Search

A prognosis of the likelihood of insemination success is valuable information for the decision to start inseminating a cow. This decision is important for the reproduction management of dairy farms. The aim of this study was to develop a prognostic model for the likelihood of successful first insemination. The parameters considered for the model are readily available on farm at the time a farmer makes breeding decisions. In the first step, variables are selected for the prognostic model that have prognostic value for the likelihood of a successful first insemination. In the second step, farm effects on the likelihood of a successful insemination are quantified and the prognostic model is cross-validated. Logistic regression with a random effect for farm was used to develop the prognostic model. Insemination and test-day milk production data from 2,000 commercial Dutch dairy farms were obtained, and 190,541 first inseminations from this data set were used for model selection. The following variables were used in the selection process: parity, days in milk, days to peak production, production level relative to herd mates, milk yield, breed of the cow, insemination season and calving season, log of the ratio of fat to protein content, and body condition score at insemination. Variables were selected in a forward selection and backward elimination, based on the Akaike information criterion. The variables that contributed most to the model were random farm effect, relative production factor, and milk yield at insemination. The parameters were estimated in a bootstrap analysis and a cross-validation was conducted within this bootstrap analysis. The parameter estimates for body condition score at insemination varied most, indicating that this effect varied most among Dutch dairy farms. The cross-validation showed that the prognosis of insemination success closely resembled the mean insemination success observed in the data set. Insemination success depends on physiologi

The goal of dry-cow therapy (DCT) is to reduce the prevalence of intramammary infections (IMI) by eliminating existing IMI at drying off and preventing new IMI from occurring during the dry period. Due to public health concerns, however, preventive use of antimicrobials has become questionable. In this study, we evaluated the effects of 8 scenarios for selecting animals for DCT, taking into account variation in parity and cow-level somatic cell count (SCC) at drying off. The aim of this study was to evaluate udder health, antimicrobial usage, and economics at the herd level when using different scenarios for selecting cows for DCT. To enable calculation and comparison of the effects of different scenarios to select cows for DCT in an "average" herd, we created an example herd, with a virtual herd size of 100 dairy cows to be calving during a year. Udder health, antimicrobial usage, and economics were evaluated during the dry period and the first 100 d in lactation, the period during which the greatest effect of DCT is expected. This leads to an estimated 13,551 cow-days at risk during a year in a 100-cow dairy herd. In addition to a blanket DCT (BDCT) scenario, we developed 7 scenarios to select cows for DCT based on SCC. The scenarios covered a range of possible approaches to select low-SCC cows for DCT, all based on cow-level SCC thresholds on the last milk recording before drying off. The incidence rate of clinical mastitis in the example herd varied from 11.6 to 14.5 cases of clinical mastitis per 10,000 cow-days at risk in the different scenarios, and the prevalence of subclinical mastitis varied from 38.8% in scenario 1 (BDCT) to 48.3% in scenario 8. Total antimicrobial usage for DCT and clinical mastitis treatment varied over the scenarios from 1.27 (scenario 8) to 3.15 animal daily dosages (BDCT), leading to a maximum reduction in antimicrobial usage of 60% for scenario 8 compared with BDCT. The total costs for each of the scenarios showed little variatio

This study aimed at determining the incidence, distribution, risk factors, and causal serotypes of foot and mouth disease (FMD) outbreaks in Ethiopia based on 5 years of retrospective outbreak data (September 2007 until August 2012). District level outbreak data were collected from 115 randomly selected districts using a questionnaire administered to district animal health officers. The national incidence of FMD outbreaks during the study period was 1.45 outbreaks per five district years. Outbreaks were geographically widespread affecting all major regional states in the country and were more frequent in the central, southern, and southeastern parts of the country. Neither long-term nor seasonal trends were observed in the incidence of outbreaks. A mixed effects logistic regression analysis revealed that the type of production system (market oriented system versus subsistence systems), presence of a major livestock market and/or route, and adjacency to a national parks or wildlife sanctuary were found to be associated with increased risk of outbreaks in the districts. FMD virus serotypes O, A, SAT 2, and SAT 1 were identified as the causal serotypes of the outbreaks during the study period. Whereas O was the dominant serotype, SAT 2 was the serotype that showed increase in relative frequency of occurrence. The estimated incidence of outbreaks is useful in assessing the economic impacts of the disease, and the identified risk factors provide important knowledge to target a progressive FMD control policy for Ethiopia.

