Effects of extending dairy cow longevity by adjusted reproduction management decisions on partial net return and greenhouse gas emissions: A dynamic stochastic herd simulation study.

Prolonging dairy cattle longevity is regarded as one of the options to contribute to more sustainable milk production. Since failure to conceive is one of the main reasons for culling, this study investigates how adjustments in reproduction management affect partial net return at herd level and greenhouse gas emissions per unit of milk, using a dynamic stochastic simulation model. The effects of reproduction decisions that extend cattle longevity on milk yield, calving interval and pregnancy rate were derived from actual performance of Dutch commercial dairy cows over multiple lactations. The model simulated lactations, calving and health status events of individual cows for herds of 100 cows. Scenarios evaluated differed in the maximum number of consecutive artificial insemination (AI) attempts (4, 5 or 6 services), or the production threshold (20, 15, or 10 kg milk per day) at which cows that failed to conceive are culled (reproductive culling). Annual partial net return was computed from revenues of sold milk, calves and slaughtered cows, and the costs from feed consumption, rearing replacement heifers, AI services and treatment for clinical mastitis and lameness. Greenhouse gas emissions were computed for feed production, enteric fermentation, and manure management, and were expressed as total CO2 equivalents. Average age at culling increased with an increased maximum number of AI services. This increase was larger when going from a maximum of 4 to 5 AI attempts (108 d) than from a maximum of 5 to 6 attempts (47 d). Similarly, the average age at culling increased from 1,968 to 2,040 and 2,132 d when the threshold for reproductive culling decreased from 20, to 15 and 10 kg milk per day, respectively. Average annual partial net return increased by 1.1% from €165,850 per 100 cows at a maximum of 4 AIs to €167,670 per 100 cows at a maximum of 6 AIs, and increased by 4.3% from €161,210 per 100 cows at a reproductive culling threshold of 10 kg/day to €168,190 per 100 cows at a threshold of 20 kg/day. Greenhouse gas emissions decreased by 1.2% from 0.926 to 0.915 kg CO2 equivalents per kg fat-and-protein-corrected milk (FPCM) with an increase in a maximum number of AIs from 4 to 6 AIs. Conversely, greenhouse gas emissions increased by 0.2% from 0.926 kg at a threshold of reproductive culling of 20 kg/day to 0.928 kg CO2 equivalents per kg FPCM at a threshold of 10 kg/day. Although lowering the threshold for reproductive culling has the potential to extend cattle longevity more than increasing the maximum number of AI services, only the increase in AI services benefits a farm's partial net return, while reducing greenhouse gas emissions.

Economic impact of subclinical mastitis treatment in early lactation using intramammary nisin.

Treatment of subclinical mastitis (SCM) during lactation is rarely recommended due to concerns related to both antimicrobial usage and the costs associated with milk discard. Nisin is a naturally produced antimicrobial peptide with a gram-positive spectrum that, when given to dairy cows, does not require milk discard. We evaluated the economic impact of the treatment of SCM during early lactation using a nisin-based intramammary treatment under different scenarios that included various treatment costs, milk prices, and cure rates. We stochastically simulated the dynamics of SCM detected during the first week of lactation. The net economic impact was expressed in US dollars per case. The probabilities of an event and their related costs were estimated using a model that was based on pathogen-specific assumptions selected from peer-reviewed articles. Nisin cure rates were based on results of pivotal studies included in the US Food and Drug Administration (FDA) approval submission. Based on our model, the average cost of a case of intramammary infection (i.e., only true-positive cases) in early lactation was $170 (90% = $148-$187), whereas the cost of a clinical mastitis case was $521 (90% range = $435-$581). Both estimates varied with etiology, parity, and stage of lactation. When comparing the net cost of SCM cases (i.e., CMT-positive tests) detected during the first week of lactation, nisin treatment generated an average positive economic impact of $19 per CMT-positive case. The use of nisin to treat SCM was beneficial 93% of the time. Based on the sensitivity analysis, treatment would result in an economically beneficial outcome for 95% and 73% of multiparous and primiparous cows, respectively. At the herd level, use of intramammary nisin to treat SCM in cows in early lactation was economically beneficial in most tested scenarios. However, the economic impact was highly influenced by factors such as rate of bacteriological cure, cost of treatment, and parity of the affected animal. These factors should be considered when deciding to use nisin as a treatment for SCM.

Smallholder milk-quality awareness in Indonesian dairy farms.

