Puslinch, Canada
Puslinch, Canada

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Dohoo I.,University of Prince Edward Island | Andersen S.,University of Prince Edward Island | Dingwell R.,Holdrege Vet Clinic PC | Hand K.,Strategic Solutions Group | And 4 more authors.
Journal of Dairy Science | Year: 2011

The objective was to examine the potential benefits of using different combinations of multiple quarter milk samples compared with a single sample for diagnosing intramammary infections (IMI) in dairy cattle. Data used in the analyses were derived from 7,076 samples from 667 quarters in 176 cows in 8 herds in 4 locations (Minnesota/Wisconsin, n = 4; Prince Edward Island, n = 2; Ontario, n = 1; New York, n = 1). Duplicate quarter milk samples were collected at morning milking for 5 consecutive days. Cows were evenly distributed between early postparturient and mid- to late-lactation cows. All samples were frozen for shipping and storage, thawed once, and cultured in university laboratories using standardized procedures consistent with National Mastitis Council guidelines. The presence of specific pathogens was confirmed and identified using the API identification system (bioMerieux, Marcy l'Etoile, France) in each laboratory. A previously developed gold standard was applied to the first sample from d 1, 3, and 5 to classify infected quarters. The data were analyzed separately for coagulase-negative staphylococci (CNS) and Streptococcus spp. Various combinations of test results from d 2 and 4 were used in the test evaluation. These consisted of single samples (n = 4), 2 sets of duplicate samples (2 samples collected on the same day), 2 sets of consecutive samples (2 samples collected 2 d apart), and 2 sets of triplicate samples (2 samples on the same day and a third sample 2 d apart). Series interpretation of duplicate or consecutive samples (i.e., positive = same pathogen isolated from both samples) resulted in the highest specificity (Sp; CNS Sp = 92.1-98.1%; Streptococcus spp. Sp = 98.7-99.6%), but lowest sensitivity (Se; CNS Se = 41.9-53.3%; Streptococcus spp. Se = 7.7-22.2%). Parallel interpretation of duplicate or consecutive samples (i.e., positive = pathogen isolated from either) resulted in the highest Se (CNS Se = 70.8-80.6%; Streptococcus spp. Se = 31.6-48.1%), but lowest Sp (CNS Sp = 72.0-77.3%; Streptococcus spp. Sp = 89.5-93.3%). The difference in estimates between single and duplicate samples was larger than between single and consecutive samples. Overall, triplicate samples provided the best combination of Se and Sp, but compared with a single sample, provided only a modest gain in Sp and little or no gain in Se. © 2011 American Dairy Science Association.


PubMed | University of Guelph, Veterinary Science and Policy Group and Strategic Solutions Group
Type: Journal Article | Journal: Journal of dairy science | Year: 2016

High ambient heat and humidity have profound effects on the production, health, profitability, and welfare of dairy cattle. To describe the relationship between summer temperature and relative humidity in the barn and determine the appropriateness of using meteorological station data as a surrogate for on-farm environmental monitoring, a study was conducted on 48 farms in Ontario, Canada, over the summer (May through September) of 2013. Within-barn environmental conditions were recorded using remote data loggers. These values were compared with those of the closest official meteorological station. In addition, farm-level characteristics and heat-abatement strategies were recorded for each farm. Environmental readings within the barn were significantly higher than those of the closest meteorological station; however, this relationship varied greatly by herd. Daily temperature-humidity index (THI) values within the barn tended to be 1 unit higher than those of the closest meteorological station. Numerically, 1.5 times more mean daily THI readings were in excess of 68 (heat stress threshold for lactating dairy cows) in the barn, relative to the closest meteorological station. In addition, tiestalls, herds that were allowed access to pasture, and herds that had no permanent cooling strategy for their cows had the highest mean and maximum daily THI values. Minimum daily THI values were almost 4 units higher for tiestall relative to freestall herds. Overall, due to farm-specific and unpredictable variability in magnitude of environmental differences between on-farm and meteorological station readings, researchers attempting to study the effects of environment on dairy cows should not use readings from meteorological stations because these will often underestimate the level of heat stress to which cows are exposed.


PubMed | University of Minnesota, University of Guelph, Veterinary Science and Policy Group and Strategic Solutions Group
Type: Journal Article | Journal: Journal of dairy science | Year: 2015

As a proactive measure toward controlling the nontreatable and contagious Johnes disease in cattle, the Ontario dairy industry launched the voluntary Ontario Johnes Education and Management Assistance Program in 2010. The objective of this study was to describe the results of the first 4 yr of the program and to investigate the variability in Risk Assessment and Management Plan (RAMP) scores associated with the county, veterinary clinic, and veterinarian. Of 4,158 Ontario dairy farms, 2,153 (51.8%) participated in the program between January 2010 and August 2013. For this study, RAMP scores and whole-herd milk or serum ELISA results were available from 2,103 farms. Herd-level ELISA-positive prevalence (herds with one or more test-positive cows were considered positive) was 27.2%. Linear mixed model analysis revealed that the greatest RAMP score variability was at the veterinarian level (24.2%), with relatively little variability at the county and veterinary clinic levels. Consequently, the annual RAMP should be done by the same veterinarian to avoid misleading or discouraging results.


PubMed | University of Guelph, Veterinary Science and Policy Group and Strategic Solutions Group
Type: Journal Article | Journal: Journal of dairy science | Year: 2015

Regionally aggregated bulk milk somatic cell count (BMSCC) data from around the world shows a repeatable cyclicity, with the highest levels experienced during warm, humid seasons. No studies have evaluated this seasonal phenomenon at the herd level. The objectives of this study were to define summer seasonality in BMSCC on an individual herd basis, and subsequently to describe the characteristics and dynamics of herds with increased BMSCC in the summer. The data used for this analysis were from all dairy farms in Ontario, Canada, between January 2000 and December 2011 (n4,000 to 6,000 herds/yr). Bulk milk data were obtained from the milk marketing board and consisted of bulk milk production, components (fat, protein, lactose, other solids), and quality (BMSCC, bacterial count, inhibitor presence, freezing point), total milk quota of the farm, and milk quota and incentive fill percentage. A time-series linear mixed model, with random slopes and intercepts, was constructed using sine and cosine terms as predictors to describe seasonality, with herd as a random effect. For each herd, seasonality was described with reference to 1 cosine function of variable amplitude and phase shift. The predicted months of maximal and minimal BMSCC were then calculated. Herds were assigned as low, medium, and high summer increase (LSI, MSI, and HSI, respectively) based on percentiles of amplitude in BMSCC change for each of the 4 seasons. Using these seasonality classifications, 2 transitional repeated measures logistic regression models were built to assess the characteristics of MSI and HSI herds, using LSI herds as controls. Based on the analyses performed, a history of summer BMSCC increases increased the odds of experiencing a subsequent increase. As herd size decreased, the odds of experiencing HSI to MSI in BMSCC increased. Herds with more variability in daily BMSCC were at higher odds of experiencing MSI and HSI in BMSCC, as were herds with lower annual mean BMSCC. Finally, a negative association was noted between filling herd production targets and experiencing MSI to HSI in BMSCC. These findings provide farm advisors direction for predicting herds likely to experience increases in SCC over the summer, allowing them to proactively focus udder health prevention strategies before the high-risk summer period.


Arruda A.G.,University of Guelph | Friendship R.,University of Guelph | Carpenter J.,Woodstock | Hand K.,Strategic Solutions Group | Poljak Z.,University of Guelph
Preventive Veterinary Medicine | Year: 2016

The objectives of this study were to describe networks of Ontario swine sites and their service providers (including trucking, feed, semen, gilt and boar companies); to categorize swine sites into clusters based on site-level centrality measures, and to investigate risk factors for porcine reproductive and respiratory syndrome (PRRS) using information gathered from the above-mentioned analyses. All 816 sites included in the current study were enrolled in the PRRS area regional control and elimination projects in Ontario. Demographics, biosecurity and network data were collected using a standardized questionnaire and PRRS status was determined on the basis of available diagnostic tests and assessment by site veterinarians. Two-mode networks were transformed into one-mode dichotomized networks. Cluster and risk factor analyses were conducted separately for breeding and growing pig sites. In addition to the clusters obtained from cluster analyses, other explanatory variables of interest included: production type, type of animal flow, use of a shower facility, and number of neighboring swine sites within 3 km. Unadjusted univariable analyses were followed by two types of adjusted models (adjusted for production systems): a generalizing estimation equation model (. GEE) and a generalized linear mixed model (. GLMM). Results showed that the gilt network was the most fragmented network, followed by the boar and truck networks. Considering all networks simultaneously, approximately 94% of all swine sites were indirectly connected. Unadjusted risk factor analyses showed significant associations between almost all predictors of interest and PRRS positivity, but these disappeared once production system was taken into consideration. Finally, the vast majority of the variation on PRRS status was explained by production system according to GLMM, which shows the highly correlated nature of the data, and raises the point that interventions at this level could potentially have high impact in PRRS status change and/or maintenance. © 2016 Elsevier B.V.


