almost Dfl. 500 per culled cow. Based on 71 farms in an investigation in the province of Overijssel, total loss per farm was determined, using the calving interval data, forced replacement data and norm-loss rate described above. On average, the calculated loss amounted to Dfl. 63 per cow per year, of which Dfl. 35.50 resulted from sub-optimal calving interval and Dfl. 27.50 from forced replacement due to reproductive failure. Costs for veterinary treatment and drugs were not included in these elements. Total loss due to reproductive failure was estimated to average about Dfl. 80 per cow per year, which equals about 2% of the gross production value or 10% of an average farmer's income.Finally, considerable differences in loss between farms have been determined. The difference between the 20% of farms with the highest and the 20% of farms with the lowest calculated loss is even more than the average loss. Thus improvment may be achieved on many farms, e.g., with the help of a herd health programme.
A total of 850 cows distributed among 13 commercial Holstein herds were involved in this study to compare the effects of 2 different dry period (DP) management strategies on milk and component yields as well as body condition score (BCS) over complete lactations. Within each herd and every 2 mo, cows were assigned to a short (35 d dry; SDP) or conventional (60 d dry; CDP) DP management based on previous lactation 305-d milk yield, predicted calving interval, and parity: primiparous (n=414) and multiparous (n=436). Cows assigned to CDP were fed a far-off dry cow ration from dry-off until 21 d prepartum, and were then switched to a precalving ration. Cows assigned to SDP were fed the precalving ration throughout their DP. Rations were different across herds, but the late-lactation, precalving, and early lactation rations were identical for both treatment groups within each herd. Additional milk was obtained at the end of lactation from cows assigned to SDP due to the extended lactation. Average daily milk yield in the following lactation was not different between treatments for third- or greater-lactation cows, but was significantly decreased in second-lactation SDP cows. However, when expressed as energy-corrected milk, this difference was not significant. Although lower for primiparous than multiparous cows, body weight and BCS were not affected by DP management strategy. Milk production and BCS responses to treatments varied among herds. Results from the present study suggest that a short DP management strategy could be more appropriate for today's dairy cows, although not suitable for all cows or all herds.
Costs and feasibility of extensive sample collection and processing are major obstacles to mastitis epidemiology research. Studies are often consequentially limited, and fundamental mastitis researchers rarely have the opportunity to conduct their work in epidemiologically valid populations. To mitigate these limitations, the Canadian Bovine Mastitis Research Network has optimized research funds by creating a data collection platform to provide epidemiologically meaningful data for several simultaneous research endeavors. This platform consists of a National Cohort of Dairy Farms (NCDF), Mastitis Laboratory Network, and Mastitis Pathogen Culture Collection. This paper describes the implementation and operation of the NCDF, explains its sampling protocols and data collection, and documents characteristics, strengths and limitations of these data for current and potential users. The NCDF comprises 91 commercial dairy farms in 6 provinces sampled over a 2-yr period. Primarily Holstein-Friesian herds participating in Dairy Herd Improvement milk recording were selected in order to achieve a uniform distribution among 3 strata of bulk tank somatic cell counts and to reflect regional proportions of freestall housing systems. Standardized protocols were implemented for repeated milk samplings on clinical mastitis cases, fresh and randomly selected lactating cows, and cows at dry-off and after calving. Just fewer than 133,000 milk samples were collected. Demographic and production data were recorded at individual cow and farm levels. Health management data are documented and extensive questionnaire data detailing farm management and cleanliness information are also captured. The Laboratory Network represents coordinated regional mastitis bacteriology laboratories using standardized procedures. The Culture Collection archives isolates recovered from intramammary infections of cows in the NCDF and contains over 16,500 isolates, all epidemiologically cross-referenced between linked databases. The NCDF is similar to Canadian dairies in relation to mean herd size, average production, and freestall percentages. Pathogen recovery was greater than anticipated, particularly for coagulase-negative staphylococci and Corynebacterium spp. International scientists are encouraged to use this extensive archive of data and material to enhance their own mastitis research.
The objective of this observational study was to investigate the risk factors for metritis, purulent vaginal discharge, and cytological endometritis. The hypothesis was that purulent vaginal discharge and cytological endometritis would have different risk factors because they represent distinct manifestations of uterine disease. Data generated from 1,363 Holstein cows (3 herds) enrolled in a randomized clinical trial were used. Calving history, periparturient disease incidence, and body condition score at calving and at 63 d in milk (DIM) were recorded. Serum nonesterified fatty acid concentration was measured once during the week before expected calving. Serum nonesterified fatty acid, β-hydroxybutyric acid, and haptoglobin (Hapto) concentrations were measured at 4 ± 3, 11 ± 3, and 18 ± 3 DIM. Serum progesterone concentration was measured at 21 ± 3, 35 ± 3, 49 ± 3, and 63 ± 3 DIM. Metritis was diagnosed by farm managers within the first 20 DIM using a standardized definition. Cows were examined at 35 ± 3 DIM by a veterinarian for purulent vaginal discharge (mucopurulent or worse vaginal discharge; Metricheck device) and cytological endometritis (≥ 6% polymorphonuclear cells on endometrial cytology; cytobrush device). Statistical analyses were performed using multivariable logistic regression models for each disease, accounting for the random effect of herd. Risk factors for metritis included increased nonesterified fatty acid prepartum (≥ 0.6 mmol/L), dystocia, retained placenta, and increased Hapto in the first week postpartum (≥ 0.8 g/L). Risk factors for purulent vaginal discharge included twinning, dystocia, metritis, and increased Hapto (≥ 0.8 g/L) in the first week postpartum. Risk factors for cytological endometritis included low body condition score at parturition (≤ 2.75), hyperketonemia (≥ 1,100 μmol/L), and increased Hapto (≥ 0.8 g/L) in the first week postpartum. These results support the hypothesis that some of the risk factors for purulent vaginal discharge and cytological endometritis are different, which supports that they are distinct manifestations of uterine disease.
