Global losses due to dairy cattle diseases: A comorbidity-adjusted economic analysis

An economic simulation was carried out over 183 milk-producing countries to estimate the global economic impacts of 12 dairy cattle diseases and health conditions: mastitis (subclinical and clinical), lameness, paratuberculosis (Johne's disease), displaced abomasum, dystocia, metritis, milk fever, ovarian cysts, retained placenta, and ketosis (subclinical and clinical). Estimates of disease impacts on milk yield, fertility, and culling were collected from the literature, standardized, meta-analyzed using a variety of methods ranging from simple averaging to random-effects models, and adjusted for comorbidities to prevent overestimation. These comorbidity-adjusted disease impacts were then combined with a set of country-level estimates for lactational incidence or prevalence or both, herd characteristics, and price estimates within a series of Monte Carlo simulations that estimated and valued the economic losses due to these diseases. It was estimated that total annual global losses are US$65 billion (B). Subclinical ketosis, clinical mastitis, and subclinical mastitis were the costliest diseases modeled, resulting in mean annual global losses of approximately US$18B, US$13B, and US$9B, respectively. Estimated global annual losses due to clinical ketosis, displaced abomasum, dystocia, lameness, metritis, milk fever, ovarian cysts, paratuberculosis, and retained placenta were estimated to be US$0.2B, US$0.6B, US$0.6B, US$6B, US$5B, US$0.6B, US$4B, US$4B, and US$3B, respectively. Without adjustment for comorbidities, when statistical associations between diseases were disregarded, mean aggregate global losses would have been overestimated by 45%. Although annual losses were greatest in India (US$12B), the United States (US$8B), and China (US$5B), depending on the measure of losses used (losses as a percentage of gross domestic product, losses per capita, losses as a percentage of gross milk revenue), the relative economic burden of these dairy cattle diseases across countries varied markedly.

Quantifying multiple burdens of dairy cattle production diseases and reproductive inefficiency: current knowledge and proposed metrics

The economic burden of diseases and reproductive inefficiency in dairy cattle is evident and has been quantified. Dairy diseases and reproductive inefficiency are however associated with other issues as well, including animal welfare, environmental pressure, and public health risks. Quantifying these other issues is becoming important to help farmers making decisions. Quantification of the non-economic burdens of diseases and reproductive inefficiency is rare and lacks an overview of approaches and metrics. The first aim of this paper is to provide trends for associating diseases and reproductive inefficiency with economic and non-economic burdens of disease. The second aim is to provide a review of approaches and metrics used to quantify the non-economic burdens of disease and reproductive inefficiency. For the economic burden of diseases and reproductive performance, only an overview of the approaches used to quantify the burden is provided. The final aim is to propose approaches and metrics for future quantification of non-economic burdens caused by individual diseases. A literature search was conducted in Web of Science to identify scientific articles on mastitis, lameness, metabolic disorders and reproductive inefficiency in dairy cows. The search was restricted to articles published between 1 January 2010 and 31 December 2022 and resulted in 7,565 articles. The total number of articles that mentioned the economic, animal welfare, public health, and environmental burden was 1,253, 428, 291, and 77, respectively. An increase in the percentage of articles mentioning the economic, animal welfare, and public health burden is observed between 2010 and 2022. Despite the 2,049 articles that mentioned one of the burdens, the results showed that approximately 10% of the articles quantified one or more of these burdens. The economic burden of diseases and reproductive inefficiency has been quantified in 154 articles and very few articles quantified the non-economic burdens (9 articles for environment, 29 articles for public health and 2 articles for animal welfare). Eleven articles were identified that quantified multiple burdens, and in all these studies the economic burden was combined with a non-economic burden through a modeling approach (mainly simulation). We propose to link the non-economic burdens to biological simulation models, and thus develop bio-burden simulation models. Well-established approaches and metrics can be used to quantify economic, environmental, and public health burdens. For the economic impact, costs per cow per year can be assessed. A life cycle assessment can be performed for environmental impact and the public health impact can be assessed by a defined daily dose for antimicrobial use and disability-adjusted life years for zoonotic diseases. Regarding animal welfare, approaches and metrics to quantify the welfare impact of a diseased animal are not well established. For animal welfare, we propose a welfare-adjusted life years approach. The mentioned approaches and metrics are a proposal, and it is up to the scientific community to use them or, based on empirics and research experience, propose changes so that we will end up with robust approaches and metrics that enable us to compare research results and provide more evidence for animal health decision makers.

Genetic analysis of health traits and their associations with longevity, fertility, production, and conformation traits in Holstein cattle

Health traits have high economic values in dairy cattle breeding, which can cause considerable financial loss through involuntary culling. In this study, fourteen health traits were analysed, including five composite health traits: reproductive disorders, udder health (UH), digestive disorders, metabolic disorders, locomotory diseases (LD), and nine independent health traits: gestation disorders and peripartum disorders, irregular estrus cycle and sterility, metritis (ME), mastitis (MA), abomasal displacement (AD), enteritis (EN), and ketosis, claw diseases (CD), laminitis complex. This study analysed variance components for health traits through both single and bivariate repeatability animal models. All health traits showed low heritability, ranging from 0.001 to 0.025. Most of the health traits in five categories showed negative genetic correlations, ranging from -0.012 (CD and EN) to -0.634 (ME and EN). Strong positive genetic correlations appeared within the same category, ranging from 0.469 (EN and AD) to 0.994 (UH and MA, LD and CD). Furthermore, approximate genetic correlations were evaluated between health traits and routinely collected traits (longevity, fertility, production, and conformation). In general, the low to moderate approximate genetic correlations were estimated between health traits and routinely collected traits. The estimated correlations between health traits and longevity, fertility, production, and conformation traits could provide an indirect reference for disease-resistance breeding in Holstein cattle.

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.

