Data quality in the Norwegian dairy herd recording system: agreement between the national database and disease recording on farm

Exploring uses for an algorithmically generated Animal Welfare Indicator for welfare assessment of dairy herds

On-farm welfare assessment is time-consuming and costly. Assessing welfare using routinely collected herd data has been proposed as a more economical alternative. The online Animal Welfare Indicator (AWI), developed by a Norwegian dairy cooperative, applies an algorithm to routinely collected health, production, and management data to "indicate" aspects of animal welfare at herd level. The overall AWI score is based on 10 AWI subindicator scores, representative of elements of animal welfare such as claw health, udder health, and mortality. Our cross-sectional study explored 2 ways in which the AWI may enable more efficient welfare assessment of Norwegian dairy herds. First, we investigated using the AWI to reduce the duration of on-farm assessments by replacing on-farm measures. Second, we examined reducing the number of on-farm welfare assessments by using the AWI to predict which herds have poorer welfare with respect to specific on-farm measures. Using Spearman rank analyses, we investigated if the AWI scores for 157 herds were associated with 24 on-farm welfare variables measured contemporaneously by Welfare Quality assessment. The mortality AWI subindicator score and the percentage mortality in the previous 12 mo were moderately correlated, as were the udder health AWI subindicator score and the percentage high somatic cell count (SCC) in the previous 3 recordings. Only negligible or weak correlations were found between the other AWI scores and the on-farm assessment variables. We built Generalized Linear Models using AWI scores as independent variables to predict herds with poorer welfare. Herds were classified as having poorer welfare based on their results in specific on-farm welfare measures. We evaluated the models' predictive ability and accuracy. Moderately accurate models were built for predicting poorer herds with respect to high SCC, mortality, and moderate or severe lameness. The other models were less accurate. The AWI scores were generally unsuitable as replacements of on-farm welfare measures. The AWI subindicators for udder health and mortality could replace the on-farm welfare measures related to those 2 topics, but there was some overlap in the data used to calculate them. Despite a lack of independence, the use of those 2 AWI subindicators may marginally reduce the duration of on-farm assessments. A prediction model based on AWI scores showed potential for identifying herds with poorer welfare in terms of moderate or severe lameness, facilitating more efficient use of resources for on-farm lameness assessment. As a consequence of the data used in the AWI, it was only reflective of health-related welfare outcomes.

Cohort profile: The FarmMERGE project-Merging human and animal databases to investigate the relationship between farmer and livestock health and welfare. The HUNT Study

Stockmanship is an important determinant for good animal welfare and health. The goal of the FarmMERGE project is to investigate the associations between farmer health and work environment, and the health, productivity and welfare of their livestock. We merged several livestock industry databases with a major total population-based health study in Norway (The Trøndelag Health Study 2017-2019 (HUNT4)). This paper describes the project's collection and merging of data, and the cohort of farmers and farms that were identified as a result of our registry merge. There were 56,042 participants of HUNT4 (Nord-Trøndelag County participants only, participation rate: 54.0%). We merged a list of HUNT4 participants whose self-reported main occupation was "farmer" (n = 2,407) with agricultural databases containing production and health data from sheep, swine, dairy and beef cattle from 2017-2020. The Central Coordinating Register for Legal Entities was used as an intermediary step to achieve a link between the farmer and farming enterprise data. We identified 816 farmers (89.5% male, mean age 51.3 years) who had roles in 771 farming enterprises with documented animal production. The cohort included 675 unique farmer-farm combinations in cattle production, 139 in sheep, and 125 in swine. We linked at least one HUNT4 participant to approximately 63% of the dairy farms, 53% of the beef cattle farms, 30% of the sheep farms, and 38% of the swine farms in Nord-Trøndelag County in the 2017-2019 period. Using existing databases may be an efficient way of collecting large amounts of data for research, and using total population-based human health surveys may decrease response bias. However, the quality of the resulting research data will depend on the quality of the databases used, and thorough knowledge of the databases is required.

Gastrointestinal nematodes and Fasciola hepatica in Norwegian cattle herds: a questionnaire to investigate farmers' perceptions and control strategies

Background

Pasture management influences the prevalence and impact of the pasture parasites (PP) in cattle herds, which cause production-limiting disease worldwide. Evaluating farmer management strategies is vital when considering sustainable PP control practices. The aim of this questionnaire-based study was to describe the pasture management and control strategies regarding PP in Norwegian beef cattle (BC) and dairy cattle (DC) production systems with a focus on gastrointestinal nematodes (GIN) and Fasciola hepatica.

