Milk Recording Research Articles

Antimicrobial Resistance and Use on Chinese Dairy Farms: Awareness and Opinions Regarding Selective Treatments of Farm Managers.

In China's expanding dairy industry, a lack of oversight regarding antimicrobial use and increasing antimicrobial resistance are evident. Selective treatments of dairy cows for clinical mastitis or dry cow therapy are proposed to promote judicious antimicrobial use without adversely impacting cattle health. These approaches have been successfully implemented on farms in other countries. On 28 October 2023, a 2-day in-person seminar was held in Beijing, China, on selective antimicrobial treatments of dairy cows for clinical mastitis or dry cow therapy on large Chinese dairy farms. Concurrently, a qualitative study involving 15 technical managers from the 13 largest Chinese dairy groups used focus group discussions and questionnaires to explore perspectives on selective treatments of dairy cows for clinical mastitis or dry cow therapy. The main outcomes assessed were opinions and concerns regarding implementing selective antimicrobial treatments. Although there was diversity of cognition on AMR and selective treatments, the technical managers were generally positive regarding adoption of selective treatments. However, they expressed a need for more evidence and tools, including anticipated economic impacts, effects of delaying treatment until diagnosis, accurate interpretation of milk recording data, safe use of internal teat sealants, and spread of pathogens. Participants stressed the need for awareness, staff training, farm management, and China-specific standards, suggesting large-scale trials to assess efficacy of selective treatments. The findings revealed key challenges and barriers currently impeding selective AMU practices. These insights could inform efforts to promote judicious AMU on farms through targeted treatment regimens, reducing mounting selective pressure driving resistance.

Development and evaluation of predictive models for pregnancy risk in UK dairy cows

One suggested approach to improve the reproductive performance of dairy herds is through the targeted management of subgroups of biologically similar animals, such as those with similar probabilities of becoming pregnant, termed pregnancy risk. We aimed to use readily available farm data to develop predictive models of pregnancy risk in dairy cows. Data from a convenience sample of 108 dairy herds in the UK were collated and each herd was randomly allocated, at a ratio of 80:20, to either training or testing data sets. Following data cleaning, there were a total of 78 herds in the training data set and 20 herds in the testing data set. Data were further split by parity into nulliparous, primiparous, and multiparous subsets. An XGBoost model was trained to predict the insemination outcome in each parity subset, with predictors from farm records of breeding, calving and milk recording. Training data comprised 74,511 inseminations in 45,909 nulliparous animals, 86,420 inseminations in 39,439 primiparous animals, and 158,294 inseminations in 32,520 multiparous animals. The final models were evaluated by predicting with the testing data, comprising 31,740 inseminations in 19,647 nulliparous animals, 38,588 inseminations in 16,215 primiparous animals, and 65,049 inseminations in 12,439 multiparous animals. Model discrimination was assessed by calculating the area under receiver operating characteristic curves (AUC); model calibration was assessed by plotting calibration curves and compared across test herds by calculating the expected calibration error (ECE) in each test herd. The models were unable to discriminate between insemination outcomes with high accuracy, with an AUC of 0.63, 0.59 and 0.62 in the nulliparous, primiparous and multiparous subsets, respectively. The models were generally well-calibrated, meaning the model-predicted pregnancy risks were similar to the observed pregnancy risks. The mean (SD) ECE in the test herds was 0.038 (0.023), 0.028 (0.012) and 0.020 (0.008) in the nulliparous, primiparous and multiparous subsets respectively. The predictive models reported here could theoretically be used to identify subgroups of animals with similar pregnancy risk to facilitate targeted reproductive management; or provide information about cows' relative pregnancy risk compared with the herd average, which may support on-farm decision-making. Further research is needed to evaluate the generalizability of these predictive models and understand the source of variation in ECE between herds; however, this study demonstrates that it is possible to accurately predict pregnancy risk in dairy cows using readily available farm data.

Impact of reproductive traits on productive life in Iranian Holstein dairy cows.