Association between genomic daughter pregnancy rate and expected milk production on the resumption of estrus behavior in Holstein cattle

The objective of this observational study was to evaluate estrous expression at the first estrus occurring between 7 to 30 d in milk (DIM), as detected by an automated activity monitor (AAM), and its association with genomic daughter pregnancy rate (GDPR) and genomic expected milk production (GEM) in lactating dairy cows. A total of 4,119 lactations from 2,602 Holstein cows were included. Cows were enrolled as first lactation (n = 1,168), second lactation (n = 1,525) and third and greater lactation (n = 1,426). Hair samples were collected from the tail switch, and cows were genotyped using an SNP platform (Clarifide, Zoetis, São Paulo, SP, Brazil). Postpartum cows were examined daily by the farm personnel from calving until 10 DIM. Calving was classified as assisted (forced calf extraction) and unassisted (normal calving). Retained fetal membranes (RFM), hyperketonemia (KET), and left displaced abomasum (LDA) were also recorded. Mean GDPR (± SD) was -0.29 ± 1.4, and the intensity and duration of the first estrus event was 15.9 ± 13.1 x-factor (intensity unit measurement) and 11.1 ± 3.8 h, respectively. Cows that had greater GDPR had greater intensity and longer duration of estrus at the alert, independent of parity. Overall resumption of estrous expression, between 7 to 30 DIM, was 41.2% (1,695/4,119), where 58.8% (2,424/4,119) did not have an estrus event, 31.0% (1,274/4,119) of cows had one event of estrus, and 10.2% (421/4,119) of cows had 2 or more events of estrus early postpartum. Mean DIM (± SD) at first estrus event, detected by the AAM, was 19.4 ± 4.4 d. Days in milk at first event for cows with one event was 20.7 ± 1.6 d and 15.9 ± 3.1 d for cows with 2 or more events of estrus. First lactation cows were more likely to have an estrus event early postpartum when compared with second and third and greater lactation cows (45.2 ± 1.4% [530/1168] vs. 41.6 ± 1.3% [636/1525] vs. 37.2 ± 1.3% [529/1426], respectively). There was an interaction of parity and GDPR on the proportion of cows demonstrating an early postpartum estrus. There was no difference in the proportion of cows with an early postpartum estrus between those with assisted or unassisted calving, RFM, or LDA. However, cows that had KET were less likely to have an alert early postpartum when compared with cows that did not have KET. Mean genomic expected milk production (± SD) was 256.8 ± 600.1 kg. There was no interaction between GEM and parity on estrous expression (i.e., intensity and duration). There was no interaction between GEM and GDPR on the proportion of estrus early postpartum.

Effects of targeted clinical examination based on alerts from automated health monitoring systems on herd health and performance of lactating dairy cows

Our objectives were to compare the proportion of lactating dairy cows diagnosed with health disorders (HD) and herd performance when using a health monitoring program designed to rely primarily but not exclusively on alerts from automated health monitoring (AHM) systems or a health monitoring program based primarily on systematic clinical examinations, milk yield monitoring, and visual observation of cows. In a clinical trial, at ∼30 d before expected parturition, nulliparous and parous Holstein cows, stratified by parity and days in gestation, were randomly assigned to the high-intensity clinical monitoring (HIC-M; n = 625) or automated monitoring (AUT-M; n = 624) treatment. Cows were fitted with a neck-attached rumination and physical activity monitoring tag, and individual daily milk yield data were collected from parlor milk meters. For cows in HIC-M, clinical examination was conducted daily until 10 d in milk (DIM) and then in response to milk yield reduction alerts or visual observation of clinical signs of HD over the course of 21 DIM. For cows in AUT-M, clinical examination until 21 DIM was because of health index (HI) score alerts and reduced milk yield alerts. The HI score alerts used were generated based on the manufacturer's settings for the system for the last 2-h period before cows were selected for examination. Visual observation of clinical signs of HD was used for identifying cows potentially missed by automated alerts. Binomial and quantitative data were analyzed by logistic regression and ANOVA with repeated measures, respectively. The percentage of cows diagnosed with at least 1 HD during the experimental treatments risk period tended to be greater and the incidence rate ratio of HD diagnosed was greater in the HIC-M than in the AUT-M treatment. We found no difference between treatments for cows that exited the herd up to 60 or 150 DIM, but more cows tended to exit the herd from 61 to 150 DIM in the HIC-M than in the AUT-M treatment. No differences were detectable between treatments in daily or total milk yield to 21 DIM or in weekly mean milk yield and total milk yield to 150 DIM. More cows were inseminated in estrus for first service if in the HIC-M treatment and had no HD diagnosed than if in the HIC-M treatment but with HD diagnosed, or in the AUT-M treatment and had no HD diagnosed. Cows in the AUT-M treatment with HD diagnosed did not differ from other groups. No differences between treatments were observed in pregnancies per artificial insemination or pregnancy loss for first service. Despite a reduction in the risk of diagnosis of HD, no evidence indicated that a health monitoring program that relied on AHM system alerts to select cows for clinical examination reduced herd performance compared with a more intensive program that included systematic clinical examinations of all cows for the first 10 DIM, reduced milk yield alerts, and visual observation. However, to obtain the same herd performance as with the HIC-M treatment, the AUT-M treatment required use of visual observation. In conclusion, a health monitoring program designed to rely primarily on targeted clinical examination based on alerts from automated health monitoring systems might be a feasible alternative to programs that rely more on clinical examination, provided that visual observation is used to identify cows not detected by automated alerts.

Metabolic-digestive clinical disorders of lactating dairy cows were associated with alterations of rumination, physical activity, and lying behavior monitored by an ear-attached sensor

The objective of this observational cohort study was to characterize the pattern of rumination time (RT), physical activity (PA), and lying time (LT) monitored by an automated health monitoring system, based on an ear-attached sensor, immediately before, during, and after clinical diagnosis (CD) of metabolic-digestive disorders. Sensor data were collected from 820 lactating Holstein cows monitored daily from calving up to 21 DIM for detection of health disorders (HD). Cows were grouped retrospectively in the no-clinical health disorder group (NCHD; n = 616) if no HD were diagnosed, or the metabolic-digestive group (METB-DIG; n = 58) if diagnosed with clinical ketosis or indigestion only. Cows with another clinical health disorder within -7 to +7 d of CD of displaced abomasum, clinical ketosis, or indigestion were included in the metabolic-digestive plus one group (METB-DIG+1; n = 25). Daily RT, PA, and LT, and absolute and relative changes within -7 to +7 d of CD were analyzed with linear mixed models with or without repeated measures. Rumination time and PA were smaller, and LT was greater for the METB-DIG and METB-DIG+1 group than for cows in the NCHD group for most days from -7 to +7 d of CD of HD. In general, daily RT, PA, and LT differences were larger between the METB-DIG+1 and NCHD groups than between the METB-DIG and NCHD groups. In most cases, RT and PA decreased to a nadir and LT increased to a peak immediately before or after CD of HD, with a return to levels similar to the NCHD group within 7 d of CD. Absolute values and relative changes from 5 d before CD to the day of the nadir for RT and PA or peak for LT were different for cows in the METB-DIG and METB-DIG+1 group than for the NCHD group. For PA, the METB-DIG+1 group had greater changes than the METB-DIG group. For cows affected by metabolic-digestive disorders, RT, PA, and LT on the day of CD and resolution of clinical signs were different than for cows in the NCHD group, but an increase in RT and PA or a decrease in LT was observed from the day of CD to the day of resolution of clinical signs. We conclude that dairy cows diagnosed with metabolic-digestive disorders including displaced abomasum, clinical ketosis, and indigestion presented substantial alterations in the pattern of RT, PA, and LT captured by an ear-attached sensor. Thus, automated health monitoring systems based on ear-attached sensors might be used as an aid for identifying cows with metabolic-digestive disorders. Moreover, RT, PA, and LT changes after CD might be positive indicators of recovery from metabolic-digestive disorders.

