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CONTEXT: In recent years, environmental policies, especially in North-western European countries have put pressure on the total livestock on a dairy farm. On closed dairy farms this primarily has resulted in a reduction of the heifer rearing unit to maintain the production unit. The economic consequences of constrained replacement heifer supply on herd level have not been investigated. OBJECTIVE: The objective of this study is to study on herd level the economic impact of suboptimal replacement decisions due to a constrained replacement heifer supply. METHODS: In this study, we combine a single-cow MDP (Markov Decision Process) optimization model with dairy herd dynamics simulation of 10 years to account for the interdependency among dairy cows within the herd of 100 cows. Besides the base scenario of following optimal replacement policy, we simulated three input scenarios of constrained, excess, and variable replacement heifer supply. RESULTS AND CONCLUSION: In the base scenario, optimal replacement policy resulted in a herd gross margin of €155,108, 11% voluntary replacement rate, 24% involuntary disposal rate annually for a herd of 100 cows. Constrained as well as excess heifer supply resulted in lower gross margins of €85,878 and €138,406 respectively, compared to the base scenario. Constrained heifer supply also resulted in 39% reduction of herd size, involuntary disposal of 17.5% and no voluntary replacements (0.1%) on average per year. Variable heifer supply scenario resulted in lower gross margins (€115,127), lower voluntary replacement rate (4%), highest involuntary disposal rate (28%) but did not result in reduction of herd size, compared to the base scenario. In conclusion, we developed a combination of cow level optimization with a herd level simulation to study the economic impacts of constrained replacement heifer supply. We found that severely constrained, excess, and variable heifer supply result in reduced herd average gross margin. SIGNIFICANCE: Op

Introduction: This article aimed to study cross-sectional associations between the performance of dairy farms and their corresponding culling proportions under the herd size constraint as imposed in 2018 by the new phosphate regulation in the Netherlands. Methods: To this end, production data from 10,540 Dutch dairy farms were analyzed to capture the inflow and outflow of both primiparous and multiparous cows. Farm performance was measured by 10 indicators structured in four areas of longevity, production, reproduction, and udder health. Farm culling proportions were represented by the overall culling (OC) and the number of culled primiparous cows in relation to (i) the total number of producing cows (PC), (ii) the number of producing primiparous cows (PPC), and (iii) the number of culled producing cows (POC). Spearman's rank correlation and weighted logistic regression were adopted to study associations. Results: In 2018, on average, 28% of producing cows were culled (OC). The number of primiparous cows culled represented 4.5% of the total number of producing cows (PC) and the mean proportion of culled primiparous cows was 18.8% of the total number of producing primiparous cows (PPC), and, of the total number of producing culled cows, 15% were primiparous cows (POC). However, the variance around the mean, and among individual farms, was high (SD 4–15% for all four culling proportions). Results from rank correlation showed very low-rank conformity (<12%) between the areas of production, reproduction, and udder health to the culling proportions. Results from logistic regression showed that higher farm levels of production and higher percentages of cows with poor udder health were associated with more overall culling but with less primiparous culling. For reproduction indicators, the associations were similar for overall and primiparous culling. However, except for the average age of culled animals, the odds ratios for indicators were close to 1 (range: 0.92–1.07 and

This ecological study aimed to associate hormone use for reproductive diseases and heat induction with reproductive performance at herd level. Hormone use, herd characteristics, and test-day recording data were obtained from 754 representative Dutch dairy farms belonging to five large veterinary practices from 2017 to 2019 (1679 observations in total). Hormone use was classified into prostaglandin, gonadotropin-releasing hormone (GnRH), and progesterone, and was expressed at herd level as the annual number of hormone doses per 100 adult dairy cows. Hormone use was categorized into four levels (no usage, low, medium, and high use), following the 33rd and 66th percentiles of herds that applied them. Three herd-level reproductive performance indicators (calving interval, calving-to-1st insemination interval, number of inseminations per cow) were analyzed using multivariable General Estimating Equations models. The median annual total hormone use was 36.1 (mean=43.1; min=0.0; max=248.2) doses per 100 adult dairy cows in all herds while the median was 39.2 (mean=46.8; min=0.4; max=248.2) doses per 100 adult dairy cows among the user-herds. The median annual group-specific hormone use was 21.3 (mean=26.1; min 0.0; max=180.0), 11.0 (mean=15.3; min=0.0; max=127.0) and 0.0 (mean=1.8; min=0.0; max=40.3) doses per 100 adult dairy cows for prostaglandin, GnRH, and progesterone, respectively. The final statistical models identified that herds with a high hormone use had a calving interval and a calving-to-1st insemination interval that was 9.3 ± 2.6 and 16.4 ± 2.1 days shorter than that of non-user herds (424.0 ± 2.7 and 114.0 ± 2.1 days), respectively. Furthermore, high-user herds needed on average 0.3 ± 0.04 inseminations more to get their cows pregnant compared to non-user herds (1.83 ± 0.04 no. of inseminations per cow). Medium-user herds had a 6.5 ± 2.6 days shorter calving interval and a 12.0 ± 2.1 days shorter calving-to-1st insemination interval with 0.2 ± 0.04 additiona