Enhancing reproductive performance is a key strategy to mitigate involuntary culling rates, thereby extending productive life (PL) and ultimately improving profitability in dairy cattle herds. A piecewise Weibull proportional hazards model was used to investigate the effect of several important reproductive traits on PL in Holstein dairy cows. Data comprised 200,747 lactation records from 82,505 cows sired by 1952 bulls across 36 dairy herds. PL was defined as the number of days from the first calving to the last milk record or censoring. The statistical model accounted for the time-dependent fixed effects of changes in herd size, year-season, milk production, fat and protein contents, and the time-independent fixed effect of age at first calving. Herd-year and sire effects were also included as random effects. Reproductive traits include calving traits such as calving ease (CE), calf size (CZ), and calf survival (CS), as well as female fertility traits such as number of inseminations per conception (NI), days from calving to first service (CFS), days from first service to conception (FSC), and days open (DO). All reproductive traits had a significant effect on PL (p < 0.001). Each reproductive trait was analysed separately. The relative risk (RR) of being culled increased as the severity of calving difficulties increased in both primiparous and multiparous cows. Cows that calved small or large calves showed a higher risk of being culled compared with those that calved medium size calves. The increased RR of culling was observed only for primiparous cows that gave birth to dead calves. In addition, cows that required more NI, a longer CFS, FSC, and DO had shorter longevity. These insights can deepen our comprehension of the factors affecting PL and provide information for refining management and breeding strategies, which could lead to increased profitability and sustainability in Iranian dairy farming.

Prediction of First Lactation Milk Yield on The Basis of Test Day Yield Using Multiple Linear Regression in Gir Cows

The test-day model is a method of choice for the study of milk yield traits, and this method is very important in countries like India, where herd size is generally smaller and lacks a well-established milk recording system. The present investigation was carried out on 365 records of Gir cows maintained at a cattle breeding farm from 1986 to 2014 with the objective of predicting the first lactation milk yield using monthly test day milk records. MLR was used with the backward elimination method. The optimum equation had total five variables (test days) viz. TD1, TD2, TD3, TD4 and TD5. This equation gave an accuracy of prediction of 77.71%. Therefore, it is concluded that first lactation 305 day milk yield could be predicted as early as 5th month of lactation with higher degree of accuracy.

Key udder health parameters on dairy farms with an automated milking system

Despite the increasing use of automated milking systems (AMS), udder health performance of farms has only been limitedly quantified on farms with a milking robot. The objective of this study was to quantify udder health parameters (UHP) at test-day and at herd level on robotic farms using routinely collected somatic cell counts (SCC) from test-day data, and to study the correlations between these UHP. Additionally, the associations between UHP and season, year and farm size as well as the association between the daily milk production and the herd milk SCC were investigated. To this end, we collected SCC test-day data from 48 Belgian and Dutch farms participating in the milk recording of a local dairy herd improvement program. The UHP were calculated based on individual animal records at each test day over three years (2017 until 2019). These UHP included herd milk SCC, average heifer SCC, average cow SCC, percentage of high SCC, percentage of new high SCC, percentage of chronic high SCC, percentage of high SCC cows after calving, and percentage of recovery during the lactation. By fitting a linear mixed model effect for each of the different UHP, significant associations between the UHP and season, year and farm size were observed at test-day level. A poorer udder health was observed during summer compared to other seasons and generally, larger farms performed worse than smaller farms. Using a linear regression model, a significant negative correlation between herd average SCC and average daily milk production was found: an increase of 6,085 cells/mL was associated with a decrease of one kg of milk production. Finally, the cow-level UHP were averaged over all test days of the entire study period per farm to investigate the farm-level udder health performance and correlations between them. At the farm level, UHP analysis revealed most farms had poor udder health performance (i.e. the herd milk SCC, average heifer SCC, average cow SCC, percentage of new high and chronic high SCC were all higher than their target values) as proposed by Barkema et al. (2013). Both at the farm and at test day level, strong correlations were found among the UHP. Similar to studies on farms with conventional milking systems, a strong correlation between herd milk SCC and the percentage of new and chronic high SCC cows was found. This study gives new insights into udder health performance on farms with an AMS and provides veterinarians and other dairy industry stakeholders with concrete benchmarks for udder health performance on farms.

Udder health outcomes in Irish herds participating in CellCheck dry cow consults

The CellCheck Dry Cow Consult (DCC) was developed by the CellCheck Technical Working Group to enable farmers to engage with their nominated vet to develop farm-specific selective dry cow therapy (SDCT) plans, where appropriate. This study evaluated the effect of the DCC on farmer decision-making around dry cow therapy, and the udder health impact of implementing SDCT, in study herds over the 2019 and 2020 dry periods. The DCC was a 3-h consult, delivered and funded as part of the Targeted Advisory Service on Animal Health (TASAH). Herds that completed a DCC were invited to register for a Dry Cow Review the following year. The combined data set for analysis across both years comprised of 439 herds and 25,357 cows. Available herd size ranged from 25 to 800. The median SCC of cows dried off with teat sealant only was 47,000 cells/ml before drying off in 2019 and 48,000 cells/ml at first milk recording in 2020, and 43,000 cells/ml before drying off in 2020 and 39,000 cells/ml at first milking recording in 2021. Following both the 2019 and 2020 dry periods, cows tended to converge toward a similar median SCC early in the following lactation, irrespective of prior dry cow treatment strategy. The uptake of SDCT was greater in Review herds, with 21% of cows receiving teat sealant only in 2020, compared with 16.3% of cows in herds participating in a Consult for the first time in 2020. At an individual cow level, in both years dry period new infection rate (NIR) was approximately 2.7% higher for cows treated with teat sealant only, than for those treated with both dry cow antibiotic tubes and teat sealant, and 1.2% higher than cows treated with antibiotic only. Regardless of treatment, there was a significant association between increasing parity and the risk of a dry period new infection. Increasing herd size had a statistically significant effect on the risk of dry period new infection rates. At a herd level, there was no statistically significant increase in NIR when SDCT was used compared with herds where blanket dry cow therapy was used. While not without risk, SDCT can be successfully implemented in Irish herds; however, constant attention to hygiene and management is essential. Though there are challenges to face, facilitating continued farmer education and engagement with professional guidance will be important.