Lipolysis inhibition as a treatment of clinical ketosis in dairy cows: A randomized clinical trial

Excessive and protracted lipolysis in adipose tissues of dairy cows is a major risk factor for clinical ketosis (CK). This metabolic disease is common in postpartum cows when lipolysis provides fatty acids as an energy substrate to offset negative energy balance. Lipolysis in cows can be induced by the canonical (hormonally induced) and inflammatory pathways. Current treatments for CK focus on improving glucose in blood (i.e., oral propylene glycol [PG], or i.v. dextrose). However, these therapies do not inhibit the canonical and inflammatory lipolytic pathways. Niacin (NIA) can reduce activation of the canonical pathway. Blocking inflammatory responses with cyclooxygenase inhibitors such as flunixin meglumine (FM) can inhibit inflammatory lipolytic activity. The objective of this study was to determine the effects of including NIA and FM in the standard PG treatment for postpartum CK on circulating concentrations of ketone bodies. A 4-group, parallel, individually randomized trial was conducted in multiparous Jersey cows (n = 80) from a commercial dairy in Michigan during a 7-mo period. Eligible cows had CK symptoms (lethargy, depressed appetite, and milk yield) and hyperketonemia (blood β-hydroxybutyrate [BHB] ≥1.2 mmol/L). Cows with CK were randomly assigned to 1 of 3 groups where the first group received 310 g of oral PG once per day for 5 d; the second group received PG for 5 d + 24 g of oral NIA once per day for 3 d (PGNIA); and the third group received PG for 5 d + NIA for 3 d + 1.1 mg/kg i.v. FM once per day for 3 d (PGNIAFM). The control group consisted of cows that were clinically healthy (HC; untreated; BHB <1.2 mmol/L, n = 27) matching for parity and DIM with all 3 groups. Animals were sampled at enrollment (d 0), and d 3, 7, and 14 to evaluate ketone bodies and circulating metabolic and inflammatory biomarkers. Effects of treatment, sampling day, and their interactions were evaluated using mixed effects models. Logistic regression was used to calculate the odds ratio (OR) of returning to normoketonemia (BHB <1.2 mmol/L). Compared with HC, enrolled CK cows exhibited higher blood concentrations of dyslipidemia markers, including nonesterified fatty acids (NEFA) and BHB, and lower glucose and insulin levels. Cows with CK also had increased levels of biomarkers of pain (substance P), inflammation, including lipopolysaccharide-binding protein, haptoglobin, and serum amyloid A, and proinflammatory cytokines IL-4, MCP-1, MIP-1α, and TNFα. Importantly, 72.2% of CK cows presented endotoxemia and had higher circulating bacterial DNA compared with HC. By d 7, the percentage of cows with normoketonemia were higher in PGNIAFM = 87.5%, compared with PG = 58.33%, and PGNIA = 62.5%. At d 7 the OR for normoketonemia in PGNIAFM cows were 1.5 (95% CI, 1.03-2.17) and 1.4 (95% CI, 0.99-1.97) relative to PG and PGNIA, respectively. At d 3, 7, and 14, PGNIAFM cows presented the lowest values of BHB (PG = 1.36; PGNIA = 1.24; PGNIAFM = 0.89 ± 0.13 mmol/L), NEFA (PG = 0.58; PGNIA = 0.59; PGNIAFM = 0.45 ± 0.02 mmol/L), and acute phase proteins. Cows in PGNIAFM also presented the highest blood glucose increment across time points and insulin by d 7. These data provide evidence that bacteremia or endotoxemia, systemic inflammation, and pain may play a crucial role in CK pathogenesis. Additionally, targeting lipolysis and inflammation with NIA and FM during CK effectively reduces dyslipidemia biomarkers, improves glycemia, and improves overall clinical recovery.

Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle

With all the sensor data currently generated at high frequency in dairy farms, there is potential for earlier diagnosis of postpartum diseases compared with traditional monitoring methodologies. Our objectives were 1) to compare the impact of sensor data pre-processing on classifier performance by using multiple time windows before a given metritis event, while considering other cow-level factors and farm-scheduled activities; 2) to compare the performance of random forest (RF), k-nearest neighbors (k-NN), and support vector machine (SVM) classifiers at different decision thresholds using different number of past observations (time-lags) for the detection of behavioral patterns associated with changes in metritis scores; and 3) to compare classifier performance between each one of the five behaviors registered every hour by an ear-tag 3-axis accelerometer (CowManager, Agis Autimatisering, Harmelen, Netherlands). A total of 239 metritis events were created by comparing metritis scores between two consecutive clinical evaluations from cows that were retrospectively selected from a dataset containing sensor data and health information during the first 21 days postpartum from June 2014 to May 2017. Hourly sensor data classified by the accelerometer as either ruminating, eating, not active (including both standing or lying), and two different levels of activity (active and high activity) behaviors corresponding to the 3 days before each metritis event were aggregated every 24-, 12-, 6-, and 3-hour time windows. Multiple time-lags were also used to determine the optimal number of past observations needed for optimal classification. Similarly, different decision thresholds were compared in terms of model performance. Depending on the classifier, algorithm hyperparameters were optimized using grid search (RF, k-NN, SVM) and random search (RF). All behaviors changed throughout the study period and showed distinct daily patterns. From the three algorithms, RF had the highest F₁ score followed by k-NN and SVM. Furthermore, sensor data aggregated every 6- or 12-h time windows had the best model performance at multiple time-lags. We concluded that the data from the first 3 days post-partum should be discarded when studying metritis, and either one of the five behaviors measured with CowManager could be used when predicting metritis when sensor data were aggregated every 6- or 12-hour time windows, and using time-lags corresponding to 2-3 days before a given event, depending on the time window used. This study shows how to maximize sensor data in their potential for disease prediction, enhancing the performance of algorithms used in machine learning.