Hand K.J.,Strategic Solutions Group | Godkin A.,Ontario Ministry of Agriculture | Kelton D.F.,University of Guelph
Journal of Dairy Science | Year: 2012

The objectives of this study were to quantify the relationship between 24-h milk loss and lactation milk loss due to mastitis at the cow level. For the year 2009, individual cow test-day production records from 2,835 Ontario dairy herds were examined. Each record consisted of 24-h milk and component yields, stage of lactation (days in milk, DIM), somatic cell count (SCC, ×10 3 cells/mL) and parity. The modeling was completed in 2 stages. In stage 1, for each animal in the study, the estimated slope from a linear regression of 24-h milk yield (kg), adjusted for DIM, the quadratic effect of DIM, and the 24-h fat yield (kg) on ln(SCC) was determined. In stage 2, the estimated slope were modeled using a mixed model with a random component due to herd. The fixed effects included season (warm: May to September, cool: October to April), milk quartile class [MQ, determined by the rank of the 24-h average milk yield (kg) over a lactation within the herd] and parity. The estimated slopes from the mixed model analysis were used to estimate 24-h milk loss (kg) by comparing to a referent healthy animal with an SCC value of 100 (×10 3 cells/mL) or less. Lactation milk loss (kg) was then estimated by using estimated 24-h milk loss within lactation by means of a test-day interval method. Lactation average milk loss (kg) and SCC were also estimated. Lastly, lactation milk loss (kg) was modeled on the log scale using a mixed model, which included the random effect of herd and fixed effects, parity, and the linear and quadratic effect of the number of 24-h test days within a lactation where SCC exceeded 100 (×10 3 cells/mL; S100). The effect of SCC was significant with respect to 24-h milk loss (kg), increasing across parity and MQ. In general, first-parity animals in the first MQ (lower milk yield animals) were estimated to have 45% less milk loss than later parity animals. Milk losses were estimated to be 33% less for animals in first parity and MQ 2 through 4 than later parity animals in comparable MQ. Therefore, the relative level of milk production was found to be a significant risk factor for milk loss due to mastitis. For animals with 24-h SCC, values of 200 (×10 3 cells/mL), 24-h milk loss ranged from 0.35 to 1.09kg; with 24-h SCC values of 2,000 (×10 3 cells/mL), milk loss ranged from 1.49 to 4.70kg. Lactation milk loss (kg) increased significantly as lactation average SCC increased, ranging from 165 to 919kg. The linear and quadratic effect of S100 was a significant risk factor for lactation milk loss (kg), where greatest losses occurred in lactations with 5 or more 24-h test days where SCC exceeded 100 (×10 3 cells/mL). © 2012 American Dairy Science Association.


Hand K.J.,Strategic Solutions Group | Godkin M.A.,Ontario Ministry of Agriculture | Kelton D.F.,University of Guelph
Journal of Dairy Science | Year: 2012

The objective of this study was to determine the effect of somatic cell count (SCC) monitoring at the cow level through Dairy Herd Improvement (DHI) programs on the risk of bulk tank SCC (BTSCC) penalties. For the year 2009, BTSCC for all producers in Ontario were examined, for a total of 2,898 DHI herds, 1,186 non-DHI herds, and 48,250 BTSCC records. Two penalty levels were examined, where BTSCC exceeded 499,000 (P500) and 399,000 (P400) cells/mL. Data were modeled first to determine the odds of a BTSCC exceeding a set penalty threshold and second to determine the odds of incurring a penalty under the Ontario Milk Act. All data were modeled as a generalized mixed model with a binary link function. Random effects included herd, fixed effects included season of BTSCC (summer, May to September, and winter, October to April), total milk shipped per month (L), fat paid per month (kg), protein paid per month (kg), and participation or not in the DHI program. The likelihood of a BTSCC exceeding a penalty threshold in a non-DHI herd compared with a DHI herd was significantly greater than 1 at both penalty levels, where the odds ratios were estimated to be 1.42 [95% confidence interval (CI): 1.19 to 1.69] and 1.38 (95% CI: 1.25 to 1.54) for P500 and P400, respectively. The likelihood of incurring a BTSCC penalty (where 3 out of 4 consecutive BTSCC exceeded penalty thresholds) was not significantly different at P500; however, it was significantly different for P400, where the odds ratio was estimated to be 1.42 (95% CI: 1.12 to 1.81). © 2012 American Dairy Science ssociation.