BHB-concentrations is mostly used for detection hyperketonaemia. Most studies applied BHB-concentrations in blood and a few studies have used BHB-measurements in milk. On-set, duration and maximum BHB-concentrations are different aspects of ketosis dynamics. The aim of this study is to investigate how different parameters of ketosis dynamics correlate with each other and the second objective of this study is to investigate if the severity of keto-sis can be reflected in an area under the curve of the repeated BHB-measurements in milk detected with the DeLaval Herd NavigatorTM. Within the Herd Navigator an inline sampler inside the AMS automatically takes a representative sample of 80 ml milk from each indi-vidual cow during the milking process and measures BHB-concentrations at least daily from 3 to 20 days in milk and between 21 and 60 days in milk at least once every 4 days. The in-vestigated data, provided by Lattec, which included 48 herds working with the Herd Naviga-tor in the Benelux in June 2020. Data of unique calving’s between July 2018 and August 2019 were included. The final dataset included 130,247 BHB-measurements of 4030 unique calving’s. The cur-rent study defined three aspects of ketosis dynamics: the onset of ketosis, the duration of ke-tosis and the maximum BHB-concentration. Descriptive statistics were performed of the dif-ferent aspects of ketosis. Mean onset of ketosis was 18 DIM, 15 DIM and 12 DIM for cows with parity 1, 2 and 3+ respectively. Mean duration of ketosis was 21 days for cows with parity 1, 27 days parity 2 cows and 32 days for cows with parity 3+. The correlation between the onset of ketosis and the duration of ketosis was -0.546 (parity 2 cows), the correlation between the duration and maximum BHB-concentration was 0.460 (parity 2 cows). The area under the curve is calculated as a sum of all BHB-measurements between 3 and 60 DIM. The mean area under the curve was 3,67 for parity 1 cows, 4,14 for parity 2 cows and 4,63 for parity

The associations between reproductive performance, milk yield and health status with the risk of culling, and thus with a cow’s longevity, have been well documented at the individual cow level. Associations at individual cow level may, however, not be valid at herd level due to interrelated herd management aspects and/or policy restrictions. The objective of this study was to explore the association of herd performance indicators with herd-level dairy cow longevity under Dutch production conditions. Longevity was expressed by three different measures, viz. age at culling, lifetime milk production of culled cows and culling rate. The evaluated herd performance indicators included factors on milk production, youngstock rearing, reproduction and health performance as registered on 10 719 Dutch commercial dairy herds during the period 2007–2016. Averaged over herds and the evaluated period, the age of culled milking cows was 2 139 days (5.8 years, SD±298 days), the lifetime milk production of culled cows was 31 238 kg (SD±7,494 kg), and the culling rate was 0.24 (SD ±0.08). A mixed linear regression modelling approach was applied to evaluate the association of each of the three longevity measures with the selected herd performance indicators. The results indicated that only four herd performance indictors (herd size, herd expansion, heifer ratio and the proportion of cows with potential subclinical ketosis) shared significant associations with all three longevity variables. Generally, the strength of the associations between each of the evaluated longevity measures and herd performance indicators was only limited. The absence of strong associations between the longevity measures and herd performance indicators reveal that there is potential of extending cattle longevity without affecting the herd performance in terms of milk production, reproduction and health. Moreover, only part of the observed variance in longevity among the herds over time was explained by the herd

More and more European countries have implemented a bovine viral diarrhea virus (BVDV) control program. The economic effects of such programs have been evaluated in simulations, but empirical studies are lacking, especially in the final stage of the program. We investigated the economic (gross margin) and production effects (milk yield, somatic cell count, and calving interval) of the herds obtaining BVDV-free certification based on longitudinal annual accounting and herd performance data from Dutch dairy herds between 2014 and 2019, the final stages of the Dutch national BVDV-free program. This study was designed as a case-control study: two types of case herds were defined for two analyses. The case herds in the first analysis are herds where the BVDV status changed from “BVDV not free” to “BVDV free” during the study period. The not-free status refers to a herd that participated in the BVDV-free program but had not yet obtained the BVDV-free certification. In the second analysis, the case herds started participating in the Dutch BVDV-free program during the study period and obtained the BVDV-free certification. Control herds in both analyses were BVDV-free during the entire study period. Potential bias between the covariates of the two herd groups was reduced by matching case and control herds using the propensity score matching method. To compare the differences between case and control herds before and after BVDV-free certification, we used the time-varying Difference-in-Differences estimation (DID) methodology. The results indicate that there was no significant change in milk yield, somatic cell count, calving interval, and gross margin upon BVDV-free certification. There are several possible explanations for the non-significant effects observed in our study, such as the final stage of the BVDV control program, not knowing the true BVDV infection situation in case herds and not knowing if control measures were implemented in case herds prior to participating i