Economic losses due to common health problems in dairy cattle were investigated in 90 Friesian/Holstein herds (average size 152 cows), which calved in England during the 1992/1993 season with an average annual yield of about 6000 l per cow. By using only the direct costs of common production diseases and other health problems (mastitis, lameness, vulval discharge, treatments for oestrus-not-observed, retained foetal membranes, milk fever, twinning, calf mortality and aid at calving), the cost of ill health in a 100 cow herd with average rates of these problems (compared with target levels) was estimated at Pounds 6300 per year. The costs ranged from Pounds 1200 (average of the top 10%) to Pounds 13600 (average of the worst 10% of the herds). The main losses were due to mastitis and lameness (38 and 27% of health cost, respectively).
Relationships between measures for reproductive performance and net revenue were studied using data that had been generated by a stochastic model in a computer simulation program for Ontario dairy herds. These measures included projected calving interval, involuntary culling rate, and adjusted calving interval. Adjusted calving interval was calculated by dividing the projected calving interval for pregnant cows by the fraction of the cows that were not culled for reproductive failure. The regression of adjusted calving interval on net revenue had an R2 of 0.72, which was higher than the R2 of 0.59 obtained by the regression of projected calving interval on net revenue. Hence, the estimation of financial losses from suboptimal reproductive performance was more accurate when adjusted calving interval was used as a measure of this performance than when projected calving interval was used. This difference is because projected calving interval did not consider cows that were culled for reproductive reasons, but those cows contributed to a reduction in profit because of suboptimal reproductive performance. The highest R2 (0.78) was obtained with a model that included projected calving interval and involuntary culling rate. However, use of that model might not be practical because herd operators differ in their ability to distinguish between involuntary and voluntary culling. The mean reduction in net revenue from a 1-d increase in adjusted calving interval was estimated at $4.7 (Canadian) per cow. This economic value was not significantly affected by level of adjusted calving interval, carcass price, feed costs, cost of a replacement heifer, or milk price.
Otherwise well-researched definitions of breeding objectives and selection criteria may never be used in practice if those definitions do not take into account the perceptions and wishes of the breeders for whom they are designed. Finding selection criteria that are widely accepted and implemented is a daunting task that requires considerable time and full interaction between the scientists and the industry. We review various aspects of this process and, in particular, how scientific principles can be used to ensure that the outcome best meets both the perceptions and needs of the users while remaining as close as possible to the technical economic optimum. Alternative methods of presenting and delivering selection indexes, such as index expression, index formulation, focus on response to selection rather than on index weights, construction of component indexes, and the use of direct accounting for costs of constraints rather than rescaling methods can all help in improving acceptance of an index. Development and implementation of selection criteria also involve consideration of the selective mating decisions that form an integral part of selection decisions in the field. The technical basis of factors that foster emphasis on individual mating decisions in the field are discussed in relation to formulation of the breeding goal and selection index and in relation to nonlinear economic and genetic parameters. Strategies that focus on use of a linear index for the selection of sires and dams followed by selective mating of selected parents have the greatest potential for implementation in the industry. We focus on examples taken from the Canadian dairy industry, but principles apply generally.
The report upon which the current discussion is based was prepared in response to the increasing interest of the dairy industry in the recording of clinical disease data. The major objective was to introduce guidelines and standards for the recording and presentation of the diseases of dairy cattle. Eight clinically identifiable diseases of economic importance to the dairy industry were considered: milk fever, retained placenta, metritis, ketosis, left displaced abomasum, cystic ovarian disease, lameness, and clinical mastitis. Standardized definitions for these diseases were established through consultation with industry partners. Two approaches to summarization and reporting were proposed. For retrospective analysis, which is used when historical data are summarized for genetic evaluation for example, lactational incidence risk (cumulative incidence) has been recommended. For current analysis, which is used for herd health monitoring, a true incidence rate has been recommended. Milk fever and retained placenta were exceptions to the latter because of their short periods of risk. For these two diseases, lactational incidence risks are reported.
