Objective dairy cow mobility analysis and scoring system using computer vision-based keypoint detection technique from top-view 2D videos.

The objective of this study was to assess the applicability of a computer vision-based keypoint detection technique to extract mobility variables associated with mobility scores from top-view 2-dimensional (2D) videos of dairy cows. In addition, the study determined the potential of a machine learning classification model to predict mobility scores based on the newly extracted mobility variables. A data set of 256 video clips of individual cows was collected, with each clip recorded from a top-view perspective while the cows were walking.

The DairyPrint model: A decision support model to help dairy farmers and other stakeholders toward improved sustainability.

Dairy farmers face increasing pressure to reduce GHG emissions (i.e., carbon dioxide, CO; methane, CH; and nitrous oxide, NO), but measuring on-farm GHG emissions directly is costly or impractical.

Economic impact of subclinical mastitis treatment in early lactation using intramammary nisin.

Treatment of subclinical mastitis (SCM) during lactation is rarely recommended due to concerns related to both antimicrobial usage and the costs associated with milk discard. Nisin is a naturally produced antimicrobial peptide with a gram-positive spectrum that, when given to dairy cows, does not require milk discard. We evaluated the economic impact of the treatment of SCM during early lactation using a nisin-based intramammary treatment under different scenarios that included various treatment costs, milk prices, and cure rates.

BullVal$: An Integrated Decision-Support Tool for Predicting the Net Present Value of a Dairy Bull Based on Genetic Merit, Semen Production Potential, and Demographic Factors.

Deciding when to replace dairy bulls presents a complex challenge for artificial insemination (AI) companies. These decisions encompass multiple factors, including a bull's age, predicted semen production, and estimated genetic merit. This study's purpose was to provide a practical, objective tool to assist in these decisions.

The Economic Evaluation of Mastitis Control Strategies in Holstein-Friesian Dairy Herds.

The economic evaluation of mastitis control is challenging. The objective of this study was to perform the economic evaluation of mastitis control, under different intervention scenarios, quantifying the total cost of mastitis caused by in Holstein cows in Argentina. A model was set for a dairy herd of Holstein cows endemically infected with .

Decision-support tool for global allocation of dairy sire semen based on regional demand, supply constraints, and genetic profiles.

An artificial insemination (AI) company seeks to allocate semen units globally by balancing perceived demand with uncertain product supply, in what is an arduous subjective process. This study aimed to objectivize this process by providing a user-friendly linear programming model to allocate bulls' semen units to regions for the next trimester sales period based on maximum revenue, and to describe the features and outcomes of this model when applied to a sample bull herd and global demand scenario reflective of a leading AI company. The objective function of maximizing revenue was calculated by summing the product of units allocated by bull and region with purchase prices assigned by bull and region.

Impact of COVID-19 in pediatric oncology care in Latin America during the first year of the pandemic.

Background: The ongoing coronavirus 2019 disease (COVID-19) pandemic strained medical systems worldwide. We report on the impact on pediatric oncology care in Latin American (LATAM) during its first year.

Method: Four cross-sectional surveys were electronically distributed among pediatric onco-hematologists in April/June/October 2020, and April/2021 through the Latin American Society of Pediatric Oncology (SLAOP) email list and St Jude Global regional partners.

Addressing Data Bottlenecks in the Dairy Farm Industry.

A survey to explore the challenges and opportunities for dairy farm data management and governance was completed by 73 farmers and 96 non-farmers. Although 91% of them find data sharing beneficial, 69% are unfamiliar with data collection protocols and standards, and 66% of farmers feel powerless over their data chain of custody. Although 58% of farmers share data, only 19% of them recall having signed a data share agreement.

Graduate Student Literature Review: Considerations for nutritional grouping in dairy farms.

Each cow in a group has different nutritional requirements even if the group is formed by cows of similar age, number of lactations, and lactation stage. Common dairy farm management setup does not support formulating a diet that accurately matches individual nutritional requirements for each cow; therefore, a proportion of cows in the group will be overfed and another proportion underfed. Overfeeding and underfeeding cows increases the risk of metabolic diseases, decreases milk production, and increases nutrient waste.

Data Governance in the Dairy Industry.

Data governance is a growing concern in the dairy farm industry because of the lack of legal regulation. In this commentary paper, we discuss the status quo of the available legislation and codes, as well as some possible solutions. To our knowledge, there are currently four codes of practice that address agriculture data worldwide, and their objectives are similar: (1) raise awareness of diverse data challenges such as data sharing and data privacy, (2) provide data security, and (3) illustrate the importance of the transparency of terms and conditions of data sharing contracts.

Integrated Decision Support Systems (IDSS) for Dairy Farming: A Discussion on How to Improve Their Sustained Adoption.

Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS.

The Ruminant Farm Systems Animal Module: A Biophysical Description of Animal Management.