Results

A total of 745 responses from BC (return rate 20.5%) and 1347 responses from DC farmers (30.7%) were included. The mean total pasture time for DC was 4.2 months for first-season grazers and 4.3 months for second-season grazers and cows, while the corresponding finding in BC was 5.4 months. Home pasture was used for most of the pasture period, particularly for first-season grazer dairy heifers (81%), which were also commonly grazed on the same pasture every year (79%). For most farmers it was necessary for grazing areas to be used for cattle for more than one season (77% of BC farmers and 89% of DC farmers). However, changing the pasture during the season was common in both DC (67%) and BC (70%) herds. The majority of DC farmers (60%) stated that they did not consider that they had a problem with PP. Of the remaining 40%, few respondents could specify whether their herds had a problem due to infection by GIN (11%) or liver flukes (12%). Treatment for GIN was performed by 52% of DC and 34% of BC farmers. Diagnostic faecal samples were collected upon suspicion of parasitic disease by 5% of DC and 16% of BC farmers. Veterinarians were stated as a central source of information about parasite management and treatment.

Conclusions

Potential risks for exposure to PP were identified, such as use of the same pasture every year for first-season grazers and frequent use of home pasture. The perception of problems related to PP appeared low. Regular anthelmintic treatment without concurrent use of diagnostic faecal samples seems to be common practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13028-021-00618-7.

Assessing the Feasibility of Retrospective and Prospective Clinical Audit in Farm Animal Veterinary Practice

As a quality improvement tool, clinical audit has been extensively described in the medical literature. There is scant literature on the use of clinical audit in the farm animal veterinary setting. This study describes the process and feasibility of prospective and retrospective data collection for farm animal clinical audit performed at three different farm animal practices in the United Kingdom. Retrospective clinical audit was difficult in all three practices due to barriers in establishing diagnosis and patient identity from clinical records. Prospective data collection proved feasible but depended on adequate case accrual and practitioner engagement. The decision to conduct clinical audit retrospectively or prospectively will depend on the topic to audit, the availability of data and the wishes of the team members involved. Further work is required looking at the use of clinical coding and alternatives to using practice management software to improve retrospective data availability for clinical audit.

Quantification of antimicrobial usage in adult cows and preweaned calves on 40 large Wisconsin dairy farms using dose-based and mass-based metrics

Use of antimicrobials in animal agriculture is under increasing scrutiny, but the quantity of antimicrobials used on large US dairy farms has not been evaluated using data from large farms and different metrics. This study investigated total antimicrobial usage (AMU) in adult dairy cows and preweaned calves (PWC) and contrasted 2 metrics used for measurement of AMU. Wisconsin dairy farms were eligible if they had >250 lactating cows, maintained computerized animal health records, and were willing to allow researchers access to treatment records. Animal health data for a 1-yr period was retrospectively collected from computerized records, and a farm visit was performed to verify case definitions and recording accuracy. Both dose-based (animal daily doses; ADD) and mass-based (total mg of antimicrobials per kg of body weight; BW) metrics were calculated at the herd, cow, and PWC levels. Descriptive statistics for AMU were examined for both age groups. Mean AMU was compared among active ingredients and route of usage using ANOVA models that included farm as a random variable. At enrollment, farms (n = 40) contained approximately 52,639 cows (mean: 1,316 ± 169; 95% CI: 975, 1657) and 6,281 PWC (mean: 180 ± 33; 95% CI: 112, 247). When estimated using ADD, total herd AMU was 17.2 ADD per 1,000 animal-days (95% CI: 14.9, 19.5), with 83% of total herd-level AMU in adult cows. When estimated using the mass-based metric, total herd AMU was 13.6 mg of antimicrobial per kilogram of animal BW (95% CI: 10.3, 17.0), with 86% of total AMU used in adult cows. For cows, 78% of total ADD (15.8 ADD per 1,000 cow-d) was administered as intramammary (IMM) preparations. In contrast, when AMU was estimated using a mass-based metric, IMM preparations represented only 24% of total AMU (12.1 mg of antimicrobial/kg of cow BW). For cows, ceftiofur was the primary antimicrobial used and accounted for 53% of total ADD, with 80% attributed to IMM and 20% attributed to injectable treatments. When estimated using a mass-based metric, ampicillin was the predominant antimicrobial used in cows and accounted for 33% of total antimicrobial mass per kilogram of BW. When AMU was estimated for PWC using ADD, injectable antimicrobials represented 79% of total usage (28.3 ADD per 1,000 PWC-d). In contrast, when AMU was estimated for PWC using a mass-based metric, injectable products represented 42% of total AMU, even though more farms administered antimicrobials using this route. When AMU in PWC was summarized using ADD, penicillin represented 32% of AMU, and there were no significant differences in ADD among ampicillin, oxytetracycline or enrofloxacin. When a mass-based metric was used to estimate AMU in PWC, oral products (sulfadimethoxine and trimethoprim-sulfa) represented more than half of the total AMU given to this group. Overall, these results showed that choice of metric and inclusion of different age groups can substantially influence interpretation of AMU on dairy farms.