Many believe the health status of organic dairy herds in Europe should be improved to meet consumers’ and legislators’ expectations to improve animal welfare. This paper reports on a study in four countries that examined dairy farmers’ intentions towards improving the health status of their organic herds through the use of the Theory of Planned Behaviour. It was found that farmers across the countries were positive about taking additional preventative measures to improve the health status of their herds. They believed this would not only improve herd physical performance, such as milk yield and fertility, but also achieve greater cost effectiveness and improved job satisfaction for them. Most study farmers would implement a tailored package of improvement measures designed by the study team with higher uptake most likely being by younger farmers, those who make greater use of veterinarians and professional advisory services, and those supplying specialist milk-marketing chains. Furthermore, farmers will be most likely to take-up additional health promotion if compatible with their everyday activities and if they have strong business performance goals aimed at maximising the physical performance of the herd.

Sustainable livestock production systems are needed to feed the larger, more urban, richer and older world population in 2050. Quantitative information about the sustainability performance of existing livestock production systems can aid the debate of which actions could be developed and implemented. Strong points of conventional and organic dairy cattle, beef cattle, pig, laying hen, and broiler production systems were identified in peer-reviewed literature for a limited number of sustainability indicators within the subjects of economy, productivity, environment, animal welfare, and public health. This study aims to identify lessons learned for sustainable livestock production.

Sustainable livestock production systems are needed to feed the larger, more urban, richer and older world population in 2050. Quantitative information about the sustainability performance of existing livestock production systems can aid the debate of which actions could be developed and implemented. Strong points of conventional and organic dairy cattle, beef cattle, pig, laying hen, and broiler production systems were identified in peer-reviewed literature for a limited number of sustainability indicators within the subjects of economy, productivity, environment, animal welfare, and public health. This study aims to identify lessons learned for sustainable livestock production.

To compare milk yields between cows or management strategies, lactations are traditionally standardized to 305-d yields. The 305-d yield, however, gives no insight into the combined effect of additional milk yield before calving, decreased milk yield after calving, and a possible shorter calving interval in the case of a shortened dry period. We aimed to develop a measure that would enable the comparison of milk yield between cows with different dry period (DP) lengths. We assessed the importance of accounting for additional milk yield before calving and for differences in calving interval. The 305-d yield was compared with a 365-d yield, which included additional milk yield in the 60 d before calving. Next, an effective lactation yield was computed, defined as the daily yield from 60 d before calving to 60 d before the next calving, to account for additional milk yield before calving and for differences in calving interval. Test-day records and drying-off dates of 15 commercial farms were used to compute the 305-d, 365-d, and effective lactation yields for individual cows. We analyzed 817 second-parity lactations preceded by no DP, a short DP (20 to 40 d), or a conventional DP (49 to 90 d). Compared with cows with a conventional DP, the 305-d yield of cows with no DP was 7.0 kg of fat- and protein-corrected milk (FPCM) per day lower, and the 305-d yield of cows with a short DP was 2.3 kg of FPCM per day lower. Including additional milk yield before calving in the 365-d yield reduced this difference to 3.4 kg of FPCM per cow per day for cows with no DP and to 0.9 kg of FPCM per cow per day for cows with a short DP. Compared with cows with a conventional DP, median days open were reduced by 25 d for cows with no DP and by 18 d for cows with a short DP. Accounting for these differences in calving interval in the effective lactation yield further decreased yield reductions for cows with no DP or a short DP by 0.3 kg of FPCM per cow per day. At the herd level, estimated

Information related to mastitis risk factors is useful for the design and implementation of clinical mastitis (CM) control programs. The first objective of our study was to model the risk of CM under Brazilian conditions, using cow-specific risk factors. Our second objective was to explore which risk factors were associated with the occurrence of the most common pathogens involved in Brazilian CM infections. The analyses were based on 65 months of data from 9,789 dairy cows and 12,464 CM cases. Cow-specific risk factors that could easily be measured in standard Brazilian dairy farms were used in the statistical analyses, which included logistic regression and multinomial logistic regression. The first month of lactation, high somatic cell count, rainy season and history of clinical mastitis cases were factors associated with CM for both primiparous and multiparous cows. In addition, parity and breed were also associated risk factors for multiparous cows. Of all CM cases, 54% showed positive bacteriological culturing results from which 57% were classified as environmental pathogens, with a large percentage of coliforms (35%). Coagulase-negative Staphylococcus (16%), Streptococcus uberis (9%), Streptococcus agalactiae (7%) and other Streptococci (9%) were also common pathogens. Among the pathogens analyzed, the association of cow-specific risk factors, such as Zebu breed (OR=5.84, 95%CI 3.77-10.77) and accumulated history of SCC (1.76, 95%CI 1.37-2.27), was different for CM caused by Coagulase-negative Staphylococcus and S. agalactiae in comparison to CM caused by coliforms. Our results suggest that CM control programs in Brazil should specially consider the recent history of clinical mastitis cases and the beginning of the lactations, mainly during the rainy season as important risk factor for mastitis.

To improve cow management in large dairy herds, sensors have been developed that can measure physiological, behavioral, and production indicators on individual cows. Recently, the number of dairy farms using sensor systems has increased. It is not known, however, to what extent sensor systems are used on dairy farms, and the reasons why farmers invest or not in sensor systems are unclear. The first objective of this study was to give an overview of the sensor systems currently used in the Netherlands. The second objective was to investigate the reasons for investing or not investing in sensor systems. The third objective was to characterize farms with and without sensor systems. A survey was developed to investigate first, the reasons for investing or not in sensor systems and, then, how the sensor systems are used in daily cow management. The survey was sent to 1,672 Dutch dairy farmers. The final data set consisted of 512 dairy farms (response rate of 30.6%); 202 farms indicated that they had sensor systems and 310 farms indicated that they did not have sensor systems. A wide variety of sensor systems was used on Dutch dairy farms; those for mastitis detection and estrus detection were the most-used sensor systems. The use of sensor systems was different for farms using an automatic milking system (AMS) and a conventional milking system (CMS). Reasons for investing were different for different sensor systems. For sensor systems attached to the AMS, the farmers made no conscious decision to invest: they answered that the sensors were standard in the AMS or were bought for reduced cost with the AMS. The main reasons for investing in estrus detection sensor systems were improving detection rates, gaining insights into the fertility level of the herd, improving profitability of the farm, and reducing labor. Main reasons for not investing in sensor systems were economically related. It was very difficult to characterize farms with and without sensor systems. Farms w

Dairy farmers often keep almost all their newborn heifer calves despite the high cost of rearing. By rearing all heifer calves, farmers have more security and retain flexibility to cope with the uncertainty in the availability of replacement heifers in time. This uncertainty is due to mortality or infertility during the rearing period and the variation in culling rate of lactating cows. The objective of this study is to provide insight in the economically optimal number of heifer calves to be reared as replacements. A herd-level stochastic simulation model was developed specific for this purpose with a herd of 100 dairy cows; the biological part of the model consisted of a dairy herd unit and rearing unit for replacement heifers. The dairy herd unit included variation in the number of culled dairy cows. The rearing unit incorporated variation in the number of heifers present in the herd by including uncertainty in mortality and variation in fertility. The dairy herd unit and rearing unit were linked by the number of replacement heifers and culled dairy cows. When not enough replacement heifers were available to replace culled dairy cows, the herd size was temporarily reduced, resulting in an additional cost for the empty slots. When the herd size reached 100 dairy cows, the available replacement heifers that were not needed were sold. It was assumed that no purchase of cows and calves occurred. The optimal percentage of 2-wk-old heifer calves to be retained was defined as the percentage of heifer calves that minimized the average net costs of rearing replacement heifers. In the default scenario, the optimal retention was 73% and the total net cost of rearing was estimated at €40,939 per herd per year. This total net cost was 6.5% lower than when all heifer calves were kept. An earlier first-calving age resulted in an optimal retention of 75%, and the net costs of rearing were €581 per herd per year lower than in the default scenario. For herds with a lower or hig

In the future veterinary herd health management (VHHM) is becoming a more and more important part to secure a responsible dairy production. However, there is room for improvement. By giving VHHM a more fixed structure and by following prewritten steps like goal setting, planning, execution and evaluation, the veterinarian and other advisors are able to tailor VHHM to the farm. This emphasizes that the execution of VHHM is changing over the years. If you can divide dairy farmers into different groups based on their interests, veterinarians are better able to target the topics that this specific farmer is interested in. This was done in a previous study. The purpose of this study is to make a cross validation of the previous study and the consequent derived (group) results. Also, we tried to create some depth and discover the reasons why farmers do or do not want to discuss a certain topic. For this study, which is a cross-validation of the previous study, nineteen randomly selected farmers were approached and were personally interviewed with an open-question interview. This interview was divided into three parts; the general data, general questions about farm(ers) data and questions about VHHM. Based on the interviews, we could draw three main conclusions: The list of topics (claw health, nutrition, young stock rearing, fertility management, udder health, housing and analysis of production numbers and milk quality), which was studied before, is nowadays still useful, but can be complemented with topics that are recent developments in the sector. Obtaining the relative importance of different topics is not very useful in practice. Because nineteen interviewed farmers all gave a different definition of VHHM, the second thing that can be concluded is that there is not a clear definition of VHHM among farmers. To avoid incomprehension and discontent among farmers and their advisors, it is important to be aware of each other’s understandings about VHHM. And the last concl

Dairy farmers often keep almost all their newborn heifer calves despite the high cost of rearing. By rearing all heifer calves, farmers have more security and retain flexibility to cope with the uncertainty in the availability of replacement heifers in time. This uncertainty is due to mortality or infertility during the rearing period and the variation in culling rate of lactating cows. The objective of this study is to provide insight in the economically optimal number of heifer calves to be reared as replacements. A herd-level stochastic simulation model was developed specific for this purpose with a herd of 100 dairy cows; the biological part of the model consisted of a dairy herd unit and rearing unit for replacement heifers. The dairy herd unit included variation in the number of culled dairy cows. The rearing unit incorporated variation in the number of heifers present in the herd by including uncertainty in mortality and variation in fertility. The dairy herd unit and rearing unit were linked by the number of replacement heifers and culled dairy cows. When not enough replacement heifers were available to replace culled dairy cows, the herd size was temporarily reduced, resulting in an additional cost for the empty slots. When the herd size reached 100 dairy cows, the available replacement heifers that were not needed were sold. It was assumed that no purchase of cows and calves occurred. The optimal percentage of 2-wk-old heifer calves to be retained was defined as the percentage of heifer calves that minimized the average net costs of rearing replacement heifers. In the default scenario, the optimal retention was 73% and the total net cost of rearing was estimated at €40,939 per herd per year. This total net cost was 6.5% lower than when all heifer calves were kept. An earlier first-calving age resulted in an optimal retention of 75%, and the net costs of rearing were €581 per herd per year lower than in the default scenario. For herds with a lower or higher

Background This research utilizes the Reasoned Action Approach framework to study which beliefs drive the intention of farmers to participate in a voluntary vaccination scheme against Bluetongue. Scope and approach Knowing the driving beliefs can help in selecting an appropriate mix of policy instruments to enhance the participation rate and thereby improve the cost-effectiveness and efficiency of voluntary vaccination strategies. Results are used to evaluate the policy instruments used by the Dutch government in their 2008 vaccination strategy (communicative intervention and vaccine subsidization). Key findings and conclusions The paper posits that social interaction mechanisms, such as peer group pressure, might advance the design of voluntary vaccination strategies.

To improve cow management in large dairy herds, sensors have been developed that can measure physiological, behavioral, and production indicators on individual cows. Recently, the number of dairy farms using sensor systems has increased. It is not known, however, to what extent sensor systems are used on dairy farms, and the reasons why farmers invest or not in sensor systems are unclear. The first objective of this study was to give an overview of the sensor systems currently used in the Netherlands. The second objective was to investigate the reasons for investing or not investing in sensor systems. The third objective was to characterize farms with and without sensor systems. A survey was developed to investigate first, the reasons for investing or not in sensor systems and, then, how the sensor systems are used in daily cow management. The survey was sent to 1,672 Dutch dairy farmers. The final data set consisted of 512 dairy farms (response rate of 30.6%); 202 farms indicated that they had sensor systems and 310 farms indicated that they did not have sensor systems. A wide variety of sensor systems was used on Dutch dairy farms; those for mastitis detection and estrus detection were the most-used sensor systems. The use of sensor systems was different for farms using an automatic milking system (AMS) and a conventional milking system (CMS). Reasons for investing were different for different sensor systems. For sensor systems attached to the AMS, the farmers made no conscious decision to invest: they answered that the sensors were standard in the AMS or were bought for reduced cost with the AMS. The main reasons for investing in estrus detection sensor systems were improving detection rates, gaining insights into the fertility level of the herd, improving profitability of the farm, and reducing labor. Main reasons for not investing in sensor systems were economically related. It was very difficult to characterize farms with and without sensor systems. Farms with

Rabies is one of the most fatal diseases in both humans and animals. A bite by a rabid dog is the main cause of human rabies in Africa. Parenteral mass dog vaccination is the most cost-effective tool to prevent rabies in humans. Our main objective was to review research articles on the parenteral dog rabies vaccination coverage in Africa. We aimed to review published research articles on percentage of dogs owned and percentage of dogs vaccinated against rabies, and on the relation between vaccination coverage and cost recovery.We followed the standard procedures of a systematic literature review resulting in a final review of 16 scientific articles. Our review results indicate that only a small percentage of African dogs is ownerless. Puppies younger than 3 months of age constitute a considerable proportion of the African dog population. There are considerably more male dogs than female dogs present within the dog population. The dog rabies parenteral vaccination coverage following a “free of charge” vaccination scheme (68%) is closer to World Health Organization recommended threshold coverage rate (70%) compared to the coverage rate achieved in “owner-charged” dog rabies vaccination schemes (18%). In conclusion, most dogs in Africa are owned and accessible for vaccination once the necessary financial arrangements have been made.

To improve management on dairy herds, sensor systems have been developed that can measure physiological, behavioral, and production indicators on individual cows. It is not known whether using sensor systems also improves measures of health and production in dairy herds. The objective of this study was to investigate the effect of using sensor systems on measures of health and production in dairy herds. Data of 414 Dutch dairy farms with (n=152) and without (n=262) sensor systems were available. For these herds, information on milk production per cow, days to first service, first calving age, and somatic cell count (SCC) was provided for the years 2003 to 2013. Moreover, year of investment in sensor systems was available. For every farm year, we determined whether that year was before or after the year of investment in sensor systems on farms with an automatic milking system (AMS) or a conventional milking system (CMS), or whether it was a year on a farm that never invested in sensor systems. Separate statistical analyses were performed to determine the effect of sensor systems for mastitis detection (color, SCC, electrical conductivity, and lactate dehydrogenase sensors), estrus detection for dairy cows, estrus detection for young stock, and other sensor systems (weighing platform, rumination time sensor, fat and protein sensor, temperature sensor, milk temperature sensor, urea sensor, ß-hydroxybutyrate sensor, and other sensor systems). The AMS farms had a higher average SCC (by 12,000cells/mL) after sensor investment, and CMS farms with a mastitis detection system had a lower average SCC (by 10,000cells/mL) in the years after sensor investment. Having sensor systems was associated with a higher average production per cow on AMS farms, and with a lower average production per cow on CMS farms in the years after investment. The most likely reason for this lower milk production after investment was that on 96% of CMS farms, the sensor system investment occurred toget

Lying behavior is a relevant indicator for the evaluation of cow welfare. Lying can be recorded automatically by data loggers attached to one of the hind legs of a cow. A threshold for the duration of a lying bout (LB) record is required, however, to discard false records caused by horizontal leg movements, such as scratching. Previously determined thresholds for similar sensors ranged from 25 s to 4 min. We aimed to validate LB recorded by the IceQube sensor (with IceManager software; IceRobotics, South Queensferry, UK) and to determine a threshold to distinguish true from false LB records in lactating dairy cows. A novel method of validation, which does not require time-consuming behavioral observations, was used to generate a larger data set with potentially more incidental short LB records. Both hind legs of 28 lactating dairy cows were equipped with an IceQube sensor for a period of 6 d and used as each other's validation. Classification of LB records as true (actual LB) or false (recorded while standing) was based on 3 assumptions. First, all standing records (absence of LB records) were assumed to occur while standing. Second, false LB records due to short leg movements could not occur in both hind legs simultaneously. Third, true LB only occurred if the LB records of the paired sensors coincided. False LB records constituted 4% of the records. Based on a maximum accuracy of 0.99, a minimum duration of LB records of 33 s was determined, implying a sensitivity of 0.99 and a specificity of 0.98. Applying this threshold of 33 s hardly affected estimates of daily lying time, but improved estimates of frequency and mean duration of LB for individual cows. The importance of distinguishing short LB was demonstrated specifically for detection of calving. The 2-sensor approach, using sensor outputs on both hind legs as each other's validation, is a time-efficient method to validate LB records that can be applied to different sensors and husbandry conditions.

