Dutch Dairy Herds Research Articles

Risk factors for introduction of BVDV in the context of a mandatory control program in Dutch dairy herds.

Bovine Viral Diarrhea Virus (BVDV) is a common viral disease in cattle, causing economic losses in naive herds where the virus is introduced. In the Netherlands, a BVDV control program has been in place since 1998, evolving from voluntary to mandatory participation for dairy herds since April 2018. Participation in the BVDV control program is not mandatory for non-dairy farms. The aim of this study was to determine risk factors for new introduction of BVDV into certified BVDV-free herds in the context of a national BVDV control program in dairy herds. In this retrospective case-control study, survey data were collected from 149 dairy farms that lost their BVDV-free status (case herds) and 148 matched dairy farms that maintained their BVDV-free status (control herds) between 2018 and 2021. The survey captured information about potential risk factors and herd characteristics in the 2 years leading up to the loss of BVDV-free status (case herds, virus detection in at least one animal or when seroconversion was detected) or remaining BVDV-free (control herds). Descriptive statistics and logistic regression with a backward selection and elimination procedure were used to identify potential risk factors associated with losing BVDV-free status. Risk factors were quantified as an Odds Ratio (OR) with the associated 95% confidence interval (CI). The risk factor with the highest OR for losing BVDV-free status was purchasing cattle from herds without BVDV-free status (OR 1.25, CI 1.10-1.41), followed by the farmer having another profession that resulted in contact with other cattle (OR 1.25, CI 1.06-1.47), housing young calves and adult cows in the same barn (OR 1.22, CI 1.08-1.38), having a permanent employee on the farm (OR 1.17, CI 1.04-1.31), having a group calving pen (OR 1.16, CI 1.03-1.32), escaped cattle from other farms that mingled with own cattle (OR 1.16, CI 1.01-1.33), and nearest distance to a non-dairy farm (OR 1.15, CI 1.03-1.28). Although the BVDV status of most dairy herds can be checked in an open register, approximately half of the farmers indicated that they purchased cattle from BVDV-free herds while they were actually purchasing from non-BVDV-free farms. Farmers should be stimulated to actively check the true BVDV status of the herd from which cattle are purchased to further reduce the risk of introduction. In addition, indirect contact with cattle from other farms through either the farmer or other on-farm staff should be avoided. It is strongly advised to work in these situations with proper biosecurity measures such as changing boots and coveralls. The results can be used to improve BVDV control programs to further decrease the prevalence.

Evaluation of a data-driven youngstock rearing quality system in Dutch dairy herds (2019–2022)

In recent years, the Netherlands exhibited an increased focus on improving youngstock rearing. Several data-driven tools were developed to assess and benchmark calf rearing practices based on key indicators, stimulate farmers to improve their youngstock rearing and reduce calf mortality. KalfOK is such a data-driven tool in which a farm-level overview with 15 indicators of youngstock rearing quality is communicated to participating farmers. In this study we assessed which farm characteristics are associated with A) a continuously high or low KalfOK score and B) frequent usage of KalfOK. Data from KalfOK and the identification and registration system were available for all participating herds over a period between 2019 and 2022 (Dataset A). Additionally, a sample of 324 dairy farmers, randomly selected from the entire Dutch dairy population (Dataset B), participated in a voluntary survey. Multivariable (logistic) regression models were used to A) identify differences in characteristics between farms with a continuously high- and farms with a continuously low KalfOK score and B) assess which farm characteristics were associated with frequent use of the KalfOK report. The results of analysis A showed multiple factors associated with having a low or high KalfOK score, including replacement rate, closed farming system, limited growth in herd size and the region where a farm was located. Sociological factors might also partly explain the difference between high and low scoring farms. Analysis B showed that approximately half of the surveyed farmers indicated that they do not frequently use their KalfOK overview in their calf rearing management. The KalfOK score of farmers who regularly use their KalfOK report was higher – and consequently better - compared to the KalfOK score of farmers who sometimes or never use KalfOK. Additionally, farmers using a milk robot and those discussing KalfOK regularly with their veterinarian, used KalfOK more often. Thirty four percent of the surveyed farmers made adjustments to their youngstock rearing management since the start of participation. The study’s insights into patterns in KalfOK utilization and associations with farm characteristics provide valuable information for ongoing efforts to enhance calf rearing practices via the use of KalfOK. It is important to consider what is needed to convince farmers that do not use the KalfOK tool to frequently start using their KalfOK report. Future research incorporating sociological aspects can provide a more comprehensive understanding of other dynamics influencing calf rearing and contribute valuable insights for enhancing the effectiveness of programs like KalfOK in promoting calf health and welfare.