Management of Dairy Farm using PHP and MySQL

A survey was prepared by the Dairy farming Working Group together with invited milk recording organizations. This paper is one part of this project and focuses on management and organizational questions. The management of recording organizations in the current climate of growing competition is more challenging than ever. The main part of this approach is how to develop a clear relationship with customers and how to provide value to farmers in regard to collected data and samples. New tools of analysis are already very common in some countries, while other participants are now focusing on maximizing increased efficiency in data capturing and processing. In those countries whose workflow is technician-based, training and certification are major components in improving human resources. The reporting of results back to farmers is also a very challenging area. The use of paper and pdf-reports is very common, but new online technologies and smartphone usage now provide new opportunities for farmers to manage information. Real value is created by additional analyses from identified milk samples. The goal was to develop a program that is flexible enough to be useful in a wide variety of management systems by providing reports suited to the individual producer. The existing program already collects farmers, employees, deliveries and their databases respectively other maintenance procedures and performance records

The association between somatic cell count and selective dry cow therapy, milking routine, and dry cow management practices in early-lactation cows from 21 commercial grazing dairy herds

This observational study aimed to explore the association of farmer-driven selective dry cow therapy (DCT), milking routine and dry cow management practices with SCC in early-lactation cows from 21 commercial dairy herds. Milking routine practices evaluated referred to cow preparation for milking, in-lactation mastitis management, and recording. Dry cow management practices related to dry cow environment and cleaning, dry-off procedure, milk cessation strategy and calving environment. Data from 2,016 multiparous cows in 21 commercial spring-calving grazing dairy herds were available for the study. Herd owners self-reported DCT (the assignment and administration of DCT was at the discretion of the herd owners with no involvement from the research team), management practices during milking and the dry period. Cow-level last test-day SCC records in 2020 (range = 105-285 DIM) and first test-day records in 2021 (range = 5-60 DIM) were obtained from milk recording databases. Quarter-level milk sampling was carried out on all cows in late lactation of 2020 (range = 240-261 DIM) for bacterial culturing. Bacteriological results were used to define cows with IMI when ≥1 quarter sample resulted in bacterial growth and there were no contaminated samples from that cow. Mixed model analyses were used to evaluate the association of selective DCT, milking routine, and dry cow management practices with cows' first test-day log 10 SCC (logSCC) in 2021. At dry-off in 2020, 47.6% of the cows were administered an internal teat sealant alone (ITS), while 52.4% were administered an antibiotic plus an internal teat sealant (AB+ITS). The mean herd-level percentage of cows with IMI was 19.7% (range = 9.8%-39.5%); Staphylococcus aureus accounted for the majority of cow-level IMI (89.9%, 357/397). Between herds, the proportion of cows administered ITS ranged from 17.7% (14/79; in a herd with an IMI prevalence of 10.1%) to 86.8% (66/76; in a herd with an IMI prevalence of 27.6%). In total, 11.8% (105/889) and 29.8% (292/980) of cows that were administered ITS or AB+ITS had an IMI in late lactation 2020, respectively. The mean untransformed SCC at the last test day in 2020 of cows administered ITS and AB+ITS was 55,000 and 197,200 cells/mL, respectively. The statistical analysis showed a significant interaction between selective DCT and milk yield at last test-day in 2020; cows with a milk yield of 15 kg and administered ITS had a 0.1 higher (untransformed SCC of 19,000 cells/mL higher) first test-day logSCC compared with cows administered AB+ITS. Additionally, greater parity, IMI in late lactation, higher logSCC at the last test-day in 2020 and longer dry periods were associated with higher logSCC at the first test-day in 2021. The current study identified cow- and herd-level management practices that could aid dairy farmers in improving the outcome of selective DCT and decrease early lactation SCC.