Staphylococcus aureus (S. aureus) strains with considerable genetic and phenotypic differences have previously been identified. The economic and epidemiologic impact of S. aureus mastitis has been investigated, but none of these studies took differences between strains into account. Here we aimed to investigate how differences between S. aureus strains affect the economic and epidemiologic outcome of various intervention strategies against clinical and subclinical intramammary infections. Five S. aureus strains were modelled using a stochastic bio-economic model simulating a dairy herd of 200 cows using single-day time steps. The strain characteristics of the five simulated S. aureus strains (general, contagious, spill-over, clinical and persistent) were based on divergent phenotypes as described in literature. Outcomes of the model included incidence (both clinical and subclinical), number of antibiotic treatment days, number of culled cows, and net income. Intervention strategies against clinical and subclinical intramammary infections were based on (variations of) intramammary antibiotic treatment, testing, and culling. Both single and multiple pathogen (intramammary infection caused by S. aureus, Escherichia coli, and non-aureus staphylococci) scenarios were simulated to determine the effect of the five S. aureus strains on the impact of 19 different intervention strategies. The results showed that the incidence (both clinical and subclinical), number of treatment days, number of culled cows, and net income varied considerably for the different S. aureus strains. Comparison of the model outcomes within and between strains showed that for most intervention strategies the relative impact differed per strain. However, the intervention strategy with the best outcome for most variables and strains was the culling of cows with a recovery probability lower than 50%. This shows that the relative economic and epidemiologic impact of most of the modelled intervention s

Supplemental material for the paper titled "The effect of new bovine viral diarrhea virus (BVDV) introduction on somatic cell count, calving interval, culling rate and calf mortality of dairy herds in the Dutch BVDV-free program".

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/c

Supplemental material for the paper titled "The impact of bovine viral diarrhea virus introduction on milk production of Dutch dairy herds".

Introduction – It has been shown in human healthcare that delegating tasks to nursing staff increases the efficiency due to improving patient flow, patient care and the overall work environment. The objective of this study is to examine whether this method can improve the efficiency of a veterinary clinic by limiting waiting time for the pet owner and consult duration by delegating the additional diagnostics to the veterinary nurse. Method and Materials – Six veterinary clinics in the Netherlands from AniCura were visited during four weeks. For each consult with additional diagnostics, several variables were recorded: gender and work experience of the veterinarian, work experience of the veterinary nurse, consult duration, waiting time for the pet owner, who handled the additional diagnostics, the type of diagnostics, the moment the results were communicated and general information from each clinic (ex. clinic size and years in business). The results were analyzed in SPSS. Results – The consult duration was not significantly shorter when the veterinary nurse handled the diagnostics. A regression analysis of all the measured variables during consults showed that the gender of the veterinarian (p=0,035) and the clinic size (p=0,043) were significant predictors of the consult duration. The waiting time was significantly shorter when the veterinary nurse handled the diagnostics (p=0,002). A regression analysis of all the measured variables during consults showed that the handler of the additional diagnostics (p=0,019), the type of diagnostics (p=0,017) and the location (p=0,023) were significant predictors of the waiting time. Conclusion – This study has shown that delegating the additional diagnostics to the veterinary nurses during consults, significantly decreases the waiting time for the pet owner. However, there are still factors that are unclear and need to be further researched.

In 2016, Lely introduced a health report; an extension of the earlier udder health report that was part of the management system (Time for Cows, T4C). The health report enables the farmer to be able to detect more diseases than just mastitis with the Lely Astronaut. The overall goal of this study is to evaluate the performance of the health report from the management system T4C from the Lely Astronaut. To get this insight, the positive and negative predictive value has been calculated, the number of false positives has been determined and the use of the health report by farmers has been examined. Six farms around Utrecht that use a Lely Astronaut were visited three to four times for the study. During the first visit, a questionnaire was taken to get an impression of the farm. Each visit the farmer was interviewed about the attentions on the health report that day. After this interview, the student examined the cows that were on the health report that day with the aid of two flowcharts. The student also visually assessed with the aid of a flowchart the health of five cows randomly selected per AMS that were not on the health report. A total of 41 attentions were examined on the health reports. In this study, a value of 90% was found for the positive predictive value and a value of 89% was found for the negative predictive value. Furthermore, a percentage of 10% false positives has been found. Farmers checked once a week to three times a day the health report and it appeared that on average 56% of all attentions are checked by the farmers. The most important reason to check a cow on the report is when the attention for increased conductivity or mastitis attention is on the report. It can be concluded that the health report of T4C van Lely has been improved compared to the udder health report of T4C. The positive predictive value has increased compared to previous findings and farmers are checking more attentions than before. However, cows are still missing on the repo