Interrelationships between cows,calves, and humans in cow-calf contact systems—An interview study among Norwegian dairy farmers

In recent years, the common dairy farming practice of early separation of dam and calf has received increased attention. Our aim was to explore how Norwegian dairy farmers with cow-calf contact (CCC) systems apply these systems in practice, and how they experience and perceive the interrelationships between cows and calves and humans within these systems. We conducted in-depth interviews with 17 farmers from 12 dairy farms and analyzed responses inductively, inspired by the grounded theory approach. The farmers in our study practiced their CCC systems differently from each other and had varying as well as common perceptions about these systems. Calves' intake of colostrum was not seen as a challenge, regardless of practice. The farmers generally perceived that any aggression shown by cows toward humans was merely an exhibition of cows' natural protective instinct. However, when the farmers had good relationships with their cows and the cows felt safe around them, the farmers could handle the calves and build good relationships with them as well. The farmers experienced the calves learning a lot from their dams. Most of the farmers' dairy housing systems were not adapted for CCC, and CCC systems could require modification in terms of placing greater emphasis on observing the animals and making adjustments in the barn and around milking. Some thought having CCC on pasture was the best and most natural, while others were reluctant to have CCC on pasture. The farmers encountered some challenges with stressed animals after later separation, but several had found methods to minimize stress. Generally, they had different opinions about workload, but agreed they spent less time on calf feeding. We found that these farmers were thriving with their CCC systems; they all described positive emotions around seeing cows and their calves together. Animal welfare and natural behavior were important to the farmers.     

Water use on nonirrigated pasture-based dairy farms: Combining detailed monitoring and modeling to set benchmarks

Water use in intensively managed, confinement dairy systems has been widely studied, but few reports exist regarding water use on pasture-based dairy farms. The objective of this study was to quantify the seasonal pattern of water use to develop a prediction model of water use for pasture-based dairy farms. Stock drinking, milking parlor, and total water use was measured on 35 pasture-based, seasonal calving dairy farms in New Zealand over 2 yr. Average stock drinking water was 60 L/cow per day, with peak use in summer. We estimated that, on average, 26% of stock drinking water was lost through leakage from water-distribution systems. Average corrected stock drinking water (equivalent to voluntary water intake) was 36 L/cow per day, and peak water consumption was 72 L/cow per day in summer. Milking parlor water use increased sharply at the start of lactation (July) and plateaued (August) until summer (February), after which it decreased with decreasing milk production. Average milking parlor water use was 58 L/cow per day (between September and February). Water requirements were affected by parlor type, with rotary milking parlor water use greater than herringbone parlor water use. Regression models were developed to predict stock drinking and milking parlor water use. The models included a range of climate, farm, and milk production variables. The main drivers of stock drinking water use were maximum daily temperature, potential evapotranspiration, radiation, and yield of milk and milk components. The main drivers for milking parlor water use were average per cow milk production and milking frequency. These models of water use are similar to those used in confinement dairy systems, where milk yield is commonly used as a variable. The models presented fit the measured data more accurately than other published models and are easier to use on pasture-based dairy farms, as they do not include feed and variables that are difficult to measure on pasture-based farms.     

Public attitude toward and perceptions of dairy cattle welfare in cow-calf management systems differing in type of social and maternal contact

Early cow-calf separation followed by individual housing of calves is standard practice on dairy farms. However, a growing body of evidence suggests that as awareness grows the public will oppose these practices, which could compromise the dairy industry's social license. Despite disagreement among different stakeholders over weighting and evaluations of effects of early separation (e.g., distress response, disease risk), recent systematic reviews indicate that there is little scientific evidence supporting this practice. The acceptability of alternative cow-calf management systems is unknown. We used a mixed methods survey with a convenience sample of 307 Canadians plus a representative sample of 1,487 Americans to investigate perceptions of these systems, examining the effects of providing social or foster cow contact following early separation or not separating cow-calf pairs. Attitudes and perceptions of animal welfare were more positive (on a 7-point scale where 1 is most negative, 7 is most positive, and 4 is a neutral midpoint) toward the system where calves were not separated from the cow (mean ± SE; 5.8 ± 0.07; 5.7 ± 0.07), compared with systems in which the calf was separated and individually housed (3.6 ± 0.07; 3.4 ± 0.07), separated and group housed (3.7 ± 0.07; 3.4 ± 0.07), or separated and kept with a foster cow (3.8 ± 0.07; 3.6 ± 0.07). Participants were consistent in their attitudes toward and perceptions of animal welfare within the system, suggesting that participants took a holistic and value-oriented approach to cow-calf management regarding separation. These results, in combination with many participants' concern for the importance of the mother cow-calf relationship and perceptions that severing of this bond was a breach of standard of care, suggest that there may be low acceptance of any cow-calf management system involving early separation as such systems are unlikely to resonate with underlying values.     