Genetic parameters for mid-infrared-spectroscopy-predicted mastitis phenotypes and related traits.

Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h2) and genetic correlations (ra) for clinical mastitis diagnosis (CM), SCS, MIR-predicted mastitis probability (MIRprob), MIR + SCS-predicted mastitis probability (MIRSCSprob) and lactoferrin estimates (LF). Data for this study were collected within the routine milk recording and health monitoring system of Austria from 2014 to 2021 and included records of approximately 54,000 Fleckvieh cows. Analyses were performed in two datasets, including test-day records from 5 to 150 or 5 to 305 days in milk. Prediction models were applied to obtain MIR- and SCS-based phenotypes (MIRprob, MIRSCSprob, LF). To estimate heritabilities and genetic correlations bivariate linear animal models were applied for all traits. A lactation model was used for CM, defined as a binary trait, and a test-day model for all other continuous traits. In addition to the random animal genetic effect, the fixed effects year-season of calving and parity-age at calving and the random permanent environmental effect were considered in all models. For CM the random herd-year effect, for continuous traits the random herd-test day effect and the covariate days in milk (linear and quadratic) were additionally fitted. The obtained genetic parameters were similar in both datasets. The heritability found for CM was expectedly low (h2 = 0.02). For SCS and MIRSCSprob, heritability estimates ranged from 0.23 to 0.25, and for MIRprob and LF from 0.15 to 0.17. CM was highly correlated with SCS and MIRSCSprob (ra = 0.85 to 0.88). Genetic correlations of CM were moderate with MIRprob (ra = 0.26 and 0.37) during 150 and 305 days in milk, respectively and low with LF (h2 = 0.10 and 0.11). However, basic selection index calculations indicate that the added value of the new MIR-predicted phenotypes is limited for genetic evaluation of udder health.

Factors Affecting the Milk Production Traits and Lactation Curve of the Indigenous River Buffalo Populations in Bangladesh.

Household buffalo dairy farming is gaining popularity nowadays in Bangladesh because of the outstanding food value of buffalo milk as well as the lower production cost of buffalo compared to cattle. An initiative has recently been taken for the genetic improvement of indigenous dairy buffaloes. The present study was carried out to determine the influence of some environmental factors like age, parity, season of calving, calving interval, dry period on the lactation yield, and lactation curve of indigenous dairy buffaloes of Bangladesh. A total of 384 indigenous dairy buffaloes from the 3rd and 4th parity of seven herds under two different agroecological zones covering four seasons were selected and ear tagged for individual buffalo milk recording. A milk yield of 300 days (MY300d) was calculated following the International Committee for Animal Recording (ICAR) and the data were evaluated using the generalized linear model (GLM). In production traits, the mean of calculated lactation period (CLP), calculated lactation yield (CLY), and milk yield of 300 days (MY300d) of the overall population were 267.28 days, 749.36 kg, and 766.92 kg, respectively, whereas calving interval (CI) and dry period (DP) as reproductive traits were 453.06 days and 185.78 days, respectively. The season of calving, age of buffalo cows, population or herd, agroecological zone, calving interval, and dry period had significant effects on production traits (p < 0.05 to p < 0.001). The season of calving, level of milk production of 300 days, population, and agroecological zone significantly affected the reproduction traits (p < 0.01 to p < 0.001). Parity was found to be non-significant for both types of traits. The average peak yield of test day (TD) milk production was highest at TD4 (4.47 kg, 98th day of lactation). The average MY300d of milk production was the highest in the Lalpur buffalo population (1076.13 kg) and the lowest in the buffalo population of Bhola (592.44 kg). The correlations between milk production traits (CLP, CLY, and MY-300d) and reproduction traits (CI and DP) were highly significant (p < 0.01 to p < 0.001). Positive and high correlation was found within milk traits and reproduction traits, but correlation was negative between milk traits and reproduction traits. Therefore, these non-genetic factors should be considered in the future for any genetic improvement program for indigenous dairy buffaloes in Bangladesh.

Bayesian approach for evaluation of lactation curve in cross bred cattle based on monthly and bimonthly test day milk yield.

