Prevalence of extended-spectrum and AmpC ß-lactamase-producing Escherichia coli in young calves on Dutch dairy farms.

In young calves on dairy farms the animal prevalence of extended-spectrum and AmpC ß-lactamase-producing Escherichia coli (ESBL/AmpC-EC) is significantly higher compared with the animal prevalence in young stock and dairy cows. Hitherto it was unknown at what age antimicrobial resistant bacteria appear for the first time in the gut of calves on dairy farms, and how long these infections persist. The aim of this study was to examine the prevalence of ESBL/AmpC-EC, the number of excreted ESBL/AmpC-EC (in cfu/g of feces), as well as the ESBL/AmpC genotypes in young dairy calves (0-21 d of age) and the variation of these parameters between calves of different ages. Next to this, the course of shedding ESBL/AmpC-EC during the first year in dairy calves was studied. In a cross-sectional study, fecal samples from 748 calves, from 0 to 88 d of age, on 188 Dutch dairy farms were collected. The prevalence of calves testing positive for ESBL/AmpC-EC in a phenotypic assay was determined for different age categories (per 2 d of age). Positive samples were subjected to a semiquantitative test to determine the numbers of ESBL/AmpC-EC per gram of feces and for a selection of ESBL/AmpC-EC isolates the ESBL/AmpC genotype was determined. Ten of the 188 farms were selected for a longitudinal study based on the presence of at least 1 female calf with ESBL/Amp-EC in the cross-sectional study. These farms were additionally visited 3 times with a 4-mo interval. All calves that were sampled in the cross-sectional study were, if still present, resampled during the follow-up visits. Results show that from the day of birth ESBL/AmpC-EC can be present in the gut of calves. The phenotypic prevalence of ESBL/AmpC-EC was 33.3% in 0- to 21-d-old calves and 28.4% in 22- to 88-d-old calves. The prevalence of ESBL/AmpC-EC positive calves varied per age category among calves up to 21 d of age: significant increases and decreases at an early age were shown. Results of the longitudinal study show that after 4, 8, and 12 mo the prevalence of ESBL/AmpC-EC positive calves dropped to 3.8% (2/53), 5.8% (3/52), and 2.0% (1/49), respectively. This indicates that early gut colonization in young calves with ESBL/AmpC-EC is transient and does not lead to long-term shedding of these bacteria.

Effect of selective dry cow treatment on udder health and antimicrobial usage on Dutch dairy farms.

Since 2013, selective dry cow treatment (SDCT) has been the standard approach in the Netherlands where farmers select cows for the use of antimicrobials at drying-off. Shortly after its introduction, antimicrobial usage decreased significantly, and no significant association was found between the level of SDCT and clinical mastitis (CM). Obviously, at that time long-term associations could not be evaluated. This study aimed to provide insight into the methods and level of implementation of SDCT on Dutch dairy farms with a conventional milking system (CMS) or an automatic milking system (AMS) in 2016 and 2017, several years after the implementation of SDCT. Udder health and antimicrobial use were also assessed. For this study, 262 farmers recorded dry cow treatments as well as all CM cases in the period from May 1, 2016, until April 30, 2017. Additionally, somatic cell count (SCC) data on cow and herd level, treatment data on herd level and questionnaire results on udder health management were collected. Data were analyzed using descriptive statistics with differences between milking systems being evaluated using nonparametric univariable statistics. In the study period, SDCT was applied on almost all (98.8%) of the participating dairy farms. The main reason for applying antimicrobials at drying-off was either the SCC history during the complete previous lactation or the SCC at the last milk recording before drying-off. The median percentage of cows treated with antimicrobials was 48.5%. The average incidence rate of CM was 27.3 cases per 100 cows per year. From all CM cases that were registered per herd, on average 32.8% were scored as mild, 42.2% as moderate, and 25.0% as severe CM. The mean bulk tank SCC of the herds was 168,989 cells/mL. A cow was considered to have subclinical mastitis (SCM) if individual SCC was ≥150,000 cells/mL for primiparous and ≥250,000 cells/mL for multiparous cows. Passing these threshold values after 2 earlier low SCC values was considered a new case of SCM. The mean incidence rate of SCM in these herds was 62.5 cases per 100 cows per year. Bulk tank SCC and the incidence rate of SCM on farms with a CMS were statistically lower than on farms with an AMS, whereas the incidence rate of CM did not significantly differ between both groups of farms. The AMS farms had more cows per herd treated with antimicrobials at drying-off and a larger proportion of severe CM cases than did CMS farms. It is unknown whether the differences are due to the milking system or to other differences between both types of farms. This study showed the level of adoption of SDCT, udder health, and antimicrobial usage parameters several years after the ban on the preventive use of antimicrobials in animal husbandry. It found that udder health parameters did not differ from those found in Dutch studies before and around the time of implementing SDCT, whereas SDCT was widely applied on Dutch dairy farms during the study period. Therefore, it was concluded that Dutch dairy farmers were able to handle the changed policy of antimicrobial use at drying-off while maintaining indicators of a good udder health.

