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Culling from the Herd's Perspective—Exploring Herd-Level Management Factors and Culling Rates in Québec Dairy Herds
Abstract
The relationship between cows’ health, reproductive performance or disorders and their longevity is well demonstrated in the literature. However these associations at the cow level might not hold true at the herd level, and herd-level variables can modify cow-level outcomes independently of the cows’ characteristics. The interaction between cow-level and herd-level variables is a relevant issue for understanding the culling of dairy cows. However it requires the appropriate group-level variables to assess any contextual effect. Based on 10 years of health and production data, the objectives of this paper are:(a) to quantify the culling rates of dairy herds in Québec; (b) to determine the profiles of the herds based on herd-level factors, such as demographics, reproduction, production and health indicators, and whether these profiles can be related to herd culling rates for use as potential contextual variables in multilevel modelling of culling risk. A retrospective longitudinal study was conducted on data from dairy herds in Québec, Canada, by extracting health information events from the dairy herd health management software used by most Québec producers and their veterinarians. Data were extracted for all lactations taking place between January 1st, 2001 and December 31st, 2010. A total of 432,733 lactations from 156,409 cows out of 763 herds were available for analysis. Thirty cow-level variables were aggregated for each herd and years of follow-up, and their relationship was investigated by Multiple Factor Analysis (MFA). The overall annual culling rate was 32%, with a 95% confidence interval (CI) of [31.6%,32.5%]. The dairy sale rate by 60 days in milk (DIM) was 3.2% [2.8%,3.6%]. The annual culling rate within 60 DIM was 8.2% [7.9%,8.4%]. The explained variance for each axis from the MFA was very low: 14.8% for the first axis and 13.1% for the second. From the MFA results, we conclude there is no relationship between the groups of herd-level indicators, demonstrating the heterogeneity among herds for their demographics, reproduction and production performance, and health status. However, based on Principal Component Analysis (PCA), the profiles of herds could be determined according to specific, single, herd-level indicators independently. The relationships between culling rates and specific herd-level variables within factors were limited to livestock sales, proportion of first lactation cows, herd size, proportion of calvings occurring in the fall, longer calving intervals and reduced 21-day pregnancy rates, increased days to first service, average age at first calving, and reduced milk fever incidence. The indicators found could be considered as contextual variables in multilevel model-building strategies to investigate cow culling risk.
... In accordance with their specific management styles, farmers follow different strategies in their decision-making process to cull dairy cows. Various studies have shown that the variation in the culling decision is not only related to individual cow performances and herd-level risk factors but also to factors such as farmers' behavior and management styles (1)(2)(3)(4). Apart from these, changes in national or global policies regarding livestock production can alter the farmers' long-term strategies regarding the culling of dairy cows. A recent survival analysis of dairy cows in the Netherlands indicated that culling intensity may vary over the years due to changes in agricultural policy, while the reasons for the culling of individual cows remained the same (5). ...
... As a result, many farmers were forced to immediately reduce their livestock numbers, temporarily increasing the efflux of dairy cows (5) and youngstock. Nor et al. (3) and Haine et al. (1) found that in the Netherlands and Canada, the long-term culling rate of dairy farms was associated with herd-level factors such as the proportion of cows with elevated somatic cell count, herd average 305-day milk, and the herd average calving intervals. Results from Armengol and Fraile (8) suggested that variation between farm characteristics and the performance of herds could be important for culling differences between herds. ...
... a Variables are farm averages calculated from the individual performance data of the producing cows on the farms from the data provided by the CRV. 1 OC, overall culling proportion; PC, primiparous culling proportion; PPC, primiparous-primiparous culling proportion; POC, primiparous-overall culling proportion. 2 Farm average milk yield on test-day converted to fat-protein corrected milk (FPCM) using the formula from Yan et al. (11): FPCM (kg) = (0.337 + 0.116 x fat % + 0.06 x protein %) x milk production (kg). 3 High somatic cell count is defined as cows having more than 150,000 cells/ml milk for primiparous and 250,000 cells/ml for multiparous cows. ...
Introduction:
This article aimed to study cross-sectional associations between the performance of dairy farms and their corresponding culling proportions under the herd size constraint as imposed in 2018 by the new phosphate regulation in the Netherlands.
Methods:
To this end, production data from 10,540 Dutch dairy farms were analyzed to capture the inflow and outflow of both primiparous and multiparous cows. Farm performance was measured by 10 indicators structured in four areas of longevity, production, reproduction, and udder health. Farm culling proportions were represented by the overall culling (OC) and the number of culled primiparous cows in relation to (i) the total number of producing cows (PC), (ii) the number of producing primiparous cows (PPC), and (iii) the number of culled producing cows (POC). Spearman's rank correlation and weighted logistic regression were adopted to study associations.
Results:
In 2018, on average, 28% of producing cows were culled (OC). The number of primiparous cows culled represented 4.5% of the total number of producing cows (PC) and the mean proportion of culled primiparous cows was 18.8% of the total number of producing primiparous cows (PPC), and, of the total number of producing culled cows, 15% were primiparous cows (POC). However, the variance around the mean, and among individual farms, was high (SD 4-15% for all four culling proportions). Results from rank correlation showed very low-rank conformity (<12%) between the areas of production, reproduction, and udder health to the culling proportions. Results from logistic regression showed that higher farm levels of production and higher percentages of cows with poor udder health were associated with more overall culling but with less primiparous culling. For reproduction indicators, the associations were similar for overall and primiparous culling. However, except for the average age of culled animals, the odds ratios for indicators were close to 1 (range: 0.92-1.07 and 0.68-1.07 for OC and PPC, respectively), indicating only weak associations to culling proportions.
Discussion:
In conclusion, although the introduction of phosphate regulation resulted in an increased outflow of cattle, corresponding culling proportions were not associated with the level of farm performance measured in terms of production, reproduction, or udder health.
... Documenting the factors responsible for culling helps to identify several problems affecting the farm from cow-level to herd-level and from a managerial to the economic point of view. 8,10, [12][13][14][15] Understanding culling rates and related factors is of utmost importance as it is often associated with managerial expertise. 8, 15 At the herd level, culling is influenced by factors such as replacement heifers plan, milk quotas, and market prices of milk and beef. ...
... 8,10, [12][13][14][15] Understanding culling rates and related factors is of utmost importance as it is often associated with managerial expertise. 8, 15 At the herd level, culling is influenced by factors such as replacement heifers plan, milk quotas, and market prices of milk and beef. 15,16 The most common reasons for culling at the cow-level include old age, diseases (udder and legs), metabolic diseases or disorders, respiratory diseases, infectious and non-infectious diseases, illness, injury, infertility, and accidents. ...
... 8, 15 At the herd level, culling is influenced by factors such as replacement heifers plan, milk quotas, and market prices of milk and beef. 15,16 The most common reasons for culling at the cow-level include old age, diseases (udder and legs), metabolic diseases or disorders, respiratory diseases, infectious and non-infectious diseases, illness, injury, infertility, and accidents. 17,18 There are several studies conducted in several countries on culling effects and culling factors, except in Africa. ...
The United Nations estimates that the global population will total 9.7 billion in 2050. Rapid population growth pose a significant obstacle to achieving the Sustainable Development Goals, particularly eradicating hunger and poverty. In view of the expanding population growth, food production ideally should triple to prevent massive food shortages. Sustainable food and nutrition security is the focal point of the dairy industry. Dairy production plays a pivotal role in addressing and advancing global food and nutrition security. It serves as a major source of protein, calcium, and phosphorus in many families in developing countries with a fast-growing population. Consequently, the dairy industry is expected to grow by approximately 26% in the next 10 years and produce an estimated 1077 million tonnes of milk by 2050. However, the growth and distribution of the dairy industry is limited by many factors such as culling and mortality of dairy cows. Several studies highlight reproduction failures, old age, poor milk yield, diseases (mastitis, lameness, and dystocia), and heat stress as some reasons for culling of dairy cows. Hence, this review highlights the factors influencing culling and mortality in dairy production farms, and discusses mitigating measures to limit culling.
... Documenting the factors responsible for culling helps to identify several problems affecting the farm from cow-level to herd-level and from a managerial to economic point of view. 8,10,[12][13][14][15] Understanding culling rates and related factors is of utmost importance as it is often associated with managerial expertise. 8,15 At the herd level, culling is influenced by several factors such as replacement heifers plan, milk quotas, and market prices of milk and beef. ...
... 8,10,[12][13][14][15] Understanding culling rates and related factors is of utmost importance as it is often associated with managerial expertise. 8,15 At the herd level, culling is influenced by several factors such as replacement heifers plan, milk quotas, and market prices of milk and beef. 15,16 The most common reasons for culling at cow-level includes old age, diseases (udder and legs), metabolic diseases or disorders, respiratory diseases, infectious and non-infectious diseases, illness, injury, infertility, and accidents. ...
... 8,15 At the herd level, culling is influenced by several factors such as replacement heifers plan, milk quotas, and market prices of milk and beef. 15,16 The most common reasons for culling at cow-level includes old age, diseases (udder and legs), metabolic diseases or disorders, respiratory diseases, infectious and non-infectious diseases, illness, injury, infertility, and accidents. 17,18 There are several studies conducted in several countries in the world on culling effects and culling factors except in Africa. ...