Risk factors for the introduction of Salmonella spp. serogroups B and D into Dutch dairy herds

Salmonella spp. infections in animals are a concern due to their zoonotic nature, welfare effects and economic impact on the livestock industry. To enable targeted surveillance, it is important to identify risk factors for the introduction of Salmonella spp. in a herd. Since 2009, Dutch dairy processors require herds delivering milk to their plants to participate in a Salmonella programme. In this programme, bulk milk is tested three times a year (i.e. test rounds) by ELISA on presence of antibodies against Salmonella spp. serogroups B and D. Based on these bulk milk results we identified newly infected herds, and aimed to identify associated risk factors. Effects of putative risk factors for becoming newly infected were studied using a multivariable population average logistic regression (PA-GEE) model with binomial distribution. Per test round in 2019–2021, 0.85–4.10 % of the Dutch dairy herds at risk became newly infected, with large regional differences. Several risk factors for becoming newly infected in the context of the low herd-level prevalence were identified. The most evident risk factors that were identified were having at least one infected or recently recovered dairy herd within 500 m (OR = 2.67), on-farm presence of pigs (OR = 1.63), introduction of more than 2 cattle from other herds in the previous 12 months (OR = 1.17), being in an area with a relative soil moisture of >0.54 % (OR = 1.31), being located in an area with a high water surface area (>2 %; OR = 1.14) and a larger herd size (OR = 1.65). These results indicate that, in addition to introduction of cattle, local transmission plays an important role in the between-herd transmission of Salmonella spp. Information on risk factors for becoming newly infected based on regularly collected data, can be used to improve surveillance and to implement targeted control measures against salmonellosis.

Effectiveness of the Leptospira Hardjo Control Programme and Detection of New Infections in Dairy Cattle in The Netherlands.

Since 2005, a mandatory L. Hardjo control programme (LHCP) has been in place for Dutch dairy herds. Almost 100 percent of dairy farms participate and have an L. Hardjo-free status. In 2020 and 2021, the number of outbreaks seemed to increase as compared to the previous years. In this study, we evaluated the effectiveness of the national LHCP in the Netherlands during 2017-2021. Cases of new infections in herds with an L. Hardjo-free status in the LHCP were described, including the role of risk factors for the introduction. Both the percentage of dairy herds with an L. Hardjo-free status that purchased cattle from herds without a free status and the number of purchased cattle increased over the years. A between-herd cluster evaluation showed that between 2017 and 2021, a suspected infection was detected 144 times in 120 dairy herds. In 26 cases (26 herds, 0.2%) new infections were identified, including within-herd transmission. No infection clusters were identified, indicating that infections never led to local transmission between dairy herds. The introduction of cattle from non-free herds appeared to be the cause of all L. hardjo infections in herds participating in the LHCP. Therefore, the national LHCP seems to be highly effective in the control of infections in dairy herds.

