Evidence of birth seasonality and clustering of Mycobacterium avium subspecies paratuberculosis infection in US dairy herds

A time series analysis of milk productivity in US dairy states

As US dairy cow production evolves, it is important to characterize trends and seasonal patterns to project amounts and fluctuations in milk and milk components by states or regions. Hence, this study aimed to (1) quantify historical trends and seasonal patterns of milk and milk components production associated with calving date by parities and states; (2) classify parities and states with similar trends and seasonal patterns into clusters; and (3) summarize the general pattern for each cluster for further application in simulation models. Our data set contained 9.18 million lactation records from 5.61 million Holstein cows distributed in 17 states during the period January 2006 to December 2016. Each record included a cow's total milk, fat, and protein yield during a lactation. We used time series decomposition to obtain each state's annual trend and seasonal pattern in milk productivity for each parity. Then, we classified states and parities with agglomerative hierarchical clustering into groups according to 2 methods: (1) dynamic time warping on the original time series and (2) Euclidean distance on extracted features of trend and seasonality from the decomposition. Results showed distinguishable trends and seasonality for all states and lactation numbers for all response variables. The clusters and cluster centroid pattern showed a general upward trend for all yields [energy-corrected milk (ECM), milk, fat, and protein] and a steady trend for fat and protein percent for all states except Texas. We also found a larger seasonality amplitude for all yields (ECM, milk, fat, and protein) from higher lactation numbers and a similar amplitude for fat and protein percent across lactation numbers. The results could be used for advising management decisions according to farm productivity goals. Furthermore, the trend and seasonality patterns could be used to adjust the production level in a specific state, year, and season for farm simulations to accurately project milk and milk components production.

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.

Within-herd prevalence threshold for the detection of Mycobacterium avium ssp. paratuberculosis antibody-positive dairy herds using pooled milk samples: A field study

Herd-level diagnosis of paratuberculosis using a pool-milk ELISA (pool size: n ≤ 50) is a novel, economical, and convenient method to identify blood serological Mycobacterium avium ssp. paratuberculosis (MAP) antibody-positive herds. To date, the diagnostic performance of the pool-milk ELISA has been described only under laboratory conditions where herd prevalence was simulated by the preparation of milk pools consisting of milk samples of cows with a known MAP status determined by fecal culture. In our observational study, test performance under field conditions was studied using pooled milk and individual blood samples. A total of 486 herds within the MAP prevalence reduction program of Lower Saxony, from which pooled milk and individual blood ELISA results were available, were assigned to this study. Data were analyzed for the period between January 1 and December 31, 2018, the first year after herd testing became obligatory in this federal state of Germany. To evaluate whether pooled milk samples reliably distinguish between herds with a MAP-apparent blood serological within-herd prevalence (MAP-Ab-WHP_app) ≥5% and herds with a MAP-Ab-WHP_app <5%, the distribution of the MAP-Ab-WHP_app was compared between pool-positive and pool-negative herds. The MAP-Ab-WHP_app was 3.4% (median; 95% confidence interval = 0-11.4%) in pool-positive herds and 1.2% (median; 95% confidence interval = 0-6.4%) in pool-negative herds. Only 10.8% (n = 12) of the pool sample-negative herds had a MAP-Ab-WHP_app ≥5% and were therefore false negatives, given the aims of the MAP prevalence reduction program. Hence, the pool-milk sampling strategy seems well suited to distinguish between herds with a MAP-Ab-WHP_app ≥ 5% and herds with a MAP-Ab-WHP_app <5% since only 10% of serum MAP-ELISA positive herds were missed. Employing a logistic regression model, we estimated that the minimum blood serological MAP-Ab-WHP_app to detect a pool-positive herd with a probability of 95% was 8%, which fits well with the aim of the MAP prevalence reduction program to focus on herds with a MAP-Ab-WHP_app of ≥5%. Despite the limitations of the control approach, which include milk pool sample collection and a low sensitivity of the ELISA used in milk pools and serum samples, the aims of the MAP prevalence reduction program can be achieved. The results of these field data support that pool-milk sample ELISA is a useful, economical, and low labor-intensive tool to identify herds seropositive for MAP in a MAP prevalence reduction program.