Short communication: Selection of extended-spectrum β-lactamase-producing Escherichia coli in dairy calves associated with antibiotic dry cow therapy—A cohort study

Antimicrobial residues in milk have been discussed as a possible selector for Enterobacteriaceae that produce extended-spectrum β-lactamases (ESBL) in dairy herds. Such residues are found in waste milk after antibiotic treatment of mastitis, but antibiotic dry cow therapy might also lead to antibiotic residues in colostrum and in milk during early lactation. While it is known that feeding of waste milk selects ESBL bacteria in calves, this was not investigated for colostrum yet, which is supposed to contain much lower antibiotic concentrations than waste milk. In this observational prospective case study on 2 farms, we hypothesized that blanket dry cow treatment with β-lactams would have more selective (here: increasing) effects on ESBL concentrations than selective (here: individually chosen) antibiotic dry cow therapy. Thus, we compared concentrations of ESBL-producing Enterobacteriaceae in feces of calves (n = 50) at 2 dairy farms with different management of antibiotic dry cow therapy. Considerably higher concentrations of ESBL-producing Escherichia coli were observed in blanket antibiotic dry cow therapy on d 3 of the calf's life (7.6 vs. 5.3 log cfu/g of calf feces). Both farms used narrow-spectrum penicillin combined with aminoglycosides for drying off, and the majority of ESBL isolates (93%) were co-resistant to aminoglycosides. No waste milk was fed to calves and no calf was treated with β-lactam antibiotics or aminoglycosides during the first 3 d of life, thus differences were most likely associated with different frequency of antibiotic dry cow therapy on farms (19 of 25 mother cows on farm A, 9 of 25 on farm B). Even though the presumable selection effect of antibiotics used for drying off decreased within the next 3 wk, this result further emphasizes the need for the reduction and prudent use of antibiotic dry cow therapy on farms.     

The creation and evaluation of a model predicting the probability of conception in seasonal-calving,pasture-based dairy cows

Reproductive performance in pasture-based production systems has a fundamentally important effect on economic efficiency. The individual factors affecting the probability of submission and conception are multifaceted and have been extensively researched. The present study analyzed some of these factors in relation to service-level probability of conception in seasonal-calving pasture-based dairy cows to develop a predictive model of conception. Data relating to 2,966 services from 737 cows on 2 research farms were used for model development and data from 9 commercial dairy farms were used for model testing, comprising 4,212 services from 1,471 cows. The data spanned a 15-yr period and originated from seasonal-calving pasture-based dairy herds in Ireland. The calving season for the study herds extended from January to June, with peak calving in February and March. A base mixed-effects logistic regression model was created using a stepwise model-building strategy and incorporated parity, days in milk, interservice interval, calving difficulty, and predicted transmitting abilities for calving interval and milk production traits. To attempt to further improve the predictive capability of the model, the addition of effects that were not statistically significant was considered, resulting in a final model composed of the base model with the inclusion of BCS at service. The models' predictions were evaluated using discrimination to measure their ability to correctly classify positive and negative cases. Precision, recall, F-score, and area under the receiver operating characteristic curve (AUC) were calculated. Calibration tests measured the accuracy of the predicted probabilities. These included tests of overall goodness-of-fit, bias, and calibration error. Both models performed better than using the population average probability of conception. Neither of the models showed high levels of discrimination (base model AUC 0.61, final model AUC 0.62), possibly because of the narrow central range of conception rates in the study herds. The final model was found to reliably predict the probability of conception without bias when evaluated against the full external data set, with a mean absolute calibration error of 2.4%. The chosen model could be used to support a farmer's decision-making and in stochastic simulation of fertility in seasonal-calving pasture-based dairy cows.     