PubMed | University of Guelph, Strategic Solutions Group and Woodstock
Type: | Journal: Preventive veterinary medicine | Year: 2016

The objectives of this study were to describe networks of Ontario swine sites and their service providers (including trucking, feed, semen, gilt and boar companies); to categorize swine sites into clusters based on site-level centrality measures, and to investigate risk factors for porcine reproductive and respiratory syndrome (PRRS) using information gathered from the above-mentioned analyses. All 816 sites included in the current study were enrolled in the PRRS area regional control and elimination projects in Ontario. Demographics, biosecurity and network data were collected using a standardized questionnaire and PRRS status was determined on the basis of available diagnostic tests and assessment by site veterinarians. Two-mode networks were transformed into one-mode dichotomized networks. Cluster and risk factor analyses were conducted separately for breeding and growing pig sites. In addition to the clusters obtained from cluster analyses, other explanatory variables of interest included: production type, type of animal flow, use of a shower facility, and number of neighboring swine sites within 3km. Unadjusted univariable analyses were followed by two types of adjusted models (adjusted for production systems): a generalizing estimation equation model (GEE) and a generalized linear mixed model (GLMM). Results showed that the gilt network was the most fragmented network, followed by the boar and truck networks. Considering all networks simultaneously, approximately 94% of all swine sites were indirectly connected. Unadjusted risk factor analyses showed significant associations between almost all predictors of interest and PRRS positivity, but these disappeared once production system was taken into consideration. Finally, the vast majority of the variation on PRRS status was explained by production system according to GLMM, which shows the highly correlated nature of the data, and raises the point that interventions at this level could potentially have high impact in PRRS status change and/or maintenance.


PubMed | Ontario Swine Health Advisory Board, University of Guelph and Strategic Solutions Group
Type: Journal Article | Journal: Transboundary and emerging diseases | Year: 2015

The main goal of this study was to investigate the occurrence of porcine reproductive and respiratory syndrome virus (PRRSV)-specific genotypes in swine sites in Ontario (Canada) using molecular, spatial and network data from a porcine reproductive and respiratory syndrome (PRRS) regional control project. For each site, location, animal movement service provider (truck companies), PRRSV status and sequencing data of the open reading frame 5 (ORF5) were obtained. Three-kilometre buffers were created to evaluate neighbourhood characteristics for each site. Social network analysis was conducted on swine sites and trucking companies to assemble the network and define network components. Three different PRRSV genotypes were used as outcomes for statistical analysis based on the regions phylogenetic tree of the ORF5. Multivariable exact logistic regression was conducted to investigate the association between being positive for a specific genotype and two main exposures of interest: (i) having at least one neighbour within threekm also positive for the same genotype outside the production system and (ii) having at least one positive site for the same genotype in the same truck network component outside the production system. Results showed that the importance of area spread and truck network on PRRSV occurrence differed according to genotype. Additionally, the Ontario PRRS database appears suitable for conducting regional disease investigations. Finally, the use of relatively new tools available for network, spatial and molecular analysis could be useful in investigation, control and prevention of endemic infectious diseases in animal populations.


PubMed | University of Guelph and Strategic Solutions Group
Type: | Journal: BMC veterinary research | Year: 2015

Heat stress is a physiological response to extreme environmental heat such as heat waves. Heat stress can result in mortality in dairy cows when extreme heat is both rapidly changing and has a long duration. As a result of climate change, heat waves, which are defined as 3 days of temperatures of 32 C or above, are an increasingly frequent extreme weather phenomenon in Southern Ontario. Heat waves are increasing the risk for on-farm dairy cow mortality in Southern Ontario. Heat stress indices (HSIs) are generally based on temperature and humidity and provide a relative measure of discomfort which can be used to predict increased risk of on-farm dairy cow mortality. In what follows, the heat stress distribution was described over space and presented with maps. Similarly, on-farm mortality was described and mapped. The goal of this study was to demonstrate that heat waves and related HSI increases during 2010-2012 were associated with increased on-farm dairy cow mortality in Southern Ontario. Mortality records and farm locations for all farms registered in the CanWest Dairy Herd Improvement Program in Southern Ontario were retrieved for 3 heat waves and 6 three-day control periods from 2010 to 2012. A random sample of controls (2:1) was taken from the data set to create a risk-based hybrid design. On-farm heat stress was estimated using data from 37 weather stations and subsequently interpolated across Southern Ontario by geostatistical kriging. A Poisson regression model was applied to assess the on-farm mortality in relation to varying levels of the HSI.For every one unit increase in HSI the on-farm mortality rate across Southern Ontario increases by 1.03 times (CI95% (IRR) = (1.025,1.035); p = 0.001). With a typical 8.6 unit increase in HSI from a control period to a heat wave, mortality rates are predicted to increase by 1.27 times.Southern Ontario was affected by heat waves, as demonstrated by high levels of heat stress and increased on-farm mortality. Farmers should be aware of these risks, and informed of appropriate methods to mitigate such risks.

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