In most low- and middle-income countries, milk is produced by smallholders, thereby contributing to the livelihood of their households. With the increasing importance of milk production in these countries, it is essential that milk quality is of a high level to ensure a safe product for consumers. It is, however, unclear whether smallholder dairy farmers are aware of the quality of their milk. The aim of this cross-sectional study was to gain insight on Indonesian smallholder dairy farmer awareness of milk quality parameters and to identify factors associated with the total plate count (TPC) and somatic cell count (SCC). A stratified sampling method was used to select smallholder farms in 4 districts in West Java, Indonesia, that were interviewed between August and September 2017. Factors putatively associated with awareness of TPC were investigated with multinomial regression models, whereas a Firth-type logistic regression was applied to identify factors associated with SCC awareness. Of the total 600 farmers surveyed, 264 (44%), 109 (18%), 170 (28%), 111 (19%), and 23 (4%) farmers were aware of TPC, total solid, fat content, milk density, and SCC, respectively, but did not know its value. Those that were conceptually aware of these quality parameters were generally unaware of their value. Furthermore, this study revealed that the following variables were significantly associated with dairy farmers' awareness of TPC: cooperative to which the farmer belonged, distance to neighboring dairy farmer, technology adoption index, TPC as the most important quality factor for the buyer, milk production information from cooperatives, and cow health information from veterinarians. Similarly, cooperative, dairy business experience, and milk quality test adoption were significantly associated with dairy farmers' awareness of SCC. Cooperative was the only variable that was significant in both final statistical models. This indicates that cooperatives play an important role in increasing farmer awareness of milk quality parameters in these smallholder dairies. This may be valid for other regions in the world also where milk production is dominated by smallholder dairy farmers.

Development of resilience indicator traits based on daily step count data for dairy cattle breeding.

BACKGROUND: Resilient animals are minimally affected by disturbances, such as diseases and heat stress, and quickly recover. Daily activity data can potentially indicate resilience, because resilient animals likely keep variations due to disturbances that threat animal homeostasis at a low magnitude. We used daily step count of cows to define resilience indicators based on theory, exploratory analysis and literature, and then investigated if they can be used to genetically improve resilience by estimating heritability and repeatability, and genetic associations with other resilience-related traits, i.e. health traits, longevity, fertility, and body condition score (BCS). RESULTS: Two groups of resilience indicators were defined: indicators describing (1) mean step count at different lactation stages for individual cows, and (2) fluctuations in step count from individual step count curves. Heritability estimates were highest for resilience indicators describing mean step count, from 0.22 for the 2-week period pre-partum to 0.45 for the whole lactation. High mean step count was consistently, but weakly, genetically correlated with good health, fertility, and longevity, and high BCS. Heritability estimates of resilience indicators describing fluctuations ranged from 0.01 for number of step count drops to 0.15 for the mean of negative residuals from individual curves. Genetic correlations with health traits, longevity, fertility, and BCS were mostly weak, but were moderate and favorable for autocorrelation of residuals (- 0.33 to - 0.44) and number of step count drops (- 0.44 to - 0.56) with hoof health, fertility, and BCS. Resilience indicators describing variability of residuals and mean of negative residuals showed strong genetic correlations with mean step count (0.86 to 0.95, absolute), which suggests that adjustment for step count level is needed. After adjustment, 'mean of negative residuals' was highly genetically correlated with hoof health, fertility, and BCS. CONCLUSIONS: Mean step count, autocorrelation and mean of negative residuals showed most potential as resilience indicators based on resilience theory, heritability, and genetic associations with health, fertility, and body condition score. Other resilience indicators were heritable, but had unfavorable genetic correlations with several health traits. This study is an important first step in the exploration of the use of activity data to breed more resilient livestock.

The economics of sensor-based management of dairy cow suboptimal mobility.

Suboptimal mobility (SOM) is a costly health condition in dairy production. Current SOM management is based on visual SOM detection by farm staff. This often leads to cows with severe SOM being detected and promptly treated, whereas the detection and subsequent treatment of cows with mild SOM is delayed or nonexistent resulting in prolonged cases of mild SOM being treated only at half-year routine hoof trimming. Using automatic SOM detection sensors may improve early detection of mild SOM allowing for improved SOM management. However, the economic value of these sensors used for sensor-based SOM management are not well known. The objective of this study was to evaluate the added economic value of automatic SOM detection sensors. A recently developed bioeconomic simulation model was extended to simulate a farm without and with automatic SOM detection sensors and farm economic performance comparisons were drawn. Moreover, for the farm with sensors, novel sensor-based SOM management strategies were designed. Within these sensor based-management strategies multiple scenarios with different sensor performance in terms of sensitivity, specificity, and mobility score detection were simulated. A new alert prioritization method was also introduced. Results from this study provide insights on the economic tradeoffs in production losses and additional labor costs for the different sensor-based management strategies, sensor performances, and alert prioritization. Simulations show that the added economic value of automatic SOM detection sensors are sensitive to the sensor-based management strategies, sensor performance, and the introduced alert prioritization method. Thirty-nine of the 80 simulated scenarios obtained a positive mean net economic sensor effect: the highest was €6,360 per year (€51/cow per yr). Based on evidence from our scenarios we suggest that twice-yearly routine hoof trimming with the addition of automatic SOM detection sensors should be replaced with cow specific hoof trimmer treatments following SOM detection by the sensor. Earlier detection and subsequent treatment of mild SOM resulted in economic gains when the alert prioritization method was introduced. Implementing automatic SOM detection sensor systems allows for many options to alter SOM management where improvements in farm economic performance can be achieved in combination with improved cow mobility. The implications for future research are discussed.