The United Nations estimates that the global population will total 9.7 billion in 2050. Rapid population growth pose a significant obstacle to achieving the Sustainable Development Goals, particularly eradicating hunger and poverty. In view of the expanding population growth, food production ideally should triple to prevent massive food shortages. Sustainable food and nutrition security is the focal point of the dairy industry. Dairy production plays a pivotal role in addressing and advancing global food and nutrition security. It serves as a major source of protein, calcium, and phosphorus in many families in developing countries with a fast-growing population. Consequently, the dairy industry is expected to grow by approximately 26% in the next 10 years and produce an estimated 1077 million tonnes of milk by 2050. However, the growth and distribution of the dairy industry is limited by many factors such as culling and mortality of dairy cows. Several studies highlight reproduction failures, old age, poor milk yield, diseases (mastitis, lameness, and dystocia), and heat stress as some reasons for culling of dairy cows. Hence, this review highlights the factors influencing culling and mortality in dairy production farms, and discusses mitigating measures to limit culling.
... These associations at individual cow level may, however, differ at herd level; partially because of interrelated herd management aspects (e.g. young stock rearing) [13] and herd size restrictions imposed by policy regulations like milk quota before 2015 or phosphate restrictions after 2018 [1]. For instance, a cow with a relatively low production has a higher probability of being culled in comparison to a high productive cow within the same herd, while a herd with a relatively low average milk production may not have a higher culling rate than a herd with a high average milk production. ...
The associations between reproductive performance, milk yield and health status with the risk of culling, and thus with a cow’s longevity, have been well documented at the individual cow level. Associations at individual cow level may, however, not be valid at herd level due to interrelated herd management aspects and/or policy restrictions. The objective of this study was to explore the association of herd performance indicators with herd-level dairy cow longevity under Dutch production conditions. Longevity was expressed by three different measures, viz. age at culling, lifetime milk production of culled cows and culling rate. The evaluated herd performance indicators included factors on milk production, youngstock rearing, reproduction and health performance as registered on 10 719 Dutch commercial dairy herds during the period 2007–2016. Averaged over herds and the evaluated period, the age of culled milking cows was 2 139 days (5.8 years, SD±298 days), the lifetime milk production of culled cows was 31 238 kg (SD±7,494 kg), and the culling rate was 0.24 (SD±0.08). A mixed linear regression modelling approach was applied to evaluate the association of each of the three longevity measures with the selected herd performance indicators. The results indicated that only four herd performance indictors (herd size, herd expansion, heifer ratio and the proportion of cows with potential subclinical ketosis) shared significant associations with all three longevity variables. Generally, the strength of the associations between each of the evaluated longevity measures and herd performance indicators was only limited. The absence of strong associations between the longevity measures and herd performance indicators reveal that there is potential of extending cattle longevity without affecting the herd performance in terms of milk production, reproduction and health. Moreover, only part of the observed variance in longevity among the herds over time was explained by the herd performance variables, indicating that differences in longevity at herd level may predominantly be determined by other factors, like farmers’ attitude and strategic management.
... Globally, diseases leading to cow death (mortality) or premature removal from the herd (involuntary culling), are considered significant constraints of dairy cattle improvement [7,8]. In fact, recently published research has indicated an alarming rise in the annual rates of mortality and culling of dairy cows due to disease or physical injuries, raising significant concerns related to animal health and welfare among consumers and community activists [9][10][11][12]. ...
Background and Aim: Dairy cow mortality and culling are important parameters reflecting on cow health, productivity, and welfare as well as important determinants of herd sustainability, growth, and profitability. There are no published reports on the causes and rates of mortality and culling of dairy cows in Jordan. Therefore, the objectives of this study were to determine the most common causes and rates of mortality and culling of adult dairy cows in Jordan using a single well-managed dairy farm as a model over 3 years.
Materials and Methods: Data extracted from the farm management record software over 3 years (January 2016–December 2018) were used in this study. Cow-specific data included the date and month of sale, death or euthanasia, age, parity, reproductive status, and daily milk yield. Cow health-specific data included physical examination findings, presumptive diagnosis, medical or surgical treatments, postmortem findings, and any available laboratory findings. Descriptive analysis was performed to determine means (± standard deviation) and frequencies of various variables using Excel Spreadsheets of Microsoft Word 10.
Results: The 3-year rolling cow population in the farm used in the study was 500 ± 35. The overall mortality and culling rates were 5.9% and 28.5%, respectively. The mean age of died and culled cows was 3 ± 1.2 and 4 ± 1.5 years, respectively. The mortality rates were highest in the colder months (January through April). The most frequent causes of mortality were infectious diseases (28%), followed by non-infectious gastrointestinal diseases (25%), udder and teat diseases (mastitis 22%), and other diseases/accidents (25%). Of the infectious diseases, the most frequently diagnosed were enterotoxemia (12%), tuberculosis (TB) (8%), enteric salmonellosis (7%), and paratuberculosis (1%). The most frequently diagnosed non-infectious gastrointestinal diseases were traumatic reticulitis (11%), vagal indigestion (9%), and abomasal ulcer (5%). The most frequently diagnosed diseases causing mortality involving other body systems were reproductive diseases (acute puerperal metritis 6%), respiratory diseases (pneumonia 5% and pulmonary embolism 1%), metabolic diseases (fatty liver 3%), musculoskeletal diseases (septic arthritis 3% and downer cow syndrome 4%), neurologic diseases (unspecified causes 2%), and finally accidents (electrocution 1%). The most frequent causes of culling were old age/low milk production (39%), followed by the poor reproductive performance (31%), diseases/accidents (24%), and unidentified causes (6%). The most frequent diseases/accidents causing culling were udder diseases (mastitis 32%), followed by non-infectious gastrointestinal diseases (28%) (vagal indigestion [15%], rumen tympany [7%], and abomasal ulcer [6%]), musculoskeletal diseases (23%) (foot and claw diseases [7%], downer cow syndrome [7%], hip luxation [5%], septic arthritis [2%], and gastrocnemius rupture [2%]), respiratory diseases (pneumonia 10%), and finally infectious diseases (9%) (paratuberculosis [3%], hemorrhagic bowel syndrome [2%], and TB [2%]).
Conclusion: Results of this study showed that the majority of deaths and culling of dairy cows in Jordan are due to infectious diseases followed by non-infectious gastrointestinal diseases and mastitis. More efforts aiming at improving biosecurity standards, nutritional management, and mastitis prevention measures are required to limit the impact of disease on farm economy, animal health and productivity, and animal welfare in Jordan.
... The culling rate in our study was 26.3%, which is comparable to the rates in previous reports [28][29][30][31][32]. Additionally, the culling rate was associated with the occurrence of each disease or parity, but there was no interaction between these factors. ...
The objective of the present study was to investigate the associations between major diseases (clinical mastitis, peracute mastitis, metabolic disorders, peripartum disorders) and four parameters related to productivity (305-day milk yield, number of days open, culling rate, death rate) on a large dairy farm in a temperate zone with approximately 2500 Holstein cows. Data were collected from 2014 to 2018 and involved 9663 calving records for 4256 cows. We found negative effects of clinical mastitis, peracute mastitis, metabolic disorders, and peripartum disorders on the productivity of cows. Clinical-mastitis-suffered cows with multiple diseases had more days open compared with those with clinical mastitis alone and the healthy group, and they had a higher death rate than the healthy group, whereas there was no difference in death rate between the clinical mastitis only and healthy groups. Cows suffering from peracute mastitis, metabolic disorders, and peripartum disorders with either single or multiple diseases exhibited reduced productivity compared with the healthy group. Our findings clearly show that major diseases of cows in a temperate zone have severely negative effects on their productivity.
... Parameter 3 -Feasibility of killing animals Culling is not a purely epidemiological measure, as welfare issues, social and psychological factors and particularly economic considerations play a vital part (Lehenbauer and Oltjen, 1998;Haine et al., 2017). As the probability of cure has a large impact on the economic benefit of treatment, cost-benefit analyses are necessary before application of any treatment, including side effects like the persistence of infected cows in a herd as a source of new contaminations (Rainard et al., 2018). ...
Staphylococcus aureus (S. aureus) was identified among the most relevant antimicrobial-resistant (AMR) bacteria in the EU for cattle and horses in previous scientific opinions. Thus, it has been assessed according to the criteria of the Animal Health Law (AHL), in particular criteria of Article 7 on disease profile and impacts, Article 5 on its eligibility to be listed, Annex IV for its categorisation according to disease prevention and control rules as in Article 9, and Article 8 for listing animal species related to the bacterium. The assessment has been performed following a methodology previously published. The outcome is the median of the probability ranges provided by the experts, which indicates whether each criterion is fulfilled (lower bound ≥ 66%) or not (upper bound ≤ 33%), or whether there is uncertainty about fulfilment. Reasoning points are reported for criteria with uncertain outcome. According to the assessment here performed, it is uncertain whether AMR S. aureus can be considered eligible to be listed for Union intervention according to Article 5 of the AHL (60-90% probability). According to the criteria in Annex IV, for the purpose of categorisation related to the level of prevention and control as in Article 9 of the AHL, the AHAW Panel concluded that the bacterium does not meet the criteria in Sections 1, 2 and 4 (Categories A, B and D; 1-5%, 5-10% and 10-33% probability of meeting the criteria, respectively) and the AHAW Panel was uncertain whether it meets the criteria in Sections 3 and 5 (Categories C and E, 33-90% and 60-90% probability of meeting the criteria, respectively). The animal species to be listed for AMR S. aureus according to Article 8 criteria include mainly mammals, birds, reptiles and fish.
... The average culling rate in the US was 33.0% in 1993, and 31.6% in 1999 (Hadley et al. 2006). Haine et al. (2017) analysed the data between the year 2001 and 2010 of Quebec, Canada and found an average culling rate of 32%. Nor et al. (2013) found the average culling rate for slaughter/death 25.4% in the Netherlands between the year 2007 À 2010. ...