In field progeny testing program milk recording at monthly or bimonthly intervals and prediction of first lactation 305-day milk yield (FL305DMY) from these test day yields have been adapted as an alternative to daily milk recording. Wood's incomplete gamma function is the one of the commonly used nonlinear lactation curve model. In recent years Bayesian approach of fitting nonlinear biological models is gaining attention among researchers. In this study Wood's incomplete gamma function was fitted using Bayesian approach using monthly (MTDY) and bimonthly test day (BTDY) yields. The lactation curve parameters thus obtained were used for prediction of FL305DMY. Efficiency of prediction based on monthly and bimonthly test day milk yield were compared using error of prediction. It was found to be 5.78% and 7.59% as root mean square error (RMSE) based on MTDY and BTDY respectively.The Breeding values of 97 Karan Fries sires were estimated using BLUP-AM based on actual and predicted FL305DMY thus obtained. The RMSE was calculated as the difference between estimated breeding values based on actual and predicted yield. It was found that RMSE calculated based on MTDY showed only a marginal superiority of 0.79% over BTDY and showed high degree of correlation with actual yield. Therefore, recording at bimonthly intervals could be an economical alternative without compromising the efficiency.

A multi-methods, multi-actor exploration of the benefits and barriers to milk recording on Irish farms using the COM-B model

Milk recording is a critical tool in dairy farming, providing individual cow information. When used effectively, this data contributes to on-farm productivity, herd health management decisions and supports prudent veterinary prescribing of antimicrobials. Although an industry and government priority, uptake has been relatively slow in Ireland. This multi-methods, three-part study aimed to gain a comprehensive understanding of the benefits to farm performance, and factors driving uptake of milk recording on Irish dairy farms. It involved an economic analysis of N=516 farms from 2008–2019, a workshop with N=26 stakeholders and an online survey of N=197 non-milk-recording farmers. Quantitative and qualitative data were analysed using econometric models and thematic analysis respectively. Results were synthesised using the COM-B model to gain a deeper understanding of what drives the target behaviour. The study revealed that agricultural education, farm location, farm specialisation in dairy and membership of a farmer discussion group were the main factors influencing uptake of milk recording. Milk recording was associated with a €39.04/cow increase in gross margin, a 177.58 litres/cow increase in milk yield and a reduction of 13,450 cells/ml in bulk milk tank somatic cell count readings. Infrastructural constraints, cost, lack of benefits and workload were the most reported perceived barriers to milk recording by farmers. The Behaviour Change Wheel illustrates how to utilise findings and systematically develop future interventions to increase milk recording uptake. This study highlights the importance of a multi-methods approach to agricultural technology adoption and the need for evidence-based methodology when developing behaviour change interventions.

Comparisons of recycled manure solids and wood shavings/sawdust as bedding material—Implications for animal welfare, herd health, milk quality, and bedding costs in Swedish dairy herds

Increasing shortages and costs of common bedding materials have led dairy farmers in Sweden to consider using recycled manure solids (RMS), which are readily available and low cost, as an alternative bedding material. The main risks are effects on udder health and milk quality, but RMS could also affect animal welfare and claw health. The advantages and disadvantages of using RMS bedding have not been fully investigated, and findings in other countries cannot be directly applied to Swedish conditions and climate. This observational cross-sectional study investigated the use of RMS as bedding, regarding associations with certain aspects of animal welfare, herd health, milk quality, and bedding costs in Swedish dairy herds. Thirty-four dairy farms using RMS or wood shavings/sawdust (each n = 17) were compared. Each farm was visited 2 times during the housing period from 2020 to 2021, once from October to December and once from March to May. Dairy barns were observed, animal welfare was assessed, and freestall dimensions were measured. Farm owners were interviewed about housing system characteristics, herd performance, and herd management. Data on milk production and herd health were obtained from the Swedish official milk recording scheme for the indoor period from October to March. The prevalence of claw disorders and abnormal claw conformation were collected from the national claw health database for the period from October to May. On each farm visit, composite samples of unused bedding outside the barn and used bedding material from the freestalls, respectively, were taken for total bacterial count and DM analysis. Samples of bulk tank milk for determination of total bacterial count were taken in connection to the visits. In addition, samples of unused and used bedding material and manure from alleys for analysis of 3 Treponema species associated with digital dermatitis (DD) were gathered and analyzed. Total bacterial count was significantly higher in unused (8.50 log10 cfu/g) and used RMS bedding (9.75 log10 cfu/g) than in wood shavings/sawdust (used 4.74; unused 8.63 log10 cfu/g), but there were no significant differences in bulk milk total bacterial count (median 4.07 vs. 3.89 log10 cfu/mL) or SCC (median 243,800 vs. 229,200 cells/mL). The aspects of animal welfare assessed did not differ significantly between the 2 bedding systems, whereas the prevalence of total claw disorders (25.9% vs. 38.0% of trimmed cows), dermatitis (6.9% vs. 16.2% of trimmed cows) and sole ulcers (2.0% vs. 4.0% of trimmed cows) were significantly lower in the RMS herds. Treponema spp. were not detected in unused RMS material, but all RMS herds had presence of DD recorded at foot trimming. An economic assessment based on the interview results and price level from winter 2021 revealed that the costs of RMS bedding varied with amount of RMS produced. Thus, RMS is a potential alternative bedding material for dairy cows in Sweden and can be a profitable option for large dairy herds. However, the high level of total bacteria in the material requires attention to bedding and milking routines as well as regular monitoring of herd health.