Effect of Major Diseases on Productivity of a Large Dairy Farm in a Temperate Zone in Japan

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.

Acidified diet is not effective in preventing hypocalcemia in dairy cows on French commercial farms

The objective of this study was to assess, in the context of typical dairy farms in western France, the preventive effects of prepartum diet acidification (AcD) and mineral intake during late gestation on the incidence of subclinical hypocalcemia (SHC) and subclinical hypophosphatemia (SHP) after calving. We conducted a longitudinal study that followed a cohort of 371 Holstein cows from 26 French dairy farms; of these, 235 cows (15 farms) were supplied with anionic salts during late gestation, and 136 cows (11 farms) were not. Blood samples were collected from 1 to 22 cows per farm (average of 14.3 cows per farm) between 24 and 48 h after calving. Total calcium and inorganic phosphorus concentrations in plasma were then quantified by inductively coupled plasma - optical emission spectrometry. The effects of AcD on the incidence of SHC and SHP were assessed using mixed linear models that evaluated the cow-level factors parity, milk yield index, and individual health events/treatments, and the farm-level factors diet calcium and magnesium content, dietary phosphorus intake, vitamin D supply, diet crude protein content, and duration of AcD, with farm as a random effect. Approximately 55% of cows were diagnosed with SHC (calcium &lt; 2.0 mmol/l, n = 203) and 37% with SHP (inorganic phosphorus &lt; 1.3 mmol/l, n=136). The first model confirmed earlier findings that the risk of SHC is higher with increased parity (P ≤ 0.0001) and revealed a higher risk associated with high milk yield (P = 0.0005), high phosphorus intake (40–60 g/cow per day, OR = 3.5; ≥ 60 g/cow per day, OR = 7.3; P = 0.0003) and high vitamin D supply (≥ 19950 IU/cow per day, OR = 3, P = 0.007). The second model highlighted a greater risk of SHP with increasing parity (P = 0.03) and showed trends for the preventive effects of AcD (OR = 0.4, P = 0.07) and moderate amounts of phosphorus in the diet (OR = 0.4, P = 0.10). Overall, our results do not support the effectiveness of AcD in preventing SHC or SHP under field conditions examined here, probably resulting from insufficient diet acidification.

Individual and Combined Effects of Diseases and Cytological Endometritis on Reproductive Performance and Culling of Dairy Cows: Preliminary Results

The objective of this study was to evaluate the effect of calving-related disorders/clinical diseases (DIS) and cytological endometritis (CYTO) on the reproductive performance and culling of dairy cows. In a total of 119 multiparous Holstein cows, DIS were recorded and CYTO was diagnosed at 40 ± 2 d in milk. Onset of luteal activity was defined as the first postpartum measurement of milk progesterone >5 ng/mL. A dummy variable containing all four possible permutations between DIS and CYTO statuses was created. The pregnancy rates after first artificial insemination were 57.7, 42.9, 23.0 and 15.8% for the DIS−/CYTO−, DIS+/CYTO−, DIS−/CYTO+ and DIS+/CYTO+ groups, respectively. The hazard of pregnancy was affected by DIS−/CYTO+ and DIS+/CYTO+ health statuses (hazard ratio (HR) = 0.43 and 0.29, respectively), whereas DIS+/CYTO− and DIS−/CYTO− cows had a similar hazard to pregnancy. The hazard of onset of luteal activity was affected by DIS+/CYTO+ health status (HR = 0.45), but not by DIS+/CYTO− and DIS−/CYTO+ health statuses. Compared with DIS−/CYTO− cows, DIS−/CYTO+ and DIS+/CYTO+ cows had 4.24 and 5.57 times the odds of being culled, respectively. Culling was not affected by DIS+/CYTO− health status. In conclusion, CYTO+ status, irrespective of DIS status, decreased the pregnancy rate. When DIS+ and CYTO+ were combined, they had an additive negative effect. Our preliminary findings suggest that the far-reaching consequences of clinical diseases on fertility and culling can be mediated through the development of CYTO. Further investigations including a larger number of cows are required to confirm these results.

Implementation of hypocalcaemia prevention programmes in commercial dairy herds: From theory to practice

Hypocalcaemia prevention programmes have been widely studied in experimental settings, but their feasibility has not been assessed under field conditions. The main objective of this study was to evaluate, in the context of small dairy farms in western France, whether and how dairy farmers implement prevention programmes and manage the feeding of dry cows to prevent hypocalcaemia. Seventy-nine commercial Holstein dairy farms in Brittany (France) were enrolled in a qualitative study in 2019. We conducted in-person interviews with the farmers to 1) understand the rationale behind the type and seasonality of prevention programmes they implemented and 2) assess how closely they followed common recommendations when implementing them. Most farmers (80 %) used at least one prevention programme, especially supplying a mineral mix formulated to meet the needs of dry cows in late gestation (53 %), acidifying the diet in late gestation (37 %), and supplying calcium at calving (oral or injectable form, 37 %). The use of programmes depended on whether the diet composition varied throughout the year. Among farmers who provided an acidified diet, 25 % did not supply a specific mineral mix to dry cows to ensure an adequate amount of P, Ca, and Mg, which could decrease the effectiveness of the acidification programme. A lack of reliability in feeding practices, such as not weighing feed or not delivering feed frequently enough, was identified for 61 % of contributing farms. Management practices could result in supplying an unsuitable amount of P, Ca, or Mg immediately before calving; for example, inappropriate batching practices around calving were identified for 22 % (cows) to 32 % (heifers) of farms. In addition, nearly all contributing farmers had no processes in place to monitor the effectiveness of the programmes implemented. Reasons for this overall lack of compliance should be explored.