Estimating the nonlinear association of online somatic cell count, lactate dehydrogenase, and electrical conductivity with milk yield.

Reduction of milk yield is one of the principal components in the cost of mastitis. However, past research into the association between milk yield and mastitis indicators is limited. Past research has not been based on online or in-line daily measurements and has not fully explored nonlinearity and the thresholds at which milk yield starts to decrease. In dairy herds with automated milking systems equipped with sensors, mastitis indicators of individual cows are measured on an intraday frequency, which provides unprecedented avenues to explore such effects in detail. The aim of this observational study was primarily to investigate the nonlinear associations of lactate dehydrogenase (LDH), electrical conductivity (EC), and somatic cell count (SCC) with milk yield at various stages of lactation, parity, and mastitis chronicity status (i.e., whether the cow had SCC ≥200,000 SCC/mL for the last 28 d). We also investigated thresholds at which mastitis indicators (LDH, EC, and SCC) started to be negatively associated with milk yield. We used data from 21 automated milking system herds measuring EC and online SCC. Of these herds, 7 of the 21 additionally measured online LDH. We operationalized milk yield as milk synthesis rate in kilograms per hour. Applying a generalized additive model, we estimated the milk synthesis rate as a function of the 3 mastitis indicators for 3 different subgroups based on parity, stage of lactation, and mastitis chronicity. Partial dependence plots of the mastitis indicators were used to evaluate the milk synthesis rate to study if the milk synthesis rate was associated with mastitis indicators at a specific level. Results showed that milk synthesis rate decreased with increasing SCC, LDH, and EC, but in a nonlinear fashion. The thresholds at which milk synthesis rate started to decrease were 2.5 LnSCC (12,000 SCC/mL) to 3.75 LnSCC (43,000 SCC/mL), 0 to 1 LnLDH (1-2.7 U/L), and 5.0 to 6.0 mS/cm for EC. Additionally, another substantial decrease of milk synthesis rate was observed at thresholds of 5.625 LnSCC (277,000 SCC/mL) and 3 LnLDH (20 LDH U/L) but not for EC. Having chronic mastitis decreased milk synthesis rate in all models. The identified nonlinearities between mastitis indicators and milk synthesis rate should be incorporated in statistical models for more accurate estimations of milk loss due to mastitis.

Progression of different udder inflammation indicators and their episode length after onset of inflammation using automatic milking system sensor data.

In automatic milking systems (AMS), sensors can measure cow behavior and milk composition at every milking. The aim of this observational study of previously collected data was to gain insight into the differences in dynamics of udder inflammation indicators between cows that recover and those that do not recover after detection of an initial inflammation. Milk diversion (milk separated from the bulk tank and thus indicating farmer intervention), conductivity, and somatic cell count (SCC) data from 4 wk before the initial inflammation to 12 wk after the initial inflammation were used to analyze 2,584 cases of udder inflammation. An udder inflammation case was defined as an initial observation of SCC ≥200,000 cells/mL as well as 1 additional SCC measurement >200,000 cells/mL within 10 d after the initial case, among other requirements. The data originated from 15 AMS herds in 6 countries. Four subsets of cows were created based on whether milk was diverted after the initial inflammation and whether the udder inflammation cases recovered, using a 10-d rolling average SCC threshold of 200,000 cells/mL and checking whether this rolling mean was below the threshold within 90 d after the initial inflammation as the indication of recovery. This formed the following subsets of cow lactations: milk diverted-recovered, milk diverted-not recovered, no milk diverted-not recovered, no milk diverted-recovered. Thresholds of 100,000 SCC/mL and 300,000 SCC/mL for the definition of case and recovery were also applied in a sensitivity analysis but with no substantial difference in results. Linear mixed models were used for each subset to study the variation in SCC (natural logarithm of SCC divided by 1,000) and σ-conductivity (natural logarithm of standard deviation of quarter conductivities). When observing the fraction of cows with SCC <200,000 cells/mL in the recovery subsets, most cows recovered within 20 d after the initial inflammation. In the recovery subsets, both σ-conductivity and SCC stabilized, mostly within 3 to 4 wk after the initial inflammation. σ-Conductivity stabilized above the pre-onset level in all subsets and did not show a clear increase in the no-milk-diverted subgroups, whereas SCC stabilized closer to the pre-onset level. Overall, this study indicated a cutoff point between nonchronic and chronic changes in indicators 3 to 4 wk after the initial inflammation for SCC and σ-conductivity.