Incidence of milk leakage after dry-off in European dairy herds, related risk factors, and its role in new intramammary infections.

The incidence of milk leakage (ML) after dry-off (DO) and related risk factors was studied in 1,175 dairy cows from 41 commercial herds in 8 European countries: Belgium, Czech Republic, Denmark, France, Germany, Italy, the Netherlands, and Spain. Milk leakage was assessed twice for 30 s each during 3 visits at 20 to 24 h, 30 to 34 h, and 48 to 52 h after DO. Information related to dry-cow management and udder health was collected at herd and cow level, including individual somatic cell count (ISCC) from test-day controls and occurrence of clinical mastitis cases from DO until 30 d in lactation. Mixed-effect logistic regression analyses were used to identify possible risk factors for ML and to study the association between ML and new intramammary infections. Intramammary infections were defined as clinical mastitis cases during the dry period and in the first 30 d in lactation or a rise in ISCC from before to after the dry period (threshold: 200,000 cells/mL) or both. Milk leakage was observed in 24.5% of the cows between 20 and 52 h after DO, where the herd incidence varied between 0.0 and 77.8%. The reduction in number of milkings in the weeks before DO had statistically significant effect on the ML incidence. When the milking frequency was reduced from 3 times/d to 2 or maintained at twice a day, cows had 11 (95% CI = 3.43-35.46) or 9 (95% CI = 1.85-48.22) times higher odds of leaking milk, respectively, compared with cows where the milking frequency was reduced from twice to once a day. Also, the milk production 24 h before DO was associated with ML incidence. Hence, cows with a milk production between 13 and 21 L or above 21 L had 2.3 (95% CI = 1.48-3.53) and 3.1 (95% CI = 1.79-5.3) times higher odds of leaking milk, respectively, compared with cows with a milk production below 13 L. A higher ML incidence was present in the group of cows with an average ISCC in the last 3 mo before DO ≥200,000 cells/mL (odds ratio = 1.7; 95% CI = 1.13-2.41) compared with cows with an average ISCC <100,000 cells/mL. Quarters with ML tended to have 2.0 times higher odds of developing clinical mastitis compared with quarters not leaking milk. Cows with ML tended to have 1.5 times higher odds of intramammary infections (i.e., an increase of ISCC or clinical mastitis) compared with cows without ML.

Factors associated with high antimicrobial use in young calves on Dutch dairy farms: A case-control study.

