Associations between farmer participation in veterinary herd health management programs and farm performance.

In the past few decades, farms have increased in size and the focus of management has changed from curative to preventive. To help farmers cope with these changes, veterinarians offer veterinary herd health management (VHHM) programs, whose major objective is to support the farmer in reaching his farm performance goals. The association between farm performance and participation in VHHM, however, remains unknown. The aim of this paper was to compare farm performance parameters between participants and nonparticipants in VHHM and to differentiate within participation to evaluate the possible added value of VHHM on the farm. Five thousand farmers received a questionnaire about the level of VHHM on their farm. Farm performance parameters of these 5,000 farms were provided. For all respondents (n=1,013), farm performance was compared between participants and nonparticipants and within level of participation, using linear mixed and linear regression models. Farmers who participated in VHHM produced 336 kg of milk/cow per year more and their average milk somatic cell count (SCC) was 8,340 cells/mL lower than farmers who did not participate in VHHM. Participating herds, however, had an older age at first calving (+12d), a lower 56-d nonreturn rate percentage (-3.34%), and a higher number of inseminations per cow (+0.09 inseminations). They also had more cows culled per year (+1.05%), and a lower age at culling (-70 d). Participants in the most-extended form of VHHM (level 3) had a lower SCC (-19,800 cells/mL), fewer cows with high SCC (-1.70%), fewer cows with new high SCC (-0.47%), a shorter calving interval (-6.01 d), and fewer inseminations per heifer (-0.07 inseminations) than participants in the least-extended form of VHHM (level 1). Level 3 participants, however, also had more cows culled per year (+1.74%) and a lower age at culling (-103 d). Discussing specific topics with the veterinarian (milk production, fertility, and udder health) had only marginal effects on improving the farm performance parameters related to those topics. Given the relevance of fertility on the farm and the focus on longevity by society, it is important to determine underlying reasons for the negative associations of these topics with participation in VHHM. A longitudinal study could provide answers to this. For now, veterinarians should be aware of the associations. The increased milk production and milk quality could help the marketing of VHHM to farmers.

Veterinary herd health management programs on dairy farms in the Netherlands: use, execution, and relations to farmer characteristics.

Veterinary herd health management (VHHM) programs are of growing importance to the dairy industry; they support farmers in the shift from curative to preventive health management, caused by increased herd sizes and quality standards in dairy farming. Farmers participating in VHHM are visited every 4 to 6 wk by their veterinarian, who checks the animals and herd management to intervene in a proactive way with problems regarding animal health and animal welfare. At present, no good overview exists of how VHHM is executed on Dutch dairy farms, and whether different farmers require different types of VHHM. Aims of this study were to (1) map out how many farmers participate in VHHM, (2) describe how VHHM is executed on the farms, and (3) see whether certain farmer characteristics are related to farmers' participation in VHHM. In 2011, a questionnaire was sent to 5,000 Dutch dairy farmers per e-mail. Part 1 of the questionnaire focused on participation in and execution of VHHM and part 2 focused on farmer characteristics regarding external information. Returned questionnaires (n=1,013) were summarized and statistically analyzed. In this study 68.6% of the responding farmers participated in any form of VHHM. The most important activities were fertility checks and advice about fertility; the least important were housing and claw health. Relationships between farmer characteristics (use of and trust in information) and participation in VHHM were found.

Short communication: influence of sampling interval on the accuracy of predicting bulk milk somatic cell count.

Although bulk milk somatic cell count (BMSCC) is, in most instances, not a good proxy for actual average herd somatic cell count (SCC), BMSCC is the only SCC parameter available to monitor trends in udder health for a large number of farms worldwide. The frequency of sampling BMSCC varies considerably between countries, and it is unknown to what extent the sampling interval of BMSCC or variation in BMSCC data itself influences the accuracy. The aim of this study was to assess the effect of sampling interval and variation of the BMSCC data on the accuracy to predict BMSCC. Because BMSCC is measured at regular time intervals, an artificial neural network (ANN) was used to determine both the effect of sampling interval and variation of the BMSCC data. The intervals examined in this study ranged from 4 to 14 d and were compared with the baseline of a standard 2-d sampling interval. The BMSCC data were collected every other day for a 24-mo period on 949 farms, and all series were created by exclusion of BMSCC data in between the original 2-d sampling interval series. The effect of variation of BMSCC was determined by comparing the error of the ANN model in 2 subsets of farms, those with the lowest SD (n=239) and those with a high SD of BMSCC data (n=236). No significant differences were found in any of the sampling intervals between the 2 cohorts of low and high SD in BMSCC. Overall, compared with the 2-d sampling interval, on average the error of the ANN model was 32,600 cells/mL for all farms included, ranging from 15,000 cells/mL (4-d interval) to 41,000 cells/mL (14-d sampling interval). Therefore, the length of the sampling interval greatly influences the usefulness of BMSCC data to monitor trends in udder health at the herd level.

