Using systems biology to understand and routinely predict health and welfare traits in dairy cattle

The welfare of dairy cattle continues to gain importance, both nationally and internationally. However, welfare research has tended to focus on indicators of poor welfare such as disease or health status and longevity. More detailed studies of welfare on-farm have tended to be limited, using small numbers of animals or farms and of short duration. Recently, however, farmers are engaging with more detailed levels of on-farm recording, aided by farm management software and encouraged by farm assurance schemes they participate in, which has been shown to be of use in national genetic evaluations and/or benchmarking. Further, recent work by the partners have shown that that routine analysis of milk samples can predict the negative body energy balance in dairy cows, a trait related to metabolic stress and that immune function measures are correlated to health and welfare events such as mastitis, lameness and difficult calvings in dairy cattle.

This project will draw together the experience of partners to holistically explore animal health and welfare traits in dairy cattle, focussing on animals from the SAC Dairy Research Centre. The research herd is participating in ongoing genetic selection and feed experiments that produce detailed lifetime records for each animal, including production, health and welfare traits. This project will supplement these data with repeated analyses of immune and inflammatory measures in the blood and milk and weekly spectral analysis of milk. Mathematical models will be used to explore the biological relationships between the immune and inflammatory measures and defined health and welfare events in dairy cows. The health and welfare events include lameness, clinical mastitis, metabolic stress, calving difficulty (including Caesarean) and major reproductive illnesses (e.g., abortion, cystic ovaries). We will explore how the immune and inflammatory traits (supplemented by other key production/fitness data such as somatic cell counts) change before, during and after the defined health and welfare events. If successful, this modelling will result in predictive indicators that could lead to practical early warning systems for poor health and welfare traits in dairy cattle that could be implemented in the field to help explore/define new traits that relate to animal welfare.

Using a systems biology approach we will explore the interactions that lead to and/or result in a poor health/welfare event in the dairy cow.

Assay. This project will routinely (bi-monthly for 2 years including a 6 month period of monthly sampling) assay the blood and milk of dairy cows for immunological parameters (natural antibodies, TNF-alpha, haptoglobin). These data will be collated with the routine collection of detailed data on MIR spectra (weekly) and other production (including product quality), health, welfare and fertility traits at the SAC Dairy Centre.

Integrate. Individual cow records collected on the farm include daily milk yield, live weight and body condition score, weekly milk fat and protein yield and milk somatic cell count, and thrice weekly feed and dry matter intake. Some of these data are routinely used as an indicator of welfare in farmed livestock (e.g., condition score). Also, these data will be collated in a SQL database to predict the ongoing body energy status of the dairy cow and therefore will be used to define the trajectory to negative body energy status (i.e., cow entering metabolic stress) and/or return to positive/neutral energy status. Health, welfare and fertility traits are also recorded under three main categories: (i) mastitis, (ii) reproductive problems (e.g., cystic ovaries, retained placenta, abortion), and (iii) lameness (e.g., sole ulcer, digital dermatitis, white line disease). Key fertility (e.g., calving interval, numbers of services) and reproductive (e.g., dystocia, stillbirth) parameters are also recorded.

Analyse. The data will be modelled to explore how temporal variation in the immunological parameters prior to welfare (including health) events could be used as early warnings of the subsequent event. Further, once sufficient samples are available, the milk MIR spectral data will be "trained" (SAC) to develop statistical predictions for the parameters that could be applied in routine milk recording.

The future sustainability of the UK dairy industry relies on farmers being able to respond to key market signals and animal welfare is a key social (and economic) requirement. Key to addressing and responding to these signals is the ability to measure (or estimate), monitor and improve animal and production system attributes as they relate to animal welfare and other drivers (e.g., environment). This project offers a unique and innovative approach to preparing the UK dairy industry to address the challenges it will face to produce dairy products in a sustainable and socially acceptable manner. The partners have successfully worked together to begin to explore some of the key attributes of defining traits that relate to animal health and welfare and how these can be adopted by the industry. For example, SAC has delivered information on, and produced, practical dairy selection tools, particularly the inclusion of fertility, health, welfare and survival traits. These have helped UK dairy producers become more sustainable by adapting to a range of challenges, including consumer concerns; breeding for improved economic performance, animal health and welfare; and reduced environmental impact. Adoption of new indexes have improved animal health and welfare and economic performance compared to continued use of previous selection practices, and has cumulatively reduced greenhouse gas emissions per breeding animal by 1.4% (reduction in CO2 equivalents) per year in dairy systems. The overall annualised economic benefits of the genetic improvement that has taken place in the years 1980-2009 is worth £105.7 million/year to the UK dairy industry. A large proportion (~ 50%) has been realised by including health, fertility and longevity traits in UK dairy breeding goals.

As part of the project the partners will develop key knowledge transfer outputs, accessible to non-specialists. This will include developing display tools for participation in local public science events, including the Doors Open Days, Knowledge Scotland, Edinburgh Science Festival and Royal Highland Show. These will also be available to policy makers linking to Centres of Expertise in Scotland, in which the partners are involved. Given the inter-disciplinary nature of this project, the impacts of the proposed work will go beyond the direct academic beneficiaries, having positive economic and societal impacts across a range of stakeholders including:
1. Policy makers and dairy industry will be able to use the outcomes from this project to help summarise the manner by which the UK dairy industry are actively working towards improving the social acceptability (and sustainability) of the dairy production chain. Animal welfare is generally viewed as a public good, but will likely be undersupplied by producers for a variety of reasons including the lack of appropriate indicators of welfare status. The results of this project will provide signals for both producers and government (who might need to regulate animal welfare).
2. Animal managers may be able to use outputs from this work to direct early intervention for improved animal welfare as well as exploring the impact of key management interventions such as feeding and breeding. By combining systems of data collection that are already in place to develop predictive models of welfare events this project will allow us to work with the dairy industry to implement such early warning systems.
3. The public will benefit from this project through having access to data allowing them to assess the cow health/welfare impacts of the dairy products they consume.
4. The UK overall will benefit through the linkages between the research innovation represented in the partners and those delivering knowledge and tools to the industry (e.g., Veterinary Surveillance Services); the outcomes will result in a shorter period from primary research through to on-farm implementation helping UK agriculture reap the benefits of this project.