Review: Perspective on high-performing dairy cows and herds

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.

The oocyte: the key player in the success of assisted reproduction technologies

The ovulation of a mature oocyte at metaphase II of meiosis, with optimal potential to undergo fertilisation by a sperm cell, complete meiosis and sustain the switch to mitotic division, and support early embryo development, involves a protracted and disrupted/delayed series of processes. Many of these are targeted for exploitation in vivo , or recapitulation in vitro , by the livestock industry. Reproductive technologies, including AI, multiple ovulation embryo transfer, ovum pick-up, in vitro embryo production, and oestrus and ovulation synchronisation, offer practitioners and producers the opportunity to produce offspring from genetically valuable dams in much greater numbers than they would normally have in their lifetime, while in vitro oocyte and follicle culture are important platforms for researchers to interrogate the physiological mechanisms driving fertility. The majority of these technologies target the ovarian follicle and the oocyte within; thus, the quality and capability of the recovered oocyte determine the success of the reproductive intervention. Molecular and microscopical technologies have grown exponentially, providing powerful platforms to interrogate the molecular mechanisms which are integral to or affected by ART. The development of the bovine oocyte from its differentiation in the ovary to ovulation is described in the light of its relevance to key aspects of individual interventions, while highlighting the historical timeline.

Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands

Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time-series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008-2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)12 time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.

On-farm investments into dairy cow health: evidence from 15 case study countries

Managing investments in dairy cow health at a national and global scale, requires an improved understanding of current on-farm expenses for cow health (e.g., expenditure for medicine and veterinary consultations). The aim of this study was to assess on-farm health investments for typical dairy farms in 15 case study countries, including Argentina, Australia, Bangladesh, Brazil, Canada, India, China, Colombia, Indonesia, Kenya, New Zealand, Uganda, UK, Uruguay, and USA. The study was conducted using a descriptive analysis of a secondary data set that was obtained from the International Farm Comparison Network (IFCN). The results suggest that health expenditures take up a relatively small proportion (<10%) of the annual total production costs per cow across all countries in the sample. The means of production costs (e.g., feed, machinery) can take up to 90% of the total production costs for highly intensive systems, while these costs can be as low as 9% for extensive systems. This study highlights the importance of understanding on-farm animal health investments as a contribution to improved national and global decision making about animal health in the dairy sector.

Use of soybean as an alternative protein source for welfare-orientated production of American alligators (Alligator mississippiensis)

Soybean meal based diets have been suggested to cause gastrointestinal issues in certain species when used as a protein alternative. Using a randomized design, we tested 1,728 alligators in one of 13 communal pens offered one of two diets (seven pens (n = 928) of soybean-based protein diets and six pens (n = 800) of animal-based protein diets) to determine if soybeans would negatively affect the growth, hide quality, behavior and health, when compared with an otherwise identical diet using animal-based protein. Both feeds were nearly identical in composition for protein and fat percentages and identical for all minerals and elements. Crude protein was a minimum of 50%, crude fat a minimum of 12%, crude fiber a minimum of 4%, and phosphorous was maintained at 1%. From this information we estimated the One Welfare of using soy as a protein in commercial diets. Although there was a statistically significant decrease in belly width (p = 0.0009; harvested hide size) for alligators fed soybean-based protein diets, all other measured parameters of soybean vs. animal-based protein diets were comparable, suggesting this environmentally sustainable alternative protein source warrants consideration as a feed base. Weight was not significantly different suggesting either diet would yield similar volumes of meat. Total length was significantly affected by diet. Hide quality was not negatively impacted by protein type, with both diets producing high quality hides free of defects (assessed at the salted hide stage prior to tanning). Behaviors were not influenced by the feed type, with animals fed either diet using the pen structures the same. Further, feeding times were the same suggesting the soybean-based protein diet was equally easy to eat and palatable as the animal-based protein diet. Behavior and feeding suggested soy-based diets do not alter time budgets or activities. There were no differences in the frequency or severity of pathologies for animals fed either diet. Respiratory (lung and trachea as a proxy to measure dust inhalation), gastrointestinal (small intestine as a proxy to measure digestive disturbances), and renal (kidney as a proxy for excretory stress) histopathology demonstrated neither diet was causing overt problems. One Welfare conclusions were feeding a soybean-based protein diet did not cause production or welfare issues. Further, soybean protein-based diets may be an environmentally sustainable alternative to currently used animal-based diets. Research examining different soybean protein concentrations and sources is warranted.