The aim of this study is to determine the effect of first-calving age (FCA) on yield parameters and productive life in dairy farms using a robotic milking system in Turkey. The cows (n ¼ 1579) were divided into five groups (24, 25, 26, 27, 28 months and above FCA. The average milk yield was highest in 24 months of FCA (9140.31 ± 145.55 kg) and was lowest in 27 months of FCA (8534.55 ± 131.00 kg) (p < .05). The average service period length in the first lactation was longer in cows of 28 months old (158.92 ± 7.28 days) than 26 and 27 months (131.96 ± 4.45and 130.51 ± 54.97 days respectively) old groups (p < .05). A number of lactations of cows that were 26 months old (2.52 ± 0.09) at FCA was higher than those FCA was 24 months and 28 months (2.03 ± 0.15 and 2.18 ± 0.09 respectively) (p < .05). Replacement rates were not differing statistically at different lactations. The most frequent reasons for culling were mastitis and reproduction in all groups. As a result, cows in 24 months of FCA had no undesirable results in terms of milk yield, service period, number of insemination per lactation. ARTICLE HISTORY
... Another example in salmonids is the attempt to set up a data integration platform described by Meyer et al. [75]. The use of animal production commercial data for research can significantly increase access to quality data with excellent coverage in time and space of the populations of interest, as shown by studies included in this work such as analyses conducted in the Chile salmonid industry [76,77] and in the Canadian dairy industry [78]. The confidentiality level required for these datasets as well as barriers to data sharing related to competitiveness and anti-trust regulation aspects suggest that there could be a fundamental incompatibility between the principles of open-access data and the use of commercial data for research. ...
Background
The FAIR (Findable, Accessible, Interoperable, Reusable) principles were proposed in 2016 to set a path towards reusability of research datasets. In this systematic review, we assessed the FAIRness of datasets associated with peer-reviewed articles in veterinary epidemiology research published since 2017, specifically looking at salmonids and dairy cattle. We considered the differences in practices between molecular epidemiology, the branch of epidemiology using genetic sequences of pathogens and hosts to describe disease patterns, and non-molecular epidemiology.
Results
A total of 152 articles were included in the assessment. Consistent with previous assessments conducted in other disciplines, our results showed that most datasets used in non-molecular epidemiological studies were not available (i.e., neither findable nor accessible). Data availability was much higher for molecular epidemiology papers, in line with a strong repository base available to scientists in this discipline. The available data objects generally scored favourably for Findable, Accessible and Reusable indicators, but Interoperability was more problematic.
Conclusions
None of the datasets assessed in this study met all the requirements set by the FAIR principles. Interoperability, in particular, requires specific skills in data management which may not yet be broadly available in the epidemiology community. In the discussion, we present recommendations on how veterinary research could move towards greater reusability according to FAIR principles. Overall, although many initiatives to improve data access have been started in the research community, their impact on the availability of datasets underlying published articles remains unclear to date.
... Managerial expertise has been proven to have a direct association with the decision to cull animals and the culling rate [ 12 , 13 ]. At the herd level, culling is influenced by several factors such as replacement heifers plan, milk quotas, market prices of milk and beef [13] . The most common reasons for culling, at cow-level, includes old age, diseases (udder and legs), metabolic diseases or disorders, respiratory diseases, infectious and non-infectious diseases; illness, injury, infertility, and accidents [15] . ...
Globally, the dairy industry estimates the production of 1077 million tonnes of milk by 2050, and this estimation is influenced by a fast-growing population. However, diseases and conditions leading to culling and mortality of dairy cows are limiting dairy production output. The objective of this study was to identify and document the factors responsible for culling and mortality in dairy farms in the Eastern Cape province, South Africa. This was done using secondary data from the dairy farms’ records and computer software. The most frequent reason for culling dairy cows was reproduction problems such as infertility (7.9%), reproductive failure (89.9%), and dystocia (1%). Various factors such as stock-theft (3.6%), indigenous breed (8.2%), and age (2.7%) accounted for the second most prevalent factors for the culling of dairy cows. Poor milk yield and health-related factors such as redwater (33.8%), milk fever (23.3%), and heartwater (6.8%) were amongst the major factors responsible for the culling of dairy cows. A total of 1774 dairy cows died in the study farms from 2015 to 2019, and cause of death was not specified except for drowning (0.01%) and snake bite (1%). There was an association between the reasons for culling and the season of culling. This study provides baseline information on the leading causes of culling and mortality. Pathogens such as Brucella abortus and Q fever need further investigation starting with the screening and testing methods, and frequencies of screening and testing to develop strategies to minimize the persistent issue of culling due to reproduction problems. Also, further research on strategies to combat the interference of nearby communities on dairy farms is recommended.
... In selected regions of the United States, the average culling rate was 31.6% in 1999 [3] but was 27.7% in Pennsylvanian herds in 2005 [21]. In Canada, Haine et al. [22] reported an average culling rate of 32% over the 2001-2010 decade and a dairy sell rate by 60 days in milk of 3.2%. The average culling rate of cows due to slaughter/death was 25.4%, ranging between 23% (in 2007) to 28% (in 2010) in Dutch dairy herds [23]. ...
Background:
Culling is a major cost for dairy farms but also an essential part in managing herd productivity. This study aimed to identify the culling rates of Estonian dairy cows, identify the farmers' stated reasons and risk factors for culling. This observational study used registry data of all cows from herds with ≥20 cow-years in 2013-2015. Cow lactation-level analyses included data of 86,373 primiparous cows from 409 herds and 177,561 lactations of 109,295 multiparous cows from 410 herds. Weibull proportional hazard regression models were used to identify risk factors for culling due to slaughter or death.
Results:
The overall culling rate of Estonian dairy cows was 26.24 (95% CI 26.02; 26.46) per 100 cow-years. The most common reasons farmers stated for culling were feet/claw disorders (26.4%), udder disorders (22.6%), metabolic and digestive disorders (18.1%) and fertility problems (12.5%). Animal-level risk factors for culling were Holstein breed, older parity, lower milk yield breeding value, older age at first calving, longer previous calving interval, having assisted calving, stillbirth and birth of twins/triplets. Lower milk yield, somatic cell count over 200,000 cells/ml and fat/protein ratio over 1.5 at first test-milking after calving were associated with greater culling hazard during the lactation. Cows from larger herds, herds with decreasing size and higher milk yields had a higher culling probability.
Conclusions:
This study emphasises the need for improved management of hoof health and prevention of mastitis and metabolic diseases. It is essential to ensure easy calving and good health of cows around calving in order to lower the culling hazard.
... in Pennsylvanian herds in 2005 (28). In Canada Haine et al. (29) reported an average culling rate of 32% over the 2001-2010 decade and a dairy sell rate by 60 days in milk of 3,2%. The average culling rate of cows due to slaughter/death was 25.4% ranging between 23% (in 2007) to 28% (in 2010) in Dutch dairy herds (30). ...
Background Culling is a major cost for dairy farms but also an essential part in managing herd productivity. The study aimed to identify the culling rates of Estonian dairy cows, identify the farmers´ stated reasons and risk factors for culling. This observational study used registry data of all cows from herds with ≥20 cow-years in 2013-2015. Cow lactation-level analyses included data of 86,373 primiparous cows and 177,561 lactations of 109,295 multiparous cows. Weibull proportional hazard regression models were used to identify risk factors for culling due to slaughter or death.
Results The overall culling rate of Estonian dairy cows was 26.24 (95% CI 26.02; 26.46) per 100 cow-years. The most common farmers´ stated reasons for culling were feet/claw disorders (26.4%), udder disorders (22.6%), metabolic and digestive disorders (18.1%) and fertility problems (12.5%). Animal-level risk factors for culling were Holstein breed, older parity, lower milk yield breeding value, older age at first calving, longer previous calving interval, having assisted calving, stillbirth and birth of twins/triplets. Lower milk yield, somatic cell count over 200,000 cells/ml and fat/protein ratio over 1.5 at first test-milking after calving were associated with greater culling hazard during the lactation. Cows from larger herds, herds with decreasing size and higher milk yields had higher culling probability.
Conclusions This study emphasises the need for improved management of hoof health and prevention of mastitis and metabolic diseases. It is essential to ensure easy calving and good health of cows around calving in order to lower the culling hazard.
... Koketsu [30] reported that it is essential to make decisions on the basis of evidence from production records to control productivity. Demographic, reproduction, production, and health factors are determinants involved in the administration of a farm enterprise [25]. Epidemiological observational studies are suitable for analyzing the factors that hinder productivity or reproductive performance [36]. ...
Peripartum disorders in dairy cows negatively influence their productivity and reproductive performance. However, only a few reports have clearly indicated the influence of such disorders on the productivity and reproductive performance at a local-area or cow-level in Japan. This study aimed to elucidate the influence of diseases occurring within 60 days after calving on subsequent productivity and reproductive performance. Accordingly, a wide-area database on dairy production was used for epidemiological analysis; subsequently, multivariable analysis was performed to investigate the association of such diseases with productivity or reproductive performance in 6,545 cows from 178 farms in Fukuoka. We used 305-day energy-corrected milk (305 ECM) as an index of productivity and conception and culling as indices of reproductive performance. With regard to causality, mixed-effects model was used for analyzing the association between disease and productivity, and Cox proportional hazard model was used for analyzing the association between disease and reproductive performance. Compared to the disease absence group, the disease presence group demonstrated significantly lower 305 ECM [−154 kg; 95% confidence interval (CI), −229 to −79] and risk of pregnancy [hazard ratio (HR), 0.85; 95% CI, 0.80–0.91] and higher risk of culling (HR, 1.36; 95% CI, 1.17–1.59). These results indicate that, in Fukuoka, dairy cows affected by diseases within 60 days after calving exhibit lower productivity and reproductive performance. Therefore, proper dairy cow management during the peripartum period to prevent diseases during early lactation may maintain or improve productivity.