Dairy farmers often keep almost all their newborn heifer calves despite the high cost of rearing. By rearing all heifer calves, farmers have more security and retain flexibility to cope with the uncertainty in the availability of replacement heifers in time. This uncertainty is due to mortality or infertility during the rearing period and the variation in culling rate of lactating cows. The objective of this study is to provide insight in the economically optimal number of heifer calves to be reared as replacements. A herd-level stochastic simulation model was developed specific for this purpose with a herd of 100 dairy cows; the biological part of the model consisted of a dairy herd unit and rearing unit for replacement heifers. The dairy herd unit included variation in the number of culled dairy cows. The rearing unit incorporated variation in the number of heifers present in the herd by including uncertainty in mortality and variation in fertility. The dairy herd unit and rearing unit were linked by the number of replacement heifers and culled dairy cows. When not enough replacement heifers were available to replace culled dairy cows, the herd size was temporarily reduced, resulting in an additional cost for the empty slots. When the herd size reached 100 dairy cows, the available replacement heifers that were not needed were sold. It was assumed that no purchase of cows and calves occurred. The optimal percentage of 2-wk-old heifer calves to be retained was defined as the percentage of heifer calves that minimized the average net costs of rearing replacement heifers. In the default scenario, the optimal retention was 73% and the total net cost of rearing was estimated at €40,939 per herd per year. This total net cost was 6.5% lower than when all heifer calves were kept. An earlier first-calving age resulted in an optimal retention of 75%, and the net costs of rearing were €581 per herd per year lower than in the default scenario. For herds with a lower or higher

The objectives of this study were to explore farmers’ intentions to implement foot and mouth disease (FMD) control in Ethiopia, and to identify perceptions about the disease and its control measures that influence these intentions using the Health Belief Model (HBM) framework. Data were collected using questionnaires from 293 farmers in three different production systems. The influence of perceptions on the intentions to implement controlmeasures were analyzed using binary logistic regression. The effect of socio-demographic and husbandry variables on perceptions that were found to significantly influence the intentions were analyzed using ordinal logistic regression. Almost all farmers (99%) intended to implement FMD vaccination free of charge. The majority of farmers in the pastoral (94%) and market oriented (92%) systems also had the intention to implement vaccination with charge but only 42%of the crop-livestockmixed farmers had the intention to do so. Only 2%of pastoral and 18%of crop-livestock mixed farmers had the intention to implement herd isolation and animal movement restriction continuously. These proportions increased to 11%for pastoral and 50%for crop-livestock mixed farmers when the measure is applied only during an outbreak. The majority of farmers in the market oriented system (>80%) had the intention to implement herd isolation and animal movement restriction measure, both continuously and during an outbreak. Among the HBM perception constructs, perceived barrier was found to be the only significant predictor of the intention to implement vaccination. Perceived susceptibility, perceived benefit and perceived barrier were the significant predictors of the intention for herd isolation and animalmovement restriction measure. In turn, the predicting perceived barrier on vaccination control varied significantly with the production systemand the age of farmers. The significant HBM perception predictors on herd isolation and animalmovement restriction con

Background: As farmers do not often keep a record of the expenditures for rearing, an economic tool that provides insight into the cost of rearing is useful. In the Netherlands, an economic tool (Jonkos) has been developed that can be used by farmers to obtain insight into the cost of rearing on their farm. The first objective of this study is to calculate the total cost of rearing young stock in Dutch dairy herds using Jonkos. The second objective is to compare the calculated total cost of rearing with the farmers' own estimation of the cost of rearing (the perceived cost). Findings: Information was available for 75 herds that reared their own young stock and who had used the Jonkos tool. The perceived cost of rearing young stock was only available for 36 herds. In the 75 herds, the average herd size was 100 dairy cows. The average calculated total cost of rearing a heifer was €1,790. The average perceived total cost of rearing a heifer (including labour and housing costs) was €1,030. Conclusion: Most Dutch farmers in the study underestimated the total cost of rearing. The Jonkos economic tool has the advantage that herd-specific information can be entered as input values. The output of the tool can improve the awareness of farmers about the total costs of rearing. This awareness can lead to a higher priority of young stock rearing and consequently to an improved quality of young stock rearing.

In dit filmpje verteld Prof. Henk Hogeveen over zijn werk en de toegevoegde waarde voor de Nederlandse samenleving. Zijn specialisatie gaat over de economie van dierziekten in de melkveehouderij. Wanneer dieren worden gehouden om inkomen te creëren moeten de kosten van dierziekte ook meegenomen worden in kosten- en baten berekeningen. Interessant is de recente toenemende eisen van de samenleving met betrekking tot de gezondheid van vee. Economische berekeningen moeten uitgebreid worden met deze eisen., In this movie Prof. Henk Hogeveen explains his work and its added value for the Dutch society. His discipline revolves around economics of animal disease in dairy farming. When animals are kept to create income the costs of animal disease need to be considered. An interesting aspect of the past couple of years is the growing demand from society. Economic calculations need to be expanded with these demands.