Trends in somatic cell count deteriorations in Dutch dairy herds transitioning to an automatic milking system.

Udder health is at risk when herds transition from a conventional milking system (CMS) to an automatic milking system (AMS). Somatic cell counts (SCC) are generally elevated for several months following a transition. However, such observations were made in studies conducted in the early 2000s. Technical improvements to AMS have likely been made since then, and farm management may have improved, learning from past experiences. This longitudinal observational study quantified national trends in SCC deteriorations in dairy herds that transitioned from a CMS to an AMS. Census data from the Dutch test day recording was used to determine these trends. It consisted of all cow-level SCC measurements conducted in the Netherlands from January 1, 2007 to December 31, 2019. Three udder health indicators, including the natural logarithm of the yield-corrected bulk-milk SCC, the proportion of cows having a composite SCC ≥200,000 cells/mL, and the proportion of cows having a new elevated SCC, were evaluated using multivariable population-averaged generalized estimation equations. Predicted values resulting from these statistical models were interpreted to determine trends in SCC deteriorations from 12 mo before and 12 mo after the transition to an AMS. Decreasing SCC deteriorations were observed during the study period for all 3 udder health indicators. Udder health deteriorations around the transition to an AMS were still observed, but they decreased in magnitude over the course of the study period. Bulk-milk SCC deteriorations were, for instance, 2.5 times lower in 2019 compared with those observed in 2007. Therefore, the effect of transitioning to an AMS on udder health became less severe in more recent years. However, deteriorations in the proportion of new SCC elevations were still evident toward the end of the study period. Efforts to lower udder health deteriorations in herds that transition to an AMS should therefore be continued and should intensify on factors lowering the proportion of cows having a new elevated SCC.

Antibiotic use and potential economic impact of implementing selective dry cow therapy in large US dairies.

In this study, our objectives were to evaluate the economic feasibility of implementing selective dry cow therapy (SDCT) in large US herds and to estimate the potential reduction in antibiotic use around the dry period if SDCT management is adopted. Cow-level data were obtained from the Dairy Herd Improvement Association (AgriTech, Visalia, CA) and individual dairy herds in California. Logistic regression models were used to predict the incidence risk of subclinical and clinical mastitis in the subsequent lactation for 96 last test-day somatic cell score categories. Linear programming was used to optimize the costs of dry cow therapy in 3 simulated large US dairy herds with different bulk tank somatic cell counts (BTSCC). The objective function was aimed at minimization of the total cost of mastitis around the dry period (TCMD), under a varying constraint of the maximum percentage of cows dried off with antibiotics. A sensitivity analysis was performed on milk price, dry-off antibiotic price, and risk ratio of mastitis in the subsequent lactation when no antibiotics and only teat sealant was used at dry-off. For all situations, blanket dry cow therapy was more expensive than SDCT. In a herd with medium BTSCC, the TCMD was $54.7 per primiparous dry cow and $58.5 per multiparous dry cow annually. In the optimal economic situation where SDCT was used, only 30% of primiparous cows received antibiotics, leading to a TCMD of $52.4 per primiparous dry cow, whereas 88% of multiparous cows received antibiotics, at a cost of $58.2 per multiparous dry cow. This corresponded with an overall reduction of 29% in the use of antibiotics around the dry period in a conservative scenario. This study showed that it is economically feasible to reduce antibiotic use associated with dry cow therapy in large US dairy herds. This contributes to the efforts of reducing antibiotic use worldwide.

The effect of new bovine viral diarrhea virus introduction on somatic cell count, calving interval, culling, and calf mortality of dairy herds in the Dutch bovine viral diarrhea virus-free program.