Since 2012, the Dutch Veterinary Medicine Authority reports antimicrobial usage (AMU) in young calves (<56 d) on dairy farms on an annual basis. The AMU distribution in this age group is skewed, with a low AMU in young calves on the majority of dairy farms and a high AMU in a relatively small number of farms. This results in a notable difference between the mean and median AMU. To further reduce the mean AMU, the AMU on the high-AMU farms must be decreased. The objective of this study was to evaluate the association between both young stock management and an indication of the farmers' mindset and AMU in young calves on Dutch dairy farms with a high and low AMU in young calves. This knowledge may be helpful in decreasing AMU in young calves on high-AMU farms. We performed a case-control study in which 200 dairy farms (100 with high AMU and 100 with low AMU in young calves) participated. Case farms were defined as farms with an animal daily-defined dose at the farm level in young calves >28 in 2012 and 2013, based on the 90th percentile of the use of antimicrobials in young calves in 2012. Control farms had an animal daily-defined dose at the farm level in young calves of <0.5 in 2012 and 2013, which was determined to be low use. A questionnaire was conducted about general farm and young stock management, hygiene, housing, vaccination, and calf health. An indication of the farmers' mindset with regard to AMU and treatment of sick calves was determined by including statements (agree/disagree) in the questionnaire. In addition, routinely collected data on herd size, growth in herd size, replacement, and calf mortality were available for analysis. Dairy farmers that immediately started antimicrobial treatment in sick calves had higher odds of being in the high-AMU group than farmers who started treatment of sick calves with supportive nonantimicrobial therapy. Other variables associated with a high AMU in young calves included housing calves on partially slatted floors, a high prevalence of respiratory disease, an unfavorable Salmonella status, and not agreeing with the statement "Young stock need specific management." Both dairy farm management and opinions of dairy producers regarding AMU, indicative of mindset, are important when distinguishing farms with high and low AMU in young calves. Although the rationale behind mindset warrants more research, likely a change in both aspects seems to be required to reduce the AMU in young calves on dairy farms.

Farmers' attitude toward the introduction of selective dry cow therapy.

The attitude of Dutch dairy farmers toward selective dry cow treatment (SDCT) is unknown, although a favorable mindset toward application of SDCT seems crucial for successful implementation. Given the fact that blanket dry cow treatment has been strongly promoted until recently, the implementation of SDCT was expected to be quite a challenge. This study aimed to provide insight into the level of implementation of SDCT in 2013 in the Netherlands, the methods used by farmers for selection of cows for dry cow treatment (DCT), the relation between SDCT and udder health and antimicrobial usage (AMU) in 2013, and the mindset of farmers toward SDCT. In 2014, a questionnaire was conducted in a group of 177 herds included in a large-scale udder health study in 2013 and for which all clinical mastitis cases during this year were recorded. In addition, data on somatic cell count (SCC) parameters and AMU was available for these herds. The questionnaire included questions with regard to DCT with a special emphasis on farmers' attitude and mindset with regard to applying DCT in 2013. The data that were obtained from the questionnaire were combined with the data on clinical mastitis, SCC, and AMU. Descriptive statistics were used to evaluate the data and to study the association between DCT, udder health, and AMU. Univariable and multivariable logistic regression models with a logit link function were applied to evaluate potential associations between DCT and farmers' mindset. Selective DCT was taken up progressively by the farmers in our study, with 75% of them implementing SDCT in 2013. The main criterion used to select cows for DCT was the SCC history during the complete previous lactation. The herds were divided into 3 groups based on the percentage of cows dried off with antibiotics in 2013 as indicated by the farmers during interviews. The first group applied BDCT, and the herds for which SDCT was applied were split in 2 equally sized groups based on the median percentage of cows dried off with antibiotics (67%). The incidence rate of subclinical and clinical mastitis were comparable between the groups. Results of the multivariable model showed that 4 factors related to farmers' mindset were associated with the probability to apply SDCT: "financial consequences of SDCT," "uncertainty whether a cow will recover without antimicrobials," the statement "I do not have a problem with the (potential) negative consequences of SDCT," and the usage of internal teat sealants. Application of SDCT appeared to be associated with farmers' attitude. The mindset of farmers with respect to reduction of AMU and the implementation of SDCT was generally positive.

Short communication: Effectiveness of tools provided by a dairy company on udder health in Dutch dairy farms.