An investigation of blood, milk, and urine test patterns for the diagnosis of ketosis in dairy cows in early lactation

Ketosis in dairy cattle is primarily diagnosed based on the concentrations of ketone bodies in the blood, milk, or urine. Cow-side tests using these fluids are available for rapid detection of elevated concentrations of ketone bodies. Although these tests have been extensively validated, the performance of different tests has not been compared over time. Our objectives were to investigate the relationship between point-of-care diagnostic tests measuring the concentrations of β-hydroxybutyrate (BHB) in blood (BT; Precision Xtra, Abbott Laboratories), BHB in milk (MT; Keto-Test, Elanco), and acetoacetate (AcAc) in urine (UT; Ketostix, Bayer Corporation) through cases of ketosis. Holstein cows (n = 148) were screened daily for hyperketonemia (HYK; blood BHB ≥1.2 mmol/L) from 3 to 16 d in milk (DIM); moreover, milk and urine samples were collected concomitantly and tested for ketones (ketosis thresholds: 100 µmol/L milk BHB and 5 mg/dL urine AcAc). Of the animals screened (n = 148), 74% were diagnosed with HYK. When diagnosed with HYK, cows were treated with propylene glycol orally once daily for 5 d. After the day of diagnosis (d 0), hyperketonemic cows were retested with BT, MT, and UT for 3 d (d 1, 2, and 3). We assessed the diagnostic test performance and time to ketosis (survival analyses and Cox proportional hazards models) of MT and UT compared with BT. Considering all paired samples (before and after diagnosis of HYK), MT had 61% sensitivity and 91% specificity, whereas the UT had 77% sensitivity and 94% specificity compared with BT. The specificity of MT and UT increased from d 0 to d 1, decreased on d 2, and increased on d 3. The median time to diagnosis of ketosis in blood was 5 DIM (95% CI 5 to 7 DIM); moreover, MT and UT had 2 d greater median time to diagnosis of ketosis compared with the BT [7 DIM (6 to 11 d); and 7 DIM (6 to 13 d), respectively]. We concluded that the UT is a more sensitive predictor of blood BHB concentration than the MT. The UT and MT tests diagnosed ketotic cows approximately 2 d later than the BT. The possible consequences of delay in detection of ketosis in milk and urine should be investigated.

Calving Ease Risk Factors and Subsequent Survival, Fertility and Milk Production in Italian Holstein Cows

Simple Summary

Several studies have analyzed the potential risk factors for assisted calvings and the consequences of calving ease on cow performances. The present study used national data to focus on Italian Holstein cow herds. In summary, herd, number of calvings/herd/year, cow parity, gestation length, twinning, calf sex, previous calving-to-conception interval, dry period and close-up length, and calving season were identified as risk factors associated with calving ease. Regarding lactation performance, cows with assisted calving had higher 30 days in milk (DIM) culling risk, lower 150 DIM pregnancy risk and lower milk yield, measured as 60-d cumulative and as 305-d predicted milk yield. These results may be beneficial to focus attention on control, management and specific factors associated with calving ease in Italian Holstein herds and also to support adequate training and education of the personnel.

Abstract

The objectives of this study were to investigate the main risk factors associated with calving ease (CE) in Italian Holstein cow herds, and to estimate the association between CE and subsequent survival, fertility and milk production. Data obtained from Holstein cows in 40 Italian herds were retrospectively investigated. Calvings were surveilled and classified into two categories of CE, unassisted calving or assisted calving, based on the need for intervention. The following factors were analyzed as possible risk factor affecting CE: herd, number of calvings/herd/year, age at first calving (AFC), cow parity, gestation length, twinning, calf sex, previous calving-to-conception interval, previous milk yield, dry period and close-up length, and season of calving. The association between CE and culling risk within the first 30 days-in-milk (DIM), cumulative 60-d milk yield and predicted 305-d milk yield, and pregnancy risk within 150 DIM were also investigated. Of the 47,672 calvings, 37,892 (79.5%) were unassisted, while 9780 (20.5%) required some type of assistance. Among the risk factors, only the AFC was not correlated with CE, while for all the other risk factors an association with CE was detected. Assisted calvings were associated with an increased culling risk at 30 DIM, decreased 60-d milk yield, decreased 305-d milk yield and reduced pregnancy risk at 150 DIM. In conclusion, dairy herd management should aim at correcting/reducing the risk factors in order to limit the incidence of assisted calving, and possibly improve the quality of calving assistance; controlling CE within the herd is crucial to reducing culling risk, and achieving higher lactation and reproductive performance.

Birth conditions affect the longevity of Holstein offspring

Studies of dairy cow longevity usually focus on the animal life after first calving, with few studies considering early life conditions and their effects on longevity. The objective was to evaluate the effect of birth conditions routinely collected by Dairy Herd Improvement agencies on offspring longevity measured as length of life and length of productive life. Lactanet provided 712,890 records on offspring born in 5,425 Quebec dairy herds between January 1999 and November 2015 for length of life, and 506,066 records on offspring born in 5,089 Quebec dairy herds between January 1999 and December 2013 for length of productive life. Offspring birth conditions used in this study were calving ease (unassisted, pull, surgery, or malpresentation), calf size (small, medium, or large), and twinning (yes or no). Observations were considered censored if the culling reason was "exported," "sold for dairy production," or "rented out" as well as if the animals were not yet culled at the time of data extraction. If offspring were not yet culled when the data were extracted, the last test-day date was considered the censoring date. Conditional inference survival trees were used in this study to analyze the effect of offspring birth conditions on offspring longevity. The hazard ratio of culling between the groups of offspring identified by the survival trees was estimated using a Cox proportional hazard model with herd-year-season as a frailty term. Five offspring groups were identified with different length of life based on their birth condition. Offspring with the highest length of life [median = 3.61 year; median absolute deviation (MAD) = 1.86] were those classified as large or medium birth size and were also the result of an unassisted calving. Small offspring as a result of a twin birth had the lowest length of life (median = 2.20 year; MAD = 1.69) and were 1.52 times more likely to be culled early in life. Six groups were identified with different length of productive life. Offspring that resulted from an unassisted or surgery calving and classified as large or medium when they were born were in the group with the highest length of productive life (median = 2.03 year; MAD = 1.63). Offspring resulting from a malpresentation or pull in a twin birth were in the group with the lowest length of productive life (median = 1.15 year; MAD = 1.11) and were 1.70 times more likely to be culled early in life. In conclusion, birth conditions of calving ease, calf size, and twinning greatly affected offspring longevity, and such information could be used for early selection of replacement candidates.