Longevity and the association with cattle health in Dutch dairy farms

Longevity of a herd is defined as the average age of all cattle over two years old at the moment of death (either natural, by euthanasia or by slaughter), and is increasing since 2018. The aim of this study was to evaluate the association between longevity and cattle health indicators in Dutch dairy herds. Anonymized census data were available for 16,200 Dutch dairy herds (∼98 % of the dairy herds) between 2016 and 2020. All herds were categorized into one of six longevity groups: herds with a high longevity (>seven years old), increasing longevity (mean increase of one year and two months between 2017 and 2020), median longevity (∼five years and eight months, without major fluctuations in longevity), decreasing longevity (mean decrease eight months), low longevity (<four years and ten months) and a varying longevity of ∼five years and eight months, with fluctuation in longevity throughout the study period. Data were aggregated to herd and quarter of the year level. Cattle health parameters were analysed with multivariable population-averaged models with health indicators as dependent variables, and longevity and other management and herd level factors as explanatory variables. Belonging to the group of herds with a high longevity was associated with lower calf- and cow mortality, a higher percentage of cows with a high somatic cell count (HSCC), fewer inseminations per pregnancy for heifers and more inseminations for cows, a higher antibiotic use in adult dairy cows and a lower antibiotic use in calves compared to the average Dutch dairy herd. Further analysis showed that the higher percentage of cows with a HSCC could be explained by the higher share of older cows. Herds with a low longevity had a higher calf- and cow mortality, a lower percentage of cows with a HSCC, more inseminations for heifers and fewer for cows, lower antibiotic use in cows and higher antibiotic use in calves compared to the average Dutch dairy herd. To conclude, there were differences in cattle health between groups of herds with a different longevity.

The association of dairy cattle longevity with farm level technical inefficiency.

Prolonging dairy cattle longevity is regarded as one of the options to contribute to a more sustainable milk production. Cattle longevity is a direct result from culling decisions, which is primarily driven by economic considerations. As a consequence, at the herd level, cattle longevity can have effects on the efficiency of dairy production. This study investigates the technical inefficiency of dairy input, and its association with cattle longevity under Dutch commercial dairy production conditions, using a two-stage data envelopment analysis (DEA) approach. First, the technical inefficiency of capital, labor, land, seed & crop protection expenses, veterinary services, livestock purchase & services, feed purchase, miscellanea, livestock units and total input on total farm revenues was computed using DEA. Secondly, a bootstrap truncated regression analysis was applied to identify the association of cattle longevity with the evaluated input-specific and total input scores for technical inefficiency. Data were compiled from performance and accountancy records of 1,037 commercial Dutch dairy herds over the period of 2007 to 2014. In general, Dutch dairy farms displayed a relatively good overall technical efficiency, represented by an average inefficiency score of 0.09. The economic benefit of extending cattle longevity was evidenced by the negative association of cattle longevity with total input inefficiency. Of the evaluated inputs, the utilization of livestock units and feed was most efficient, with inefficiency scores below 0.26. This contrasts with the poor input efficiency of capital and livestock purchase & services with inefficiency scores around 0.52. Although the strength of the evaluated associations was generally low, the regression results illustrated that, except for labor, the age of culled cows was significantly negatively associated (P < 0.05) with each of the input inefficiencies. This contrasts with the significant associations of input inefficiencies with lifetime milk production, which were mostly positive. Since lifetime milk production is driven by length of cattle lifespan in combination with production level of the cows, the reverse direction of the associations with the two longevity indices illustrates that prolonging cattle longevity can improve efficiency performance of a dairy farm as long as the milk yield per cow remains unchanged.

Disentangling the relationships between lameness, milking frequency and milk production in Dutch dairy herds using an automatic milking system

Lameness in dairy cattle commonly results in milk production losses. Previous studies have quantified this effect in cows milked with conventional milking systems. The effect of lameness on milk production in herds with automatic milking systems (AMS) might differ because lame cows may visit the AMS less frequently, resulting in an additional milk production loss. This observational study aimed to quantify the direct and indirect effect, through milking frequency, of lameness on milk production in AMS herds by means of a multilevel mediation analysis. Lameness scores, the number of visits to the AMS, and daily milk production records of 1608 dairy cows from 87 Dutch AMS herds were available for analysis. Of those cows, 27 % were mildly lame and 11 % were severely lame. Using a multilevel Poisson regression model, both mildly lame and severely lame cows were identified to visit the AMS less frequently. A separate multilevel linear regression model identified that the direct effect of severe lameness on milk production was 1.4 kg per day. The subsequent multilevel mediation analysis showed that the relationship between severe lameness and milk production was mediated through a reduced milking frequency; 63 % of the total milk production losses (3.9 kg per day) were due to a reduced milking frequency (2.5 kg per day). The mediating effect of a reduced milking frequency was most prominent in cows with parity ≥ 4 and in cows that were ≥ 30 days in lactation. This study suggests that milk production losses due to severe lameness of dairy cows might be higher in AMS herds compared with cows in herds with a conventional milking system, a likely result of a reduced milking frequency of those cows. Prevention of severe lameness may be more cost-effective in AMS herds than in herds with a conventional milking system.