A Dutch dairy company initiated a quality system to support dairy farmers to improve sustainability on their farm. Improvement of udder health is defined by the dairy company as one of the sustainability items. A part of that quality system is to offer farmers 3 tools to improve the udder health status of the herd. The first tool is an Udder Health Workshop at which farmers make a farm-specific action plan to improve the udder health situation in their herd. The second tool is the Udder Health Navigator, which is an internet-based program to gain insight in the actual udder health situation at the farm. The third tool is the Udder Health Checklist, which is available on the internet and it identifies farm-specific risks for udder health problems. The aim of this study was to evaluate the effectiveness of these tools in improving udder health. The bulk milk somatic cell count (BMSCC) was used as the measure of herd udder health performance. In total, 605 farms attended the Udder Health Workshop, 988 farms completed the Udder Health Navigator, and 1,855 farms completed the Udder Health Checklist in 2012. Information on BMSCC records (2 records per month) was available for 12,782 Dutch dairy farms during the years 2011 and 2012. For every farm, the average BMSCC of all months during the years 2012 and 2011 were calculated. This resulted in 306,768 average monthly observations of the BMSCC. Subsequently, all months after the completion of one of the tools were assigned a 1, and all other months were assigned a 0. A statistical analysis was carried out to compare the average monthly BMSCC of the farms that completed one or more tools with farms that did not complete one of the tools. Both completing the Udder Health Navigator and the Udder Health Checklist had a significant association with a lower average monthly BMSCC. The effect of the Udder Health Navigator and Udder Health Checklist on the BMSCC were greater in herds with a BMSCC in 2011 of 200,000 to 250,000 cells/mL and even greater for herds with a BMSCC above 250,000 cells/mL compared with herds with a BMSCC in 2011 of 150,000 to 200,000 cells/mL or less than 150,000 cells/mL. It is difficult to draw conclusions on the effect of the Udder Health Workshop due to overlap in participation between the tools. The results suggest that completing the web tools is associated with a reduction in the BMSCC of the herd.

Do farm audits improve milk quality?

Milk quality is assessed using bulk milk analysis and by farm audits in the Netherlands. However, the extent of the effect that dairy farm audits have on milk quality is unknown. Data from over 13,000 audits performed on 12,855 dairy farms from February 2006 to April 2008 were merged with laboratory test results of 325,150 bulk milk samples collected 6 mo before and after the audit. A linear mixed model with the method of restricted maximum likelihood was conducted to study whether the total bacterial counts (TBC) of bulk milk were lower during the periods before and after the dairy farm audit. Results showed that TBC values were 2 to 6% lower (i.e., 0.010 to 0.026 log cfu/mL) for a period from 1.5 to at least 6 mo after an audit. Additionally, several variables were significantly associated with bulk milk TBC values: seasonality, total number of attention points (given if some checklist points were not appropriate), audit type, audit result, and the categories milking equipment maintenance, and utility room-tank maintenance. The TBC values increased with a higher level of attention points. Furthermore, the farms rejected based on the audit results had the highest average TBC values and the approved farms had the lowest values. If dairy farms had an overall negative audit assessment and consequently needed a re-audit in the following year, the TBC values of bulk milk were more likely to be higher. Auditing may provide dairy farmers the opportunity to receive advice about factors that influence bulk milk TBC values, for a period of at least 6 mo following the audit.

Risk-based audit selection of dairy farms.