Integrating diverse data sources to predict disease risk in dairy cattle-a machine learning approach

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making them impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia), and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1 = 0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, such as housing, nutrition, or climate, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Our findings pave the way toward data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

Lipopolysaccharide induces lipolysis and insulin resistance in adipose tissue from dairy cows

Intense and protracted adipose tissue (AT) fat mobilization increases the risk of metabolic and inflammatory periparturient diseases in dairy cows. This vulnerability increases when cows have endotoxemia-common during periparturient diseases such as mastitis, metritis, and pneumonia-but the mechanisms are unknown. Fat mobilization intensity is determined by the balance between lipolysis and lipogenesis. Around parturition, the rate of lipolysis surpasses that of lipogenesis, leading to enhanced free fatty acid release into the circulation. We hypothesized that exposure to endotoxin (ET) increases AT lipolysis by activation of classic and inflammatory lipolytic pathways and reduction of insulin sensitivity. In experiment 1, subcutaneous AT (SCAT) explants were collected from periparturient (n = 12) Holstein cows at 11 ± 3.6 d (mean ± SE) before calving, and 6 ± 1 d and 13 ± 1.4 d after parturition. Explants were treated with the endotoxin lipopolysaccharide (LPS; 20 µg/mL; basal = 0 µg/mL) for 3 h. The effect of LPS on lipolysis was assessed in the presence of the β-adrenergic agonist and promoter of lipolysis isoproterenol (ISO; 1 µM; LPS+ISO). In experiment 2, SCAT explants were harvested from 24 nonlactating, nongestating multiparous Holstein dairy cows and exposed to the same treatments as in experiment 1 for 3 and 7 h. The effect of LPS on the antilipolytic responses induced by insulin (INS = 1 µL/L, LPS+INS) was established during ISO stimulation [ISO+INS, LPS+ISO+INS]. The characterization of lipolysis included the quantification of glycerol release and the assessment of markers of lipase activity [adipose triglyceride lipase (ATGL), hormone-sensitive lipase (HSL), and phosphorylated HSL Ser563 (pHSL)], and insulin pathway activation (AKT, pAKT) using capillary electrophoresis. Inflammatory gene networks were evaluated by real-time quantitative PCR. In periparturient cows, LPS increased AT lipolysis by 67 ± 12% at 3 h across all time points compared with basal. In nonlactating cows, LPS was an effective lipolytic agent at 3 h and 7 h, increasing glycerol release by 115 ± 18% and 68.7 ± 16%, respectively, relative to basal. In experiment 2, LPS enhanced ATGL activity with minimal HSL activation at 3 h. In contrast, at 7 h, LPS increased HSL phosphorylation (i.e., HSL activity) by 123 ± 11%. The LPS-induced HSL lipolytic activity at 7 h coincided with the activation of the MEK/ERK inflammatory pathway. In experiment 2, INS reduced the lipolytic effect of ISO (ISO+INS: -63 ± 18%) and LPS (LPS+INS: -45.2 ± 18%) at 3 h. However, the antilipolytic effect of INS was lost in the presence of LPS at 7 h (LPS+INS: -16.3 ± 16%) and LPS+ISO+INS at 3 and 7 h (-3.84 ± 23.6% and -21.2 ± 14.6%). Accordingly, LPS reduced pAKT:AKT (0.11 ± 0.07) compared with basal (0.18 ± 0.05) at 7 h. Our results indicated that exposure to LPS activated the classic and inflammatory lipolytic pathways and reduced insulin sensitivity in SCAT. These data provide evidence that during endotoxemia, dairy cows may be more susceptible to lipolysis dysregulation and loss of adipocyte sensitivity to the antilipolytic action of insulin.

Effect of body condition change and health status during early lactation on performance and survival of Holstein cows

Body condition score (BCS) and disease records are commonly available in dairy operations. However, the effect of BCS changes (ΔBCS) considering specific health profiles has not been investigated extensively. The objective of this study was to assess the effects of different levels of ΔBCS on fertility, milk yield, and survival of Holstein cows diagnosed with reproductive disorders (REP; dystocia, twins, retained fetal membranes, metritis, and clinical endometritis), other health disorders (OTH; subclinical ketosis, left displaced abomasum, lameness, clinical mastitis, and respiratory disease), or with no disease events (HLT) within 40 days in milk (DIM). Data included lactation information from 11,733 cows calving between November 2012 and October 2014 in 16 herds across 4 geographical regions in the United States (Northeast, Midwest, Southwest, Southeast). Cows were evaluated for BCS at 5 ± 3 DIM (BCS5) and at 40 ± 3 DIM (BCS40) and the difference between BCS40 and BCS5 was classified as excessive loss of BCS (EL; ΔBCS ≤-0.75), moderate loss (ML; ΔBCS = -0.5 to -0.25), no change (NC; ΔBCS = 0), or gain of BCS (GN; ΔBCS ≥0.25). Multivariable logistic regression was used for assessing potential associations between the outcomes of interest and ΔBCS and health. The effect of the interaction term ΔBCS by health group was not statistically significant for any of the study outcomes. The odds of resumption of ovarian cyclicity (ROC), in GN, NC, and ML cows were 1.94 (95% CI: 1.57-2.40), 1.59 (1.28-1.97), and 1.27 (1.10-1.47) times greater than the odds of ROC in EL cows, respectively. The odds of pregnancy at 150 DIM (P150) in GN cows were 1.61 (1.20-2.17) times greater than the odds of P150 in EL cows. Cows with REP or OTH disorders had smaller odds of ROC compared with HLT cows [REP: OR = 0.65 (0.56-0.76) and OTH: OR = 0.79 (0.68-0.92)]. For pregnancy outcomes, REP cows had smaller odds of pregnancy at the first artificial insemination compared with HLT cows [0.70 (0.58-0.84)]. Similarly, REP cows had smaller odds of being diagnosed pregnant by 150 and 305 DIM compared with HLT cows [P150: 0.73 (0.59-0.87), P305: 0.58 (0.49-0.69)]. Overall, average daily milk within the first 90 DIM was greater in EL (39.5 ± 1.13 kg/d) and ML (38.9 ± 1.11 kg/d) cows than in NC (37.8 ± 1.12 kg/d) and GN (36.2 ± 1.12 kg/d) cows. On the other hand, average daily milk within the first 90 DIM was lower in REP (37.0 ± 1.11 kg/d) cows compared with OTH (38.7 ± 1.12 kg/d) and HLT cows (38.6 ± 1.11 kg/d). The magnitude of ΔBCS and the health status of early lactation cows should be considered when assessing subsequent cow performance and survival.