Invited Review: Increasing Milk Yield and Negative Energy Balance: A Gordian Knot for Dairy Cows?

The continued increase in milk production during the last century has not been accompanied by an adequate dry matter intake (DMI) by cows, which therefore experience a negative energy balance (NEB). NEB is low and of minor importance at low milk yield (MY), such as for the nutrition of one calf, and under these circumstances is considered "natural". MY and low DMI around parturition are correlated and are the reason for the genetic correlation between increasing MY and increasing NEB up to 2000 MJ or more for 2-3 months postpartum in high-genetic-merit dairy cows. The extension and duration of NEB in high-producing cows cannot be judged as "natural" and are compensated by the mobilization of nutrients, particularly of fat. The released non-esterified fatty acids (NEFAs) overwhelm the metabolic capacity of the cow and lead to the ectopic deposition of NEFAs as triglycerides (TGs) in the liver. The subsequent lipidosis and the concomitant hampered liver functions cause subclinical and clinical ketosis, both of which are associated with "production diseases", including oxidative and endoplasmatic stress, inflammation and immunosuppression. These metabolic alterations are regulated by homeorhesis, with the priority of the physiological function of milk production. The prioritization of one function, namely, milk yield, possibly results in restrictions in other physiological (health) functions under conditions of limited resources (NEB). The hormonal framework for this metabolic environment is the high concentration of growth hormone (GH), the low concentration of insulin in connection with GH-dependent insulin resistance and the low concentration of IGF-1, the so-called GH-IGF-1 axis. The fine tuning of the GH-IGF-1 axis is uncoupled because the expression of the growth hormone receptor (GHR-1A) in the liver is reduced with increasing MY. The uncoupled GH-IGF-1 axis is a serious impairment for the GH-dependent stimulation of gluconeogenesis in the liver with continued increased lipolysis in fat tissue. It facilitates the pathogenesis of lipidosis with ketosis and, secondarily, "production diseases". Unfortunately, MY is still increasing at inadequate DMI with increasing NEB and elevated NEFA and beta-hydroxybutyric acid concentrations under conditions of low glucose, thereby adding health risks. The high incidences of diseases and of early culling and mortality in dairy cows are well documented and cause severe economic problems with a waste of resources and a challenge to the environment. Moreover, the growing public concerns about such production conditions in agriculture can no longer be ignored.

Impact of postpartum time period associated body condition score on reproductive performance in dairy cows

This study was designed to identify the time period at which the body condition score (BCS) most critically affects the reproductive performance after calving in dairy cows. Data were collected from 4865 lactation records (1821 from primiparous and 3044 from multiparous cows) on 28 dairy farms, including the BCS (at calving, and month 1 and the first artificial insemination [AI] after calving), the presence of peri- or postpartum disorders, reproductive data, and the weather. The data on the extent of BCS loss from calving to the first AI was divided into two periods, namely, between calving and month 1 after calving (period 1), and between month 1 and the first AI after calving (period 2). Cows with BCS of 3.0, 3.25 and ≥ 3.5 at the first AI after calving were more likely (P < 0.05-0.01) to be pregnant at 30 (odds ratio [OR]: 1.36, 1.64, and 1.90) and 45 (OR: 1.39, 1.75, and 1.99) days post-AI and more likely (P < 0.05-0.01) to be pregnant within 180 days after calving (hazard ratio [HR]: 1.18, 1.43, and 1.58) than cows with BCS of ≤2.75. In addition, cows who suffered a BCS loss of ≥0.5 units during period 1 tended to be less likely (HR: 0.79, P < 0.1) to be pregnant within 180 days after calving than cows without a BCS loss. Cows with BCS of 3.0, 3.25, and ≥3.5 at calving were less likely (P < 0.1-0.05) to lose their pregnancy (OR: 0.37, 0.33, and 0.16) than cows with BCS of ≤2.75. These results indicate that the linearly higher BCS (3.0, 3.25, and ≥3.5) at the first AI is positively associated with the probability of pregnancy per AI after the first AI and the likelihood of pregnancy within 180 days after calving, whereas a higher BCS loss of ≥0.5 units during period 1 was adversely associated with the likelihood of pregnancy within 180 days after calving.