Evaluating the effects of grass management technologies on the physical,environmental, and financial performance of Irish pasture-based dairy farms

Grass management technologies (grass measuring devices and grassland management decision support tools) have been identified as important tools to improve the performance of pasture-based dairy farms. They have the potential to significantly improve the efficiency and sustainability of dairy systems by increasing milk production through enhanced pasture growth and utilization, which would reduce the need for supplementary feeds, along with increased output, therefore increasing farm profitability and environmental sustainability. Despite the several potential benefits of grass management technologies, there is a lack of empirical research around the effects of these technologies on the performance of pasture-based dairy systems. The current study aimed to fill this knowledge gap by using a 2018 nationally representative survey of Irish dairy farms and a propensity score matching approach to determine the effects of adopting grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms. The findings showed that dairy farms utilizing grass management technologies had, on average, higher farm physical, environmental, and financial performance (in terms of grazed pasture use, total pasture use, length of the grazing season, milk yield, milk solids, greenhouse gas emissions per kilogram of fat- and protein-corrected milk, gross output, and gross margin) compared with dairy farms not utilizing these technologies. However, when controlling for selection bias, we can only attribute a positive causal effect of grass management technology adoption on the use of grazed pasture per cow, grazing season length, milk yield per cow, and milk solids per cow. This might be due to dairy farmers not yet using the technologies to their full potential, 2018 being an unusual year in terms of weather (and therefore not being able to capture the full range of farm performance benefits), or because grass management technologies need to be adopted in association with other technologies and practices to achieve their expected performance outcomes. Future research should include updated farm-level data to capture the weather and learning effects and so be able to determine the impact of grass management technologies on a wider range of performance indicators.     

Estimating the effect of different work practices and technologies on labor efficiency within pasture-based dairy systems

Herd size expansion combined with the seasonal workload on pasture-based dairy farms has led to an increased focus on techniques that can improve farm labor efficiency such as work practices and technologies. The objective of this study was to identify the work practices and technologies associated with labor efficiency of particular tasks, and estimate the time savings that could be made through their implementation during the period of peak labor input on spring-calving dairy farms. Data from an existing labor time-use study, completed from February 1 to June 30, 2019 (150 d), on 76 Irish dairy farms was used in conjunction with a survey on work practice and technology implementation. One hundred ten work practices and technologies were included in the initial survey, and of these, 59 were found to have an association with labor efficiency for their respective tasks. Best practice, regarding labor efficiency, was identified for the 59 work practices and technologies. An accumulation score was compiled for work practice and technology implementation; each farm received one point for each work practice or technology implemented. On average, farms implemented 31 labor-efficient work practices and technologies (ranging from 10–45). The most labor-efficient 25% of farms implemented a greater number of work practices and technologies (n = 37) than the least labor-efficient 25% of farms (n = 25). Multiple regression models estimated that each additional work practice or technology implemented would improve farm labor efficiency by 0.6 h/cow. Additionally, backward-regression models were used to predict the labor-savings associated with the most important work practices and technologies. Labor-savings were estimated for 12 significant individual work practices and technologies, of which 5 were related to milking, 4 to calf care, 2 to cow care, and one to grassland management. The work practices and technologies that offered the largest labor-savings included having one person in the milking pit during the mid-lactation period (?3.04 h/cow), having automatic cluster removers present (?2.55 h/cow) and contracting slurry spreading (?1.78 h/cow). This study focused on the variety of labor-efficient work practices and technologies available and highlighted those that farmers should focus on to improve labor efficiency. The results indicated that there is scope for improvement in the adoption of labor-saving work practices and technologies on many farms. The positive effect of implementing the identified labor-saving techniques on labor efficiency could be used to support future adoption.     