The present study aimed to reveal the culling-related metrics, identify the culling reason patterns for cows by developing and implementing the cow culling form (CCF), and analyse the concordance of farmers' stated culling reasons with those identified based on the CCF.
A CCF was developed to register the disease history and conditions of cows that were related to culling. CCFs were completed by farm managers and veterinarians in eight dairy herds over a one-year period for slaughtered (n = 686) and dead (n = 250) cows. Completed CCFs were interpreted by the study authors to identify underlying, intermediate, influential, and immediate culling reasons. The identified culling reasons were compared to those reported by producers.
The mean annual cow culling rate of the study farms was 31.8%, and the average on-farm mortality was 9.3%. Of the 250 cows that died on the farms, 43.6% were euthanised. Only 2% of the cows were slaughtered due to low milk yield. In total, 260 and 119 unique three-reason culling codes were created for slaughtered and dead cows, respectively. Single disease or condition causing slaughter or death of cows was identified in 44.8% and 52.0% of the cases, respectively. Producers' reported culling reasons concurred with the underlying culling reason in 72.6% and 63.6% of slaughtered and dead cows, respectively. A high variety of agreements between the farmers' reported and CCF-based culling codes was identified across single culling reasons. Improved registration of culling reasons is required to support informed herd-based decisions.
Herd culling rates and longevity represent herd health and welfare status as well as farm economic performance. The contribution of endemic circulation of the main cattle pathogens to herd performance has not been previously analysed. The aim of this study was to estimate the herd prevalence of selected endemic bovine pathogens among large commercial dairy herds and to analyse their associations with herd culling rates and longevity.
Bulk tank milk (BTM) samples and 10 heifer serum samples were collected from 120 Estonian dairy herds with at least 100 cows, between August 2019 and July 2020. All samples were tested for antibodies against bovine herpesvirus 1 (BHV-1), bovine viral diarrhoea virus (BVDV), bovine respiratory syncytial virus (BRSV), Mycoplasma bovis, Mycobacterium avium spp. paratuberculosis (MAP) and Salmonella Dublin using commercial ELISA. Data on herd size, milk yield, culling rate (CR) and mean age of the culled cows (MAofCC) were collected from the Estonian Livestock Performance Recording Ltd. The apparent herd and animal prevalences were calculated, and linear regression models were used to identify associations between the herd status of six tested pathogens and CR and MAofCC.
The herd seroprevalences for antibodies based on BTM and heifer serum sample testing were BHV-1 56.7% (95% CI 47.3; 65.7), Mycoplasma bovis 48.3% (95% CI 39.1; 57.6), MAP 2.5% (95% CI 0.5; 7.1) and S. Dublin 24.2% (95% CI 16.8; 32.8) in all tested herds. Excluding vaccinated herds, herds prevalence for BVDV was 27.0% (95% CI 19.0; 36.3) and for BRSV 94.7% (95% CI 88.1; 98.3).
Herd seropositive status for BRSV was associated with lower MAofCC, and herds with BSRV-seropositive youngstock had increased CR. Herds with positive BTM test results for S. Dublin culled cows at an older average age (Coef = 3.79 months, 95% CI 0.52; 7.07, p = 0.023). MAP-positive herds had somewhat lower herd MAofCC (Coef = -6.18 months, 95% CI -12.98; 0.63, p = 0.075). There was also a tendency of BVDV-negative herds to have a lower CR than BVDV-positive herds (Coef = -3.03%, 95% CI -6.54; 0.49, p = 0.090), and vaccination against BVDV tended to be protective against high CR (Coef = -6.26%, 95% CI -12.61; 0.09, p = 0.053 compared to infected herds).
This study shows that Estonian large-scale dairy herds are endemically infected with several important cattle pathogens. Most of the studied pathogens influence longevity and culling rates, thus entailing health and economic consequences.
A useful term to describe the reproductive function of a herd of cows is 21‐day pregnancy rate. Heat stress is among the most consequential changes in the environment of the cow that can reduce 21‐day pregnancy rate. This chapter focuses on effects of heat stress on reproductive events important for establishment of pregnancy. It describes what is known about the physiological and cellular mechanisms by which heat stress compromises reproductive function. The chapter addresses strategies for minimizing heat stress effects on reproduction. Consequences of heat stress on the physiology and productive potential of the cow depend on both physiological changes that an animal engages to regulate body temperature and the direct negative effects of elevated body temperature on cellular function.
Background Culling is a major cost for dairy farms but also an essential part in managing herd productivity. This study aimed to identify the culling rates of Estonian dairy cows, identify the farmers’ stated reasons and risk factors for culling. This observational study used registry data of all cows from herds with ≥20 cow-years in 2013-2015. Cow lactation-level analyses included data of 86,373 primiparous cows from 409 herds and 177,561 lactations of 109,295 multiparous cows from 410 herds. Weibull proportional hazard regression models were used to identify risk factors for culling due to slaughter or death.
Results The overall culling rate of Estonian dairy cows was 26.24 (95% CI 26.02; 26.46) per 100 cow-years. The most common reasons farmers stated for culling were feet/claw disorders (26.4%), udder disorders (22.6%), metabolic and digestive disorders (18.1%) and fertility problems (12.5%). Animal-level risk factors for culling were Holstein breed, older parity, lower milk yield breeding value, older age at first calving, longer previous calving interval, having assisted calving, stillbirth and birth of twins/triplets. Lower milk yield, somatic cell count over 200,000 cells/ml and fat/protein ratio over 1.5 at first test-milking after calving were associated with greater culling hazard during the lactation. Cows from larger herds, herds with decreasing size and higher milk yields had a higher culling probability.
Conclusions This study emphasises the need for improved management of hoof health and prevention of mastitis and metabolic diseases. It is essential to ensure easy calving and good health of cows around calving in order to lower the culling hazard.
Background Culling is a major cost for dairy farms but also an essential part in managing herd productivity. The study aimed to identify the culling rates of Estonian dairy cows, identify the farmers´ stated reasons and risk factors for culling. This observational study used registry data of all cows from herds with ≥20 cow-years in 2013-2015. Cow lactation-level analyses included data of 86,373 primiparous cows from 409 herds and 177,561 lactations of 109,295 multiparous cows from 410 herds. Weibull proportional hazard regression models were used to identify risk factors for culling due to slaughter or death.
Results The overall culling rate of Estonian dairy cows was 26.24 (95% CI 26.02; 26.46) per 100 cow-years. The most common farmers´ stated reasons for culling were feet/claw disorders (26.4%), udder disorders (22.6%), metabolic and digestive disorders (18.1%) and fertility problems (12.5%). Animal-level risk factors for culling were Holstein breed, older parity, lower milk yield breeding value, older age at first calving, longer previous calving interval, having assisted calving, stillbirth and birth of twins/triplets. Lower milk yield, somatic cell count over 200,000 cells/ml and fat/protein ratio over 1.5 at first test-milking after calving were associated with greater culling hazard during the lactation. Cows from larger herds, herds with decreasing size and higher milk yields had higher culling probability.
Conclusions This study emphasises the need for improved management of hoof health and prevention of mastitis and metabolic diseases. It is essential to ensure easy calving and good health of cows around calving in order to lower the culling hazard.
Several health disorders, such as milk fever, displaced abomasum, and mastitis, as well as impaired reproductive performance, are known risk factors for the removal of affected cows from a dairy herd. While cow-level risk factors are well documented in the literature, herd-level associations have been less frequently investigated. The objective of this study was to investigate the effect of cow- and herd-level determinants on variations in culling risk in Québec dairy herds: whether herd influences a cow's culling risk. For this, we assessed the influence of herd membership on cow culling risk according to displaced abomasum, milk fever, and retained placenta.
In this paper it is shown for four sets of real data, all published examples of principal component analysis, that the number of variables used can be greatly reduced with little effect on the results obtained. Five methods for discarding variables, which have previously been successfully tested on artificial data (Jolliffe, 1972), are used. The methods are compared and all are shown to be satisfactory for real, as well as artificial, data, although none is shown to be overwhelmingly superior to the others.
This paper discusses statistical modelling for data with a hierarchical structure, and distinguishes in this context between three different meanings of the term hierarchical model: to account for clustering, to investigate variability and separate predictive equations at different hierarchical levels (multi-level analysis), and in a Bayesian framework to involve multiple layers of data or prior information. Within each of these areas, the paper reviews both past developments and the present state, and offers indications of future directions. In a worked example, previously reported data on piglet lameness are reanalyzed with multi-level methodology for survival analysis, leading to new insights into the data structure and predictor effects. In our view, hierarchical models of all three types discussed have much to offer for data analysis in veterinary epidemiology and other disciplines.
Optimising the number of replacement heifers needed will have positive economic and environmental consequences on herds that rear their own young stock. The number of heifers needed to be kept is closely related with the number of culled dairy cows in the herd. This study therefore looked at the variation that exists in culling rate and herd level factors associated with it. A dataset from 1903 dairy herds available included information at animal level (dates of culling, slaughter/death) and herd level (characteristics of reproduction, performance, health) over the years 2007 to 2010. The average culling rate for slaughter/death was used and was defined for each year as percentage of the herd size that died within 30 d after they were culled. The analysis of the association between average culling rate for slaughter/death and the characteristics of the herd was performed using a mixed model. The results showed that the average culling rate for slaughter/death was 25·4% and varied between 23% (2007) and 28% (2010). More than 70% of the herds have an average culling rate for slaughter/death of less than 30%, showing that there is room for lowering the average culling rate for slaughter/death. A higher average culling rate for slaughter/death is associated with a longer average calving interval, a higher average 305-d protein production, a higher average somatic cell count (SCC), a higher percentage of new high SCC, a more than 5% decrease in herd size, and herds that bought more than 1% of animals per year. A lower average culling rate for slaughter/death is associated with a longer average age, herds that bought less than 1% of animals per year and a more than 5% increase in herd size. In conclusion, the average culling rate for slaughter/death is associated with fertility, udder health and openness of the herd.
Risk of culling consequent to the main health disorders occurring in the current production systems is reviewed. Survival analysis including health disorders as time-dependent variables is considered to be the most appropriate method to assess their effects because they allow a better description of the exact follow-up of disease history. Farmers preferentially consider health events in the current lactation and/or those occurring in early stages of lactation for making culling decisions. The unfavourable direct effects on culling of dystocia and udder disorders (mastitis and teat injuries) are clearly demonstrated, whereas there are variations between studies on the association between metabolic and reproductive disorders and culling. These variations may be due to differences in study designs, populations involved and methods. Consequences, in terms of estimated effect of health disorders, of methodological choices (e.g. whether or not including in the models descriptors for milk yield and/or reproductive performance) are discussed. Metabolic and reproductive disorders may act indirectly through a subsequent decrease in milk yield and reproductive performance. The impact of health disorders on longevity is on average weak, compared to the impact of low milk yield potential and poor reproductive performance. Herd characteristics (availability of heifers, quota, farmer's attitude towards risk and uncertainty...) modify the risk for a cow to be culled for a given health disorder. Aims of further studies could be (1) to interpret the meaning and to analyse the reliability of culling reasons information, (2) to evaluate the relative effect on culling of health disorders and performance (milk yield and reproduction) in different parities, (3) to investigate the role of components of the herd effect on the risk of culling.
In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.
A number of commentators have argued that there is a distinctive geography of health-related behaviour. Behaviour has to be understood not only in terms of individual characteristics, but also in relation to local cultures. Places matter, and the context in which behaviour takes place is crucial for understanding and policy. Previous empirical research has been unable to operationalize these ideas and take simultaneous account of both individual compositional and aggregate contextual factors. The present paper addresses this shortcoming through a multi-level analysis of smoking and drinking behaviours recorded in a large-scale national survey. It suggests that place, expressed as regional differences, may be less important than previously implied.
There is much debate about possible antagonism between high milk production and reproductive performance. This paper reviews methods of measuring reproductive performance and the association of the level of milk production with pregnancy rate at the herd and individual levels. The main question is whether fertility (the capacity for reproductive function and successful pregnancy) of dairy cows has in fact declined, as opposed to the success of management systems and people at meeting the metabolic, nutritional, housing, and social needs of increasingly productive animals but with no less inherent capacity to achieve and maintain pregnancy; and if fertility really has diminished, the extent to which this decline is caused by increased milk production. There is no doubt that production per cow has increased, but it is unclear how much of this increase can explain the apparent decrease in fertility. It is important to separate the biology of reproductive function from the effects of economically based management decisions about culling and continuation of breeding. Most traditionally-used measures of reproductive performance (calving interval, conception rate, non-return rate) are incomplete or severely biased outcome measures. Both herd and cow-level data should include as much information as possible on confounders of the relationship of production with reproduction. Population or herd-level data should not be used to make inferences about individual-level associations. Considering the quality of data and analytic methods in the published literature, it is not clear if there is any association between higher milk yield and the probability and timing of pregnancy, either among cows at various levels of production in a population at one time, or with increasing production over time.
Knowledge of reproductive risk factors for culling is useful in making insemination and culling decisions and helps motivate efforts to reduce or eliminate risk factors. The objective of this study was to describe survival and reproductive risk factors for culling in Holstein dairy herds with at least 200 cows. Results were calculated from 2,345,015 DHI lactation records from 727 herds with at least 200 cows from 2001 to 2006. Herds were located in 36 states primarily located east of the Mississippi River. Kaplan-Meier survival curves were obtained and daily hazards of culling were calculated with the actuarial method. Cox regression was performed with the GLIMMIX procedure in SAS (SAS Institute Inc., Cary, NC). The hazard of culling increased with parity number. Cows in their sixth parity had 3 times greater hazards than cows in their first parity. Medium remaining productive life for cows calving in parity 1 to 6 were 907, 697, 553, 469, 423, and 399 d, respectively. Daily hazards of culling first peaked approximately 30 d after calving and then again later in lactation, after 280 d, for older cows. Hazards for first-parity cows peaked earlier, around d 10 after calving, and the first-parity cows had lower risks of culling later in lactation than older cows. Pregnant cows had 3 to 7 times lower hazards of culling than open cows. Hazards of culling increased for cows that had greater calving difficulty, gave birth to males or twins, were in herds with shorter days to first insemination, or had longer days to conception. The possible to likely use of a synchronized breeding program increased from 21.9% in 2001 to 41.4% in 2006. Cows in herds that did not use a synchronized breeding program had slightly lower risks of culling than those in herds that at least possibly used a synchronized breeding program.
A survey was carried out in a random sample of 123 dairy farms from the east of Ireland. The monthly mean production per cow was 315 l of milk and 11.5 kg of fat. The mean log herd somatic cell count was 5.45 (arithmetic mean = 372,573 cells/ml), with almost 50% of the monthly counts over 300,000 cells/ml in a 12-month period. Bivariate and multivariate analysis was performed to assess the relative impact of the personal characteristics of the farmer and the management policies he applied on the amount and quality of the milk produced. In five out of six models the group of variables related to farmers' attitudes, values, and sociodemographic profile explained a similar or greater amount (between 14.44 and 34.35%) of the variation of farm performance than the group of management variables (between 14.33 and 25.99%) as measured by the R2. These results stress the importance of the human factors in explaining variation in farm performance.
Data from a survey performed from 1986 to 1990 were analyzed to assess the effects of diseases on length of productive life of 3589 Holstein cows from 47 herds, using a proportional hazard model. The probability of a cow being culled, or hazard function, was supposed to be the product of an unspecified baseline hazard function and log-linear, time-dependent explanatory variables that possibly influence culling rate (Cox's regression). The effect of 16 health events was studied according to lactation number of occurrence. The model included adjustments for effects of herd-year-season (considered to be random), month of calving, stage of lactation, lactation number, reproductive performance, and milk production. The probability of a cow being culled increased in early and late stages of lactation in older cows, in low producing cows, and in cows with poor reproductive performance. Mastitis before the peak of lactation or during the dry period increased the risk (relative culling rate in first lactation, 1.3 and 4.0, respectively). Teat injuries and nontraumatic udder disorders had a large impact on longevity. Cows with late metritis or early abortion had poor survival. The decrease in median length of productive life could be over a standard lactation in particular cases. Expected survivor curves, computed after assumption of a priori values of covariates and their evolution over time, appear to be powerful tools for examining the effect of health disorders on length of productive life of cows.
Ecological studies have been evaluated in epidemiological contexts in terms of the "ecological fallacy." Although the empirical evidence for a lack of comparability between correlations derived from ecological- and individual-level analyses is compelling, the conceptual meaning of the ecological fallacy remains problematic. This paper argues that issues in cross-level inference can be usefully conceptualized as validity problems, problems not peculiar to ecological-level analyses. Such an approach increases the recognition of both potential inference problems in individual-level studies and the unique contributions of ecological variables. This, in turn, expands the terrain for the location of causes for disease and interventions to improve the public's health.
The effect of seven diseases on culling was measured in 7523 Holstein cows in New York State. The cows were from 14 herds and had calved between January 1, 1994 and December 31, 1994; all cows were followed until September 30, 1995. Survival analysis was performed using the Cox proportional hazards model to incorporate time-dependent covariates for diseases. Different intervals representing stages of lactation were considered for effects of the diseases. Five models were fitted to test how milk yield and conception status modified the effect of diseases on culling. Covariates in the models included parity, calving season, and time-dependent covariates measuring diseases, milk yield of the current lactation, and conception status. Data were stratified by herd. The seven diseases and lactational risks under consideration were milk fever (0.9%), retained placenta (9.5%), displaced abomasum (5.3%), ketosis (5.0%), metritis (4.2%), ovarian cysts (10.6%), and mastitis (14.5%). Older cows were at a much higher risk of being culled. Calving season had no effect on culling. Higher milk yield was protective against culling. Once a cow had conceived again, her risk of culling dropped sharply. In all models, mastitis was an important risk factor throughout lactation. Milk fever, retained placenta, displaced abomasum, ketosis, and ovarian cysts also significantly affected culling at different stages of lactation. Metritis had no effect on culling. The magnitude of the effects of the diseases decreased, but remained important, when milk yield and conception status were included as covariates. These results indicated that diseases have an important impact on the actual decision to cull and the timing of culling. Parity, milk yield, and conception status are also important factors in culling decisions.
Dairy Herd Improvement Holstein herd summary records (n = 11,259) were obtained for the year ending 1998. Reasons cows reportedly left the herd based on termination codes were analyzed for the effect of region, herd size, and herd milk production level. Regions were: North, Midsouth, and South. Herd sizes were: small (25 to 99), low medium (100 to 149), high medium (150 to 299), and large (greater than or equal to 300 cows). Milk production levels were: low (less than 7258 kg), medium (7258 to 9072 kg), and high (greater than 9072 kg). The overall percentage of cows leaving the herd was higher in the Midsouth than the South and increased with herd size. Low producing herds reported a lower percentage of cows left than high producing herds. Herds in the South reported more cows leaving for reproduction, death, and low production and fewer leaving for mastitis. Herds in the North and Midsouth reported more cows leaving for injury/other and disease, respectively. Cows left herds for disease less frequently in the North. Large herds in the South had a higher percentage leaving for low production than any herd size group in the North. Small herds reported more cows leaving for reproduction and mastitis than high medium and low medium size herds. The percentage of cows leaving for feet and leg problems was lowest for small size herds. High producing herds reported more cows leaving for reproduction, mastitis, feet and legs and disease.