The variability in cow’s recovery potential regarding the milk recording season

The purpose of the study was to investigate how different milk recording seasons affected the prevalence of mastitis and the potential for cows to recover from it. After conducting logical data validation, a total of 3,953,637 test-day records of Holstein cows obtained during the milk recording period from January 1, 2005, to December 31, 2022, were used. The findings revealed that mastitis was most prevalent in the autumn season, and during this period, there was a lower total increase in milk production. On the other hand, the prevalence of healthy cows was maximum in summer and winter, while the winter season showed the highest overall increase in milk production. These results suggest that the prevalence of mastitis and the potential for recovery in cows are significantly influenced by the season, and the values may vary considerably. During the winter season, which is characterized by lower temperatures and humidity levels, the animals have the maximum likelihood of recuperation and regaining their production capacity in a manner that is consistent with their genetic potential.

Genetic analysis of phenotypic indicators for heat tolerance in crossbred dairy cattle

Climate change-induced rise in global temperatures has intensified heat stress on dairy cattle and is contributing to the generally observed low milk productivity. Selective breeding aimed at enhancing animals’ ability to withstand rising temperatures while maintaining optimal performance is crucial for ensuring future access to dairy products. However, phenotypic indicators of heat tolerance are yet to be effectively factored into the objectives of most selective breeding programs. This study investigated the response of milk production to changing heat load as an indication of heat tolerance and the influence of calving season on this response in multibreed dairy cattle performing in three agroecological zones Kenya. First-parity 7-day average milk yield (65 261 milk records) of 1 739 cows were analyzed. Based on routinely recorded weather data that were accessible online, the Temperature-Humidity Index (THI) was calculated and used as a measure of heat load. THI measurements used represented averages of the same 7-day periods corresponding to each 7-day average milk record. Random regression models, including reaction norm functions, were fitted to derive two resilience indicators: slope of the reaction norm (Slope) and its absolute value (Absolute), reflecting changes in milk yield in response to the varying heat loads (THI 50 and THI 80). The genetic parameters of these indicators were estimated, and their associations with average test-day milk yield were examined. There were no substantial differences in the pattern of milk yield response to heat load between cows calving in dry and wet seasons. Animals with ≤50% Bos taurus genes were the most thermotolerant at extremely high heat load levels. Animals performing in semi-arid environments exhibited the highest heat tolerance capacity. Heritability estimates for these indicators ranged from 0.06 to 0.33 and were mostly significantly different from zero (P < 0.05). Slope at THI 80 had high (0.64–0.71) negative correlations with average daily milk yield, revealing that high-producing cows are more vulnerable to heat stress and vice versa. A high (0.63–0.74) positive correlation was observed between Absolute and average milk yield at THI 80. This implied that low milk-producing cows have a more stable milk production under heat-stress conditions and vice versa. The study demonstrated that the slope of the reaction norms and its absolute value can effectively measure the resilience of crossbred dairy cattle to varying heat load conditions. The implications of these findings are valuable in improving the heat tolerance of livestock species through genetic selection.

The effect of first-lactation calving season, milk production, and morphology on the survival of Simmental cows

Longevity in dairy and dual-purpose cattle is a complex trait which depends on many individual and managerial factors. The purpose of the present study was to investigate the survival (SURV) rate of Italian Simmental dual-purpose cows across different parities. Data of this study referred to 2 173 primiparous cows under official milk recording that calved between 2002 and 2020. Only cows linearly classified for type traits, including muscularity (MU) and body condition score (BCS) were kept. Survival analysis was carried out, through the Cox regression model, for different pairwise combinations of classes of milk productivity MU, BCS, and calving season. Herd-year of first calving was also considered in the model. SURV (0 = culled; 1 = survived) at each lactation up to the 6th were the dependent variables, so that, for example, SURV2 equal to 1 was attributed to cows that entered the 2nd lactation. Survival rates were 98, 71, 63, 56, and 53% for 2nd, 3rd, 4th, 5th, and 6th lactation, respectively. Results revealed that SURV2 was not dependent on milk yield, while in subsequent parities, low-producing cows were characterized by higher SURV compared to high-producing ones. Additionally, cows starting the lactation in autumn survived less (47.38%) than those starting in spring (53.49%), suggesting that facing the late gestation phase in summer could increase the culling risk. The present study indicates that SURV in Italian Simmental cows is influenced by various factors in addition to milk productivity. However, it is important to consider that in this study all first-calving cows culled before the linear evaluation - carried out between mid- and late lactation in this breed - were not accounted for. Finding can be transferred to other dual-purpose breeds, where the cows’ body conformation and muscle development - i.e. meat-related features - are often considered as important as milk performance by farmers undertaking culling decisions.