Logistic regression models were used to examine the relationship between milk yield and incidence of certain disorders. Lactations (n = 2197) of 1074 Holstein-Friesian cows from 10 dairies (25 to 146 cows per dairy) in Lower Saxony were studied. The 305-d yield from the previous and current lactations served as the standards for milk yield. Eight disorder complexes were considered: retained placenta, metritis, ovarian cysts, mastitis, claw diseases, milk fever, ketosis, and displaced abomasum. Each disorder complex was modeled separately. In addition to milk yield, the influences of the lactation number, the calving season and the other disorder complexes were examined with the "herd" factor taken into account. A correlation between retained placenta, mastitis, and milk fever to milk yield during the previous lactation was found to be probable and for ketosis and displaced abomasum such a correlation was found to be possible. A connection to the yield in the current lactation was shown for ovarian cysts, claw diseases, and milk fever. No relationship to milk yield existed for metritis. An influence of the lactation number was also demonstrated in various models. Single models allowed a demonstration of the influences of both milk yield and lactation number. Limitations of the model types are discussed.
The objective of the current research was to examine the association of herd level disease incidence with the return over feed (ROF) (milk income minus feed cost) herd profit index offered through Canwest Dairy Herd Improvement. The lactational incidence risks (LIR) for displaced abomasum, retained placenta, clinical mastitis, milk fever, clinical ketosis, and lameness submitted by producers (n = 48) were similar to previous reports. However, there was no negative association of clinical disease LIR's with ROE Subclinical ketosis and subclinical mastitis cumulative incidence were determined during the early postpartum period by using a cow-side test for betahydroxybutyrate in milk and the California Mastitis Test, respectively. Subclinical mastitis was not associated with ROE However, a unit increase in the cumulative incidence of subclinical ketosis was associated with a decrease of dollars 0.015/cow/day in the ROE The results highlight the economic significance that subclinical ketosis may have in Ontario dairy herds.
Monte Carlo simulation was used to predict the long-term financial performance related to the technical performance of dairy herds. The indicators addressed were derived from data collected routinely in the herd. They indicated technical performance that can be affected by the farmer or the consultant, and they were derived from expected cause-effect relations between technical performance and financial performance at the herd level. The study included the indicators shape of lactation curve, reproduction efficiency, heifer management, variation between cows in lactation curve persistency, mortality in cows and calves, dynamics of body condition, and somatic cell counts. Each indicator was defined by 2 or 3 levels, and 2- and 3-factor interactions were included in the simulation experiment, which included 72 scenarios. Each scenario was replicated 200 times, and the resulting gross margin per cow was analyzed as the measure of financial performance. The potential effects of the selected indicators on the gross margin were estimated by means of an ANOVA. The final model allowed estimation of the financial value of specific changes within the key performance indicators. This study indicated that improving the shape of the herd-level lactation curve by 1 quartile was associated with an increase in gross margin of euro 227 per cow year. This represents 53% of the additional available gross margin associated with all the management changes included in the study. The improved herd-level lactation curve increased the gross margin 2.6 times more than improved reproduction efficiency, which again increased the gross margin 2.6 to 5.9 times more than improved management related to heifers, body condition score, mortality, and somatic cell counts. These results were implemented in a simple "metamodel" that used data extracted from ordinary management software to predict herd-specific financial performance related to major management changes. The metamodel was derived from systematic experiments with a complex simulation model that was used directly for advanced herd-specific decision support. We demonstrated the use of these key performance indicators to forecast the financial consequences of different "what-if" herd management options, with emphasis on herd health economics.
No nationwide studies of the incidence rate of clinical mastitis (IRCM) have been conducted in Canada. Because the IRCM and distribution of mastitis-causing bacteria may show substantial geographic variation, the primary objective of this study was to determine regional pathogen-specific IRCM on Canadian dairy farms. Additionally, the association of pathogen-specific IRCM with bulk milk somatic cell count (BMSCC) and barn type were determined. In total, 106 dairy farms in 10 provinces of Canada participated in the study for a period of 1 yr. Participating producers recorded 3,149 cases of clinical mastitis. The most frequently isolated mastitis pathogens were Staphylococcus aureus, Escherichia coli, Streptococcus uberis, and coagulase-negative staphylococci. Overall mean and median IRCM were 23.0 and 16.7 cases per 100 cow-years in the selected herds, respectively, with a range from 0.7 to 97.4 per herd. No association between BMSCC and overall IRCM was found, but E. coli and culture-negative IRCM were highest and Staph. aureus IRCM was lowest in low and medium BMSCC herds. Staphylococcus aureus, Strep. uberis, and Streptococcus dysgalactiae IRCM were lowest in the Western provinces. Staphylococcus aureus and Strep. dysgalactiae IRCM were highest in Québec. Cows in tie-stalls had higher incidences of Staph. aureus, Strep. uberis, coagulase-negative staphylococci, and other streptococcal IRCM compared with those in free-stalls, whereas cows in free stalls had higher Klebsiella spp. and E. coli IRCM than those in tie-stall barns. The focus of mastitis prevention and control programs should differ between regions and should be tailored to farms based on housing type and BMSCC.
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