Dairy production is an important source of nutrients in the global food supply, but environmental impacts are increasingly a concern of consumers, scientists, and policy-makers. Many decisions must be integrated to support sustainable production-which can be achieved using a simulation model. We provide an example of the Ruminant Farm Systems (RuFaS) model to assess changes in the dairy system related to altered animal feed efficiency.

A Preliminary Investigation of Social Network Analysis Applied to Dairy Cow Behavior in Automatic Milking System Environments.

We have applied social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). The SNA was applied to social groups defined by four pens on a dairy farm, each served by an automatic milking system (AMS). Each pen was equipped with an automatic sorting gate that identified when cows voluntarily moved from the resting area to either milking or feeding areas.

Economics of Twin Pregnancies in Dairy Cattle.

Twinning in Holstein dairy cows has increased over time concurrent with increased milk production. Twinning in dairy cattle is not desirable due to the negative effects on both cows that calve twins and calves born as twins that result in economic losses to dairy farms. Although a twin pregnancy could bring additional income from extra calves and shorten gestation length, twinning compromises milk production, increases the incidence of dystocia and perinatal mortality, decreases calf birth weight, increases the incidence of metabolic diseases, decreases fertility, increases the incidence of freemartinism, increases overall culling risks, and shortens the productive lifespan of cows.

CBASS Immunity Uses CARF-Related Effectors to Sense 3'-5'- and 2'-5'-Linked Cyclic Oligonucleotide Signals and Protect Bacteria from Phage Infection.

cGAS/DncV-like nucleotidyltransferase (CD-NTase) enzymes are immune sensors that synthesize nucleotide second messengers and initiate antiviral responses in bacterial and animal cells. Here, we discover Enterobacter cloacae CD-NTase-associated protein 4 (Cap4) as a founding member of a diverse family of >2,000 bacterial receptors that respond to CD-NTase signals. Structures of Cap4 reveal a promiscuous DNA endonuclease domain activated through ligand-induced oligomerization.

Profiles of Women With Fibromyalgia and Social Comparison Processes.

Background: Due to uncertainty regarding chronic pain in Fibromyalgia (FM) patients, there has been a growing interest in social comparison and its influence on emotional responses.

Aims: to analyze profiles in FM patients according to pain perception, social comparison strategies and anxiety and depression.

Methods: The sample consisted of 131 FM outpatients (Mean age: 50.

Improving nutritional accuracy and economics through a multiple ration-grouping strategy.

The objective of this study was to develop a model application to systematize nutritional grouping (NG) management in commercial dairy farms. The model has 4 sub-sections: (1) real-time data stream integration, (2) calculation of nutritional parameters, (3) grouping algorithm, and (4) output reports. A simulation study on a commercial Wisconsin dairy farm was used to evaluate our NG model.

Symposium review: Real-time continuous decision making using big data on dairy farms.

We are developing a real-time, data-integrated, data-driven, continuous decision-making engine, The Dairy Brain, by applying precision farming, big data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multidisciplinary scientists, dairy farmers, and industry professionals. Dairy farms have embraced large and diverse technological innovations such as sensors and robotic systems, and procured vast amounts of constant data streams, but they have not been able to integrate all this information effectively to improve whole-farm decision making.

Economic evaluation of sexed semen use in Iranian dairy farms according to field data.

In this study, at first, the reproductive consequences of sexed semen use were quantified and then the cost-benefit of sexed semen use on Iranian commercial dairy farms was evaluated. Retrospective data collected during 2006 to 2013 from four large dairy farms in the Isfahan province of Iran were used for this study. These data included of 13,003 heifers records, from which 11.

Robotic milking: Feeding strategies and economic returns.

Cows in herds equipped with conventional milking parlors follow a structured, consistent, and social milking and feeding routine. Furthermore, in most cases cows in conventional herds receive all their nutrients from a total mixed ration, whereas in herds equipped with robotic or automatic milking systems (AMS) a fraction of their nutrients is provided during milking, mainly as a means to attract cows to the milking system. In this regards, AMS present both a challenge and an opportunity for feeding cows.

Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors.

The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, while ignoring (or absorbing) the costs associated with semen and labor directed toward low-fertility cows that are unlikely to conceive. Modern analytical methods, such as machine learning algorithms, can be applied to cow-specific explanatory variables for the purpose of computing probabilities of success or failure associated with upcoming insemination events. Lift chart analysis can identify subsets of high fertility cows that are likely to conceive and are therefore appropriate targets for insemination (e.

Short communication: Prediction of retention pay-off using a machine learning algorithm.

Replacement decisions have a major effect on dairy farm profitability. Dynamic programming (DP) has been widely studied to find the optimal replacement policies in dairy cattle. However, DP models are computationally intensive and might not be practical for daily decision making.

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle.