| For a scientific researcher, publishing in scientific journals is important. Publishing in scientific journals started as a means to share your findings in your research field, so that others could learn from your findings, and apply that newly gained knowledge in their own work. Since a long time, the scientific community has established a quality system. In so-called peer-reviewed journals, manuscripts are reviewed by colleague researchers (peers). When the editor of a journal receives a new manuscript, he or she will send it out to two or three peers in the field. These peers are able to, anonoumously, read the manuscript and give comments. Sometimes a manuscript need to be clarified, sometimes the statistical analyses were insufficient and an advice is given to redo these analyses and sometimes the work is not good and the manuscript is rejected. There are differences in scientific journals. All journals have some kind of minimum standard. Crap research will not be published. However, some better quality journals have higher standards than the poorer quality journals, so to be able to publish research in these better quality journals is a proof that your research work is of better quality (in general). In the modern research society, it is becoming more and more important that administrators are able to get an idea how good their researchers are. They do that by looking at the journals where researchers publish. This has a couple of nice consequences, which I will not discuss further. To make a long story short, publications in peer-reviewed journals are of a different quality than publications by other means, such as book chapters or publictions in proceedings of scientific conferences. I am authoring quite a long list of publications in proceedings and books, but the most important list of publications is that in peer-reviewed scientific journals. In total I am co-authoring in 96 of this type of publications. This list is given below. Of the most recent publications an abstract and a link to the original publicaiton is given.
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ParaCalc®—A novel tool to evaluate the economic importance of worm infections on the dairy farm | |||
| J. Charlier, M. van der Voort, H. Hogeveen and J. Vercruysse, 2012. Subclinical infections with gastrointestinal nematodes and liver fluke are important causes of production losses in grazing cattle. Although there is an extensive compilation of literature describing the effect of these infections on animal performance, only a few attempts have been made to convert these production losses to an economic cost. Here, we propose a novel tool (ParaCale®), available as a web-application, to provide herd-specific estimates of the costs of these infections on dairy farms. ParaCalc®) is a deterministic spread-sheet model where results from diagnostic methods to monitor the helminth infection status on a herd and anthelmintic usage are used as input parameters. Default values are provided to describe the effects of the infections on production and the cost of these production losses, but the latter can be adapted to improve the herd-specificity of the cost estimate. After development, ParaCalc® was applied on input parameters that were available for 93 Belgian dairy herds. In addition, the tool was provided to 6 veterinarians and their user experiences were evaluated. The estimated median [25th-75th percentile] cost per year per cow was € 46 [29-58] and € 6 [0-19] for gastrointestinal nematode and liver fluke infection, respectively. For both infections, the major components in the total costs were those associated with milk production losses in the adult cows. The veterinarians evaluated ParaCalc® as a useful tool to raise the farmers' awareness on the costs of worm infections, providing added value for their services. However, the score given for user-friendliness was diverse among users. Although the model behind ParaCalc® is a strong simplification of the real herd processes inducing economic losses, the tool may be used in the future to support economic decisions on helminth control. |
Veterinary Parasitology 184: 204-211
Success of first and following inseminations in Dutch dairy cows
Inchaisri, C., R. Jorritsma, J.C.M.Vernooij, P.L.A.M. Vos, G.C. van der Weijden and H. Hogeveen (2011)The objective of this research was to determine the contribution of cow factors to the probability of successful insemination accounting for the serial number of inseminations in analysis. The investigation was performed with 101 297 insemination records in 51 525 lactations of different cows from 1368 herds obtained from the Dutch milk production recording database. Cows that had a first insemination (AI) between 40 and 150 days post-partum with one or more inseminations (<= 6 inseminations) were selected. An insemination was defined successful when not followed by another insemination and when the cow calved between 267 and 295 days after insemination, or when the cow was culled between 135 and 295 days after the last insemination. Breed, parity, days in milk, lactation curve characteristics, milk production traits, moment of AI related to peak milk yield time (before or after peak milk yield), the last calf (female, male, twin or stillbirth) and season of insemination were selected as independent parameters for a model with successful rate of insemination as dependent parameter. A multivariable logistic regression model was used within cow and farm as a random effect. The probability of successful insemination was the highest in the first insemination and decreased in the following inseminations. However, the success rate of all inseminations increased in a later stage of lactation. The improvement in the successful inseminations in a later stage of lactation was better in multiparous cows than in first parity cows. Insemination in summer and before peak milk yield time reduced the success of insemination. The success rate was the lowest in 100% Holstein Friesian cows compared with other breeds and was the highest when the last calf was a female calf compared to a male calf, twin or stillbirth. In conclusion, the success of first and following inseminations depended on parity, breed, season of insemination, last calf status, daily milk yield at insemination date, serial insemination number and days in milk at insemination date.
Reproduction in Domestic Animals, 46: 1043-1049.
Het economische rendement van de activiteiten van een rundveepracticus op verschillende melkveebedrijven
The economic margins of activities of a bovine practitioner on dairy farmsVan Genugten, A.J.M., J.A. van Haaften and H. Hogeveen (2011).
Because of lower margins and market liberalisation veterinarians and farmers are increasingly negotiating rates. Therefore, the margins of veterinarians are under pressure. In addition, the sales if drugs, performance of operations or giving of advice arc more and more separated. These developments give veterinarians uncertainty about the profitability of their activities for dairy fanners. Not much is known about margins on veterinary activities on dairy farms. Moreover; it is interesting to see how much margins of the bovine practitioner differ between veterinary practises and dairy farms. In this study, invoices for bovine activities of 14 veterinary practises were combined with milk production registration data of the dairy farms of these practices. This way, the gross margin per bovine practitioner could be studied for the different veterinary practise. Moreover; the relation between gross margin and specification of the veterinary practise could be studied. Finally, the gross margin per dairy farm and the factors that influenced this gross margin were studied. The most important result was the observation that the gross margin per bovine practitioner was dependent on the number of dairy farms per practitioner, the margin on drugs and the region of the veterinary practise. The size of the veterinary practise, the share of the dairy farming within the practise and the source of the gross margin (drugs, time or operations) did not influence the gross margin. Variables that explained the gross margin per dairy farm were, amongst others, the number of dairy cows, the milk production level of the farms and participation in PIR-DAP system to support the veterinarians herd health and management program). There is no relation of gross margin per dairy farm and the veterinary practise or region.
Tijdschrift voor Diergeneeskunde, 136: 794-800.
The relation between milking interval and somatic cell count in automatic milking systems
Mollenhorst, H., M.M. Hidayat, J. van den Broek, F. Neijenhuis and H. Hogeveen (2011).
The aim of this study was to explore whether, during automatic milking, milking interval or its variation is
related to somatic cell count (SCC), even when corrected for effects of production, lactation stage, and
parity. Data on milking interval and production level were available from the automatic milking systems of
151 farms. Data on SCC, parity, and lactation stage were derived from dairy herd improvement records of
the same farms. Mainly due to incomplete records, data of 100 farms were used in the final analysis. For
every cow, only 1 test day was used in the final analysis. Milking interval, the coefficient of variation of
milking interval, production rate, the difference in production rate between short- and long-term, parity, days
in milk, and some biologically relevant interactions were used in a linear mixed model with farm as random
variable to assess their association with log10-transformed SCC. None of the interactions was significantly
related to SCC, whereas all main effects were, and thus, stayed in the final model. The effect of milking
interval was, although significant, not very strong, which shows that the effect of milking interval on SCC is
marginal when corrected for the other variables. The variation in milking intervals was positively related with
SCC, showing that the variation in milking interval is even more important than the milking interval itself. In
the end, this study showed only a limited association between milking interval and SCC when milking with an
automatic milking system.
Analysis of the economically optimal voluntary waiting period
The voluntary waiting period (VWP) is defined as the time between parturition and the time at which the cow is first eligible for insemination. Determining the optimal VWP from field data is difficult and unlikely to happen. Therefore, a Monte-Carlo dynamic-stochastic simulation model was created to calculate the economic effects of different VWP. The model is dynamic and uses time steps of 1 wk to simulate the reproductive cycle (ovulation, estrous detection, and conception), the occurrence of postpartum disorders, and the lactation curve. Inputs of the model were chosen to reflect the situation of Dutch dairy cows. In the model, we initially created a cow of a randomly selected breed, parity, month of calving, calf status of last calving, and expected 305-d milk yield. The randomly varied variables were based upon relevant distributions and adjusted for cow statuses. The lactation curve was modeled by Wood's function. The economic input values in the analysis included: cost of milk production ((sic)0.07 to (sic)0.20 per kg), calf price ((sic)35 to (sic)150 per calf), AI cost ((sic)7 to (sic)24 per AI), calving management cost ((sic)137 to (sic)167 per calving), and culling cost, expressed as the retention pay-off ((sic)118 to (sic)1,117). A partial budget approach was used to calculate the economic effect of varying the VWP from 7 to 15 wk postpartum, using a VWP of 6 wk as reference. Per iteration, the VWP with either the lowest economic loss or the maximum profit was determined as the optimal VWP. The optimal VWP of most cows (90%) was less than 10 wk. On average, every VWP longer than 6 wk gave economic losses. Longer VWP were in particular optimal for the first parity of breeds other than Holstein-Friesian, cows calving in winter with low milk production, high milk persistency, delayed peak milk yield time, a delayed time of first ovulation, or occurrence of a postpartum disorder, and while costs of milk production are low and costs for AI are high.
Journal of Dairy Science 94: 3811-3824.
Sensor measurement patterns revealed; predicting the Gram-status of clinical mastitis causal pathogen
Kamphuis, C., H. Mollenhorst and H. Hogeveen (2011)Automatic milking systems produce mastitis alert lists that report cows likely to have clinical mastitis (CM). A farmer has to check these listed cows to confirm a CM case and to start an antimicrobial treatment if necessary. In order to make a more informed decision, it would be beneficial to have information about the CM causal pathogen at the same time a cow is listed on the mastitis alert list. Therefore, this study explored whether decision-tree induction was able to predict the Gram-status of CM causal pathogens using in-line sensor measurements from automatic milking systems. Data were collected at nine Dutch dairy farms milking with automatic milking systems and included 140 bacteriological cultured CM cases with sensor measurements of electrical conductivity, colors red, green, and blue and milk yield for analyses. In total, 110 CM cases were classified as Gram-positive CM cases and 30 as Gram-negative. Stratified randomization was used to divide the data in a training set (n = 96) for model development, and a test set (n = 44) for validation. The decision tree used three variables to predict the Gram-status of the CM causal pathogen; two variables were based on electrical conductivity measurements, and one on measurements of the color blue. This decision tree had an accuracy of 90.6% and a kappa value of 0.76 based on data in the training set. When only those CM cases were considered with extreme high probability estimates for their Gram-status (either positive or negative), 74% of all records in the training set could be classified with a stratified accuracy of 97.1%. When validated, the decision tree performed poorly; accuracy dropped to 54.5% and the kappa value to -0.20. The stratified accuracy calculated for 75% of all records in the test set was 66.7%. Predicting the CM causal pathogen showed a similar poor result; the decision tree had an accuracy of 27.9% and a kappa of 0.12, based on data in the test set. Based on these results, it is concluded that decision-tree induction in conjunction with sensor information from the electrical conductivity, color, and milk yield provide insufficient discriminative power to predict the Gram-status or the CM causal pathogen itself.
Computers and Electronics in Agriculture 77: 86-94
Economic aspects of mastitis: New developments
Hogeveen, H., K. Huijps and T.J.G.M. Lam (2011)Good udder health is not only important for the dairy farmer but, because of increasing interest of consumers in the way dairy products are produced, also for the dairy production chain as a whole. An important role of veterinarians is in advising on production diseases such as mastitis. A large part of this advice is given around the planning of management to maintain or improve the udder health status of a farm. Mastitis is a costly disease, due to losses (a reduction of output due to mastitis) and expenditure (additional inputs to reduce the level of mastitis). Worldwide, published estimates of the economic losses of clinical mastitis range from 61 to 97 per cow on a farm, with large differences between farms, e.g. in The Netherlands, losses due to clinical and subclinical mastitis varied between 17 and 198 per cow per year. Moreover, farmers tended to underestimate these costs. This indicates that for a large proportion of farms there are many avoidable losses. In order to provide good support to farmers' decision-making, it is important to describe the mastitis setting not only in terms of disease, e.g. incidence of clinical mastitis, but also in monetary terms; and to make good decisions, it is necessary to provide the dairy farmer with information on the additional expenditure and reduced losses associated with alternative decisions. Six out of 18 preventive measures were shown to have a positive nett benefit, viz blanket use of dry-cow therapy, keeping cows standing after milking, back-flushing of the milk cluster after milking a cow with clinical mastitis, application of a treatment protocol, washing dirty udders, and the use of milkers' gloves. For those measures that included a large amount of routine labour or investment, the reduced losses did not outweigh the additional expenditure. The advisor cannot expect that measures that are cost-effective are always implemented. Reasons for this are the objectives of the dairy farmer can be other than maximisation of profit, resources to improve the mastitis situation compete with other fields of management, risk involved with the decision, economic behaviour of the dairy farmer, and valuation of the cost factors by the dairy farmer. For all decision-makers this means that, although financial incentives do have an effect on the management of mastitis, it is not always sufficient to show the economic benefits of improved management to induce an improvement of management of mastitis.
What veterinarians need to know about communication to optimise theirrole as advisors on udder health in dairy herds
Lam, T.J.G.M., J. Jansen, B.H.P. van den Borne, R.J. Renes and H. Hogeveen (2011)The veterinary practitioner is one of the most important advisors for farmers in the field of udder health. He or she has the tools to improve udder health if farmers are motivated to do so. Many farmers think that udder health is important, but this does not always mean that management of mastitis is up to standard. Many veterinarians are of the opinion that they are unable to convince their clients of the possible profits to be gained from investing in management of mastitis. Something is required to bridge this gap. This article, based on data and experiences from The Netherlands, describes the communication issues that can be considered in order to improve the role of the veterinarian as advisor, to achieve better udder health. The outcome is beneficial for both farmers and veterinarians, the former for reasons of economics, welfare and ease of work; the latter because it creates extra, challenging work. It is concluded that the veterinary practitioner is in an ideal situation to advise and motivate farmers to improve udder health but, to do this, the means of communication need to take account of the different learning styles of farmers. The most important aspects of such communication are found to be a pro-active approach, personalisation of messages, providing a realistic frame of reference for the farmer, and use of the farmer's social environment. Importantly, all persons and organisations in a farmer's social environment should articulate the same message.
Cow-specific treatment of clinical mastitis: An economic approach
Steeneveld, W., T. van Werven, H.W. Barkema and H. Hogeveen (2011)Under Dutch circumstances, most clinical mastitis (CM) cases of cows on dairy farms are treated with a standard intramammary antimicrobial treatment. Several antimicrobial treatments are available for CM, differing in antimicrobial compound; route of application, duration; and cost. Because cow factors (e.g., parity; stage of lactation, and somatic cell count history) and the causal pathogen influence the probability of cure; cow-specific treatment of CM is often recommended. The objective of this study was to determine if cow-specific treatment; of CM is economically beneficial. Using a stochastic Monte Carlo simulation model; 20;000 CM cases were simulated. These CM cases were caused by Streptococcus uberis and Streptococcus dysgalactiae (40%), Staphylococcus aureus (30%), or Escherichia coli (30%). For each simulated CM case; the consequences of using different antimicrobial treatment regimens (standard 3-d intramammary, extended 5-d intramammary, combination 3-d intramammary + systemic; combination 3-d intramammary + systemic + 1-d nonsteroidal antiinflammatory drugs; and combination extended 5-d intramammary + systemic) were simulated simultaneously. Finally, total costs of the 5 antimicrobial treatment regimens were compared. Some inputs for the model were based on literature information and assumptions made by the authors were used if no information was available. Bacteriological cure for each individual cow depended on the antimicrobial treatment regimen; the causal pathogen, and the cow factors parity; stage of lactation; somatic cell count history; CM history, and whether the cow was systemically ill. Total costs for each case depended on treatment costs for the initial CM case (including costs for antibiotics; milk withdrawal, and labor); treatment costs for follow-up CM cases, costs for milk production losses; and costs for culling. Average total costs for CM using the 5 treatments were (US) $224, $247, $253, $260, and $275, respectively. Average probabilities of bacteriological cure for the 5 treatments were 0.53, 0.65; 0.65, 0.68; and 0.75; respectively For all different simulated CM cases, the standard 3-d intramammary antimicrobial treatment had the lowest total costs. The benefits of lower costs for milk production losses and culling for cases treated with the intensive treatments did not outweigh the higher treatment costs. The stochastic model was developed using information from the literature and assumptions made by the authors. Using these information sources resulted in a difference in effectiveness of different antimicrobial treatments for CM. Based on our assumptions, cow-specific treatment of CM was not economically beneficial.
Journal of Dairy Science, 94: 174-188.
Sub-optimal decision making of dairy farmers with respect to mastitis management.
Huijps, K., H. Hogeveen, G. Antonides, N. Valeeva, T.J.G.M. Lam and A.G.J.M. Oude Lansink (2010)This study analyses sub-optimal economic behaviour in decision-making of Dutch dairy farmers regarding measures to improve udder health. A low adoption rate and a low level of compliance with advice given to the dairy industry suggest the presence of inertia. Farmers who already had implemented a specific management measure were more likely to continue doing this than farmers who applied a different management regime, regardless of the availability of more effective or lower cost alternatives. Additionally, the results showed that farmers were more sensitive to penalties rather than bonuses aimed at stimulating desired behaviour.
European Review of Agricultural Economics. 37: 553-568
Effect of milk yield characteristics, breed and parity on success of the first insemination in Dutch dairy cows
Economic consequences of reproductive performance in dairy cattle
Inchaisri, C., R. Jorritsma, P.L.A.M. Vos, G.C. van der Weijden and H. Hogeveen (2010)The net economic value of reproductive efficiency in dairy cattle was estimated using a stochastic dynamic simulation model. The objective was to compare the economic consequences of reproductive performance scenarios ("average" and "poor") of a cow having a good reproductive performance and to explore which reproductive factors have an important impact on economic efficiency. A "good" reproductive performance scenario was defined with 1 ovulation rate (POVU(i)), 0.7 estrus detection rate (PEst), 0.7 conception rate (PCon), 0.03 incidence rate of postpartum disorders prolonging the ovarian cyclicity (CO), 0.2 incidence rate of postpartum disorders reducing conception (ME), 0.05 embryonic death rate (ED), and voluntary waiting period (VWP) of 9 wks pp (post partum). In the current situation of dairy cows in the Netherlands, an "average" reproductive scenario (0.95 POVU(i), 0.5 PEst, 0.5 Peon, 0.07 CO, 0.27 ME, 0.07 ED and VWP of 12 wks pp) and a "poor" reproductive scenario (0.90 POVU(i), 0.3 PEst, 0.3 Peon, 0.11 CO, 0.33 ME, 0.09 ED and VWP of 15 wks pp) were identified. A sensitivity analysis was performed by comparing changes of single effect of factors in a good and poor scenario with the average scenario. The mean net economic loss (NEL(i)) compared with the good scenario was (sic)34 and (sic)231 per cow per year for the average and poor reproductive performance scenario, respectively. Increasing the calving interval resulted in greater economic loss. The important factors on the cost of reproductive efficiency were the involuntary culling cost and the return of milk production. Variation in PCon, PEst, ME, ED, and VWP had large impacts on economic benefits.
Theriogenology, 74: 835-846
Relationship between udder health and hygiene on farms with an automatic milking system
Dohmen, W., F. Neijenhuis and H. Hogeveen (2010)
Poor hygiene is an important risk factor for reduced udder health. Because the teat cleaning process is done
automatically on farms with an automatic milking system (AMS), hygiene management might differ. The aim of this study was to determine the relationship between hygiene and udder health on farms with an AMS at the farm level as well as at the cow level. Information on hygiene and udder health was collected on 151 Dutch dairy farms with an AMS. Teams of 2 veterinary students collected data with the use of a partially open-ended questionnaire and scoring protocols for hygiene of the cows, cleanliness of the AMS, and functioning of the AMS. Milk production records from the Dutch dairy herd information association were also collected. Stepwise general linear models were used to analyze the relation between hygiene and udder health at farm level. Dependent variables were average herd somatic cell count (SCC), the average percentage of new cows with a high SCC, and the incidence rate of clinical mastitis, all in the year preceding the farm visit. The annual average herd SCC was positively related to the proportion of cows with dirty teats before milking and the proportion of cows with dirty thighs. The annual average percentage of new cows with a high SCC was positively related to the proportion of cows with dirty teats before milking and the proportion of milkings where teats were not covered with teat disinfecting spray by the AMS. The annual incidence rate of clinical mastitis was positively related to the frequency of replacing the milking filters. At the cow level, hygiene scores of the udder, thighs, and legs (range 1 to 4, where 1 is clean and 4 is very dirty) were related with cow SCC from the milk production test day closest to the farm visit using a
general linear mixed model. The relationship between cow SCC and the hygiene score of
the udder was positive.
Journal of Dairy Science, 93: 4019-4033
Bio-economic modeling of lactational antimicrobial treatment of new bovine subclinical intramammary infections caused by contagious pathogens
Van den Borne, B.H.P., T. Halasa, G. van Schaik, H. Hogeveen and M. Nielen (2010)
This study determined the direct and indirect epidemiologic and economic effects of lactational
treatment of new bovine subclinical intramammary infections (IMI) caused by contagious pathogens using an existing bioeconomic model. The dynamic and stochastic model simulated the dynamics of Staphylococcus aureus, Streptococcus uberis, Streptococcus dysgalactiae, and Escherichia coli during lactation and the dry period in a 100-cow dairy herd during 1 quota year. Input parameters on cure were obtained from recent Dutch field data. The costs of clinical IMI, subclinical IMI, and intervention were calculated into the combined total annual net costs of IMI per herd. The cost effectiveness of 4 scenarios with lactational intervention was determined; scenarios included no intervention, treatment after 1 mo of infection, treatment after 2 mo of infection, and treatment after 1 mo of infection and culling of uncured cows after 2 mo of infection. Model behavior was observed for variation in parameter input values. Compared with no lactational intervention, lactational intervention of new subclinical IMI resulted in fewer clinical flare ups, less transmission within the herd, and much lower combined total annual net costs of IMI in dairy herds. Antimicrobial treatment of IMI after 1 mo of infection and culling of uncured cows after 2 mo of infection resulted in the lowest costs, whereas treatment after 2 mo of infection was associated with the highest costs between the scenarios with intervention. Changing the probability of cure resulted in a nonlinear change in the cumulative incidence of IMI cases and associated costs. Lactational treatment was able to prevent IMI epidemics in dairy herds at high transmission rates of Strep. uberis, Strep. dysgalactiae, and E. coli. Lactational treatment did not limit the spread of Staph. aureus at high transmission rates, although the associated costs were lower compared with no intervention. To improve udder health in a dairy herd, lactational treatment of contagious subclinical IMI must therefore be preceded by management measures that lower the transmission rate. Lactational treatment of environmental subclinical IMI seemed less cost effective. Detection of subclinical IMI needs improvement to be able to most effectively treat subclinical IMI caused by contagious pathogens during lactation.
Sensors and clinical mastitis – the quest for the perfect alert
Hogeveen, H., C. Kamphuis, W. Steeneveld and H. Mollenhorst (2010)
When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors,
clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe
the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor
systems. Several detection models based on different sensors were studied in the past. When evaluating these
models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the
similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80%
and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should
be as similar to practical farm circumstances as possible. The study design should comprise more than one
farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and
evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes
these results not very comparable. There is a also large difference in performance between the detection models
and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to
compare the overall performance of the different CM detection models. The sensitivity and specificity found in the
different studies could, for a large part, be explained in differences in the used time window. None of the
described studies satisfied the demands for CM detection models
Sensors, 10: 7991-8009
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Neijenhuis, F., H.W. Barkema, H. Hogeveen en J.P.T.M. Noordhuizen (2000). Classification and longitudinal examination of callused teat ends in dairy cows and related factors. Journal of Dairy Science, 83: 2795-2804.
Klungel, G.H., B.A. Slaghuis en H. Hogeveen (2000). The effect of the introduction of automatic milking systems on milk quality. Journal of Dairy Science, 83:1998-2006.
Neijenhuis, F., H. Hogeveen en G.H. Klungel (2001). Recovery of cow teats after milking determined by ultrasonographic scanning. Journal of Dairy Science, 84: 2599-2606.
Hogeveen, H., W. Ouweltjes, C.J.A.M. de Koning, and K. Stelwagen, 2001. Milking interval, milk yield and milk flow rate in an automatic milking system. Livestock Production Science, 72: 157-167
Neijenhuis, F., H.W. Barkema, H. Hogeveen and J.P.T.M. Noordhuizen (2001). Relationship between teat-end callosity and occurrence of clinical mastitis. Journal of Dairy Science, 84: 2664-2672.
Hogeveen, H. and W. Ouweltjes (2003). Sensors and management support in high-tech milking. Journal of Animal Science 81 (Suppl. 3): 1-10.
Norberg, E., H. Hogeveen, I.R. Korsgaard, N.C. Friggens, K.H.M.N. Sloth and P. Løvendahl (2004). Electrical conductivity of milk: Ability to predict mastitis status. Journal of Dairy Science 87: 1099-1107.
Berry
Swinkels, J.M., J. Rooijendijk, R.N. Zadoks and H. Hogeveen (2005). Economic consequences of antibiotic treatment during lacation on chronic subclinical mastitis caused by Streptococcus uberis or Streptococcus dysgalactiae. Journal of Dairy Research, 72: 75-85.
Swinkels, J.M., H. Hogeveen and R.N. Zadoks (2005). A partial budget model to estimate economic benefits of lactational treatment of subclinical Staphylococcus aureus mastitis. Journal of Dairy Science 88: 4273-4287.
Vosough Ahmadi, B., A.G.J. Velthuis, H. Hogeveen and R.B.M. Huirne (2006). Simulating E. coli O157 transmission to asess effectiveness of slaughterhouse inverventions. Preventive Veterinary Medicine, 77 (1): 15-30.
Bijl, R., S.J. Kooistra and H. Hogeveen (2007). The profitability of automatic milking on Dutch dairy farms. Journal of Dairy Science, 90: 238-240.
Huijps, K. and H. Hogeveen (2007). Stochastic modelling to determine the economic effects of blanket, selective and no dry cow therapy. Journal of Dairy Science, 90: 1225-1234.
Halasa, T. , K. Huijps and H. Hogeveen (2007). Bovine mastitis, a review. Veterinary Quarterly, 29: 18-31.
Valeeva, N.I., T.J.G.M. Lam and H. Hogeveen (2007). Motivation of dairy farmers to improve mastitis management. Journal of Dairy Science, 90: 4466-4477.
Vosough Ahmadi, B., K. Frankena, J. Turner, A.G.J. Velthuis, H. Hogeveen and R.B.M. Huirne (2007). Effectiveness of interventions in reducing the prevalence of E.coli O157:H7 in lactating cows in Dutch dairy herds. Veterinary Research, 38: 755-771.
Steeneveld, W. H. Hogeveen and J.M. Swinkels. 2007. Stochastic modelling to assess economic effects of chronic subclinical mastitis caused by Streptococcus uberis. Journal of Dairy Research 74: 459-467.
Huijps, K., T.J.G.M. Lam and H. Hogeveen (2008). Costs of mastitis: facts and perception. Journal of Dairy Research 75: 113-120.
Kamphuis, C. , D. Pietersma, R. van der Tol, M. Wiedemann and H. Hogeveen (2008). Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milk. Computers and Electronics in Agriculture 62: 169-181.
Steeneveld, W. H. Hogeveen, H.W. Barkema, J. van den Broek and R.B.M. Huirne (2008). The influence of cow factors on the incidence of clinical mastitis in dairy cows. Journal of Dairy Science 91: 1391-1402.
Kamphuis, C. , R. Sherlock, J. Jago, G. Mein and H. Hogeveen (2008). Automatic detection of clinical mastitis is improved by in-line monitoring of somatic cell count. Journal of Dairy Science, 91: 4560-4570.
Huijps, K., S. de Vliegher, T.J.G.M. Lam and H. Hogeveen (2009). Cost estimation of heifer mastitis by stochastic modelling. Veterinary Microbiology, 134: 121-127.
Halasa, T., M. Nielen, A. P. W. de Roos, R. van Hoorne, G. de Jong, T. J. G. M. Lam, T. van Werven and H. Hogeveen (2009). Production loss due to new subclinical mastitis in Dutch dairy cows estimated with a test day model. Journal of Dairy Science, 92: 599-606.
Steeneveld, W. , L.C. van der Gaag, H.W. Barkema and H. Hogeveen (2009). Providing probability distributions for the causal pathogen of clinical mastitis using naive Bayesian networks. Journal of Dairy Science, 92: 2598-2609.
Benedictus, A., H. Hogeveen and B.R. Berends (2009). The price of the precautionary principle: cost-effectiveness of BSE intervention strategies in the Netherlands
Halasa, T., H. Hogeveen, M. Nielen and R.B.M. Huirne (2009). Bio-economic modelling of mastitis. Livestock Science, 124: 295-305.
Halasa, T., M. Nielen, O. Osteras, T. Van Werven and H. Hogeveen (2009). Meta analysis of dry cow management for dairy cattle. Part 1: Protection against new intramammary infections. Journal of Dairy Science, 92: 3134-3149.
Lievaart, J.J., H.W. Barkema, H. Hogeveen and W.D.J. Kremer (2009). Reliability of the bulk milk somatic cell count as indication of the average herd milk somatic cell count. Journal of Dairy Research, 76: 490-496.
Huijps, K., H. Hogeveen, T.J.G.M. Lam and R.B.M. Huirne (2009). Preferences of cost factors for mastitis management among Dutch dairy farmers using Adaptive Conjoint Analysis. Preventive Veterinary Medicine, 92: 351-359.
Ellis-Iversen, J., A.J. Cook, E. Watson, M. Nielen, L. Larkin, M. Wooldridge and H. Hogeveen (2010). Perceptions, circumstances and motivators that influence implementation of zoonotic control programs. Preventive Veterinary Medicine, 93: 276-285.
Kamphuis, C. , H. Mollenhorst, A. Feelders, D. Pietersma and H. Hogeveen (2010). Decision-tree induction to detect clinical mastitis with automatic milking. Computers and Electronics in Agriculture, Computers and Electronics in Agriculture 70: 60-68.
Huijps, K., H. Hogeveen, T.J.G.M. Lam and A.J.G.M. Oude Lansink (2010). Efficiency of management measures to improve the mastitis situation on farm level. Journal of Dairy Science, 93:115-124.
Steeneveld, W. , L. C. van der Gaag, H. W. Barkema and H. Hogeveen (2010). Simplify the interpretation of alert lists for clinical mastitis in automatic milking systems. Computers and Electronics in Agriculture 71: 50-56.
Halasa, T., M. Nielen, T. van Werven and H. Hogeveen (2010). Stochastic simulation model to calculate costs and benefits of dry period interventions in dairy cattle. Livestock Science, 129: 80-87.
Bruijnis, M.R.N., H. Hogeveen and E.N. Stassen (2010). Assessing the economic consequences of foot disorders in dairy cattle using a dynamic stochastic simulation model. Journal of Dairy Science, 93: 2419-2432.
Steeneveld, W. L.C. van der Gaag, W. Ouweltjes , H. Mollenhorst and H. Hogeveen (2010) Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems. Journal of Dairy Science, 93: 2559-2568.
Mollenhorst, H. , P. P. J. van der Tol and H. Hogeveen (2010). Somatic cell count assessment at quarter or cow milking level. Journal of Dairy Science, 93: 3358-3364.
Kamphuis, C. , H. Mollenhorst, J.A.P. Heesterbeek and H. Hogeveen (2010). Data mining to detect clinical mastitis with sensor data. Journal of Dairy Science, 93: 3616-3627.
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Stefanowska, J., Devir, S., and H. Hogeveen (1997). The time consumption of dairy cows inside an automatic milking system with selection unit and one-way cow traffic. Canadian Agricultural Engineering, 39 (3): 221-229.
Michalopoulos, T., M. Korthals and H. Hogeveen (2008). Trading “ethical preferences” in the market: Outline of a politically liberal framework for the ethical characterization of foods. Journal of Agricultural and Environmental Ethics 21: 3-27.
Noordhuizen, J.P.T.M., M.J. van Egmond, R. Jorritsma, H. Hogeveen, T. van Werven, P.L.A.M. Vos en J.J. Lievaart (2008). Veterinary advice for entrepreneurial Dutch dairy farmers – From curative practice to coach-consultant: what needs to be changed? Tijdschrift voor Diergeneeskunde 133: 4-8.
Van Asseldonk, M.A.P.M., R.J. Renes, T.J.G.M. Lam and H. Hogeveen (2010). Awareness and perceived value of economic information in controlling somatic cell count. Veterinary Record, 166: 263-267.
Hogeveen, H., Schreinemakers, J.F. and E.N. Noordhuizen‑Stassen (1992). Application of conditional causality in an integrated knowledge‑based system for dairy farms. AI Applications 6, 5‑16.
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Meuwissen, M.P.M., A.G.J. Velthuis, H. Hogeveen and R.B.M. Huirne (2003). Traceability and certification in meat supply chains. Journal of Agribusiness 21 (2): 131-148.
Vosough Ahmadi, B., A.G.J. Velthuis, H. Hogeveen and R.B.M. Huirne (2006). Cost-effectiveness of beef slaughterhouse decontamination measures in the Netherlands
Velthuis, A.G.J., H. Hogeveen, M.C.M. Mourits, M.A. Dolman, H. Van Wichen and A. Gaaff (2009). Costs and reliability of livestock traceability systems for the Dutch sheep and goat sectors. Food Economics – Acta Agriculturae Scandinavica C, 6: 31-42.
Bewley, J.M., M.D. Boehlje, A.W. Gray, H. Hogeveen, S.J. Kenyon, S.D. Eicher, M.A.S. Russell and M.M. Schutz (2010). Stochastic simulation using @Risk for dairy business investment decisions. Agricultural Finance Review 70:97-125.
Bewley, J.M., M.D. Boehlje, A.W. Gray, H. Hogeveen, S.J. Kenyon, S.D. Eicher, M.A.S. Russell and M.M. Schutz (2010). Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation. Agricultural Finance Review, 70: 126-150.
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Inchaisri, C., H. Hogeveen, P.L.A.M. Vos, G.C. van der Weijden and R. Jorritsma (2010)
The objective of this study was to determine the contribution of cow factors to the probability of a successful first
insemination (SFI). The investigation was performed with 51,791 lactations from 1,396 herds obtained from the
Dutch dairy cow database of the Cattle Improvement Co-operative (CRV). Cows that had the first insemination
(AI) between 40 and 150 d postpartum were selected. The first AI was classified as successful when cows were
not reinseminated and either calved between 267 and 295 d later or were culled within 135 to 295 d after first AI.
The lactation curve characteristics of individual lactations were estimated by Wilmink's curve using the test-day
milk records from CRV. The lactation curve characteristics (peak milk yield, milk yield at the first-AI date, time of
peak yield (PT), and milk persistency) were calculated. Breed, parity, interval from calving to first AI (CFI),
lactation curve characteristics, milk production traits, moment of AT related to PT (before or after PT), calf status,
month of AI, and month of calving were selected as independent variables for a model with SFI as a dependent
variable. A multivariable logistic regression model was used with farm as a random effect. Overall SFI was 44%.
The effect of parity on SFI depended on CFI. The first-parity cows had the greatest SFI (0.43) compared with
other parities (0.32-0.39) at the same period of CFI before 60 d in milk (DIM), and cows in parity >= 5 had the
least SFI (0.38-0.40) when AI was after 60 DINT. After 60 DIM, extending CFI did not improve SFI in the first
parity cows, but SFI was improved in multiparous cows. Holstein-Friesian cows had lesser SFI (0.37) compared
with cross-breed cows (0.39-0.46). Twin and stillbirth calving reduced SFI (0.39) compared with a single female
calf (0.45) or a male calf (0.43) calving. The SFI in different months of AT varied and depended on CFI. Cows that
received AI before 60 DIM had a lesser SFI, especially in March, June, and July (0.18, 0.35, and 0.34,
respectively). Artificial insemination before PT reduced SFI (0.39) in comparison with AI after PT (0.44). The effect
of milk yield at the first-AI date on SFI varied depending on CFI. After 60 DIM at the same period of CFI, a high
level of milk yield at the first-AI date reduced SFI. In conclusion, knowledge of the contribution of cow factors on
SFI can be applied to support decision making on the moment of insemination of an individual cow in estrus.
Journal of Dairy Science, 93: 5179-5187
Journal of Dairy Science, 93: 5179-5187