Costs of mastitis: facts and perception

A model to calculate the economic losses of mastitis on an average Dutch dairy farm was developed and used as base for a tool for farmers and advisors to calculate farm-specific economic losses of mastitis. The economic losses of a clinical case in a default situation were calculated as euro210, varying from euro164 to euro235 depending on the month of lactation. The total economic losses of mastitis (subclinical and clinical) per cow present in a default situation varied between euro65 and euro182/cow per year depending on the bulk tank somatic cell count. The tool was used to measure perception of the total economic losses of mastitis on the farm and the farmers' assessment of the cost factors of mastitis on 78 dairy farms, of which 64 were used for further analyses. Most farmers (72%) expected their economic losses to be lower than those revealed by our calculation made with their farm information. Underestimating the economic losses of mastitis can be regarded as a general problem in the dairy sector. The average economic losses assessed by the farmers were euro78/cow per year, but a large variation was given, euro17-198/cow per year. Although the average assessment of the farmers of the different cost factors is close to the default value, there is much variation. To improve the adoption rate of advice and lower the incidence of mastitis, it is important to show the farmers the economic losses of mastitis on their farm. The tool described in this paper can play a role in that process.     

Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems

Precision livestock farming (PLF) technologies have been widely promoted as important tools to improve the sustainability of dairy systems due to perceived economic, social, and environmental benefits. However, there is still limited information about the level of adoption of PLF technologies (percentage of farms with a PLF technology) and the factors (farm and farmer characteristics) associated with PLF technology adoption in pasture-based dairy systems. The current research aimed to address this knowledge gap by using a representative survey of Irish pasture-based dairy farms from 2018. First, we established the levels of adoption of 9 PLF technologies (individual cow activity sensors, rising plate meters, automatic washers, automatic cluster removers, automatic calf feeders, automatic parlor feeders, automatic drafting gates, milk meters, and a grassland management decision-support tool) and grouped them into 4 PLF technology clusters according to the level of association with each other and the area of dairy farm management in which they are used. The PLF technology clusters were reproductive management technologies, grass management technologies, milking management technologies, and calf management technologies. Additionally, we classified farms into 3 categories of intensity of technology adoption based on the number of PLF technologies they have adopted (nonadoption, low intensity of adoption, and high intensity of adoption). Second, we determined the factors associated with the intensity of technology adoption and with the adoption of the PLF technology clusters. A multinomial logistic regression model and 4 logistic regressions were used to determine the factors associated with intensity of adoption (low and high intensity of adoption compared with nonadoption) and with the adoption of the 4 PLF technology clusters, respectively. Adoption levels varied depending on PLF technology, with the most adopted PLF technologies being those related to the milking process (e.g., automatic parlor feeders and milk meters). The results of the multinomial logistic regression suggest that herd size, proportion of hired labor, agricultural education, and discussion group membership were positively associated with a high intensity of adoption, whereas age of farmer and number of household members were negatively associated with high intensity of adoption. However, when analyzing PLF technology clusters, the magnitude and direction of the influence of the factors in technology adoption varied depending on the PLF technology cluster being investigated. By identifying the PLF technologies in which pasture-based dairy farmers are investing more and by detecting potential drivers and barriers for the adoption of PLF technologies, the current study could allow PLF technology companies, practitioners, and researchers to develop and target strategies that improve future adoption of PLF technologies in pasture-based dairy settings.     