Dairy farms are audited in the Netherlands on numerous process standards. Each farm is audited once every 2 years. Increasing demands for cost-effectiveness in farm audits can be met by introducing risk-based principles. This implies targeting subpopulations with a higher risk of poor process standards. To select farms for an audit that present higher risks, a statistical analysis was conducted to test the relationship between the outcome of farm audits and bulk milk laboratory results before the audit. The analysis comprised 28,358 farm audits and all conducted laboratory tests of bulk milk samples 12 mo before the audit. The overall outcome of each farm audit was classified as approved or rejected. Laboratory results included somatic cell count (SCC), total bacterial count (TBC), antimicrobial drug residues (ADR), level of butyric acid spores (BAB), freezing point depression (FPD), level of free fatty acids (FFA), and cleanliness of the milk (CLN). The bulk milk laboratory results were significantly related to audit outcomes. Rejected audits are likely to occur on dairy farms with higher mean levels of SCC, TBC, ADR, and BAB. Moreover, in a multivariable model, maxima for TBC, SCC, and FPD as well as standard deviations for TBC and FPD are risk factors for negative audit outcomes. The efficiency curve of a risk-based selection approach, on the basis of the derived regression results, dominated the current random selection approach. To capture 25, 50, or 75% of the population with poor process standards (i.e., audit outcome of rejected), respectively, only 8, 20, or 47% of the population had to be sampled based on a risk-based selection approach. Milk quality information can thus be used to preselect high-risk farms to be audited more frequently.

Invited review: sensors to support health management on dairy farms.

Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

Optimizing bulk milk dioxin monitoring based on costs and effectiveness.

Dioxins are environmental pollutants, potentially present in milk products, which have negative consequences for human health and for the firms and farms involved in the dairy chain. Dioxin monitoring in feed and food has been implemented to detect their presence and estimate their levels in food chains. However, the costs and effectiveness of such programs have not been evaluated. In this study, the costs and effectiveness of bulk milk dioxin monitoring in milk trucks were estimated to optimize the sampling and pooling monitoring strategies aimed at detecting at least 1 contaminated dairy farm out of 20,000 at a target dioxin concentration level. Incidents of different proportions, in terms of the number of contaminated farms, and concentrations were simulated. A combined testing strategy, consisting of screening and confirmatory methods, was assumed as well as testing of pooled samples. Two optimization models were built using linear programming. The first model aimed to minimize monitoring costs subject to a minimum required effectiveness of finding an incident, whereas the second model aimed to maximize the effectiveness for a given monitoring budget. Our results show that a high level of effectiveness is possible, but at high costs. Given specific assumptions, monitoring with 95% effectiveness to detect an incident of 1 contaminated farm at a dioxin concentration of 2 pg of toxic equivalents/g of fat [European Commission's (EC) action level] costs €2.6 million per month. At the same level of effectiveness, a 73% cost reduction is possible when aiming to detect an incident where 2 farms are contaminated at a dioxin concentration of 3 pg of toxic equivalents/g of fat (EC maximum level). With a fixed budget of €40,000 per month, the probability of detecting an incident with a single contaminated farm at a dioxin concentration equal to the EC action level is 4.4%. This probability almost doubled (8.0%) when aiming to detect the same incident but with a dioxin concentration equal to the EC maximum level. This study shows that the effectiveness of finding an incident depends not only on the ratio at which, for testing, collected truck samples are mixed into a pooled sample (aiming at detecting certain concentration), but also the number of collected truck samples. In conclusion, the optimal cost-effective monitoring depends on the number of contaminated farms and the concentration aimed at detection. The models and study results offer quantitative support to risk managers of food industries and food safety authorities.

Comparative analysis as a management tool for broiler breeder farms: simulated individual farm analysis (IFAS).

The objective of this study was to develop a management information system to evaluate the tactical management of a breeder flock using individual farm analysis with a deterministic simulation model (IFAS). Individual farm analysis is a method that evaluates the performance of individual farms by comparing them with standards. In the first step of IFAS, a farm accounting system is used to compare performance indicators of a flock with the same performance indicators of the average of a group of flocks that produced in the same time period. In the next step, a deterministic simulation model is used to determine the factors causing the traced deviations in performances. Then, relevant deviations are determined based on the economic and statistical importance of each traced deviation. Finally, the deviations are identified by relevance to give farmers an indication of their strong and weak management practices.

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.

Process audits versus product quality monitoring of bulk milk.