Genetic and phenotypic trends for female fertility traits derived from service records of South African Holstein cattle

The objective of this study was to estimate breeding values (EBV) and genetic and phenotypic trends for female fertility traits derived from artificial insemination (AI) records. Data were collected from automated milk recording systems: 64464 records from 18 South African (SA) Holstein herds. Breeding values were estimated for heifer traits, i.e., age at first service (AFS), age at first calving (AFC), number of services per conception (SPCh); and for cow traits, i.e., calving to first service (CFS), number of days open (DO), number of services per conception (SPC), first service within 80 days post-partum (FS80d), and whether cows were confirmed pregnant within 100 days post-partum (PD100d) or within 200 days post-partum (PD200d) using THRGIBBSF90. Trends were estimated for the period 1984 to 2011. There was no clear trend on average breeding values for heifer traits. However, phenotypic trends for heifer traits showed a desirable downward trend, with a decrease of 0.14 and 0.13 months/year for AFS and AFC, respectively. Phenotypic trends for cow fertility traits appeared to increase at the rate 0.16 and 0.83 days/year for CFS and DO, respectively, with animals requiring 0.02 more services per year for SPC. The average EBV for binary traits FS80d, PD100d, and PD200d, showed no distinct trend. In general, there was no clear genetic and phenotypic trend for all traits defined. The trends that appeared to follow trait improvement could have been coincidental. Thus, efforts have to be made towards the improvement of heifer and cow fertility traits explored in this study

Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models

Mastitis has a substantial impact on the dairy industry across the world, causing dairy producers to suffer losses due to the reduced quality and quantity of produced milk. A further problem, related to this issue, is the excessive use of antibiotics that leads to the development of resistance in different bacterial strains. The growing consumer awareness oriented toward food safety and rational use of antibiotics has promoted the search for new methods of early identification of cows that may be at risk of developing the disease. Subclinical mastitis does not cause any visible changes to the udder or milk, and therefore it is more difficult to detect than clinical mastitis. The collection of large amounts of data related to milk performance of cows allows using machine learning (ML) methods to build models that could be used for classifying cows into healthy and at risk of subclinical mastitis. The data used for the purpose of this study included information from routine milk recording procedures. The data set consisted of 19,856 records of 2,227 Polish Holstein-Friesian cows from 3 herds. The authors decided to use the approach of building ensemble ML models, in particular bagging, boosting, stacking and super learner models, and comparing them for accuracy of identification of disease-affected cows against single ML models based on the Support Vector Machines, Logistic Regression, Gaussian Naïve Bayes, k-Nearest Neighbors and Decision Tree algorithms. The models were trained and evaluated based on the information recorded for herd 1 and using an 80:20 train-test split ratio according to animal ID (to avoid data leakage). The information recorded for herds 2 and 3 was only used to evaluate on unseen data models developed using the herd 1 data set. Among the single ML models, the Support Vector Machines model was found to be the most accurate in predicting subclinical mastitis at subsequent test-day when used both for the training set (mean F1-score of 0.760) and the testing sets containing data for herds 1, 2 and 3 (F1-score of 0.778, 0.790 and 0.741 respectively). The Gradient Boosting model was found to be the best performing model among the ensemble ML models (F1-score of 0.762, 0.779, 0.791 and 0.723 for the training set and the testing sets respectively). The super learner model, featuring the most advanced design and Logistic Regression in the meta layer, achieved the highest mean F1-score of 0.775 during the cross-validation, however, it was characterized by a slightly worse prediction accuracy of the testing sets (mean F1-score of 0.768, 0.790 and 0.693 for herds 1, 2 and 3 respectively). The study findings confirm the promising role of ensemble ML methods that were found to be slightly superior with respect to most of the single ML models.