Estimating the Effect of a Bovine Viral Diarrhea Virus Control Program: An Empirical Study on the Performance of Dutch Dairy Herds.

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 in the BVDV-free program. In our study, the effects of BVDV-free certification might have been underestimated, given that the Dutch BVDV control program became mandatory during the study period, and some of the case herds might have never experienced any BVDV infection. The results of this study suggest that in the final stage of the BVDV control program, the program may no longer have a clear benefit to the herd performance of participating dairy herds. When designing national programs to eradicate BVDV, it is therefore important to include incentives for such farms to motivate them to join the program.

Dynamics of Mycoplasma bovis in Dutch dairy herds during acute clinical outbreaks

Mycoplasma bovis (M. bovis) can cause serious illness in cattle, presenting as arthritis and mastitis in dairy cows and pneumonia, arthritis and otitis media in calves. This study aimed to provide insight into the dynamics of M. bovis within dairy herds, experiencing an acute outbreak in dairy cows. Twenty farms were followed with laboratory testing of suspected dairy cows. Each outbreak farm was sampled five times, at 2–3 week intervals, sampling blood and milk and conjunctival fluid from clinically suspected dairy cows and healthy animals from three different age groups: dairy cows, young stock (7–24 months) and calves (1–6 months). Additionally, bulk tank milk was sampled every visit and environmental samples were taken on the first and last visits. The presence of M. bovis was tested by evaluating antibody titres in blood, bacterial DNA in conjunctival fluid and environmental samples and viable bacteria in milk samples. All data were analysed using logistic regression models, corrected for repeated sampling and within-herd correlation.Sixty percent (12/20) of the herds showed a combination of arthritis and mastitis, while other herds experienced only clinically mastitis (3/20) or arthritis (5/20). From the time an outbreak was confirmed, M. bovis infection was not only present in dairy cows, but also in young stock and calves (80% of the farms). Laboratory tests also confirmed the presence of M. bovis in healthy animals. The M. bovis PCR levels of calves and young stock were highly correlated at all visits (rtotal = 0.81, P < 0.01). Furthermore, M. bovis was present in the environment of the animals. At the end of the 3-month study period, none of the 20 clinical outbreak farms were M. bovis-‘negative’, based on laboratory testing, although hardly any clinical cases were observed at that time.

Correlations of feed intake predicted with milk infrared spectra and breeding values in the Dutch Holstein population

Feed is a major cost in dairy production, and substantial genetic variation in feed efficiency exists between cows. Therefore, breeders aim to improve feed efficiency of dairy cattle. However, phenotypic data on individual feed intake on commercial farms is scarce, and accurate measurements are very costly. Several studies have shown that information from Fourier-transformed infrared spectra of milk samples (milk infrared, milk IR) can be used to predict phenotypes such as energy balance and energy intake, but this is usually based on small data sets obtained under experimental circumstances. The added value of information from milk IR spectra for estimation of breeding values is unknown. The objectives of this study were (1) to develop prediction equations for dry matter intake (DMI) and residual DMI (rDMI) from milk IR spectra; (2) to apply these for a data set of milk IR spectra from commercial Dutch dairy farms; (3) to estimate genetic parameters for these traits; and (4) to estimate correlations between these predictions and other traits in the breeding goal. We used data from feeding trials where individual feed intake was recorded daily and for which milk IR spectra were determined weekly to develop prediction equations for DMI and rDMI with partial least squares regression. This data set contained over 7,600 weekly averaged DMI records linked with milk IR spectra from 271 cows. The equations were applied for a data set with test day information from 676 Dutch dairy herds with 621,567 records of 78,488 cows. Both milk IR-predicted DMI and rDMI were analyzed with an animal model to obtain genetic parameters and sire effect estimates that could be correlated with breeding values. A partial least squares regression model with 10 components from the milk IR spectra explained around 25% of DMI variation and less than 10% of rDMI variation in the validation set. Nearly all variation in the milk IR spectra was captured by 7 components; additional components contributed marginally to the spectral variation but decreased prediction errors for both traits. Accuracies of predictions of DMI and rDMI from milk IR spectra for a large feeding experiment were 0.47 and 0.26 on average, respectively, with small differences between ration treatments (ranging from 0.43 to 0.55 and from 0.21 to 0.34, respectively) and among lactation stages (ranging from 0.24 to 0.59 and from 0.13 to 0.36, respectively), with the highest prediction accuracies in early lactation. The estimated heritabilities for predicted DMI and rDMI were 0.3 and 0.4, respectively, which suggests genetic potential for both predicted traits. The correlations of sire estimates for milk IR-predicted DMI with official Dutch breeding values were strongest with milk production (0.33), longevity (0.26), and fertility (-0.27), indicating that cows that eat more produce more, live longer, and have poorer fertility. The correlations of sire estimates for predicted DMI and rDMI with the official breeding values for DMI were low (0.14 and 0.03, respectively). This implies that the added value of including milk IR-predicted DMI information in the estimation procedure of breeding values for DMI would be considered insufficient for practical application.