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.

A review of paratuberculosis in dairy herds - Part 1: Epidemiology

Bovine paratuberculosis is a chronic infectious disease of cattle caused by Mycobacterium avium subspecies paratuberculosis (MAP). This is the first in a two-part review of the epidemiology and control of paratuberculosis in dairy herds. Paratuberculosis was originally described in 1895 and is now considered endemic among farmed cattle worldwide. MAP has been isolated from a wide range of non-ruminant wildlife as well as humans and non-human primates. In dairy herds, MAP is assumed to be introduced predominantly through the purchase of infected stock with additional factors modulating the risk of persistence or fade-out once an infected animal is introduced. Faecal shedding may vary widely between individuals and recent modelling work has shed some light on the role of super-shedding animals in the transmission of MAP within herds. Recent experimental work has revisited many of the assumptions around age susceptibility, faecal shedding in calves and calf-to-calf transmission. Further efforts to elucidate the relative contributions of different transmission routes to the dissemination of infection in endemic herds will aid in the prioritisation of efforts for control on farm.

Identifying individual animal factors associated with Mycobacterium avium subsp. paratuberculosis (MAP) milk ELISA positivity in dairy cattle in the Midwest region of the United States

BACKGROUND

Mycobacterium avium subsp. paratuberculosis (MAP) is a widespread chronic disease of ruminants that causes severe economic losses to the dairy cattle industry worldwide. The objective of this study was to evaluate the association between individual cow MAP-ELISA and relevant milk production predictors in dairy cattle using data routinely collected as part of quality and disease control programs in the Midwest region of the U.S. Milk ELISA results of 45,652 animals from 691 herds from November 2014 to August 2016 were analyzed.

RESULTS

The association between epidemiological and production factors and ELISA results for MAP in milk was quantified using four individual-level mixed multivariable logistic regression models that accounted for clustering of animals at the farm level. The four fitted models were one global model for all the animals assessed here, irrespective of age, and one for each of the categories of < 4 year-old, 4-8 year-old, and > 8 year-old cattle, respectively. A small proportion (4.9%; n = 2222) of the 45,652 tested samples were MAP-seropositive. Increasing age of the animals and higher somatic cell count (SCC) were both associated with increased odds for MAP positive test result in the model that included all animals, while milk production, milk protein and days in milk were negatively associated with MAP milk ELISA. Somatic cell count was positively associated with an increased risk in the models fitted for < 4 year-old and 4-8 year-old cattle. Variables describing higher milk production, milk protein content and days in milk were associated with significantly lower risk in the models for 4-8 year-old cattle and for all cattle.

CONCLUSIONS

Our results suggest that testing cows with high SCC (> 26 × 1000/ml), low milk production and within the first 60 days of lactation may maximize the odds of detecting seropositive animals. These results could be useful in helping to design better surveillance strategies based in testing of milk.

Case definition terminology for paratuberculosis (Johne's disease)

Paratuberculosis (Johne's disease) is an economically significant condition caused by Mycobacterium avium subsp. paratuberculosis. However, difficulties in diagnosis and classification of individual animals with the condition have hampered research and impeded efforts to halt its progressive spread in the global livestock industry. Descriptive terms applied to individual animals and herds such as exposed, infected, diseased, clinical, sub-clinical, infectious and resistant need to be defined so that they can be incorporated consistently into well-understood and reproducible case definitions. These allow for consistent classification of individuals in a population for the purposes of analysis based on accurate counts. The outputs might include the incidence of cases, frequency distributions of the number of cases by age class or more sophisticated analyses involving statistical comparisons of immune responses in vaccine development studies, or gene frequencies or expression data from cases and controls in genomic investigations. It is necessary to have agreed definitions in order to be able to make valid comparisons and meta-analyses of experiments conducted over time by a given researcher, in different laboratories, by different researchers, and in different countries. In this paper, terms are applied systematically in an hierarchical flow chart to enable classification of individual animals. We propose descriptive terms for different stages in the pathogenesis of paratuberculosis to enable their use in different types of studies and to enable an independent assessment of the extent to which accepted definitions for stages of disease have been applied consistently in any given study. This will assist in the general interpretation of data between studies, and will facilitate future meta-analyses.

