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Supplementary information/ material for manuscript titled "Dutch dairy farmers’ perspectives on culling reasons and strategies"

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.

Several studies have stated the various effects of an increased dairy cow longevity on economic herd performance, but empirical studies are lacking. This study aimed to investigate the association between longevity of dairy cows and the economic performance of dairy herds based on longitudinal Dutch accounting data. Herd and farm accounting data (n = 855 herds) over the years 2007–2016 were analyzed. Herd data contained yearly averages on longevity features, herd size and several production variables. Longevity was defined as the age of cows at culling and by lifetime milk production of culled cows. Farm accounting data contained yearly averages on revenues, fixed and variable costs of the herds, by which gross margins were defined. Data was analyzed using generalized linear mixed modeling, with gross margin as dependent variable. The independent variables consisted of average age of culled cows, average lifetime production of culled cows, year, herd size, herd intensity (milk production per ha), herd expansion rate, soil type, milking system, successor availability, total full-time equivalent, heifer ratio (% of heifers per cow) and use of outsourced heifer rearing. Herd was included as a random effect to account for the heterogeneity among herds. Descriptive statistics showed that the average age of culled cows was 5.87 (STD = 0.78) years and the average lifetime milk production of culled cows was 31.87 (STD = 7.56) tons per cow with an average herd size of 89 cows (STD = 38.85). The average age of culled cows was stable over the 10 years (variation between 5.79 AND 5.90 years). The gross margin was on average €24.80/100 kg milk (STD = 4.67), with the lowest value in year 2009 and the highest value in year 2013. Gross margin was not significantly associated with age of culled cows and lifetime milk production of culled cows. Variance in longevity between herds was large (STD = 0.78 years) but herds with a higher longevity did not perform economically better nor worse than herds resulting in lower longevity. This indicates that, within current practice, there is potential for improving longevity in order to meet society's concerns on animal welfare and environmental pollution, without affecting the economic performance of the herd.

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.

Clinical ketosis (CK) and subclinical ketosis (SCK) are associated with lower milk production, lower reproductive performance, an increased culling of cows and an increased probability of other disorders. Quantifying the costs related to ketosis will enable veterinarians and farmers to make more informed decisions regarding the prevention and treatment of the disease. The overall aim of this study was to estimate the combined costs of CK and SCK using assumptions and input variables from a typical Dutch context. A herd level dynamic stochastic simulation model was developed, simulating 385 herds with 130 cows each. In the default scenario there was a CK probability of almost 1% and a SCK probability of 11%. The herds under the no risk scenario had no CK and SCK, while the herds under the high-risk scenario had a doubled probability of CK and SCK compared to the default scenario. The results from the simulation model were used to estimate the annual cash flows of the herds, including the costs related to milk production losses, treatment, displaced abomasum, mastitis, calf management, culling and feed, as well as the returns from sales of milk and calves. The difference between the annual net cash flows of farms in the no risk scenario and the default scenario provides the estimate of the herd level costs of ketosis. Average herd level costs of ketosis (CK and SCK combined) were €3,613 per year for a default farm and €7,371 per year for a high-risk farm. The costs for a single CK case were on average €709 (with 5 and 95 percentiles of €64 and €1,196, respectively), while the costs for a single SCK case were on average €150 (with 5 and 95 percentiles of €18 and €422, respectively) for the default farms. The differences in costs between cases occurred due to differences between cases (e.g., cow culled vs cow not culled, getting another disease vs not getting another disease).

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.

Clinical mastitis (CM) can be caused by a wide variety of pathogens and a farmer has to start treatment before the actual causal pathogen is known. Knowing the Gram-status of CM cases would aid in the decision for the most appropriate treatment. By providing a probability distribution for the Gram-status, rather than only providing the most likely Gram-status, the involved uncertainty is visible for a farmer, thereby allowing a more informed treatment decision. The objective of this study was to examine the value of providing probability distributions for the Gram-status of CM cases to a farmer to take a more informed treatment decision. A naive Bayesian network (NBN) based on data from 274 Dutch dairy herds in which the occurrence of CM was recorded over an 18-month period was constructed. The dataset contained 3,534 CM cases, all classified accordingto their Gram-status. Two-third of the dataset was used for the construction and one-third was retained for validation. Information usually available at a dairy farm was included in the NBN under construction (parity, month in lactation, season of the year, quarter position, somatic cell count history and CM history, being sick or not, and color and texture of the milk). For getting insight in the quality of the constructed NBN, the accuracy was determined. The accuracy of classifying CM cases into Gram-positive or Gram-negative pathogens was 73%. Because only CM cases with a high probability for Gram-negative or Gram-positive pathogens will be considered for specific treatment, it was interesting to have a closer look at CM cases with probabilities > 0.90. We foundthat the accuracy of the classification increased with the calculated probability for Gram-negative or Gram-positive pathogens. The probability distributions for the Gram-status provide the farmer with considerable additional information about the most likely Gram-status of a CM case and the uncertainty involved.

