Recipient Organization
UNIVERSITY OF VERMONT
(N/A)
BURLINGTON,VT 05405
Performing Department
(N/A)
Non Technical Summary
Good animal welfare is paramount to the dairy industry, including producers, processors, distributors, and cooperatives. The development of a new, accurate, and remote welfare assessment benchmark using validated multi-variable precision dairy technologies (PDTs) has the potential to increase the sustainability of the dairy industry. PDTs allow for real-time, continuous recording of animal behavior and other animal-based outcomes at the individual animal level. Before these technologies can be useful in assessing animal welfare, predictive models and validations must first be done. Additionally, although technology may be useful to identify animal welfare concerns onfarm, dairy producers must be willing to adopt these technologies, see value and trust in these tools, and interpret the data. Concurrently, there is a risk that investment in and adoption of novel technologies may be futile if these technologies are ultimately rejected by society. Therefore, the public must be engaged to establish which aspects of these technologies may generate social acceptance or concern. Thus, our proposed integrated research and extension project aims to bridge the use of PDTs with the social aspects of animal welfare. We will develop models and validate the use of multiple, integrated technologies to predict animal welfare assessment outcomes that can be monitored remotely while simultaneously engaging dairy producers and the public in two-way conversations about the role of these technologies on-farm. Our multidisciplinary project will integrate scientific assessments of animal welfare, artificial intelligence, machine learning, dairy production knowledge, and social science to provide practical recommendations for the sustainable use of PDT on-farm.
Animal Health Component
80%
Research Effort Categories
Basic
20%
Applied
80%
Developmental
(N/A)
Goals / Objectives
Our proposed integrated research and extension project aims to bridge PDTs and animal welfare assessments with social aspects of animal welfare. We will validate the use of automated integrated technologies to predict common animal welfare assessment outcomes while simultaneously engaging dairy producers and the public about the role of these technologies on-farm. Our multidisciplinary project will integrate the scientific assessment of animal welfare, artificial intelligence, machine learning, extension, and social science to provide practical recommendations for the future sustainable use of PDTs on dairy farms.Long-term Goal and Supporting ObjectivesOur long-term goal is to create evidence-based recommendations for the sustainable use of precision technologies on dairy farms, with a focus on improving animal welfare assessment and individual animal management on-farm using PDT data. We intend to accomplish this goal by pursuing 3 specific objectives:Objective 1: Develop and validate the use of PDTs to predict animal-based measurements collected manually from animal welfare assessments (e.g., lameness, injuries, and body condition) using algorithms created from machine learning. We predict that the PDTs, in combination with variables routinely collected from farms, will yield precise and accurate data comparable to manual assessments.Objective 2: Identify key variables collected by PDTs that best predict animal-based outcomes at the herd- and cow-level. We predict that a combination of variables from the PDTs will be able to accurately detect welfare concerns at the herd- and cow-level.Objective 3: Identify support, concerns, and trust relative to PDTs among producers, other industry stakeholders (e.g. processors, veterinarians, nutritionists, consultants, and extension specialists) and the public, as well as disseminate project findings to dairy producers using extension efforts. We predict that increased familiarity and use of data collected by PDTs will improve producer and other industry stakeholder trust in PDTs as a component of management and animal welfare assessments. Public acceptance is predicted to associate with the extent to which PDTs are perceived to confer welfare improvements to animals.
Project Methods
This study aims to bridge precision dairy technologies and animal welfare assessments, the methodology for each objective is explained below:Objective 1: Develop and Validate Predictive Technologies for Animal Welfare Assessment**Facilities and Recruitmen*: Fifty dairy farms in the Southeast and Midwest of the U.S. will be enrolled. Farms will be identified through study teams, extension agents, veterinarians, dairy farmers' associations (like Vermont Dairy Development Council), and industry experts over two years. Producers will be contacted by phone, and participation is voluntary, with no remuneration beyond access to the predictive technologies (PDTs) and data.Animals and Study Design: Forty dairy cows per farm will be selected based on specific criteria. Data will be collected from 20 days before calving until 305 days after calving using two PDTs: a collar measuring activity, rumination, and rest, and a 3D camera measuring body condition scores (BCS). Data will be collected twice daily for BCS and hourly for other metrics. Additional data on milk yield, components, and health records will also be collected.Manual Assessment: Conducted at the start and every 4 months, this assessment will evaluate lameness, lesions, broken tails, and BCS using the National Dairy F.A.R.M. Program guidelines. The entire lactating herd will be scored, and trained evaluators will ensure reliability. Farms will be categorized as "compliant" or "needs improvement" based on specific thresholds.Model Development: Using the F.A.R.M. program's thresholds, a machine learning model will be created to predict animal-based outcomes using data from PDTs and routine farm data. The model aims to accurately identify farms needing improvement.Hypothesis: The machine learning model will accurately predict thresholds in animal-based measurements, identifying herds needing improvement.Objective 2: Identify Key Variables for Predictive ModelsAnimals and Study Design: Using the same farms and data from Objective 1, this objective aims to optimize the machine learning model to predict welfare concerns at both herd and cow levels.Design and Model Development: A model will be developed to determine the accuracy of PDTs in detecting welfare concerns like lameness, lesions, broken tails, and low BCS. Machine learning will combine data from PDTs and farm records to create data clusters predicting at-risk animals. The final model will identify the most accurate variables for notifying producers of issues.Hypothesis: The machine learning model will identify the best combination of variables to predict welfare outcomes, requiring a sub-sample of animals per farm for accurate assessment.Objective 3: Assess and Disseminate Perspectives on PDTsProducer Perspectives: Surveys and focus groups will capture changes in producers' perspectives on PDTs over time. Surveys will be administered before, during, and after PDT use to assess expectations and perceptions. Focus groups will explore factors influencing these changes.Industry Stakeholders: Surveys will gauge the willingness of veterinarians, nutritionists, and other stakeholders to use and promote PDTs, focusing on data utility, security, and ownership.Public Acceptance: A national online survey will determine public acceptance and concerns about PDTs. Information framing (cow welfare, ease of management, economic benefits) will be tested for its impact on acceptance and trust.Engagement and Outreach: Demonstration farms will host field days to discuss and demonstrate PDTs, engaging producers in peer discussions. Surveys will assess knowledge and attitudes before and after visits. Findings from Objectives 1 and 2 will be shared, and producer feedback will refine the model.Hypothesis:Producer trust and comfort with PDTs will increase with use, and stakeholders will have positive perceptions if data supports animal welfare. Public acceptance will vary by demographics and values but may increase with information framing emphasizing animal benefits.