Prediction of the herd somatic cell count of the following month using a linear mixed effect model.

An accurate prediction of the average somatic cell count (SCC) for the next month would be a valuable tool to support udder health management decisions. A linear mixed effect (LME) model was used to predict the average herd SCC (HSCC) for the following month. The LME model included data on SCC, herd characteristics, season, and management practices determined in a previous study that quantified the contribution of each factor for the HSCC. The LME model was tested on a new data set of 101 farms and included data from 3 consecutive years. The farms were split randomly in 2 groups of 50 and 51 farms. The first group of 50 farms was used to check for systematic errors in predicting monthly HSCC. An initial model was based on older data from a different part of the Netherlands and systematically overestimated HSCC in most months. Therefore, the model was adjusted for the difference in average HSCC between the 2 sets of farms (from the previous and current study) using the data from the first group of 50 farms. Subsequently, the data from the second group of 51 farms were used to independently assess this final model. A null model (no explanatory variables included) predicted 48 and 59% of the HSCC within the predetermined range of 20,000 and 30,000 cells/mL, respectively. The final LME model predicted 72 and 81% of the HSCC of the next month correctly within these 2 ranges. These outcomes indicate that the final LME model was a valid additional tool for farmers that could be useful in their short-term decisions regarding udder health management and could be included in dairy herd health programs.

[Evaluation of veterinary advice given to dairy farmers]. / Beoordeling van de bedrijfsadvisering door dierenartsen op rundveebedrijven.

In this pilot study, the manner and effectiveness of advice given by four cattle practitioners was investigated during health management visits to 34 dairy farms. Farmers were asked about their satisfaction with the advice given, and an independent observer evaluated the manner in which practitioners carried out the farm visit. There were no major differences between the cattle practitioners, but there were minor differences in the way practitioners gave advice, in the duration of the herd visit, and in the subjects talked about. The farmers were positive about the manner in which advice was given and its effect. The method used in this pilot study could be used to gain insight into the manner in which veterinary advice is given by veterinary practitioners and veterinary practices, and may contribute to improving the quality of veterinary services provided.

Effect of herd characteristics, management practices, and season on different categories of the herd somatic cell count.

In this study, the contribution of management practices, herd characteristics, and seasonal variables to the herd somatic cell count (SCC) was quantified in herds with low (<150,000 cells/mL), medium (150,000-200,000 cells/mL), and high (>200,000 cells/mL) herd SCC (HSCC). Selection of the variables was performed using a linear mixed effect model; HSCC was calculated as the arithmetic mean of the individual cow's SCC. The data concerning management practices were derived from 3 questionnaires on mastitis prevention and management practices on 246 Dutch dairy farms. The monthly Dairy Herd Improvement test data of these 246 farms were used to calculate the herd characteristics and seasonal effects. None of the management practices were associated with HSCC in all 3 HSCC categories. Some variables only had a significant association with HSCC in one HSCC category, such as dry premilking treatment (-9,100 cells/mL in the low HSCC category) or feeding calves with high SCC milk (11,100 cells/ mL in the medium HSCC category). Others had an opposite effect on HSCC in different HSCC categories, such as average parity (-6,400 and 11,000 cells/mL in the low and medium HSCC category, respectively) and feeding calves with fresh milk (10,300 and -9,700 cells/ mL in the low and high HSCC category, respectively). We conclude that, given the individual Dairy Herd Improvement data and information on management practices of an individual farm, it is possible to provide quantitative insight into the contribution of these different variables to the HSCC of an individual farm. Being able to provide such insight is a prerequisite for interpretation, prediction, and control of HSCC on individual dairy farms.

Short communication: Comparison of bulk milk, yield-corrected, and average somatic cell counts as parameters to summarize the subclinical mastitis situation in a dairy herd.

In this study, the correlation was determined between the prevalence of high cow-level somatic cell count (SCC >250,000 cells/mL), a summary of the subclinical mastitis situation in a dairy herd, and 3 average herd SCC parameters: bulk milk SCC (BMSCC), yield-corrected test-day SCC (CHSCC), and the arithmetic average test-day SCC (HSCC) of the lactating herd. The herd prevalence of cows with an SCC of >250,000 cells/mL was calculated by using Dairy Herd Improvement data. Herds were included if BMSCC was sampled within 2 d of the Dairy Herd Improvement test day and if the BMSCC did not exceed 400,000 cells/mL. The interval between sampling, 0, 1, or 2 d, did not significantly influence the correlation between BMSCC and the prevalence of high SCC. The correlations between the prevalence of high SCC and BMSCC, yield-corrected test-day SCC, and HSCC, examined by using a linear regression model, were 0.64, 0.78, and 0.89, respectively. Therefore, it can be concluded that, based on the highest correlation, HSCC is a more appropriate parameter than BMSCC to summarize the average herd subclinical mastitis situation in a dairy herd.