Analysis of Longevity Traits in Holstein Cattle: A Review

Dairy cow longevity is an essential economic trait that can supplement the breeding value of production traits, which is related to the herd time and lifetime milk yield of dairy cows. However, longevity is a relatively difficult trait to select for dairy cow breeding due to low heritability and numerous influence factors of the longevity in dairy cows. Longevity trait has been used as an important breeding target of a comprehensive selection index in many dairy developed countries; however, it has not been included in performance index in many developing countries. At present, cows in these countries are still in the primary stage of “large quantity, low quality, high cost, and low yield.” The average parity of dairy cows is less than 2.7, which is difficult to maintain the production efficiency to meet the demands of the dairy industry. Therefore, there is an urgent need to select and breed for the longevity of dairy cows. The various definitions and models (including linear, threshold, random regression, sire, and survival analysis) of longevity were reviewed and standardized. Survival analysis is the optimal model to evaluate longevity, and the longevity heritability is 0.01–0.30 by using different definitions and models. Additionally, the relationship between longevity and other traits was summarized, and found that longevity was regulated by multiple factors, and there were low or medium genetic correlations between them. Conformation traits, milk production traits, reproductive traits, and health traits may be used as indicators to select and breed the longevity of dairy cows. The genetic assessment methods, heritability, influencing factors, importance, breeding, and genetics of longevity were reviewed in the manuscript, which could provide a valuable reference for the selective breeding to extend the productive life of Holstein cattle.

The Whole and the Parts—A New Perspective on Production Diseases and Economic Sustainability in Dairy Farming

The levels of production diseases (PD) and the cow replacement rate are high in dairy farming. They indicate excessive production demands on the cow and a poor state of animal welfare. This is the subject of increasing public debate. The purpose of this study was to assess the effect of production diseases on the economic sustainability of dairy farms. The contributions of individual culled cows to the farm’s economic performance were calculated, based on milk recording and accounting data from 32 farms in Germany. Cows were identified as ‘profit cows’ when they reached their individual ‘break-even point’. Data from milk recordings (yield and indicators for PD) were used to cluster farms by means of a principal component and a cluster analysis. The analysis revealed five clusters of farms. The average proportion of profit cows was 57.5%, 55.6%, 44.1%, 29.4% and 19.5%. Clusters characterized by a high proportion of cows with metabolic problems and high culling and mortality rates had lower proportions of profit cows, somewhat irrespective of the average milk-yield per cow. Changing the perception of PD from considering it as collateral damage to a threat to the farms’ economic viability might foster change processes to reduce production diseases.

Sustainable Management of Voluntary Culling Risk in Primiparous Zaraibi Goats in Egypt: Roles of Season and Reproductive and Milk Production-Related Traits

Simple Summary

Sustainable production of livestock farms mainly depends on efficient management and precise decisions. One of the most important managemental decisions that play a great role in the continuation of production and profitability is culling. Excluding non-efficient animals from the production cycle is a very critical decision as a wrong and aggressive culling decision may cause a certain loss in farm profitability by increasing costs of replacement. Identifying factors that affect culling risk can aid breeders in creating suitable interventions to maintain productive animals. In this study, logistic regression analysis was used to study the pattern of culling risk factors in primiparous Nubian goats. The effect of ten independent factors including season, reproductive traits, and milk production traits on the percentage of voluntary culling risk was studied. The presented outcomes clearly indicated that birth weight, total litter size at birth, litter size at weaning, number of kids dead, total milk yield, average daily milk yield, lactation period, and age at first kidding had significant effects on culling risk, while both the birth season of the dam and kidding season did not exhibit significant effects on culling risk. In practice, monitoring of traits that indicate milk imbalance and older age at first kidding could promote identifying goats at high risk of being culled.

Abstract

The purpose of the current study is to reconnoiter the relationships between season (birth season, BS, and kidding season, KS), reproductive traits (birth weight, BW; total litter size at birth, TLS; litter size at weaning, LSW; the number of kids dead, NKD, and; age at first kidding, AFK) and milk production (total milk yield, TMY; average daily milk yield, DMY, and; lactation period, LP), with voluntary culling risk in primiparous Zaraibi goats. Records of 637 primiparous does were collected during the period 2008–2014 from a herd of Zaraibi goats raised at the El-Serw Experimental Station, which belongs to the Animal Production Research Institute. Our data revealed that the voluntary and involuntary culling was 89.12% and 10.88%, respectively. Moreover, the BW, TLS, LSW, NKD, TMY, DMY, LP, AFK had significant effects on culling risk (p < 0.05), while both the season of birth and kidding did not exhibit significant effects on culling risk. The thinnest goats at birth were more likely to be culled compared to those with body energy reserves. Does with weaned twins and triplets kids were 9.5% (OR = 0.905) and 43% (OR = 0.570), respectively less likely to be culled compared to those with singles. Likewise, does with dead twins and triplets kids had 2.566 and 2.138 times, respectively higher odds of culling compared to those with singles. Interestingly, culling risk decreased with 74.6% (OR = 0.254), 79.8% (OR = 0.202), and 75.6% (OR = 0.244) in does with TMY, LP, and DMY more than 230 kg, 260 days, and 0.800 kg, respectively compared to their counterparts (less than 185 kg, 240 day, and 0.500 kg, respectively). Moreover, culling risk increased steadily along with increasing AFK; the animals with an AFK of more than 24 months had 2.974 times higher odds of culling compared to those with an AFK that varied between 22 and 24 months. It could be concluded that the most critical issues for higher culling probability in dairy goats were attributed to the lower TMY (<185 kg) and DMY (<0.5 kg), and shorter LP (<245 days), as well as older age at first kidding (>2 years). This putative information could be used as indicators to enhance the management and genetic approaches in dairy goats and thus sustain productivity with low cost.

Assessment of the Relationship between Postpartum Health and Mid-Lactation Performance, Behavior, and Feed Efficiency in Holstein Dairy Cows

The objective of this study was to investigate the relationships between postpartum health disorders and mid-lactation performance, feed efficiency, and sensor-derived behavioral traits. Multiparous cows (n = 179) were monitored for health disorders for 21 days postpartum and enrolled in a 45-day trial between 50 to 200 days in milk, wherein feed intake, milk yield and components, body weight, body condition score, and activity, lying, and feeding behaviors were recorded. Feed efficiency was measured as residual feed intake and the ratio of fat- or energy-corrected milk to dry matter intake. Cows were classified as either having hyperketonemia (HYK; n = 72) or not (n = 107) and grouped by frequency of postpartum health disorders: none (HLT; n = 94), one (DIS; n = 63), or ≥2 (DIS+; n = 22). Cows that were diagnosed with HYK had higher mid-lactation yields of fat- and energy-corrected milk. No differences in feed efficiency were detected between HYK or health status groups. Highly active mid-lactation time was higher in healthy animals, and rumination time was lower in ≥4th lactation cows compared with HYK or DIS and DIS+ cows. Differences in mid-lactation behaviors between HYK and health status groups may reflect the long-term impacts of health disorders. The lack of a relationship between postpartum health and mid-lactation feed efficiency indicates that health disorders do not have long-lasting impacts on feed efficiency.