A cross-sectional study of associations between herd-level calf mortality rates, compliance with legislation on calf welfare, and milk feeding management in Norwegian dairy herds

Our aim was to investigate the associations between management factors, compliance with current legislation, and herd-level calf mortality. In a national calf welfare audit, veterinary inspectors from the Norwegian Food Safety Authority assessed compliance with current legislation on calf welfare (n = 912 herds). Nine criteria were assessed and rated as satisfactory (1) or not satisfactory (0): housing, natural behavior, single pens, colostrum feeding, feeding, water, surveillance, illness/injuries, and <5% mortality rate. In addition, a short questionnaire on milk feeding management for 3-wk-old calves was distributed to all national calf welfare audit herds, and data on mortality and disease recordings were obtained from the Norwegian Dairy Herd Recording System (NDHRS). A herd welfare compliance score (WCS) for each farm was constructed, summarizing the results for the individual criteria. Most herds had a high WCS (median 9.0, range 2-9). Fifty-six percent of the national calf welfare audit herds (508/912) responded to the questionnaire. We performed a cross-sectional study using a data set from 431 herds with available data on recorded disease and mortality events from the NDHRS, recordings from the national calf welfare audit, and the questionnaire. A mixed-effects negative binomial model with Norwegian Food Safety Authority district as the random effect was fitted to the data. Of the 416 herds with available data on calf mortality, 108 (25.9%) reported no mortality in 2016, and the median 6-mo mortality rate was 0.064 (interquartile range 0-0.11) dead calves per 6 calf-months at risk, based on NDHRS recordings. Calf mortality rates were higher in herds whose calves did not have free access to water (incidence rate ratio 1.29; 95% confidence interval 1.02-1.64) and higher in herds that had reported calf disease events (incidence rate ratio 1.31; 95% confidence interval 1.09-1.54). Neither the WCS nor any of the calf milk feeding management factors were associated with mortality, but more than half of producers (59.6%) fed less milk than currently recommended for 3-wk-old calves (8 L/d first 3 to 4 wk). These results indicate that a lack of access to water was associated with higher calf mortality rates. Herds with registered calf disease events had a higher incidence rate ratio of mortality. This finding may be linked to suboptimal calf management, leading to more calf diseases and mortality; or it may be that veterinary consultancy occurs too late or only for the worst cases. There is room for improvement in Norwegian dairy calf management, and water should be provided to young calves.

Herd level estimation of probability of disease freedom applied on the Norwegian control program for bovine respiratory syncytial virus and bovine coronavirus

A national control program against bovine respiratory syncytial virus (BRSV) and bovine coronavirus (BCV) was launched in Norway in 2016. A key strategy in the program is to test for presence of antibodies and protect test-negative herds from infection. Because these viruses are endemic, the rate of re-introduction can be high, and a disease-free status will become more uncertain as time from testing elapses. The aim of this study was to estimate the probability of freedom (PostPFree) from BRSV and BCV antibodies over time by use of bulk tank milk (BTM) antibody-testing, geographic information and animal movement data, and to validate the herd-level estimates against subsequent BTM testing.

BTM samples were collected from 1148 study herds in West Norway in 2013 and 2016, and these were analyzed for BRSV and BCV antibodies. PostPFree was calculated for herds that were negative in 2013/2014, and updated periodically with new probabilities every three months. Input variables were test sensitivity, the probability of introduction through animal purchase and local transmission. Probability of introduction through animal purchase was calculated by using real animal movement data and herd prevalence in the region of the source herd. The PostPFree from the final three months in 2015 was compared to BTM test results from March 2016 using a Wilcoxon Rank Sum Test.

The probability of freedom was generally high for test-negative herds immediately after testing, reflecting the high sensitivity of the tests. It did however, decrease with time since testing, and was greatly affected by purchase of livestock. When comparing the median PostPFree for the final three months to the test results in 2016, it was significantly lower (p < 0.01) for test positive herds. Furthermore, there was a large difference in the proportion of test positive herds between the first and fourth quartile of PostPFree. The results show that PostPFree provides a better estimate of herd-level BTM status for both BRSV and BCV than what can be achieved by relying solely on the previous test-result.