Paratuberculosis in Latin America: a systematic review

Latin America is the definition of the American group, where languages of Latin origin are spoken, including countries in South, Central, and North America. Paratuberculosis is a gastrointestinal contagious chronic disease that affects ruminants, whose etiological agent is the bacilli Mycobacterium avium subsp. paratuberculosis (MAP). Paratuberculosis is characterized by intermittent diarrhea, decreased milk production, dehydration, and progressive weight loss and is possibly involved in Crohn's disease, a human intestinal disease. MAP is resistant to environmental factors, pasteurization, and water disinfection, which coupled with the subclinical-clinical nature of the disease, and makes paratuberculosis a relevant socioeconomic and public health issue, justifying the descriptive review of research on the disease carried out in Latin American countries. A survey of articles, published until September 2016, on the Scopus database, PubMed, Agris, and Science Direct, about detection of the agent and the disease in Latin America, without restrictions to the date of the research was performed. The keywords were as follows: "paratuberculosis," "Mycobacterium avium subsp. paratuberculosis," "cattle," "milk," "wildlife," "goat," "ovine," "dairy," and the name of each country in English. Studies found from nine of the 20 Latin America countries, 31 related to Brazil, 17 to Argentina, 14 to Chile, eight to Colombia, six to Mexico, two to Peru, two to Venezuela, and one to Panama and to Bolivia, each. The agent was detected in cattle, goats, sheep, domesticated water buffalo, and wild animals. Microbiological culture, PCR, and ELISA were the frequent techniques. The small number of studies may result in overestimation or underestimation of the real scenario.

Genome-wide association analysis and genomic prediction of Mycobacterium avium subspecies paratuberculosis infection in US Jersey cattle

Paratuberculosis (Johne’s disease), an enteric disorder in ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP), causes economic losses in excess of $200 million annually to the US dairy industry. To identify genomic regions underlying susceptibility to MAP infection in Jersey cattle, a case-control genome-wide association study (GWAS) was performed. Blood and fecal samples were collected from ∼5,000 mature cows in 30 commercial Jersey herds from across the US. Discovery data consisted of 450 cases and 439 controls genotyped with the Illumina BovineSNP50 BeadChip. Cases were animals with positive ELISA and fecal culture (FC) results. Controls were animals negative to both ELISA and FC tests that matched cases on birth date and herd. Validation data consisted of 180 animals including 90 cases (positive to FC) and 90 controls (negative to ELISA and FC), selected from discovery herds and genotyped by Illumina BovineLD BeadChip (∼7K SNPs). Two analytical approaches were used: single-marker GWAS using the GRAMMAR-GC method and Bayesian variable selection (Bayes C) using GenSel software. GRAMMAR-GC identified one SNP on BTA7 at 68 megabases (Mb) surpassing a significance threshold of 5×10⁻⁵. ARS-BFGL-NGS-11887 on BTA23 (27.7 Mb) accounted for the highest percentage of genetic variance (3.3%) in the Bayes C analysis. SNPs identified in common by GRAMMAR-GC and Bayes C in both discovery and combined data were mapped to BTA23 (27, 29 and 44 Mb), 3 (100, 101, 106 and 107 Mb) and 17 (57 Mb). Correspondence between results of GRAMMAR-GC and Bayes C was high (70–80% of most significant SNPs in common). These SNPs could potentially be associated with causal variants underlying susceptibility to MAP infection in Jersey cattle. Predictive performance of the model developed by Bayes C for prediction of infection status of animals in validation set was low (55% probability of correct ranking of paired case and control samples).