Bovine viral diarrhea virus (BVDV) infection has a major effect on the health of cows and consequently on herd performance. Many countries have implemented control or eradication programs to mitigate BVDV infection and its negative effects. These negative effects of BVDV infection on dairy herds are well documented, but there is much less information about the effects of new introduction of BVDV on dairy herds already participating in a BVDV control program. The objective of our study was to investigate the effect of a new BVDV introduction in BVDV-free herds participating in the Dutch BVDV-free program on herd performance. Longitudinal herd-level surveillance data were combined with herd information data to create 4 unique data sets, including a monthly test-day somatic cell count (SCC) data set, annual calving interval (CIV) and culling risk (CR) data sets, and a quarterly calf mortality rate (CMR) data set. Each database contained 2 types of herds: herds that remained BVDV free during the whole study period (defined as free herds), and herds that lost their BVDV-free status during the study period (defined as breakdown herds). The date of losing the BVDV-free status was defined as breakdown date. To compare breakdown herds with free herds, a random breakdown date was artificially generated for free herds by simple random sampling from the distribution of the breakdown month of the breakdown herds. The SCC and CIV before and after a new introduction of BVDV were compared through linear mixed-effects models with a Gaussian distribution, and the CR and CMR were modeled using a negative binomial distribution in generalized linear mixed-effects models. The explanatory variables for all models included herd type, BVDV status, year, and a random herd effect. Herd size was included as an explanatory variable in the SCC, CIV, and CMR model. Season was included as an explanatory variable in the SCC and CMR model. Results showed that free herds have lower SCC, CR, CMR, and shorter CIV than the breakdown herds. Within the breakdown herds, the new BVDV introduction affected the SCC and CMR. In the year after BVDV introduction, the SCC was higher than that in the year before BVDV introduction, with a factor of 1.011 [2.5th to 97.5th percentile (95% PCTL): 1.002, 1.020]. Compared with the year before BVDV breakdown, the CMR in the year of breakdown and the year after breakdown was higher, with factors of 1.170 (95% PCTL: 1.120; 1.218) and 1.096 (95% PCTL: 1.048; 1.153), respectively. This study reveals that a new introduction of BVDV had a negative but on average relatively small effect on herd performance in herds participating in a BVDV control program.

Novel ways to use sensor data to improve mastitis management.

Current sensor systems are used to detect cows with clinical mastitis. Although, the systems perform well enough to not negatively affect the adoption of automatic milking systems, the performance is far from perfect. An important advantage of sensor systems is the availability of multiple measurements per day. By clearly defining the need for detection of subclinical mastitis (SCM) and clinical mastitis (CM) from the farmers' management perspective, detection and management of SCM and CM may be improved. Sensor systems may also be used for other aspects of mastitis management. In this paper we have defined 4 mastitis situations that could be managed with the support of sensor systems. Because of differences in the associated management and the epidemiology of these specific mastitis situations, the required demands for performance of the sensor systems do differ. The 4 defined mastitis situations with the requirements of performance are the following: (1) Cows with severe CM needing immediate attention. Sensor systems should have a very high sensitivity (>95% and preferably close to 100%) and specificity (>99%) within a narrow time window (maximum 12 h) to ensure that close to all cows with true cases of severe CM are detected quickly. Although never studied, it is expected that because of the effects of severe CM, such a high detection performance is feasible. (2) Cows with mastitis that do not need immediate attention. Although these cows have a risk of progressing into severe CM or chronic mastitis, they should get the chance to cure spontaneously under close monitoring. Sensor alerts should have a reasonable sensitivity (>80%) and a high specificity (>99.5%). The time window may be around 7 d. (3) Cows needing attention at drying off. For selective dry cow treatment, the absence or presence of an intramammary infection at dry-off needs to be known. To avoid both false-positive and false-negative alerts, sensitivity and specificity can be equally high (>95%). (4) Herd-level udder health. By combining sensor readings from all cows in the herd, novel herd-level key performance indicators can be developed to monitor udder health status and development over time and raise alerts at significant deviances from predefined thresholds; sensitivity should be reasonably high, >80%, and because of the costs for further analysis of false-positive alerts, the specificity should be >99%. The development and validation of sensor-based algorithms specifically for these 4 mastitis situations will encourage situation-specific farmer interventions and operational udder health management.

The effect of bovine viral diarrhea virus introduction on milk production of Dutch dairy herds.

Dairy cows are negatively affected by the introduction of bovine viral diarrhea virus (BVDV), and consequently, produce less milk. Existing literature on potential milk production losses is based on relatively outdated data and hardly evaluates milk production loss in relation to a new BVDV infection in a surveillance system. This study determined the annual and quarterly loss in milk production of BVDV introduction in 3,126 dairy herds participating in the Dutch BVDV-free program between 2007 and 2017. Among these herds, 640 were "breakdown-herds" that obtained and subsequently lost their BVDV-free status during the study period, and 2,486 herds obtained and retained their BVDV-free status during the study period. Milk yields before and after BVDV introduction were compared through annual and quarterly linear mixed models. The fixed variables for both models included herd type (breakdown-herd or free-herd), bovine viral diarrhea status (on an annual and quarterly basis), year, season, and a random herd effect. The dependent variable was the average daily milk yield on the test day. To define the possible BVDV-introduction dates, 4 scenarios were developed. In the default scenario, the date of breakdown (i.e., loss of the BVDV-free status) was assumed as the BVDV-introduction date. For the other 3 scenarios, the BVDV-introduction dates were set at 4, 6, and 9 mo before the date of breakdown, based on the estimated birth date of a persistently infected calf. In the default scenario, the loss in milk yield due to BVDV introduction occurred mainly in the first year after breakdown, with a reduction in yield of 0.08 kg/cow per day compared with the last year before breakdown. For the other 3 scenarios, the greatest yield reduction occurred in the second year after BVDV introduction, with a loss of 0.09, 0.09, and 0.1 kg/cow per day, respectively. For the first 4 quarters after BVDV introduction in the default scenario, milk yield loss was 0.14, 0.09, 0.02, and 0.08 kg/cow per day, respectively. These quarterly results indicated that milk yield loss was greatest in the first quarter after BVDV introduction. Overall, BVDV introduction had a negative, but on average a relatively small, effect on milk yield for herds participating in the BVDV-free program. This study will enable dairy farmers and policymakers to have a clearer understanding of the quantitative milk production effect of BVDV on dairy farms in a control program.