A cohort study of the effect of winter dysentery on herd-level milk production

Winter dysentery (WD) is a contagious disease caused by bovine coronavirus. It is characterized by acute onset of diarrhea, fever, depression, and reduced milk yield in adult cattle. Although production loss is a well-known consequence of WD, large-scale studies estimating the effect on milk production are lacking. The objective of this study was to estimate the effect of farmer-reported WD on herd-level milk production and milk composition. A cohort study was performed based on reports of herd outbreaks of winter dysentery during a regional epidemic in Norway during the winter of 2011–2012. Reports were made by farmers, and diagnosis was based on a herd outbreak of acute diarrhea in adults. Milk shipment data were retrieved from the dairy company, and information on herd size and milking system were retrieved from the Norwegian Dairy Herd Recording System. We compared milk production in herds with reported outbreaks of WD (n = 224) with all herds in the same area without a reported outbreak (n = 2,093) during the same period. The outcome variable in the analysis was milk volume per cow per day, and the main predictor was whether the herd had a reported outbreak of WD or not. We assessed the effect of WD on milk production by fitting a linear mixed model, adjusting for milk production in the herd before the outbreak. Similarly, we assessed the effect of WD on milk composition using linear regression, adjusting for the levels of milk components before the outbreak. This study estimated a total loss of 51 L/cow during the study period, from 7 d before to 19 d after a reported outbreak. The lowest estimated production was 2 d after the outbreak was reported, when the average milk yield was 19.4 L/cow per day, compared with 23.0 L/cow per day 7 days before notification (i.e., a difference of 3.6 L/cow, or 15%). The effect gradually declined with time. The estimated effect on milk composition was modest, but an increase of 11% in free fatty acids and a small increase in fat/protein ratio indicated that WD might put cows into negative energy balance. Descriptive analysis indicated that herd milk yield was still reduced 4 mo after an outbreak. This cohort study showed that WD causes considerable decreases in milk production, and it alters milk composition. These findings highlight the important negative consequences of WD, and should motivate actions to prevent between-herd spread of bovine coronavirus.

Bovine respiratory syncytial virus and bovine coronavirus antibodies in bulk tank milk - risk factors and spatial analysis

Bovine respiratory syncytial virus (BRSV) and bovine coronavirus (BCoV) are considered widespread among cattle in Norway and worldwide. This cross-sectional study was conducted based on antibody-ELISA of bulk tank milk (BTM) from 1347 herds in two neighboring counties in western Norway. The study aims were to determine the seroprevalence at herd level, to evaluate risk factors for BRSV and BCoV seropositivity, and to assess how these factors were associated with the spatial distribution of positive herds. The overall prevalence of BRSV and BCoV positive herds in the region was 46.2% and 72.2%, respectively. Isopleth maps of the prevalence risk distribution showed large differences in prevalence risk across the study area, with the highest prevalence in the northern region. Common risk factors of importance for both viruses were herd size, geographic location, and proximity to neighbors. Seropositivity for one virus was associated with increased odds of seropositivity for the other virus. Purchase of livestock was an additional risk factor for BCoV seropositivity, included in the model as in-degree, which was defined as the number of incoming movements from individual herds, through animal purchase, over a period of five years. Local dependence and the contribution of risk factors to this effect were assessed using the residuals from two logistic regression models for each virus. One model contained only the x- and y- coordinates as predictors, the other had all significant predictors included. Spatial clusters of high values of residuals were detected using the normal model of the spatial scan statistic and visualized on maps. Adjusting for the risk factors in the final models had different impact on the spatial clusters for the two viruses: For BRSV the number of clusters was reduced from six to four, for BCoV the number of clusters remained the same, however the log-likelihood ratios changed notably. This indicates that geographical differences in proximity to neighbors, herd size and animal movements explain some of the spatial clusters of BRSV- and BCoV seropositivity, but far from all. The remaining local dependence in the residuals show that the antibody status of one herd is influenced by the antibody status of its neighbors, indicating the importance of indirect transmission and that increased biosecurity routines might be an important preventive strategy.

Evaluation of the usefulness at national level of the dairy cattle health and production recording systems in Great Britain

The aim of this study was to formally evaluate, qualitatively, the ability of existing recording systems to generate accurate and reliable estimates of the frequency of selected health conditions in the dairy herd of Great Britain. Fifty-nine recording systems were identified, of which 36 had their key characteristics defined through a web-based questionnaire. Nineteen of them were further assessed following the SERVAL, a SuRveillance EVALuation framework against a set of 12 attributes: benefit, bias, communication, coverage, data collection, data management, data analysis, data completeness, flexibility, multiple utility, representativeness and stability/sustainability. The evaluated systems showed considerable differences in their coverage, implementation and objectives. There were overlaps in recorded conditions, with Johne's disease, bovine viral diarrhoea, mastitis and lameness being recorded by most of the systems. Selection bias, data ownership and lack of integration of data from different systems appeared to be a key limitation on the future use of existing systems for nationwide monitoring. The results showed that even though the individual systems can provide reliable estimates of dairy health for individual farmers, none of the systems alone could provide accurate and reliable estimates for any of the conditions of interest at national level.