An examination of labor time-use on spring-calving dairy farms in Ireland

The seasonal workload associated with pasture-based dairy farms, combined with increasing herd sizes, has led to a renewed focus on labor time-use and efficiency on dairy farms. The objective of this study was to examine labor time-use on pasture-based dairy farms in the spring and summer seasons. A total of 82 spring-calving Irish dairy farms completed the study from February 1 to June 30, 2019 (150 d). Each farmer recorded their labor input on one alternating day each week using a smartphone app. Any labor input by farm workers not using the app was recorded through a weekly online survey. Farms with data for each month (n = 76) were classified into 1 of 4 herd size categories (HSC) for analysis: farms with 50 to 90 cows (HSC 1); 91 to 139 cows (HSC 2); 140 to 239 cows (HSC 3); and ≥240 cows (HSC 4). Total hours of labor input was similar on HSC 1 (1,821 h) and HSC 2 (2,042 h) farms, but predictably as HSC increased further, total hours of labor input increased (HSC 3: 2,462 h, HSC 4: 3,040 h). On a monthly basis, labor input peaked in February (15.4 h/d) and March (15.7 h/d). The farmer worked on average 60.0 h/wk over the duration of the study period. Hired labor and contractors completed a greater amount of work as HSC increased. Labor efficiency, as measured by hours/cow, improved as HSC increased (HSC 1: 26.3 h/cow, HSC 2: 17.7 h/cow, HSC 3: 14.3 h/cow, HSC 4: 10.9 h/cow), though there were large variations in labor efficiency within HSC. Milking was the most time-consuming task, representing 31% of farm labor input making it an important focus for potential improvements in efficiency. The next 5 most time-consuming tasks were calf care (14%), grassland management (13%), cow care (10%), repairs and maintenance (10%), and administration/business (8%). This study contributes to the understanding of labor use during the busiest (most labor demanding) time of the year on pasture-based dairy farms and points to areas where labor efficiency improvements can be made on farms. The considerable variation in farm labor efficiency observed within HSCs emphasizes the necessity for a greater focus on knowledge transfer of methods to achieve improved labor efficiency and a better work–life balance on many dairy farms. As the 2 busiest months on most dairy farms, February and March require the most focus for identification of potential labor savings.     

Temporal and spatial water use on irrigated and nonirrigated pasture-based dairy farms

Robust information for water use on pasture-based dairy farms is critical to farmers' attempts to use water more efficiently and the improved allocation of freshwater resources to dairy farmers. To quantify the water requirements of dairy farms across regions in a practicable manner, it will be necessary to develop predictive models. The objectives of this study were to compare water use on a group of irrigated and nonirrigated farms, validate existing water use models using the data measured on the group of nonirrigated farms, and modify the model so that it can be used to predict water use on irrigated dairy farms. Water use data were collected on a group of irrigated dairy farms located in the Canterbury, New Zealand, region with the largest area under irrigation. The nonirrigated farms were located in the Manawatu region. The amount of water used for irrigation was almost 52-fold greater than the amount of all other forms of water use combined. There were large differences in measured milking parlor water use, stock drinking water, and leakage rates between the irrigated and nonirrigated farms. As expected, stock drinking water was lower on irrigated dairy farms. Irrigation lowers the dry matter percentage of pasture, ensuring that the amount of water ingested from pasture remains high throughout the year, thereby reducing the demand for drinking water. Leakage rates were different between the 2 groups of farms; 47% of stock drinking water was lost as leakage on nonirrigated farms, whereas leakage on the irrigated farms equated to only 13% of stock drinking water. These differences in leakage were thought to be related to regional differences rather than differences in irrigated versus nonirrigated farms. Existing models developed to predict milking parlor, corrected stock drinking water, and total water use on nonirrigated pasture-based dairy farms in a previous related study were tested on the data measured in the present research. As expected, these models performed well for nonirrigated dairy farms but provided poor predictive power for irrigated farms. Partial least squares regression models were developed specifically to simulate corrected stock drinking water, milking parlor water, and total water use on irrigated dairy farms.     

Determinants of antimicrobial treatment for udder health in Danish dairy cattle herds

Societal pressure to limit the use of antibiotics in livestock production systems, including dairy cattle systems, is consistently increasing. To motivate farmers to reduce antibiotic usage, it is important to understand the factors that determine whether a cow will be treated with antibiotics or not. If farmers' usual practices regarding antibiotic treatments are taken into account, they may be motivated to adopt control measures that can facilitate prudent use of antibiotics and are at the same time cost-effective. In this study, we analyzed database recordings of milk yield and somatic cell count from the routine milk recording scheme, clinical registrations of mastitis and PCR results, and cow factors such as days in milk and parity in relation to antibiotic treatments for 518 dairy herds in Denmark. Farm-wise logistic regressions were used to predict antimicrobial treatment based on these factors. The resulting regression coefficients of 422 herds were further analyzed by principal component analysis and clustering to determine the driving predictors for treatment in different groups of farms. The results showed that determinants that were most important for predicting antibiotic treatments vary from one farm to another. Health indicators such as PCR or somatic cell count were most indicative for treatment on some farms, whereas other groups seemed to depend more on production factors (milk yield) or later culling of the cows. This shows that farmers behave differently and differences can be identified in register data. This information can be considered when developing cost-effective herd-specific control measures of mastitis to promote prudent use of antibiotics in Danish dairy cattle farms.     