Serum retinol, β-carotene, and α-tocopherol as biomarkers for disease risk and milk production in periparturient dairy cows

The effectiveness of using serum vitamin concentrations as biomarkers to predict diseases in dairy cows during the periparturient period is not well known. The objective of this study was to evaluate the association between serum β-carotene, retinol, and α-tocopherol concentrations and periparturient cow diseases in commercial dairies. We measured serum concentrations of these vitamin-active compounds at dry-off and during close-up (approximately 3 wk before calving) and early lactation (approximately 7 d post-calving), and we examined their association with clinical diseases in the first 30 d in milk. Diseases were diagnosed by trained personnel and recorded using database software. Blood samples were taken from 353 cows from 5 different farms over a 3-yr period. Blood samples were analyzed for β-carotene, retinol, α-tocopherol, and cholesterol. We built separate mixed logistic regression models for each disease outcome: hyperketonuria, lameness, mastitis, uterine diseases (retained placenta or metritis), and an aggregate outcome. For the aggregate outcome, a cow was considered positive if she had one or more of the following: hyperketonuria, lameness, mastitis, uterine disease, pneumonia, milk fever, or displaced abomasum. Concentrations of all 3 fat-soluble vitamins decreased significantly in early lactation relative to the 2 prepartum sampling times. Serum retinol concentrations at close-up and early lactation were negatively associated with odds of developing postpartum hyperketonuria. At early lactation, cows with uterine disease had lower serum retinol concentrations than cows without uterine disease. Similarly, lower serum retinol concentrations were associated with greater odds of having any one disease in the aggregate outcome. First-test 305-d mature-equivalent milk yield was positively correlated with increased serum α-tocopherol and negatively correlated with β-carotene concentrations. This study demonstrates the potential for serum β-carotene, retinol, and α-tocopherol to serve as biomarkers for disease risk.

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

Review: Importance of colostrum supply and milk feeding intensity on gastrointestinal and systemic development in calves

Feeding management of the postnatal and preweaning calf has an important impact on calf growth and development during this critical period and affects the health and well-being of the calves. After birth, an immediate and sufficient colostrum supply is a prerequisite for successful calf rearing. Colostrum provides high amounts of nutrient as well as non-nutrient factors that promote the immune system and intestinal maturation of the calf. The maturation and function of the neonatal intestine enable the calf to digest and absorb the nutrients provided by colostrum and milk. Therefore, colostrum intake supports the start of anabolic processes in several tissues, stimulating postnatal body growth and organ development. After the colostrum feeding period, an intensive milk feeding protocol, that is, at least 20% of BW milk intake/day, is required to realise the calf potential for growth and organ development during the preweaning period. Insufficient milk intake delays postnatal growth and may have detrimental effects on organ development, for example, the intestine and the mammary gland. The somatotropic axis as the main postnatal endocrine regulatory system for body growth is stimulated by the intake of high amounts of colostrum and milk and indicates the promotion of anabolic metabolism in calves. The development of the forestomach is an important issue during the preweaning period in calves, and forestomach maturation is best achieved by solid feed intake. Unfortunately, intensive milk-feeding programmes compromise solid feed intake during the first weeks of life. In the more natural situation for beef calves, when milk and solid feed intake occurs at the same time, calves benefit from the high milk intake as evidenced by enhanced body growth and organ maturation without impaired forestomach development during weaning. To realise an intensive milk-feeding programme, it is recommended that the weaning process should not start too early and that solid feed intake should be at a high extent despite intensive milk feeding. A feeding concept based on intensive milk feeding prevents hunger and abnormal behaviour of the calves and fits the principles of animal welfare during preweaning calf rearing. Studies on milk performance in dairy cows indicate that feeding management during early calf rearing influences lifetime performance. Therefore, an intensive milk-feeding programme affects immediate as well as long-term performance, probably by programming metabolic pathways during the preweaning period.

Assessment of associations between transition diseases and reproductive performance of dairy cows using survival analysis and decision tree algorithms

This study aimed to evaluate the associations between transition cow conditions and diseases TD with fertility in Holstein cows, and to compare analytic methods for doing so. Kaplan-Meier, Cox proportional hazard, and decision tree models were used to analyze the associations of TD with the pregnancy risk at 120 and 210 DIM from a 1-year cohort with 1946 calvings from one farm. The association between TD and fertility was evaluated as follows: 1 cows with TD whether complicated with another TD or not TD-all, versus healthy cows, and 2 cows with uncomplicated TD TD-single, versus cows with multiple TD TD+; complicated cases, versus healthy cows. The occurrence of twins, milk fever, retained placenta, metritis, ketosis, displaced abomasum, and clinical mastitis were recorded. Using Kaplan-Meier models, in primiparous cows the 120 DIM pregnancy risk was 62% (95% CI: 57-67 %) for healthy animals. This was not significantly different for TD-single (58%; 95% CI: 51-66 %) but was reduced for TD+ (45%; 95% CI: 33-60 %). Among healthy primiparous cows, 80% (95% CI: 75-84 %) were pregnant by 210 DIM, but pregnancy risk at that time was reduced for primiparous cows with TD-single (72%; 95% CI: 65-79 %) and TD+ (62%; 95% CI: 49-75 %). In healthy multiparous cows, the 120 DIM pregnancy risk was 53% (95% CI: 49-56 %), which was reduced for TD-single (36%; 95% CI: 31-42 %) and TD+ (30%; 95% CI: 24-38 %). The 210 DIM pregnancy risk for healthy multiparous cows was 70% (95% CI: 67-72 %), being higher than the 210 DIM pregnancy risk for multiparous cows with TD-single (47%; 95% CI: 42-53 %) or TD+ (46%; 95% CI: 38-54 %). Cows with TD-all presented similar pregnancy risk estimates as for TD + . Cox proportional hazards regressions provided similar magnitudes of effects as the Kaplan-Meier estimates. Survival analysis and decision tree models identified parity as the most influential variable affecting fertility. Both modeling techniques concurred that TD + had a greater effect than TD-single on the probability of pregnancy at 120 and 210 DIM. Decision trees for individual TD identified that displaced abomasum affected fertility at 120 DIM in primiparous while metritis was the most influential TD at 120 and 210 DIM for multiparous cows. The data were too sparse to assess multiple interactions in multivariable Cox proportional hazard models for individual TD. Machine learning helped to explore interactions between individual TD to study their hierarchical effect on fertility, identifying conditional relationships that merit further investigation.