Assessment of milk quality is based on bulk milk testing and farm certification on process quality audits. It is unknown to what extent dairy farm audits improve milk quality. A statistical analysis was conducted to quantify possible associations between bulk milk testing and dairy farm audits. The analysis comprised 64.373 audit outcomes on 26,953 dairy farms, which were merged with all conducted laboratory tests of bulk milk samples 12 mo before the audit. Each farm audit record included 271 binary checklist items and 52 attention point variables (given to farmers if serious deviations were observed), both indicating possible deviations from the desired farm situation. Test results included somatic cell count (SCC), total bacterial count (TBC), antimicrobial drug residues (ADR), level of butyric acid spores (BAB), freezing point depression (FPD), level of free fatty acid (FFA), and milk sediment (SED). Results show that numerous audit variables were related to bulk milk test results, although the goodness of fit of the models was generally low. Cow hygiene, clean cubicles, hygiene of milking parlor, and utility room were positively correlated with superior product quality, mainly with respect to SCC, TBC, BAB, FPD, FFA, and SED. Animal health or veterinary drugs management (i.e., drug treatment recording, marking of treated animals, and storage of veterinary drugs) related to SCC, FPD, FFA, and SED. The availability of drinking water was related to TBC, BAB, FFA, and SED, whereas maintenance of the milking equipment was related mainly to SCC, FPD, and FFA. In summary, bulk milk quality and farm audit outcomes are, to some degree, associated: if dairy farms are assessed negatively on specific audit aspects, the bulk milk quality is more likely to be inferior. However, the proportion of the total variance in milk test results explained by audits ranged between 4 and 13% (depending on the specific bulk milk test), showing that auditing dairy farms provides additional information but has a limited association with the outcome of a product quality control program. This study suggests that farm audits could be streamlined to include only relevant checklist items and that bulk milk quality monitoring could be used as a basis of selecting farms for more or less frequent audits.

Standardized data in the broiler value chain.

In the Dutch broiler chain, data are collected as a routine practice. However, there is wide variation in the content of data collected and in data collection systems. This variability hampers the use of field data in management information systems to support decisions. The objective of this study was to analyze the quality of data and to standardize the content of data sets in the broiler production chain. To evaluate the quality of data, data sets from 3 Dutch hatcheries, from 23,637 batches of eggs, were assessed. The quality of data was assessed intuitively based on 7 quality attributes. To standardize the content of the data set, a protocol was proposed and validated. The protocol was validated at 30 breeder farms, 3 hatcheries, and 104 broiler farms by using 3 quality attributes: consistency, uniformity, and completeness. Results of the data quality analysis of the 3 Dutch hatcheries showed that the data sets had some fields with inaccurate, incorrect, inconsistent, nonuniform, incomprehensible, missing relevant, or incomplete data. Results of the validation protocol were as follows: feedback was obtained from 23 (77%) breeder farms, 3 (100%) hatcheries, and 7 (7%) broiler farms. Of all the questions, on average 88% were answered on breeder farms; 57, 65, and 82% were answered at each of the 3 hatcheries, respectively; and 79% were answered on the broiler farms. Data collected at 2 hatcheries were more consistent than those collected at the third hatchery. Hatchery data were less consistent than breeder farm data, but the number of data entries at hatcheries far exceeded the number at the farm level. Data from the hatcheries, breeder farms, and broiler farms were not always uniform, possibly because of differences in management strategies. This protocol enables the listing of relevant and standard contents of a data set whereby information exchange along the chain can be simplified. However, it is recommended that the protocol be supplemented with some rules for data collection and management, for example, that variables must be recorded in the provided fields, and that a variable must have one and only one name or code, the same unit of measurement, and the same definition.

Recall costs balanced against spoilage control in Dutch custard.