An investigation of dairy calf management practices,colostrum quality,failure of transfer of passive immunity,and occurrence of enteropathogens among Australian dairy farms

Calf preweaning morbidity and mortality risks have been reported as high in several countries, with average values approximating 35 and 7%, respectively. However, limited data are available for calf morbidity and mortality risks on Australian dairy farms. The aims of this study were (1) to investigate current calf management practices on dairy farms in Australia and their association with herd-level morbidity and mortality using a questionnaire-based, cross-sectional study; and (2) to estimate the prevalence of common enteropathogens causing diarrhea, the failure of passive transfer of immunity, and poor colostrum quality in a sample of Australian dairy farms. We analyzed 106 completed questionnaires and samples from 23 farms (202 fecal, 253 calf serum, and 221 colostrum samples). Morbidity and mortality risks reported by farmers in preweaned heifers were 23.8 and 5.6%, respectively. These risks were above the Australian dairy industry targets in 75.5 and 66.7% of respondents. The zoonotic pathogens Cryptosporidium spp. and Salmonella spp. were the most prevalent enteropathogens, with a true prevalence of 40.9 and 25.2%, respectively. Salmonella O-group D was present in 67.9% of Salmonella-positive samples, followed by O-groups B (17.9%) and C (10.7%). Failure of transfer of passive immunity (IgG <10 g/L) was observed in 41.9% of calves (mean herd-level prevalence of 36.2%), and only 19.5% of colostrum samples met the standards for immunoglobulin content and microbiological quality. Collectively, these data indicate that there is still considerable room for improvement in calf-rearing practices on Australian dairy farms, particularly with regard to colostrum management and feeding hygiene.     

Early detection of subclinical mastitis in lactating dairy cows using cow-level features

Subclinical mastitis in cows affects their health, well-being, longevity, and performance, leading to reduced productivity and profit. Early prediction of subclinical mastitis can enable dairy farmers to perform interventions to mitigate its effect. The present study investigated how well predictive models built using machine learning techniques can detect subclinical mastitis up to 7 d before its occurrence. The data set used consisted of 1,346,207 milk-day (i.e., a day when milk was collected on both morning and evening) records spanning 9 yr from 2,389 cows producing on 7 Irish research farms. Individual cow composite milk yield and maximum milk flow were available twice daily, whereas milk composition (i.e., fat, lactose, protein) and somatic cell count (SCC) were collected once per week. Other features describing parity, calving dates, predicted transmitting ability for SCC, body weight, and history of subclinical mastitis were also available. The results of the study showed that a gradient boosting machine model trained to predict the onset of subclinical mastitis 7 d before a subclinical case occurs achieved a sensitivity and specificity of 69.45 and 95.64%, respectively. Reduced data collection frequency, where milk composition and SCC were recorded only every 15, 30, 45, and 60 d was simulated by masking data, to reflect the frequency of recording of this data on commercial dairy farms in Ireland. The sensitivity and specificity scores reduced as recording frequency reduced with respective scores of 66.93 and 80.43% when milk composition and SCC were recorded just every 60 d. Results demonstrate that models built on data that could be recorded routinely available on commercial dairy farms, can achieve useful predictive ability of subclinical mastitis even with reduced frequency of milk composition and SCC recording.     