Farm-level nutritional factors associated with milk production and milking behavior on Canadian farms with automated milking systems

The objective of this study was to describe nutritional strategies utilized on Canadian dairy farms with automated milking systems (AMS), both at the feed bunk and the concentrate offered at the AMS, as well as to determine what dietary components and nutrients, as formulated, were associated with milk production and milking behaviors on those farms. Formulated diets, including ingredients and nutrient content, and AMS data were collected from April 1, 2019, until September 30, 2020, on 160 AMS farms (Eastern Canada [East] = 8, Ontario [ON] = 76, Quebec [QC] = 22, and Western Canada [West] = 54). Both partial mixed ration (PMR) and AMS concentrate samples were collected from May 1 to September 30, 2019, on 169 farms (East = 12, ON = 63, QC = 42, West = 52). AMS milking data were collected for 154 herds. For each farm (n = 160), milk recording data were collected and summarized by farm to calculate average milk yield and components. Multivariable regression models were used to associate herd-level formulated nutrient composition and feeding management practices with milk production and milking behavior. Milk yield (37.0 ± 0.3 kg/d) was positively associated with the PMR ether extract (EE) concentration (PMR % EE; +0.97 kg/d per percentage point (p.p.) increase) and with farms that fed barley silage as their major forage source on farm (n = 16; +2.18 kg/d) compared with haylage (n = 42), while farms that fed corn silage (n = 96; +1.23 kg/d) tended to produce more milk than farms that fed haylage. Greater milk fat content (4.09 ± 0.28%) was associated with greater PMR-to-AMS concentrate ratio (+0.02 p.p. per unit increase) and total diet net energy for lactation (+0.046 p.p. per 0.1 Mcal/kg increase), but lesser % non-fiber carbohydrates (NFC) of the PMR (-0.016 p.p. per p.p. increase of % NFC). Milk protein content (3.38 ± 0.14%) was positively associated with forage % of the PMR (+0.003 p.p. per p.p. increase of % forage) and total diet % starch (+0.009 p.p. per p.p. increase of % starch), but negatively associated with farms feeding corn silage (-0.1 p.p. compared with haylage) as their major forage. Greater milking frequency (2.77 ± 0.40 milkings/d) was observed on farms with free-flow cow traffic systems (+0.62 milkings/d) and positively associated with feed push-up frequency (+0.013 milkings/d per additional feed push-up), while being negatively associated with PMR NFC content and % forage of the total ration (-0.017 milkings/d per p.p. increase of % forage). Lastly, greater milking refusal frequency (1.49 ± 0.82 refusals/d) was observed on farms with free-flow cow traffic systems (+0.84 refusals/d) and farms feeding barley silage (+0.58 refusals/d) than guided flow and farms feeding either corn silage or haylage, respectively. These data give insight into the ingredients, nutrient formulations and type of diets fed on AMS dairy farms across Canada and the association of those factors with milk production and milking behaviors.

How does a beef × dairy calving affect the dairy cow's following lactation?

For beef semen usage on dairy cows, much of the research has focused on the performance of the crossbred calves, yet little focus has been given to the subsequent performance of the cow herself. This study aimed to evaluate the performance of dairy cows for milk yield, fertility, and survival traits after giving birth to beef x dairy crossbred calves and compare this with the performance after giving birth to purebred dairy calves. Further, we aimed to study if the effect of a difficult calving was the same regardless of whether the calf was purebred dairy or beef x dairy crossbred. Phenotypic records from 587,288 calving events from 1997 to 2020 were collected from the Swedish milk recording system from cows of the dairy breeds Swedish Red (SR) and Swedish Holstein (SH). The sire beef breeds studied were Aberdeen Angus, Hereford (combined in category LIGHT), Charolais, Limousin, and Simmental (category HEAVY). Sixteen traits were defined and grouped in 3 categories: cumulative and 305-d milk, fat, and protein yield, daily milk yield, and 75-d milk yield as yield traits; calving to first insemination interval, calving to last insemination interval, first to last insemination interval, calving interval, and number of inseminations as fertility traits; and survival to 75 d or to next calving and lactation length as measures of survival. The data were analyzed for all traits for first and second parities separately using mixed linear models, with a focus on the estimates of cow breed by service sire breed combinations. All traits in parity 2 were adjusted for previous 305-d milk yield based on the expectation that low-yielding cows would more likely to be inseminated with beef semen. Overall, milk yield was lower after beef x dairy calvings compared with the purebred dairy calvings. The largest effects were found on cumulative yields and in second parity, with lower effects for yields early in lactation and yields in first parity. The largest decrease was 13-14 kg (0.12 phenotypic SD) for cumulative fat yield when breeding beef breed sires with purebred SR dams. For fertility traits, for most breed combinations, the effects were not large enough to be significant. Conversely, all beef x dairy crossbred combinations showed significantly lower results for survival to the next lactation, and mostly also for lactation length. There was some indication that dairy cows with beef x dairy calvings in parity 2 that were the result of maximum 2 inseminations in parity 1, had lower survival than corresponding calvings resulting from more than 2 inseminations. This could indicate that the former cows were marked for culling already when inseminated. There was generally an unfavorable effect of a difficult calving on all traits, however, there were almost no significant interactions between calving performance and dam by sire breed combination, and these interactions were never significant in first parity.