The relation between the moment at which a recall of Dutch custard is initiated and the direct costs of this recall was investigated. A simulation model of the custard supply chain was developed to compare scenarios with and without a quarantine of 48 h at the storage of the production plant. The model consists of 3 parts: 1) the distribution of a 24,000-L batch of custard over the supply chain over time is simulated; 2) the time to detect spoilage bacteria with a recontamination test procedure is simulated; and 3) the direct recall costs of custard over the different parts of the supply chain are calculated. Direct recall costs increase from about 25,000 euros/batch to 36,171 euros/batch from 57 to 135 h in the situation without quarantine and from 25,000 euros/batch to 36,648 euros/batch from 123 h to 163 h for the situation with quarantine. Then costs decrease because more and more custard is at the consumer level and only 0.13% of the consumers will ask for a refund. With low true contamination probabilities quarantine is not profitable, but at later detection moments with high probabilities it is. We conclude that a simulation model is a helpful tool to evaluate the efficiency of risk management strategies like end product testing and a quarantine situation.

Field study on broilers' first-week mortality.

In the Dutch poultry meat production chain, first week mortality (FWM) of the chicks is an important measure to quality and is therefore highly related to the price of the chicks that the broiler farm has to pay to the hatchery. Therefore, next to the total number of broiler eggs produced per hen and hatchability, this figure is often used as a measure of efficiency in the breeder-hatchery-broiler production chain. In this study, factors that are related to chick mortality in the first week at broiler farms were investigated. Field data obtained from 2 commercial Dutch hatcheries, for which 482 broiler farms voluntarily recorded FWM of 16,365 flocks of broiler chicks over the years 2004, 2005, and 2006, were analyzed. These represented 79% of the total number of day-old chicks delivered to separate broiler farms. First week mortality was significantly related to breeder age, egg storage length at the hatchery, season, strain, feed company of the breeder farm, year, and hatchery. Furthermore, FWM differed significantly between chicks originating from eggs of different breeder flocks and which were kept for grow-out at different broiler farms.

Field study on broiler eggs hatchability.

The aim of this research was to explore factors that are related with hatchability in the field. Data from 3 Dutch hatcheries for the years 2004, 2005, and 2006 were analyzed using a random regression model with the method of restricted maximum likelihood. In total, 24,234 batches of 724,750,444 eggs, originating from 511 breeder flocks, were included. Annually, 241,583,481 eggs were set on average, which is 37% of the total annual eggs set in the Netherlands. A significant difference in hatchability among eggs from different breeder flocks was found. Hatchability was significantly related with flock age, egg storage length, strain, feed company, season, year, as well as hatchery (P < 0.001). There was also significant interaction between flock age and age at first delivery, egg storage length at hatchery, strain, feed company, and season. Other 3-way interaction terms were also significant. The variation in hatchability was larger among the breeder farms than within breeder farms. The average estimated difference in hatchability among the hatcheries was 8%. The average estimated hatchability at 25 wk of age was 66%; it increased to 86% between 31 and 36 wk and decreased to 50% at 65 wk of age. On average, an extra day of storage until d 7 reduced hatchability by 0.2% and from d 7 to 14 by 0.5%. Eggs from older flocks were less sensitive to prolonged storage, whereas they were more sensitive to season. Hatchability was greater during late summer than during spring. The average estimated differences in hatchability among strains and feed companies of the breeder farms were 8 and 2%, respectively. Based on the relations found, optimization of hatchery results depends not only on good management at the hatchery but also on the hatching egg quality and therefore on the breeder farm management. It can be concluded that production data that are collected by the hatcheries can be used to adjust the management decisions at hatcheries as well as breeder farms.

Using routinely collected data to evaluate risk factors for mortality of veal calves.

From 2009 to 2012 a gradual increase in on-farm mortality of Dutch veal calves was observed. In 2012, the cattle industry decided that more information was needed on risk factors for mortality in both veal herds and herds of origin to enable implementation of risk mitigating measures. Routinely collected data were available from seven different data sources and contained information from 2.4 million white veal calves that were fattened in the period between 1 January 2011 and 30 June 2014. Survival analysis techniques (Kaplan-Meier), multilevel Poisson and multilevel Logistic regression models were applied to analyse the data. Two different models were assembled in which risk factors for veal calf mortality in respectively veal herds and herds of origin were identified. Univariable and multivariable regression techniques were used to detect risk factors significantly associated with mortality of veal calves during the fattening period. During the study period, the mean mortality was 4.9% per production cycle. The probability to die was highest during the first weeks after arrival in the veal herds and declined thereafter. Important risk factors included a veal herds with a higher use of antimicrobials, hair colour as proxy for breed, certain countries of origin, veal herd management with a limited amount of supplied feed and a not having an all-in / all-out system. A higher body weight at arrival in the veal herd was associated with lower mortality as well as veal calves that were fed an above median amount of milk, roughage and concentrates. From the calves that were fattened during the study period, observations of 1.1 million calves originated from the Netherlands and were available to study risk factors for veal calf mortality associated with the herd of origin. Important risk factors included purchase, herds with high mortality rates in the quarter in which the calf was born, fast growth in herd size, high cattle replacement rates and a higher antibiotic use in the quarter of birth. Calves that originated from herds that were certified BVD-free, Salmonella-unsuspected or Paratuberculosis-unsuspected, had a lower odds to die during the subsequent fattening period in a veal herd. Veal calf mortality was influenced by risk factors at the herd of origin as well as at veal herds. Adequate collaboration between the different industries is necessary to optimize veal calf management leading to a reduction in veal calf mortality during the fattening period.

Comparing methods to quantify experimental transmission of infectious agents.

Transmission of an infectious agent can be quantified from experimental data using the transient-state (TS) algorithm. The TS algorithm is based on the stochastic SIR model and provides a time-dependent probability distribution over the number of infected individuals during an epidemic, with no need for the experiment to end in final-size (e.g., where no more infections can occur). Because of numerical limitations, the application of the TS algorithm is limited to populations with only a few individuals. We investigated the error of using the easily applicable, time-independent final-size (FS) algorithm knowing that the FS situation was not reached. We conclude that the methods based on the FS algorithm: (i) underestimate R(0), (ii) are liberal when testing H(0):R(0)1 against H(1):R(0)<1, (iii) are conservative when testing H(0):R(0)1 against H(1):R(0)>1, and (iv) are conservative when testing H(0):R(control)=R(treatment) against H(1):R(control)>R(treatment). Furthermore, a new method is presented to find a difference in transmission between two treatment groups (MaxDiff test). The MaxDiff test is compared to tests based on FS and TS algorithms. The TS test and the MaxDiff test were most powerful (approximately equally powerful) in finding a difference, whereas the FS test was less powerful (especially, when both R(control) and R(treatment) are >1).

Effectiveness of movement-prevention regulations to reduce the spread of foot-and-mouth disease in The Netherlands.

After the foot-and-mouth disease (FMD) outbreak in 2001 the Dutch government implemented movement-prevention regulations to reduce the number of contacts between farms and consequently the risk of spread of highly contagious animal infections in the future. We studied the efficacy of these regulations by comparing registered cattle-movement data from 2000 to those from 2002. We also used the spatial and stochastic simulation model InterFMD to evaluate the consequences of the observed alterations in cattle-contact structure on the spread and control of a FMD epidemic. There was a significant decrease in the number of cattle movements "for live use", no difference in the number of group movements "for live use" and a distinct change in the overall contact structure. The most important structure changes were a decrease in the number of group movements from dairy farms to cattle-collection centres (-44%), and an increase in the number of group movements from dairy farms to beef farms (111%). Our simulations demonstrated that the implemented regulations result in a concentration of the FMD-affected area and therefore in a reduction in size of the epidemics. Based on the intended Dutch strategy to control future FMD outbreaks, the decrease in extreme epidemics (95th percentiles) went from 31 infected farms in an epidemic-length of 65 days to 8 infected farms in an epidemic-length of 53 days in sparsely populated areas. In densely populated areas this decrease went from 135 infected farms to 103, while the duration reduced from 88 days to 81.