The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end-users appreciate the essence of the method. The examples are simple enough to see the behind-the-scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data.
Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR). The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.
This book explores the field of geographical variations in disease. Especially with respect to variations in environmental exposures at the small-area scale, the book gives an account of current practice and developments. The recent and rapid expansion of the field looks set to continue in line with growing public, governmental, and media concern about environmental and health issues, and the scientific need to understand and explain the effects of environmental pollutants on health. The book is concerned with fostering an understanding of the geographical distribution of disease and the effects of environmental exposures on human health.
Under certain conditions aggregate-level data provide unbiased estimates of individual-level relationships. Here I present these conditions in the form of a single theoretical decision rule: bias is absent when, and only when, the group mean of the independent variable (X) has no effect on Y, with X controlled. This paper introduces this rule, demonstrates it for the general n-variable case, compares it with prior discussions of cross-level inference, and illustrates it with the 1930 census data used by Robinson (1950). The final section discusses the implications of this rule for the converse type of cross-level inference: the use of individual-level data to estimate aggregate-level relationships.
We investigate the effect of measurement error on principal component analysis in the high-dimensional setting. The effects of random, additive errors are characterized by the expectation and variance of the changes in the eigenvalues and eigenvectors. The results show that the impact of uncorrelated measurement error on the principal component scores is mainly in terms of increased variability and not bias. In practice, the error-induced increase in variability is small compared with the original variability for the components corresponding to the largest eigenvalues. This suggests that the impact will be negligible when these component scores are used in classification and regression or for visualizing data. However, the measurement error will contribute to a large variability in component loadings, relative to the loading values, such that interpretation based on the loadings can be difficult. The results are illustrated by simulating additive Gaussian measurement error in microarray expression data from cancer tumours and control tissues.
Handling missing values is an unavoidable problem in the practice of statistics. We focus on multiple factor analysis in the sense of Escofier and Pagès (2008), a principal component method that simultaneously takes into account several multivariate datasets composed of continuous and/or categorical variables. The suggested strategy to deal with missing values, named regularised iterative MFA, is derived from a method available in principal component analysis which consists in alternating a step of estimation of the axes and components and a step of estimation of the missing values. The pattern of missing values considered can be structured with missing rows in some datasets. Some simulations and real examples that cover several situations in sensory analysis are used to illustrate the methodology. We focus on the important issue of the maximum number of products that can be assessed during an evaluation task.
The data were taken from 2,534 Holstein herds on Record of Performance (R.O.P.) for milk. Herd size was determined from the average inventory of cows on July 1, 1967 and July 1, 1968. Herd production was obtained for cows with records from March 1, 1967 to February 29, 1968. The cows disposed of from September 1, 1967 to August 31, 1968 were recorded in three main sections: (1) cows sold for beef, (2) cows sold for dairy purposes, and (3) cows that died in the herd. Section 1 was divided into four subsections: cows sold due to breeding or calving problems, low production, unsatisfactory type, and other reasons. Herds were categorized as constant, increasing, or decreasing in size. Within each size grouping and disposal reason, multiple regression analyses were completed for percent of cows disposed of in relation to herd size and production level. In herds of constant size, production level increases were associated with increases in total percent of cows disposed of (P < 0.01), increases in percent sold for breeding purposes (P < 0.01) and increases in percent sold for beef because of poor type (P < 0.01). There was also a decrease (P < 0.05) in percent sold for beef due to low production as the production level increased. Herd size had no effect on cow disposals.
An assessment of the importance of farm managers' attitudes, i.e., the farm managers' socio-psychological characteristics, relative to management practices, and with respect to farm performance on southwestern Ontario dairy farms is presented. The assessment was done using a multiple linear regression analysis. Eight dependent variables measuring farm performance were used; namely, retained placenta (%), metritis (%), ovarian disorders (%), other reproductive disorders (%), calving interval (months), culling (%) and herd BCA (breed class average) for fat and milk. Socio-psychological variables appeared in the best regression models of each of the dependent variables, and were found to be quantitatively as important or more so than the management group of variables. In the best regression model for the first six dependent variables, the group of socio-psychological variables explained between 10.8% and 24.5% of the variation in the dependent variable as compared with 0% to 15.5% for the management group of variables. The best regression model for the last two dependent variables showed a synergistic association between the two groups of independent variables.
The results of this study indicate that farm managers' attitudes are important when studying farm performance. Attitudes should be measured and considered before proposing management practices to improve farm performance since interactions between attitudes and management practices suggest that attitudes act as effect modifers on the management practices-herd performance relationship. However, further research is needed to better understand the mode of action of managers' attitudes in the dairy farm system.
It is demonstrated that Cattell's scree test and Bartlett's chi-square test for the number of factors are both based on the same rationale, so the former reflects statistical (subject sampling) variability and the latter usually involves psychometric (variable sampling) influences. If the alpha-level (implicit in the scree test) is set the same, the two tests should lead to the same conclusions. Analyses with some examples suggest that if the alpha-level for the Bartlett test is set (explicitly) in the neighborhood of .0003 for sample Ns of 100 to 150, the results from applications of this test will indicate approximately the same number of factors as estimated on the basis of a scree test determined on a much larger (N 600) sample. Used in this way, the Bartlett test may yield fairly good "population" estimates of the number of factors. Relationships between the Bartlett test, hence the scree test, and tests for a common factor model and for the significance of a correlation matrix are explicated.
Multiple factor analysis (MFA, also called multiple factorial analysis) is an extension of principal component analysis (PCA) tailored to handle multiple data tables that measure sets of variables collected on the same observations, or, alternatively, (in dual‐MFA) multiple data tables where the same variables are measured on different sets of observations. MFA proceeds in two steps: First it computes a PCA of each data table and ‘normalizes’ each data table by dividing all its elements by the first singular value obtained from its PCA. Second , all the normalized data tables are aggregated into a grand data table that is analyzed via a (non‐normalized) PCA that gives a set of factor scores for the observations and loadings for the variables. In addition, MFA provides for each data table a set of partial factor scores for the observations that reflects the specific ‘view‐point’ of this data table. Interestingly, the common factor scores could be obtained by replacing the original normalized data tables by the normalized factor scores obtained from the PCA of each of these tables. In this article, we present MFA, review recent extensions, and illustrate it with a detailed example. WIREs Comput Stat 2013, 5:149–179. doi: 10.1002/wics.1246
This article is categorized under: Data: Types and Structure > Categorical Data
Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis
Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
Data from an observational study, carried out during a 4.5 year period (1986–1990), were used to quantify the effects of health disorders on the risk of culling. The study population consisted of 47 commercial Holstein dairy herds from western France, comprising 4123 cows.Logistic regression was used to assess the relationships between health disorders and early and late culling. Fourteen main health disorders with clinical signs and one subclinical disease were studied: abortion, periparturient accident, calving provided with assistance, digestive disorders, ketosis, locomotor disorders, mastitis, metritis, milk fever, cystic ovaries, respiratory disorders, retained placenta, teat injuries, non-traumatic udder disorders and status with respect to milk somatic cell count. Adjustments were made for year, month of calving, parity, breeding value for milk, best of the two first milk production records and reproductive performance. The possible effects of interactions among variables were also studied. The herd effect was taken into account using random effect models.Non-traumatic udder disorders, teat injuries, milk fever and the occurrence of both ketosis and assistance at calving were significantly associated with an increased risk of being early culled (odds ratios (OR) ranging from 1.6 to 10.3). Early and late abortion, late metritis, poor peproductive performance, retained placenta, non-traumatic udder disorders within 45 days post-partum and mastitis occurring in the first 3 months of the lactation were positively associated with a late culling (OR ranging from 1.2 to 6.6). Cows with lower breeding value for milk and higher parities were high risk groups for culling. A lower level of milk production and occurrence of both reproductive disorders and poor reproductive performance were risk factors for late culling.
During 1986 and 1987, mortality, morbidity, case-fatality, and culling rates were estimated for endemic disease conditions in 43 randomly selected California dairy farms. Data were collected mainly by farmer interview.Age-specific mortality rates were: calves 22.8 per 1000 calf-months at risk; young stock 1.2 and cows 2.0 per 100 animal-years at risk. No bulls died during the observational period. For calves, diarrhea/enteritis and pneumonia had the highest incidence rates, 115.8 and 76.5 per 1000 calf-months at risk, respectively. Average incidence rates per 100 cow-years for the five most commonly reported cow diseases were: mastitis 30.3; infertility 7.9; metritis 7.0; footrot 5.5; retained placenta 4.7. The highest case-fatality rate was 32% for cows with traumatic reticulitis (hardware disease). About 25% of all cows were culled. Low production, infertility, and mastitis accounted for 87.6% of all culled cows.Characteristics of participating herds and diagnostic criteria are presented. Limitations of the data and of the collection methods are discussed.
The prediction of the unknown part of a 305-day lactation is usually based on regression models. For these models the relation between known test-day yields and remaining lactation yield as well as their means within classes of environmental effects need to be known. Owing to environmental changes, means may vary severely. In this study a procedure for estimation of the means is presented and single regression (SR), multiple regression (MR), and factor analysis (FA) models for prediction are compared. Both MR and FA use all known test-day yields as information sources. Method SR uses the last known test-day yield. Lactation curves were estimated per herd on all records, adjusted for age and season of calving. By back-adjustment for age and season of calving, lactation curves were obtained for a group of cows pertaining to a certain class of herd-age-month of calving. Means necessary for prediction were calculated from these lactation curves (Type II). Correlations with means, which were directly calculated in the data set (Type I), ranged from 0.61 to 0.78 for test-days and from 0.51 to 0.69 for remaining lactation milk yield. The average difference between Types I and II means was almost zero. Using Type II means, no differences were found between SR, MR and FA. The correlation between predicted and realized 305-day milk yield was 0.87, 0.88 and 0.88 respectively for parity 1, 2 and >2, when the last test-day yield was known at 50 days postpartum. The use of Type II means resulted in a more precise adjustment.