Economic and epidemiological impact of different intervention strategies for clinical contagious mastitis.

The overall aim of this study was to compare different intervention strategies for clinical intramammary infections (IMI). We conducted a simulation study to represent a Danish dairy cattle herd with IMI caused mostly by Staphylococcus aureus and 9 different intervention strategies for clinical IMI. A standard intervention of 3 d of treatment consisting of intramammary injections for all clinical cases was used. Two of the strategies reflected the use of more antibiotics and 6 strategies reflected cow-specific treatment or culling decisions. For these strategies, we assessed the cost and effectiveness of culling as an IMI intervention. Our results showed that nearly all strategies could reduce the number of IMI cases [e.g., a median of 37 clinical cases with the extended intramammary treatment over 5 d strategy (Basic5) and 30 clinical cases with the cow culled with recovery probability below 50% (Before50)] compared with the standard intervention (median of 42 clinical cases). This happened alongside either increased antibiotic usage (e.g., from a median of 123 treatment days up to 179 treatment days with strategy Basic5) or an increased number of cows culled in relation to IMI (e.g., from a median of 16 up to 24 cows with strategy Before50). Strategies with more antibiotics or reactive culling had a slightly higher net income (e.g., €190,014 median net income with strategy Basic5 or €196,995 with strategy Before50 compared with €187,666 with the standard strategy). This shows that a cow-specific clinical intervention approach can be cost-effective in reducing IMI incidence.

Economic evaluation of 4 bovine leukemia virus control strategies for Alberta dairy farms.

Bovine leukemia virus (BLV) is a production-limiting disease common in North American dairy herds. To make evidence-based recommendations to Canadian dairy producers and their consultants regarding cost and financial benefits of BLV on-farm control, an economic model that takes the supply-managed milk quota system into account is necessary. Alberta-specific input variables were used for the presented analysis. A decision tree model program was used to evaluate economic aspects of decreasing a 40% BLV within-herd prevalence on dairy farms by implementing various control strategies over 10 yr. Investigated strategies were (1) all management strategies, including 3 options for colostrum management; (2) some management strategies; (3) test and cull; and (4) test and segregate. Each of these strategies was compared with a no control on-farm approach. The prevalence for this no-control approach was assumed to stay constant over time. Each control strategy incurred specific yearly cost and yielded yearly decreases in prevalence, thereby affecting yearly partial net revenue. Infection with BLV was assumed to decrease milk production, decrease cow longevity, and increase condemnation of carcasses at slaughter from cattle with enzootic bovine leukosis, thereby decreasing net revenue. Cows infected with BLV generated a yearly mean partial net revenue of Can$7,641, whereas noninfected cows generated Can$8,276. Mean cost for the control strategies ranged from Can$193 to Can$847 per animal over 10 yr in a 146-animal herd. Net benefits of controlling BLV on farm, as compared with not controlling BLV, per cow in a 146-animal herd over a 10-yr period for each strategy was: Can$1,315 for all management strategies (freezer); Can$1,243 for all management strategies (pasteurizer); Can$785 for all management strategies (powdered colostrum); Can$1,028 for some management strategies; Can$1,592 for test and cull; and Can$1,594 for test and segregate. Consequently, on-farm BLV control was financially beneficial. Even though negative net benefits were possible and expected for some iterations, our sensitivity analysis highlighted the overall robustness of our model. In summary, this model provided evidence that Canadian dairy farmers should be encouraged to control BLV on their farm.

Veterinary medicinal product usage among food animal producers and its health implications in Central Ethiopia.