Farm characteristics and calf management practices on dairy farms with and without diarrhea: A case-control study to investigate risk factors for calf diarrhea

Calf diarrhea is one of the most important problems in calf rearing on dairy farms worldwide. Besides pathogens, several noninfectious management factors, especially management around birth, colostrum management, calf housing, feeding, and hygiene are important in the pathogenesis of diarrhea. To date, few data are available concerning calf rearing management on small and medium-sized dairy farms that are typical for Austria and the alpine region. Consequently, the objectives of this case-control study were to evaluate routine calf management practices on Austrian dairy farms and to examine differences in management between farms with and without the presence of calf diarrhea to identify risk factors. Overall, 100 dairy farms were visited. Of these farms, 50 were chosen based on the history and presence of calf diarrhea (case farms). Another 50 farms with no presence of calf diarrhea were chosen to serve as a standard of comparison (control farms). On farms, management was evaluated by face-to-face interview, and health status and hygiene were surveyed. Several calf rearing management procedures were similar on all of the visited farms, especially in areas regulated by national and European law. These factors include colostrum management and feeding. Consequently, no influence of these factors on the appearance of calf diarrhea could be detected. In contrast, other areas such as hygiene measures differed between farms and showed a partial association with the presence of calf diarrhea on farm. Variables related to diarrhea on farm were farm size; that is, the number of cows on farm. Farms with diarrhea cases were larger (median 40 cows, interquartile range 24.5 to 64.0) compared with farms with no presence of diarrhea (median 28 cows, interquartile range 18.8 to 44.0). Other risk factors that influenced the presence of diarrhea were the presence of other farm animal species on the farm [odds ratio (OR) 26.89, 95% confidence interval (CI): 2.64 to 273.5], frequency of cleaning of the calving area (OR 0.12, 95% CI: 0.02 to 0.79), the placement of individual calf housings (barn vs. outdoors; OR 0.02, 95% CI: 0.00 to 0.47), and the presence of respiratory tract disease (OR 52.49, 95% CI: 1.26 to 2,181.83). The possible influence of these factors on the appearance of calf diarrhea should be considered when farmers are advised.     

Precipitation and Temperature Effects on Mortality and Lactation Parameters of Dairy Cattle in California

Data from 3 commercial rendering companies located in different regions of California were analyzed from September 2003 through August 2005 to examine the relationship of dairy calf and cow mortality to monthly average daily temperature and total monthly precipitation respectively. Yearly average mortality varied between rendering regions from 2.1 to 8.1% for mature cows. The relationship between cow and calf monthly mortality and monthly average daily temperature was U-shaped. Overall, months with average daily temperatures less than 14 and greater than 24°C showed substantial increases in both calf and cow mortality with calf mortality being more sensitive to changes in these temperature ranges than cow mortality. Temperature changes were reflected in a 2-fold difference between the minimum and maximum mortality in cows and calves. Precipitation showed a weak effect with calf mortality and no effect with cow mortality. Data from Dairy Herd Improvement Association were used from 112 California herds tested over a 24-mo period to examine the relationship of milk production and quality with monthly average daily temperature and monthly precipitation. Somatic cell count and percent milk fat were either weakly or not associated with monthly average daily temperature and total monthly precipitation. However, total monthly precipitation was negatively associated with test day milk per milking cow regardless of the dairy's geographical location. Housing-specific associations for test day milk per milking cow were greater for total monthly precipitation than monthly average daily temperature, with the strongest negative association seen for dairies that do not provide shelter for cows. This suggests that providing suitable housing for lactating dairy cattle may ameliorate the precipitation-associated decrease in test day milk per milking cow.     

Survey of calf and heifer housing on Pennsylvania dairy farms

Research has established a link between calf and heifer housing and calf health. To determine current calf and heifer housing practices in Pennsylvania, 329 dairy farms were surveyed. The study was designed to increase awareness on the part of dairy farmers in housing and management and to develop education programs and materials in the area of calf and heifer management. All surveys were conducted on the farm by personal interviews. Results showed 24.9% of the farms had maternity pens in a building separate from the milking herd, although half of these farms used maternity pens in conjunction with facilities of lesser quality for the health and management of the animals. The same number of farms used calf hutches as those keeping calves in dairy barns with cows. A high percentage of the farms weaned calves (moved from milk diets to dry feed diets) to recommended types of facilities that included group pens, loose housing, and group or superhutches. However, 49.5% of the facilities used for weaned calves were in conjunction with other dairy animals. Animal restraint facilities have also been identified as an area that needs more emphasis on dairy farms. Many areas of dairy replacement housing on commercial dairy farms were determined to be unsatisfactory according to recommended Pennsylvania standards.     