Multiple Factor Analysis (MFA) studies several groups of variables (numerical and/or categorical) defined on the same set of individuals. MFA approaches this kind of data according to many points of view already used in others methods as: factor analysis in which groups of variables are weighted, canonical analysis, Procrustes analysis, STATIS, INDSCAL. In MFA, these points of view are considered in a unique framework. This paper presents the different outputs provided by MFA and an example about sensory analysis of wines.
SUMMARY Statistical methods are proposed for estimating relative rate parameters, based on estimated disease rates and covariate
data from random samples of individuals from each of several cohorts. A random effects model is used to derive mean and variance
models for estimated disease rates. Estimating equations for relative rate parameters are then developed by replacing cohort
covariate averages by corresponding sample averages. The asymptotic distribution of regression parameter estimates is derived,
and the asymptotic bias is shown to be small, even if covariates are contaminated by classical random measurement errors,
provided the covariate sample size in each cohort is not small. Simulation studies, motivated by international data on diet
and breast cancer, provide insights into the properties of the proposed estimators.
The objectives were to describe culling patterns and reasons for culling across lactation, estimate mortality and the proportion of cows leaving from 21 d before an expected calving date through 60 d in milk (DIM; CULL60) for Pennsylvania (PA) dairy herds, and to describe production measures for herds with high and low mortality and CULL60. Weekly culling frequencies and reasons for culling from 3 wk before a reported expected calving date through >or= 100 wk of lactation were calculated for all PA cows with at least 1 Dairy Herd Improvement test in 2005. It was estimated that at least 5.0% of PA dairy cows died in 2005, and that at least 7.6% were culled by 60 DIM. The majority of cows exiting the herd by 60 DIM either died (35.1%) or had a disposal code of injury/other (29.9%). A total of 137,951 test-day records from 20,864 cows in herds with high mortality (>8.0%) and CULL60 (>12.0%) and 136,906 test-day records from 12,993 cows in herds with low mortality (<1.4%) and CULL60 (<2.9%) were retained to describe differences among herds with high and low survival. Least squares means for weekly milk yield, fat and protein percentages, and somatic cell score (SCS) were estimated with a model that included fixed effects for herd environment (high or low survival) and week nested within herd environment and lactation; random effects were cow, herd-test-day, and error. Cows from herds with high mortality and CULL60 produced more milk in lactations 1 (+1.9 +/- 0.15 kg/d) and 2 (+0.9 +/- 0.16 kg/d), but less in lactations 4 (-0.7 +/- 0.22 kg/d), 5 (-1.4 +/- 0.29 kg/d), and >or= 6 (-0.7 +/- 0.32 kg/d) and had higher SCS (+0.24 +/- 0.02), more change in early-lactation fat percentage (-1.77% vs. -1.59%), and a greater frequency of fat-protein inversions (3.6 +/- 0.3%). There is an opportunity to manipulate management practices to reduce mortality and early-lactation culling rates, which will improve cow welfare and the efficiency of dairy production by capturing a greater proportion of potential lactation milk yield, increasing cow salvage values, and reducing replacement costs.
Cows are culled at a relatively low age, which causes considerable economic loss. The annual culling rate in the Netherlands has increased from 18.8 per cent in 1951 to 25-30 per cent of the average number of cows in more recent years. The productive life is now about 3.5 years. On the thirty farms of the program group and thirty-one farms of the control group, the main reasons for culling were reproductive failure followed by mastitis and teat injuries. About 60 per cent of culling was due to health problems, the other 40 per cent to low productive capacity, old age, poor workability etc. The annual culling rate varied per farm per year but also per month. The moment of culling in the current lactation, the slaughter value and the age differed per reason. The calculated loss of forced replacement consists of reduction in both production prior to culling and slaughter value. Additionally, there is an idle production period due to lack of an immediate replacement. The biggest loss is caused by lost future income. There was a considerable difference per farm in the loss caused by culling for health problems. A low culling rate due to health problems was associated with longer longevity and a relatively lower loss. Tangible effects due to changes in the farm culling policy may not be evident for some years. The reduction in loss of culling in the program farms compared with the controls was small because the duration (2 1/2 years) of the herd health and management program was too short.(ABSTRACT TRUNCATED AT 250 WORDS)
An ecologic study focuses on the comparison of groups, rather than individuals; thus, individual-level data are missing on the joint distribution of variables within groups. Variables in an ecologic analysis may be aggregate measures, environmental measures, or global measures. The purpose of an ecologic analysis may be to make biologic inferences about effects on individual risks or to make ecologic inferences about effects on group rates. Ecologic study designs may be classified on two dimensions: (a) whether the primary group is measured (exploratory vs analytic study); and (b) whether subjects are grouped by place (multiple-group study), by time (time-trend study), or by place and time (mixed study). Despite several practical advantages of ecologic studies, there are many methodologic problems that severely limit causal inference, including ecologic and cross-level bias, problems of confounder control, within-group misclassification, lack of adequate data, temporal ambiguity, collinearity, and migration across groups.
This paper addresses ecological studies in public health research in terms of the logic of their analysis. It makes several distinctions between studies based on ecological and individual units. First, it identifies the variables common to both types of study and those particular to ecological studies. Second, it shows how ecological and individual units combine in two classes: unmixed (purely ecological, purely individual) and mixed. Third, it details how the relationships among and between individual and grouped units (expressed in terms of regression coefficients between independent and dependent variables) yield four coefficients: for all individual members; for all groups; for all individuals within each group; and for all individuals within groups (a weighted average). Equipped with an understanding of the dimensions involved at ecological and individual levels and of the relationships between them, researchers are in a position to exploit the public health potential of the ecological approach.
A survey of 50 Friesian/Holstein dairy herds (average size 178 cows) in England investigated the rate of culling and the reasons for disposal and death over three years from 1990 to 1992. The average total annual culling rate was 23.8 per cent (22.0 per cent sold and 1.8 per cent died). Of the disposals, 54 per cent were culled by the end of their fourth lactation. Poor fertility was the most important reason for culling (36.5 per cent of disposals), followed by management policy (11.5 per cent), mastitis (10.1 per cent), bovine spongiform encephalopathy (BSE) (7.4 per cent) and lameness (5.6 per cent). The most common causes of death were mastitis (8.9 per cent) and BSE (11.5 per cent), but 46 per cent died for unknown reasons.
A large portion of current epidemiologic research is based on methodologic individualism: the notion that the distribution of health and disease in populations can be explained exclusively in terms of the characteristics of individuals. The present paper discusses the need to include group- or macro-level variables in epidemiologic studies, thus incorporating multiple levels of determination in the study of health outcomes. These types of analyses, which have been called contextual or multi-level analyses, challenge epidemiologists to develop theoretical models of disease causation that extend across levels and explain how group-level and individual-level variables interact in shaping health and disease. They also raise a series of methodological issues, including the need to select the appropriate contextual unit and contextual variables, to correctly specify the individual-level model, and, in some cases, to account for residual correlation between individuals within contexts. Despite its complexities, multilevel analysis holds potential for reemphasizing the role of macro-level variables in shaping health and disease in populations.
The report upon which the current discussion is based was prepared in response to the increasing interest of the dairy industry in the recording of clinical disease data. The major objective was to introduce guidelines and standards for the recording and presentation of the diseases of dairy cattle. Eight clinically identifiable diseases of economic importance to the dairy industry were considered: milk fever, retained placenta, metritis, ketosis, left displaced abomasum, cystic ovarian disease, lameness, and clinical mastitis. Standardized definitions for these diseases were established through consultation with industry partners. Two approaches to summarization and reporting were proposed. For retrospective analysis, which is used when historical data are summarized for genetic evaluation for example, lactational incidence risk (cumulative incidence) has been recommended. For current analysis, which is used for herd health monitoring, a true incidence rate has been recommended. Milk fever and retained placenta were exceptions to the latter because of their short periods of risk. For these two diseases, lactational incidence risks are reported.
Incidences of diseases and their effects on reproductive performance and risk of culling in herds stratified by production and estrus detection efficiency were studied. Data were from the Swedish milk and disease recording systems and consisted of records for 33,748 first parity Swedish Friesian cows. A standardized mixed threshold model was used for statistical analyses of categorical outcome variables, and an ordinary linear mixed model was used for continuous outcome variables. An increase in production was associated with increased frequencies of treatments of most diseases, shorter intervals from calving to first artificial insemination, fewer days open, and lower culling rates. Cows treated for metritis, silent estrus, and cystic ovaries had an increased number of days to first artificial insemination and more days open. However, the negative consequences of these diseases on reproductive performance decreased as herd production increased. The risk of culling was higher for cows treated for dystocia, cystic ovaries, and mastitis, but the increase in the risk of culling was lower for higher producing herds. Similar trends were observed when herds were stratified by estrus detection efficiency. The results support the hypothesis that herd management, as characterized by milk production or estrus detection efficiency, is important in the incidences and consequences of diseases. Herd management, measured directly or indirectly, should be considered when the health status or cost of disease for a given herd is evaluated.