BACKGROUND: Antimicrobials and anthelmintics are the most commonly used veterinary drugs to control animal diseases. However, widespread use of these drugs could contribute to the emergence of drug resistance. Information on the practice of antimicrobial usage among food animal raising communities in Central Ethiopia is scarce. We used a standardised questionnaire survey to assess knowledge, awareness, and practices related to drug use and resistance in food animals among the farmers in and around Bishoftu town. RESULTS: Of the total of 220 livestock owners interviewed, around 80% of the respondents were not able to define what antimicrobials are and for what purposes they are used. Only 14.1% (n = 31) of the respondents had awareness about antimicrobial resistance (AMR) and its consequences; and 35.5% (n = 11/31) and 9.7% (n = 3/31) of them agreed that the irrational use of antimicrobials in animals could lead to AMR in animals and humans. Oxytetracycline was the most commonly available antibiotic in veterinary drug shops/pharmacies and the most widely used drug in the area. However, 43.3% of the respondents did not see clinical improvements after using antibiotics. Similarly, the respondents explained that no response was observed in 73.3, 70.8 and 52.5% of the cases after medication with anthelmintics, antiprotozoal and acaricides, respectively. About 56.7% of the respondents considered traditional medicines equally important to modern medicines. It was also noted that there were illegal drug vendors, dispensing medicines under unfavourable conditions which include a direct exposure to sunlight, which practice violates the drug handling and storage recommendations given by WHO. CONCLUSION: The study revealed that there is a general lack of awareness among food animal owners about the correct use of antibiotics and anthelmintics. The widespread misuse and improper drug dispensing and handling practices observed in this study can affect the drug quality and can also contribute to the development of drug resistance in central Ethiopia.

Dynamic forecasting of individual cow milk yield in automatic milking systems.

Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.

Addition of meloxicam to the treatment of bovine clinical mastitis results in a net economic benefit to the dairy farmer.

Recently, it has been shown that the addition of meloxicam to standard antimicrobial therapy for clinical mastitis (CM) improves the conception rate of dairy cows contracting CM in the first 120 d in milk. The objective of our study was to assess whether this improved reproduction through additional treatment with meloxicam would result in a positive net economic benefit for the farmer. We developed a stochastic bio-economic simulation model, in which a dairy cow with CM in the first 120 d in milk was simulated. Two scenarios were simulated in which CM cases were treated with meloxicam in conjunction with antimicrobial therapy or with antimicrobial therapy alone. The scenarios differed for conception rates (31% with meloxicam or 21% without meloxicam) and for the cost of CM treatment. Sensitivity analyses were undertaken for the biological and economic components of the model to assess the effects of a wide range of inputs on inferences about the cost effectiveness of meloxicam treatment. Model results showed an average net economic benefit of €42 per CM case per year in favor of the meloxicam scenario. Cows in the no-meloxicam treatment scenario had higher returns on milk production, lower costs upon calving, and reduced costs of treatment. However, these did not outweigh the savings associated with lower feed intake, reduced number of inseminations, and the reduced culling rate. The net economic benefit favoring meloxicam therapy was a consequence of the better reproductive performance in the meloxicam scenario in which cows had a shorter calving to conception interval (132 vs. 143 d), a shorter intercalving interval (405 vs. 416 d), and fewer inseminations per conception (2.9 vs. 3.7) compared with cows in the no-meloxicam treatment scenario. This resulted in a shorter lactation, hence a lower lactational milk production (8,441 vs. 8,517 kg per lactation) with lower feeding costs in the meloxicam group. A lower culling rate (12 vs. 25%) resulted in lower replacement costs in the meloxicam treatment scenario. All of the scenarios evaluated in the sensitivity analyses favored meloxicam treatment over no meloxicam. This study demonstrated that improvements in conception rate achieved by the use of meloxicam, as additional therapy for mild to moderate CM in the first 120 d in milk, have positive economic benefits. This inference remained true over a wide range of technical and economic inputs, demonstrating that use of meloxicam is likely to be cost effective across many production systems.

Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis.