Sensor based time budgets in commercial Dutch dairy herds vary over lactation cycles and within 24 hours.

Cows from 8 commercial Dutch dairy farms were equipped with 2 sensors to study their complete time budgets of eating, rumination, lying, standing and walking times as derived from a neck and a leg sensor. Daily sensor data of 1074 cows with 3201 lactations was used from 1 month prepartum until 10 months postpartum. Farms provided data over a 5 year period. The final models (lactational time budget and 24h time budget) showed significant effects of parity, farm and calving season. When primiparous cows were introduced in the lactational herd, they showed a decrease in lying time of 215 min (95% CI: 187–242) and an increase in standing time of 159 min (95% CI: 138–179), walking time of 23 min (95% CI: 20–26) and rumination time of 69 min (95% CI: 57–82). Eating time in primiparous cows increased from 1 month prepartum until 9 months in lactation with 88 min (95% CI: 76–101) and then remained stable until the end of lactation. Parity 2 and parity 3+ cows decreased in eating time by 30 min (95% CI: 20–40) and 26 min (95% CI: 18–33), respectively, from 1 month before to 1 month after calving. Until month 6, eating time increased 11 min (95% CI: 1–22) for parity 2, and 24 min (95% CI: 16–32) for parity 3+. From 1 month before calving to 1 month after calving, they showed an increase in ruminating of 17 min (95% CI: 6–28) and 28 min (95% CI: 21–35), an increase in standing time of 117 min (95% CI: 100–135) and 133 min (95% CI: 121–146), while lying time decreased with 113 min (95% CI: 91–136) and 130 min (95% CI: 114–146), for parity 2 and 3+, respectively. After month 1 in milk to the end of lactation, lying time increased 67 min (95% CI: 49–85) for parity 2, and 77 min (95% CI: 53–100) for parity 3+. Lactational time budget patterns are comparable between all 8 farms, but cows on conventional milking system (CMS) farms with pasture access appear to show higher standing and walking time, and spent less time lying compared to cows on automatic milking system (AMS) farms without pasture access. Every behavioral parameter presented a 24h pattern. Cows eat, stand and walk during the day and lie down and ruminate during the night. Daily patterns in time budgets on all farms are comparable except for walking time. During the day, cows on CMS farms with pasture access spent more time walking than cows on AMS farms without pasture access. The average 24h pattern between parities is comparable, but primiparous cows spent more time walking during daytime compared to older cows. These results indicate a specific behavioral pattern per parameter from the last month prepartum until 10 months postpartum with different patterns between parities but comparable patterns across farms. Furthermore, cows appear to have a circadian rhythm with varying time budgets in the transition period and during lactation.

Detection of a high prevalence of paratuberculosis in a previously test‐negative conventional dairy herd in the Netherlands

AbstractJohne's disease, or paratuberculosis, in cattle, caused by Mycobacterium avium subspecies paratuberculosis (MAP), can cause substantial economic losses on dairy farms. This case study describes the finding of a MAP prevalence of 8% in a Dutch dairy herd with a 10‐year history of biennial enzyme‐linked immunosorbent assay‐negative testing in individual milk samples of lactating cows. This case illustrates the strengths and limitations of the different laboratory tests and testing schemes for MAP screening in dairy herds. A cluster of MAP‐shedding cattle on this farm was related to a birth cohort, which suggests a common source of infection early in life. Our observations stress the importance of preventive management to reduce the potential and hidden spread of MAP in test‐negative herds.