The effects of 15 diseases on time until culling were studied in 39,727 Finnish Ayrshire cows that calved during 1993 and were followed until the next calving or culling. The diseases studied were: dystocia, milk fever, retained placenta, displacement of the abomasum, metritis, non-parturient paresis, ketosis, rumen disorders, acute mastitis, hypomagnesemia, lameness, traumatic reticuloperitonitis, anestrus, ovarian cysts, and teat injuries. Survival analysis, using the Cox proportional hazards model, was performed and diseases were modeled as time-dependent covariates. Different stages of lactation when culling can occur were also considered. Parity, calving season and herd were included as covariates in every model. Parity had a significant effect on culling, the risk of culling being four times higher for a cow in her sixth or higher parity than for a first parity cow. The effects of diseases varied according to when the diseases occurred and when culling occurred. Mastitis, teat injuries and lameness had a significant effect on culling throughout the whole lactation. Anestrus and ovarian cysts had a protective effect against culling at the time when they were diagnosed. In general, diseases affected culling decisions mostly at the time of their occurrence. The effect seemed to decrease with time from the diagnosis of the disease. However, milk fever, dystocia and metritis also had a significant effect on culling at the end of the lactation.
The effects of 15 diseases, pregnancy status and milk yield on culling were studied in 39727 Finnish Ayrshire cows that calved in 1993 and were followed until culling or next calving. Survival analysis, using the Cox proportional hazards model, was performed with diseases, pregnancy status and milk yield as time-dependent covariates. Effects of parity, calving season and herd were also accounted for. Pregnancy status was the single most influential factor affecting culling decisions, followed by milk yield. Several diseases also had a significant effect on culling, the most influential ones being mastitis, lameness, teat injuries, and milk fever. The effects of all of these factors varied according to the stage of lactation. Milk yield had a significant effect on culling decisions, depending on the stage of lactation. At the beginning of lactation, milk production did not have any effect on culling decisions, but later on, the highest producers were at the lowest risk of being culled and the lowest producers had the highest risk. Adjusting for milk yield modified the effects of parity, most diseases and also pregnancy status on culling. Effects of parity increased after including milk yield in the model, indicating that milk yield and parity are interrelated in their effects on culling. The effects of pregnancy status also increased towards the end of lactation when milk yield was accounted for in the model. The effects of mastitis, teat injuries and lameness decreased after adjusting for milk production. These diseases lower milk yield and thus, part of their effect on culling was mediated through milk production. The effects of anestrus and ovarian cysts were mainly modified by pregnancy status, but not by milk yield. The effects of milk fever on culling increased at the beginning of lactation after including milk yield in the model. This suggests that even though cows with milk fever tend to be higher producers, it is the disease as such that triggers the culling decision early in the lactation. The changes in the effects of other diseases after adjusting for milk yield varied, depending on the disease and the stage of lactation.
The effects of 15 diseases and reproductive performance on culling were studied in 39727 Finnish Ayrshire cows that calved in 1993 and were followed until culling or next calving. Survival analysis, using the Cox proportional hazards model, was performed with diseases and pregnancy status as time-dependent covariates. Parity, calving season and herd were included as covariates in every model. The effect of the number of inseminations was also studied. The farmer's knowledge of the cow's pregnancy status had a significant effect on culling. It varied according to the stage of lactation a cow was in; the earlier the farmer knew a cow was pregnant, the smaller was the risk of culling. If a cow had not been inseminated at all, her risk of culling was 10 times higher than if she was inseminated once. If a cow was inseminated more than once, she had a slightly lower risk of being culled than a cow inseminated only once. The effect of parity decreased when pregnancy status and number of inseminations were added to the model, indicating that part of the parity effect was accounted for by reproductive performance. Including diseases in the model with pregnancy status and the number of inseminations did not change the effects of reproductive performance on culling. Mastitis, teat injuries and lameness had the greatest effect on culling (whether adjusted for reproductive performance or not), increasing the risk of culling, followed by anestrus, ovarian cysts and milk fever. In general, the effects of diseases decreased when reproductive performance was also accounted for in the model. When pregnancy status was included in the model, the effects of anestrus and ovarian cysts became slightly more protective, but when the number of inseminations was also considered, they became non-significant at the beginning of lactation and they increased the risk of culling at the end of lactation. Sensitivity analysis, which was run to evaluate the effects of our censoring mechanism on the results, indicated that the censoring times (i.e., the time of next calving) were not fully independent of the event (culling) times; the effects of the diseases and pregnancy status at the very end of the lactation changed slightly from the original model.
This paper investigates generalized estimating equations for association parameters, which are frequently of interest in family studies, with emphasis on covariance estimation. Separate link functions are used to connect the mean, the scale, and the correlation to linear predictors involving possibly different sets of covariates, and separate estimating equations are proposed for the three sets of parameters. Simulations show that the robust 'sandwich' variance estimator and the jackknife variance estimator for the correlation parameters are generally close to the empirical variance for the sample size of 50 clusters. The results contradict Ziegler et al. and Kastner and Ziegler, where the 'sandwich' estimator obtained from the software MAREG was shown to be unsuitable for practical usage. The problem appears to arise because the MAREG variance estimator does not account for variability in estimation of the scale parameters, but may be valid with fixed scale. We also find that the formula for the approximate jackknife variance estimator in Ziegler et al. is deficient, resulting in systematic deviations from the fully iterated jackknife variance estimator. A general jackknife formula is provided and performs well in numerical studies. Data from a study on the genetics of alcoholism is used to illustrate the importance of reliable variance estimation in biomedical applications.
Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
http://deepblue.lib.umich.edu/bitstream/2027.42/55371/1/2004ADREpidlogicRev.pdf
Data derived over four years from 434 dairy herds in 1998/99 to 244 in 2001/02 revealed average disposal rates of 22.6 per cent per year, half of which were for poor fertility, mastitis and lameness. The quartile of herds with the lowest disposal rates sold an average of 11.5 per cent annually and the quartile with the highest rates sold 35.5 per cent. Average annual disease rates over the four years were as follows: for assisted calving 7.8 per cent, for digestive disease 1.2 per cent, for ketosis 0.5 per cent, for hypomagnesaemia 0.5 per cent, for hypocalcaemia 5.0 per cent and for injuries 0.8 per cent. The incidence of mastitis increased from 36.0 to 43.3 per cent of cows per year. The incidence of lameness decreased from 23.3 per cent in 1998/99 to 20.7 per cent in 2000/01 but increased to 21.9 per cent in 2001/02. Data received from the same 219 farms during the first three years showed no effective differences from the full set of data for each of the three years. The lowest annual incidences of mastitis and lameness on individual farms were below 7 per cent and 2.5 per cent, respectively. In general, housing cows in cubicles was associated with a greater risk of lameness, and housing them in straw yards with a greater risk of mastitis. However, some of the lowest rates of lameness were recorded in cubicle-housed cows and some of the lowest rates of mastitis were recorded in cows housed in yards. Larger herds were not associated, in general, with higher rates of mastitis.
Culling patterns in the Upper Midwest and Northeast regions were examined from Dairy Herd Improvement records from 1993 through 1999. There were 7,087,699 individual cow lactation observations of which 1,458,936 were complete. A probit regression model was used to determine how individual cow and herd characteristics affected the likelihood of a cow being culled. The model predicted whether individual cows were culled each month. With a combination of observable cow and herd characteristics, as well as variables to capture state, year, and farm effects, the model was able to explain, with a 79.5 and 79.9% accuracy rate, individual cow cull decisions in the Upper Midwest and Northeast regions, respectively. Summer (- 11.5% in the Upper Midwest; - 6.4% in the Northeast) and fall (- 18.7% in the Upper Midwest; - 7.9% in the Northeast) calving vs. spring calving, higher than average milk production (- 1.7% per hundredweight in the Upper Midwest; - 0.5% in the Northeast), higher than average protein content (- 0.2% per additional percentage milk protein in the Upper Midwest; - 0.1% in the Northeast), higher milk production persistency (- 2.1% per additional percent persistent in the Upper Midwest; - 1.8% in the Northeast), and expansion (during the early years following the expansion) were associated with a reduced likelihood of a cow being culled. Lower than average fat content (0.04% per additional percentage butterfat in the Upper Midwest; 0.02% in the Northeast), and higher than average somatic cell count (8.8% for each unit increase in somatic cell count score in the Upper Midwest; 7.8% in the Northeast) were associated with an increased likelihood of a cow being culled. The study results are useful in describing patterns of culling and relating them to cow, herd, geographic, and time variables and can act as a benchmark for producers.
The interaction of the effects of pregnancy status and veterinary-treated clinical mastitis on culling in Swedish dairy cattle was analyzed with survival analysis. The data were from 978,780 cows with first calvings between 1988 and 1996. Four breeds (Swedish Red and White (SRB), Swedish Friesian (SLB), Swedish Polled Breed and Jersey) were included in the analysis, together with the SRB x SLB crossbreds. Length of productive life was defined as the number of days between first calving and culling or censoring (end of data collection). The model (Weibull proportional hazard) included the interaction of parity by pregnancy status by veterinary-treated clinical mastitis, peak test-day milk-yield deviation within herd-year-parity, age at first calving, year by season, region, breed, herd production level, and the random effect of herd. The effects of pregnancy status and veterinary-treated clinical mastitis were modeled as time-dependent covariates. The lactation was divided into five stages during which a veterinary-treated clinical mastitis and culling might occur and in which the pregnancy status was assumed to be known and culling could occur. Open cows had a pronounced effect on culling: they had a very high risk of being culled in all lactations, and it was even higher if they were treated for mastitis in early lactation. For pregnant cows, the later they got pregnant during the lactation, the greater their risk to be culled. The risk associated with cases of veterinary-treated clinical mastitis remained important throughout the lactation.