Rapid detection of dairy cow mastitis is important so corrective action can be taken as soon as possible. Automatically collected sensor data used to monitor the performance and the health state of the cow could be useful for rapid detection of mastitis while reducing the labor needs for monitoring. The state of the art in combining sensor data to predict clinical mastitis still does not perform well enough to be applied in practice. Our objective was to combine a multivariate dynamic linear model (DLM) with a naïve Bayesian classifier (NBC) in a novel method using sensor and nonsensor data to detect clinical cases of mastitis. We also evaluated reductions in the number of sensors for detecting mastitis. With the DLM, we co-modeled 7 sources of sensor data (milk yield, fat, protein, lactose, conductivity, blood, body weight) collected at each milking for individual cows to produce one-step-ahead forecasts for each sensor. The observations were subsequently categorized according to the errors of the forecasted values and the estimated forecast variance. The categorized sensor data were combined with other data pertaining to the cow (week in milk, parity, mastitis history, somatic cell count category, and season) using Bayes' theorem, which produced a combined probability of the cow having clinical mastitis. If this probability was above a set threshold, the cow was classified as mastitis positive. To illustrate the performance of our method, we used sensor data from 1,003,207 milkings from the University of Florida Dairy Unit collected from 2008 to 2014. Of these, 2,907 milkings were associated with recorded cases of clinical mastitis. Using the DLM/NBC method, we reached an area under the receiver operating characteristic curve of 0.89, with a specificity of 0.81 when the sensitivity was set at 0.80. Specificities with omissions of sensor data ranged from 0.58 to 0.81. These results are comparable to other studies, but differences in data quality, definitions of clinical mastitis, and time windows make comparisons across studies difficult. We found the DLM/NBC method to be a flexible method for combining multiple sensor and nonsensor data sources to predict clinical mastitis and accommodate missing observations. Further research is needed before practical implementation is possible. In particular, the performance of our method needs to be improved in the first 2 wk of lactation. The DLM method produces forecasts that are based on continuously estimated multivariate normal distributions, which makes forecasts and forecast errors easy to interpret, and new sensors can easily be added.

Economic evaluation of stall stocking density of lactating dairy cows.

An increase in stall stocking density (SSD), as measured by the number of lactating cows per stall in a freestall barn, reduces cow performance, such as milk yield and fertility, but may increase farm profitability. Our objectives were to calculate effects of varying SSD on profit per stall for a range of effects on cow performances and external farm factors and store results in regression metamodels. The literature on quantified effects of SSD on cow performance that directly affects cash flow was found to be weak. We assumed effects of SSD on milk yield, probability of conception, and probability of culling. External farm factors were probability of insemination, feed price, and milk price. A herd budget-simulation model was used which mimics the performance of cows in a herd and calculates profit per stall per year and other results. The SSD varied from 100 (no overstocking) to 150% (severe overstocking) in steps of 10%. Sensitivity analyses for effects of SSD on cow performance and effects of external farm factors were performed. Three regression metamodels were developed. The first metamodel accurately predicted profitability at 100% SSD for all variations in the external farm factors. Optimal SSD varied from 100 to 150% SSD, depending on the combination of inputs, and was very sensitive to changes in the size of the milk loss and milk and feed prices. Average optimal SSD of all 2,187 combinations of inputs was 120% SSD and average maximum increase in profit was $99/stall per year. Of the 2,187 combinations of inputs, 18% were ascending (maximum increase in profit >150% SSD), 33% were descending (maximum profit at 100% SSD), and 50% had a maximum increase in profit between 100 and 150% SSD. The second metamodel accurately captured changes in profit for all combinations of biological and external inputs and SSD. A third metamodel captured breakeven daily milk losses which would result in the same profit as at 100% SSD given the same external farm factors. In conclusion, overstocking was profitable under plausible economic conditions in the United States. The 3 metamodels accurately captured the results for a wide range of values of the input variables. A tradeoff will occur between economically optimal SSD and animal welfare in some situations.

Failure and preventive costs of mastitis on Dutch dairy farms.

Mastitis is an important disease from an economic perspective, but most cost assessments of mastitis include only the direct costs associated with the disease (e.g., production losses, culling, and treatment), which we call failure costs (FC). However, farmers also invest time and money in controlling mastitis, and these preventive costs (PC) also need to be taken into account. To estimate the total costs of mastitis, we estimated both FC and PC. We combined multiple test-day milk records from 108 Dutch dairy farms with information on applied mastitis prevention measures and farmers' registration of clinical mastitis for individual dairy cows. The aim was to estimate the total costs of mastitis and to give insight into variations between farms. We estimated the average total costs of mastitis to be €240/lactating cow per year, in which FC contributed €120/lactating cow per year and PC contributed another €120/lactating cow per year. Milk production losses, discarded milk, and culling were the main contributors to FC, at €32, €20, and €20/lactating cow per year, respectively. Labor costs were the main contributor to PC, next to consumables and investments, at €82, €34, and €4/lactating cow per year, respectively. The variation between farmers was substantial, and some farmers faced both high FC and PC. This variation may have been due to structural differences between farms, different mastitis-causing pathogens, the time at which preventive action is initiated, stockmanship, or missing measures in PC estimates. We estimated the minimum FC to be €34 per lactating cow per yr. All farmers initiated some preventive action to control or reduce mastitis, indicating that farmers will always have mastitis-related costs, because mastitis will never be fully eradicated from a farm. Insights into both the PC and FC of a specific farm will allow veterinary advisors and farmers to assess whether current udder health strategies are appropriate or whether there is room for improvement from an economic perspective.