This manuscript presents the idea of providing dairy farmers with probability distributions to support decisions on mastitis management and illustrates its feasibility by two applications. Naive Bayesian networks were developed for both applications. The networks in the first application were used to compute probability distributions to support decisions on which cows from a mastitis alert list generated by detection sensors in an automatic milking system should be visually inspected for clinical mastitis. The computed probability distribution allows farmers to interpret the uncertainty in an alert. The network in the second application was used to compute probability distributions to support treatment decisions for clinical mastitis cases. The computed probability distribution allows for distinguishing a situation in which a single pathogen has a high probability from the situation where two or more pathogens have almost equal probabilities. The first situation would support the choice for a pathogen-specific treatment.

Modern dairy herds resemble factories. Cows, organized in production units, are manufacturing milk from many components (e.g. concentrates, silage). However, both production units and components can greatly differ between each other. Therefore, production optimization based on general recommendations might be inefficient. Instead, as in manufacturing industry, decision support could be based on systematic experimentation within ongoing production system. This concept, known as Evolutionary Operations (EVOP), is based on small changes to the production system. However, a challenge here is lack of a tool which would allow a farmer to assess how small changes, for example in feeding, influence productivity. The objective of this study was to construct a multivariate dynamic linear model (DLM) to assess the intervention effect on milk production.The DLM was built to account for intervention at individual and herd level. It consisted of an observation and a system equation. The observation equation links the observations to parameters describing the herd (lactation curve), individual cows and an intervention effect. The system equation expresses how the parameters may change over time. The lactation curve was modeled by two linear expressions and was parameterized using: milk yield 60 days after calving, slope over the first 60 days in milk and slope after 60 days in milk. The variance components of the DLM were estimated using a maximum likelihood method. The application of the model was demonstrated on a field experiment in a commercial herd with 4 automatic milking systems (AMS). The herd was split into 2 groups based on the AMS. The experiment relied on two steps. The first step was to reduce the feed energy given to cows in the AMS and instead supply the feed energy to the cows at the feed bunk. The second step was to reduce the feed energy given in two of the four AMS. The DLM presented here was successful in providing estimates of the effects on milk yield of change in feed energy given to the cows in the AMS. The DLM results support the sequential tactical decisions within EVOP and are readily applicable in other herd experiments.

The increasing intensiveness of agriculture has contributed to environmental pollution through a higher production of waste materials. The environmental and economic pressures mean that it is nowadays important that milk is produced in a more sustainable way. The young stock rearing enterprise also contributes to the sustainability of dairy farming. For example, decisions about the number of young stock to retain on the farm have consequences for the amount of waste produced. A more sustainable young stock rearing enterprise can be achieved if the optimal decisions are taken during the rearing period. However, these decisions are very complex because many different factors are involved. For instance, retaining too few young stocks can result in not enough replacement heifers to replace culled dairy cows, and a shorter rearing period might negatively influence the future performance of dairy cows. Therefore, it is necessary to obtain insight into these decisions. The general objective of this thesis was to provide insight in the economic consequences of the decisions taken during young stock rearing. For this thesis, we focused on 2 important decisions that are directly related to the number of animals on the farm, and therefore to waste production. These decisions are about the first calving age and the number of 2-week-old heifer calves to be retained. Results of data analysis on 8,454 heifers showed that the first calving age can be lowered by starting inseminations earlier. However, earlier inseminations need to be accompanied by an adjustment of the rearing management to ensure sufficient development. Otherwise, earlier inseminations will lead to a lower milk production. These findings showed that an economic optimum exists between rearing costs and the first calving age. The decision about the optimal number of 2-week-old heifer calves to be retained is analysed using a stochastic herd level simulation model. In that model, the optimal percentage of 2-week-old heifer calves to be retained was defined as the percentage of heifer calves which minimized the average net cost of rearing replacement heifers. Inputs for this model were based on literature, expert opinion and authors’ expertise. In addition, inputs that are important but not readily available for Dutch circumstances were also studied. These were the cost of rearing and the culling rate of dairy cows. The cost of rearing was estimated using a cow level simulation model. The culling rate of dairy cows was studied by using a multivariate regression analysis. The results on costs and culling rate were used as inputs in the stochastic herd level simulation model. Results of the model showed that in a 100-cow herd, the optimal retention of 2-week-old heifer calves in default scenario was 73%. The net cost of rearing when retaining the optimal percentage of heifer calves was estimated at €40,939 per herd per year. This amount is 6.5% lower compared to when a farm retain all heifer calves. In conclusion, for Dutch dairy farms, it is economically optimal not to retain all heifer calves.