Why Test Purchased Cattle in BVDV Control Programs?

Bovine viral diarrhea (BVD) is controlled in many countries by detection and culling of persistently infected (PI) animals. The most important risk factor for BVDV introduction is purchase. An introduced cow can be PI and transmit the virus to other cattle in the herd. If she is not PI but is pregnant, there is still a risk because the subsequently born calf may be PI, when she encountered the virus in early pregnancy. To control this risk, all cows > 1 year from non-BVDV-free herds that are introduced in herds that participate in the Dutch BVDV control program are tested for virus and antibodies. Depending on the results, subsequent measures such as suspension of the BVDV-free status, removing the animals from the herd, or testing the off-spring of the cow for virus, are undertaken. The aim of this study was to evaluate the results of this risk mitigating measure. Data on cattle movements, calving's, herd-level BVDV status, and animal-level test data were available from all dairy herds that participated in the national BVDV control program (>14,000 dairy herds) for the year 2019. The data were combined and parameters of interest were calculated, i.e., (i) the number of purchased BVD virus positive cattle and (ii) the number of BVD virus positive calves born from purchased cows within 9 months after introduction. In 2019, 217,301 cattle were introduced in Dutch dairy herds that participated in the BVDV control program. Of these, 49,820 were tested for presence of BVD virus and 27 (0.05%) cows introduced in 21 different herds tested BVD virus positive. Out of 46,727 cattle that were tested for antibodies, 20.5% tested positive. The seropositive cows produced 4,341 viable calves, of which 3,062 were tested for virus and subsequently, 40 (1.3%) were found BVD virus positive. These 40 BVD virus positive calves were born in 23 herds. The risk mitigating measure led to detection of 67 BVD virus positive animals in 44 unique herds in 2019. This study makes plausible that the probability and impact of re-introduction of BVDV can be minimized by testing introduced cattle and their subsequently born calves.

Transmission dynamics of Staphylococcus aureus and Streptococcus agalactiae in a Dutch dairy herd using an automatic milking system

Staphylococcus aureus and Streptococcus agalactiae are important contagious mastitis pathogens and are considered to mainly transmit between cows through the milking machine. Controlling contagious mastitis on dairy farms requires a reduction of the transmission rate or the duration of intramammary infections (IMI), or both. These parameters may differ in dairy herds milked with an automatic milking system (AMS) as compared to those milked with a conventional milking system (CMS). The aims of this prospective longitudinal study were to estimate the transmission rate, the median duration of IMI and the basic reproduction number (R0) of Staph. aureus and Strep. agalactiae in a Dutch AMS herd. Bacteriological cultures of quarter milk samples were collected every 2 wks. Using 3 different definitions of IMI, we estimated the transmission rate for Staph. aureus to be within the range of 0.002 (95 % CI: 0−0.005) quarter-day−1 to 0.019 (95 % CI: 0.010−0.032) quarter-day−1, and for Strep. agalactiae of 0.007 (95 % CI: 0.005−0.010) quarter-day−1 to 0.019 (95 % CI: 0.011−0.032) quarter-day−1, the median duration of chronic IMI at 95 (95 % CI: 72−125) days for Staph. aureus and at 86 (95 % CI: 67−111) days for Strep. agalactiae, and the R0 between 0.16 (95 % CI: 0.05−0.27) and 0.34 (95 % CI: 0.20−0.48) for Staph. aureus, and between 0.64 (95 % CI: 0.41−0.87) and 0.68 (95 % CI: 0.48−0.88) for Strep. agalactiae.Transmission of these two contagious pathogens in this herd was limited and theoretically the IMI would not sustain, given that R0 of both pathogens was lower than 1. The estimated transmission rate of Staph. aureus in this AMS herd was found to be comparable to those described for CMS herds, while for Strep. agalactiae, it was slightly higher than in CMS herds. The duration of Staph. aureus IMI was in line with results from CMS farms, while the duration of Strep. agalactiae was lower than what has been described in CMS herds. The R0 of these contagious pathogens was found to be lower than the estimates in CMS herds. Our study suggests that the transmission rate of these two contagious pathogens in this AMS herd were comparable to what has been reported about well-performing CMS herds that have a low rate of transmission.

Dry period management and new high somatic cell count during the dry period in Dutch dairy herds under selective dry cow therapy

The aim of this study was to describe the current management practices that could influence udder health during the dry period and to investigate the associations between management and new high SCC during the dry period. In 2008 the Netherlands started to improve transparency and decrease the use of antimicrobials in animal husbandry. Prevention of new high SCC during the dry period via antimicrobial dry cow treatment (DCT) was no longer allowed in animals with a low SCC before the dry period. An increase of new high SCC during the dry period was expected in dairy cows without antimicrobial protection, but was not observed. In 2018 an online questionnaire was conducted among 1,942 Dutch dairy farms using 12 different veterinary clinics. The questionnaire asked about the management of dry cows at the start of and during the dry period, and around calving, considerations in the use of DCT, and knowledge of the 2012 guidelines for selective DCT. A total of 690 farmers (36%) responded to the questionnaire. Data on new high SCC during the dry period, use of antimicrobials for intramammary DCT and mastitis treatment, herd size, and milking system were available from other sources. Descriptive statistics were used to evaluate the associations between the different variables. For analysis of new high SCC on herd level, explanatory variables were analyzed using a generalized linear mixed model. Respondents indicated that the most important management factor to reduce the risk of new high SCC was reduction of milk yield before dry-off. The variables associated with a lower proportion of new high SCC on herd level during the dry period were the use of dip or spray after drying off, a higher animal-defined daily dose of intramammary antimicrobials for DCT, the use of DCT in low-SCC cows based on SCC or mastitis history, correct knowledge of the guideline, and awareness of importance of low infection rate and good hygiene during dry-off. The variables associated with a higher proportion of new high SCC on herd level were dry cow housing other than cubicles and a higher animal-defined daily dose for intramammary antimicrobials for mastitis. This research clearly indicates that farmers can balance limited use of antimicrobials at dry-off with management measures to maintain good udder health during the dry period.

Evaluation of the association between the introduction of data-driven tools to support calf rearing and reduced calf mortality in dairy herds in the Netherlands

Between 2009 and 2017, calf mortality in the Dutch dairy sector showed a slight but steady increase. The Dutch dairy industry decided to act and supported the development of several data-driven tools that were implemented from 2018 on. The tools informed farmers about their calf mortality rates and stimulated them to improve. The Trend Analysis Surveillance Component of the Dutch cattle Health Surveillance System provided the possibility to evaluate the calf mortality in Dutch dairy herds before and after implementation of these tools. The aim of this study was to evaluate the association between calf mortality and i) all actions that were taken by the Dutch dairy industry to improve the quality of calf rearing and ii) other potential management or environmental factors associated with calf mortality in Dutch dairy herds.Census data from approximately 98 % of all Dutch dairy herds were available from July 2014 until June 2019. Four different calf mortality indicators were defined: perinatal calf mortality risk (i.e., mortality before, during, or shortly after the moment of birth up to the moment of ear-tagging), postnatal calf mortality risk (ear-tagging till 14 d), preweaned calf mortality rate (15 d-55 d) and weaned calf mortality rate (56 d-1 yr.). All data were aggregated to herd and monthly level and were analysed using Population-Averaged Generalized Estimating Equations (PA GEE models) with a Poisson distribution and log link function.When the period before implementation of the tools (2016–2017) was compared to the period thereafter (2018–2019), all four calf mortality indicators decreased. The relative decrease varied from 3 % (postnatal calves) and 10 % (perinatal calves) up to 18 % and 30 % in preweaned and weaned calves, respectively. Registrations of veterinary treatments such as antimicrobial use, vaccinations (calf or cow) and antiparasitic treatments were associated with calf mortality. Additionally, herds with a higher level of metabolic problems in transition cows had a higher calf mortality and also extreme outside temperatures were associated with higher calf mortality.Given that the different tools were implemented nation-wide and a control group was lacking, we could not prove that implementing the different tools caused the reduction in calf mortality. We do however, believe that all the actions and communication towards improvement of calf rearing in dairy herds led to an increased awareness among farmers towards the importance of calf rearing management and therefore a reduction in calf mortality on national level.

Survey on Colostrum Management by Dairy Farmers in the Netherlands.

Colostrum feeding is essential for the transfer of passive immunity and health of newborn calves. Information on current colostrum management practices to reduce calf morbidity and mortality is important but lacking for Dutch dairy herds. We therefore conducted a survey to investigate colostrum management strategies on Dutch dairy farms. The survey was specifically focused on the most recently born calf and was returned by 107 respondents (response rate of 13.4%). The mean amount of colostrum fed at first feeding was 2.9 liters. Overall, 79% of farmers provided the calf with at least 6 liters of colostrum in up to three feedings. The majority of respondents (84%) claimed to provide the calf with colostrum for the first time within 2 h post-partum. Using ordinal logistic regression and Wilcoxon rank sum test, we found no differences in time to first colostrum feeding or total amount of colostrum fed between bull calves and heifer calves, respectively. Ordinal logistic regression showed no significant differences in time to first colostrum feeding or time between calving and removing the calf from the dam between AMS and conventional milking herds. Two sample T-test comparing the total volume of colostrum showed no significant difference between AMS and conventional milking herds. Time of day at which a calf was born affected both volume fed at first colostrum feeding and time until first colostrum feeding. Calves born between 00.00 and 06.00 were significantly at risk of receiving the first colostrum later as compared to calves born at other times. Calves born in the evening received on average a lower amount of colostrum at first feeding. Survey results on colostrum management on most Dutch dairy farms are in agreement with the advice to feed as soon as possible after parturition and to provide at least 6 liters within 24 h of age. The current study points at time of calving as a potential risk factor for sub-optimal colostrum feeding. Further research is necessary to determine the consequences of this observation.

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.

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.

An observational study on the management of digital dermatitis through a repeated risk assessment on 19 Dutch dairy herds

The etiopathogenesis of bovine digital dermatitis (DD) is not well understood, but its risk factors on dairy farms have been studied extensively. The objective of this study was to identify associations between a DD risk score [determined by a DD risk assessment questionnaire (RAQ)] and DD prevalence (determined by an in-parlor M-score). We also investigated whether feedback for farmers on their DD management using the DD RAQ resulted in changes that decreased DD prevalence in their herds. The DD RAQ consisted of multiple-choice questions related to foot health, housing, and general management that were used to create a total risk score (TRS). In 2016 and 2018, the DD RAQ-together with a DD prevalence determination in the lactating herd-was used on 19 Dutch dairy farms from 1 veterinary practice. After each visit, farmers and their consulting veterinarians received a 1-page summary that identified herd-specific strengths and weaknesses in DD management. In 2018, the summary included suggestions for improvement. In 2019, farmers and veterinarians were contacted to ask whether the use of the DD RAQ and the 1-page summary had led them to implement changes in their DD management in 2016 and 2018. We tested the association between TRS and DD prevalence using linear mixed model analysis. The TRS ranged from 13 to 65% and 20 to 68% in 2016 and 2018, respectively. Herd DD prevalence ranged from 15 to 59% and 27 to 69% in 2016 and 2018, respectively. For both years, the DD RAQ identified that DIM, herd size, and breed were often present in a manner associated with increased risk for DD. The linear mixed model analysis identified that each 10-point increase in TRS was associated with an increase in herd DD prevalence of less than 1%. The association between TRS and herd DD prevalence was caused mainly by risk factors related to housing. We found no important relationship between change in TRS and change in DD prevalence between the 2 visits. Only a few farmers indicated some form of change in their DD management following a visit. Veterinarians in general said that they discussed the 1-page summaries and DD control with farmers during a routine visit, but the majority admitted a lack of follow-up. We propose that the DD RAQ could be used as a tool to start a discussion on DD control on farm, but simply undertaking a DD RAQ and providing a 1-page summary of the results was insufficient to initiate behavioral change that led to a decrease in DD prevalence.