Uncovering Patterns in Dairy Cow Behaviour: A Deep Learning Approach with Tri-Axial Accelerometer Data

The accurate detection of behavioural changes represents a promising method of detecting the early onset of disease in dairy cows. This study assessed the performance of deep learning (DL) in classifying dairy cows' behaviour from accelerometry data acquired by single sensors on the cows' left flanks and compared the results with those obtained through classical machine learning (ML) from the same raw data. Twelve cows with a tri-axial accelerometer were observed for 136 ± 29 min each to detect five main behaviours: standing still, moving, feeding, ruminating and resting. For each 8 s time interval, 15 metrics were calculated, obtaining a dataset of 211,720 observation units and 15 columns. The entire dataset was randomly split into training (80%) and testing (20%) datasets. The DL accuracy, precision and sensitivity/recall were calculated and compared with the performance of classical ML models. The best predictive model was an 8-layer convolutional neural network (CNN) with an overall accuracy and F1 score equal to 0.96. The precision, sensitivity/recall and F1 score of single behaviours had the following ranges: 0.93-0.99. The CNN outperformed all the classical ML algorithms. The CNN used to monitor the cows' conditions showed an overall high performance in successfully predicting multiple behaviours using a single accelerometer.

High-resolution structural variants catalogue in a large-scale whole genome sequenced bovine family cohort data

BACKGROUND

Structural variants (SVs) are chromosomal segments that differ between genomes, such as deletions, duplications, insertions, inversions and translocations. The genomics revolution enabled the discovery of sub-microscopic SVs via array and whole-genome sequencing (WGS) data, paving the way to unravel the functional impact of SVs. Recent human expression QTL mapping studies demonstrated that SVs play a disproportionally large role in altering gene expression, underlining the importance of including SVs in genetic analyses. Therefore, this study aimed to generate and explore a high-quality bovine SV catalogue exploiting a unique cattle family cohort data (total 266 samples, forming 127 trios).

RESULTS

We curated 13,731 SVs segregating in the population, consisting of 12,201 deletions, 1,509 duplications, and 21 multi-allelic CNVs (> 50-bp). Of these, we validated a subset of copy number variants (CNVs) utilising a direct genotyping approach in an independent cohort, indicating that at least 62% of the CNVs are true variants, segregating in the population. Among gene-disrupting SVs, we prioritised two likely high impact duplications, encompassing ORM1 and POPDC3 genes, respectively. Liver expression QTL mapping results revealed that these duplications are likely causing altered gene expression, confirming the functional importance of SVs. Although most of the accurately genotyped CNVs are tagged by single nucleotide polymorphisms (SNPs) ascertained in WGS data, most CNVs were not captured by individual SNPs obtained from a 50K genotyping array.

CONCLUSION

We generated a high-quality SV catalogue exploiting unique whole genome sequenced bovine family cohort data. Two high impact duplications upregulating the ORM1 and POPDC3 are putative candidates for postpartum feed intake and hoof health traits, thus warranting further investigation. Generally, CNVs were in low LD with SNPs on the 50K array. Hence, it remains crucial to incorporate CNVs via means other than tagging SNPs, such as investigation of tagging haplotypes, direct imputation of CNVs, or direct genotyping as done in the current study. The SV catalogue and the custom genotyping array generated in the current study will serve as valuable resources accelerating utilisation of full spectrum of genetic variants in bovine genomes.

Herd Level Yield Gap Analysis in a Local Scale Dairy Farming System: A Practical Approach to Discriminate between Nutritional and Other Constraining Factors

This study performed a yield gap analysis to help farmers understand whether their constraints were mainly due to nutritional factors or management and health issues. Twenty-nine farms were periodically evaluated. Milk yield (MY), dry matter intake (DMI), total mixed ration (TMR) composition and homogeneity index (HI), TMR digestibility, income over feed cost (IOFC), and MY summer-winter ratio (SWR) were collected. Farms were divided and compared according to the average annual MY: Low (L), Medium (M) and High (H), characterised by <31.1, 31.1-36.7 and >36.7 kg/head/day. An ANOVA mixed model and a stepwise regression to assess the relationship between nutritional variables and MY were run. H farms showed higher IOFC (p < 0.001), DMI (p = 0.006), DDM (p < 0.001), digestible crude protein (DCP, p = 0.019), HI (p = 0.09), SWR (p = 0.041) and lower HI coefficient of variation (p = 0.04). The conversion of DDM into milk was higher in H and M farms. Stepwise regression for MY selected DDM and CP (R² = 0.716, p < 0.05). M farms were mainly constrained by nutritional factors, whereas L farms were also affected by other factors such as those related to management and health.

Clinical and diagnostic characteristics of the development of hepatocardial syndrome in black and white cows in the early lactation period

Background and Aim:

It is known that during the early postpartum and lactation periods in dairy cows, metabolic disorders develop, that is, ketosis, which can lead to secondary damage to internal organs. Therefore, it is important to address the issues of changing the lactating cows’ clinical, laboratory, and physiological parameters regarding the development of hepatocardial syndrome. This study aimed to provide clinical and diagnostic justification for developing hepatocardial syndrome in highly productive dairy cows.

Materials and Methods:

The study was conducted on 20 black and white cows in the early postpartum period (7–10 days after birth), with a milk production level of >4500 kg of milk during the previous lactation period, a positive result in the formol colloid sedimentary test, the presence of deafness and splitting of heart sounds, changes in the size, or increased pain sensitivity of the percussion field of the liver. Clinically healthy dairy cows in the early postpartum period were used as controls (n = 24). Clinical, electrocardiographic, echocardiographic, and biochemical parameters were also evaluated.

Results:

Dairy cows with hepatocardial syndrome developed arterial hypertension and sinus tachycardia, which led to a significant decrease in PQ and QT intervals at ECG. A significant increase in the diastolic size of the interventricular septum, systolic size of the free wall of the left ventricle, and diastolic and systolic sizes of the left ventricle and a significant decrease in the shortening fraction of the left ventricular myocardium were observed in the cows due to the development of hepatocardial syndrome. The affected cows demonstrated a significant increase in serum activity of gamma-glutamyl transferase, alanine aminotransferase, lactate dehydrogenase, creatine phosphokinase, alkaline phosphatase, troponin, malondialdehyde, diene conjugates, and ceruloplasmin and a decrease in glucose concentration. In addition, they demonstrated decreased activity of superoxide dismutase, catalase, glutathione peroxidase, and glutathione reductase.

Conclusion:

Hepatocardial syndrome in dairy cows occurs due to ketosis, characterized by arterial hypertension, sinus tachycardia, a moderate decrease in myocardial contractility, oxidative stress, and cytolysis of cardiomyocytes and hepatocytes. Therefore, the control and prevention of the development of hepatocardial syndrome will make it possible to maintain the productive health and longevity of dairy cows.