Epidemiology of bovine colostrum production in New York Holstein herds: Cow,management, and environmental factors

Adequate supply of high-quality colostrum is essential for calf health. Colostrum production, at first milking, varies between animals and seasons, but herd-level and management associations with colostrum production have not been well described. Our objectives were to (1) describe colostrum production and colostrum handling practices and (2) to identify individual cow, herd management, and environmental factors associated with colostrum production. A convenience sample of 19 New York Holstein dairy farms (620 to 4,600 cows) were enrolled in this observational study to describe colostrum production and to evaluate cow, management, and prepartum environmental factors associated with colostrum yield and Brix %. Herd owners or managers were given a colostrum management questionnaire, and farm personnel recorded individual colostrum yield and Brix % for primiparous (PP; n = 5,978) and multiparous (MPS; n = 13,228) cows between October 2019 and February 2021. Temperature, relative humidity, and light intensity were measured by sensors placed in each farm's close-up dry cow pens for the entire length of the study. Median colostrum yield for each farm ranged from 2.5 to 7.6 kg for PP and 4.0 to 7.7 kg for MPS cows. Mean Brix % from each farm ranged from 22.2 to 27.9% for PP and 22.0 to 28.8% for MPS cows. Lowest colostrum yield from PP animals was associated with calf sex (female) and colostrum Brix % (≤22%). Greatest colostrum yield from MPS cows was associated with colostrum Brix % (≤22%), calf sex (twin), dry period length (>67 d), gestation length (283–293 d), an alive calf, second parity, previous lactation length (>344 d) and previous lactation 305-d mature equivalent milk yield (>13,091 kg), heat and humidity exposure area under the curve (AUC) 7 d before calving (>69.2 average temperature-humidity index per 30-min interval), and light intensity AUC 14 d before calving (>154.2 average lux per 15-min interval). Greatest colostrum Brix % from PP animals was associated with calf sex (male), an alive calf, and light intensity AUC 14 d before calving (≤64.0 average lux per 15-min interval). Greatest colostrum Brix % from MPS cows was associated with dry period length (>67 d), an alive calf, 305-d mature equivalent milk yield of previous lactation (≤15,862 kg), gestation length (274–282 d), colostrum yield (<6 kg), fifth or greater parity, and heat and humidity exposure AUC 7 d before calving (≤50.1 average temperature-humidity index per 30-min interval). Dairy producers can use this information to recognize the variation in colostrum production and alter colostrum management programs in anticipation of periods of low production or quality.     

An examination of the effects of labor efficiency on the profitability of grass-based,seasonal-calving dairy farms

The seasonality of grass-based, seasonal-calving dairy systems results in disproportionately higher labor demands during the spring, when cows are calving, than in the remaining seasons. This study aimed to (1) examine the relationship between labor efficiency and profitability; (2) investigate strategies to reduce the hours worked per day by the farmer, family, and farm staff in the spring by having certain tasks outsourced; and (3) quantify the economic implications of those strategies. Data from an existing labor efficiency study on Irish dairy farms were used in conjunction with economic performance data from the farms. Tasks that required the highest level of farm labor per day in the spring were identified and hypothetical strategies to reduce the farm hours worked per day were examined. A stochastic budgetary simulation model was then used to examine the economic implications of employing these strategies and the effects of their use in conjunction with a proportionate increase in cow numbers that would leave the hours worked per day unchanged. The strategies were to use contractors to perform calf rearing, machinery work, or milking. Contracting out milking resulted in the greatest reduction in hours worked per day (5.6 h/d) followed by calf rearing (2.7 h/d) and machinery work (2 h/d). Reducing the hours worked per day by removing those tasks had slight (i.e., <5%) negative effects on profitability; however, maintaining the farm hours worked per day while utilizing the same strategies and increasing herd sizes resulted in profitable options. The most profitable scenario was for farms to increase herd size while contracting out milking.