Improving feed efficiency in dairy cows: understanding its key determinants using precision phenotyping, to allow tailored genetic selection strategies according to environment. – Deffilait

The general organization of the project is structured in 5 work packages (WP):
- WP1 “Improving large-scale phenotyping for traits associated to feed efficiency” will improve the measures of important traits for feed efficiency determination and our understanding of the
involved mechanisms.- The outputs of this WP1 will be used to extend the phenotyping capacity for WP2 and 3 to better understand the “Determinants of feed efficiency in dairy cows”. WP2 will manage important trials with individual phenotyping relating intake to many traits associated with energy expenses, to understand the mechanisms behind the between animal variation in FE and its different indicators.
- WP3 will focus on the “Genetic study of body mobilization during the lactation cycle”. Body reserves are strongly linked to energy balance and thereby are a component of feed efficiency. However, they also have downstream effects on reproduction, health and thus lifespan. Thus the strategic (genetically determined) role of body mobilization needs to be better characterized.
- WP4 “Optimizing use of efficiency measures and modelling tools to predict the consequences of selection and management strategies on long-term efficiency” will make the link with the work done in WP2 and 3 through a modelling approach to conceive the best strategies to assess
and select on FE. These strategies will not only consider short-term rates of gain in FE but also the effect of the strategies on long term efficiency, and thereby their durability.
- WP5 “Database management and valorization” will work closely with the four previous WPs to organize the database structures and management. It will also manage the valorization of the different results of the project through demonstration and dissemination to the expected end-users.
- WP0 will coordinate the project

This project will generate a powerful set of results to improve robustness of the dairy cow selection and to give to dairy farmers new tools for future dairy farming. It fits with the fifth of the major societal challenges of ANR and the first research theme of the Apis-Gene company; on feed efficiency and limitation of azote pollution and methane emission by ruminants to improve the overall efficiency of ruminants.
- The main expected result will be a new, durable, strategy to improve feed efficiency of dairy cows without impairing robustness of cows. This strategy will combine new methods and indicators to assess feed efficiency on a large scale in order to develop a selection program on this factor. It will indicate the best periods, duration, and indicators that can be used to assess feed efficiency of dairy cows.
- New phenotyping tools will become available for both research and farmers at the end of the project. New tools using the new phenotyping devices and models will become available to help farmers in monitoring livestock management, including possible precision feeding.
- New models simulating interactions between functions and explaining robustness capacities of the cows will become available to simulate consequences on life function priorities, and robustness, across the animal’s productive lifespan.
- New algorithms for genomic selection on body mobilization profiles will be developed allowing genetic selection to reduce the intensive use of body reserves generally observed in early lactation. Better body condition score, improved reproduction performances and less metabolic diseases are expected from this selection. This is a further element of the goal of increasing dairy cow’s robustness

The future prospects are :
- To combine a genetic (medium-long term) and feed (short-term) approach for the dairy cow's nutritional efficiency.
- Provide the essential elements for developing genetic selection and feeding strategies to improve this parameter.
- Better quantify the possibilities for progress on feed efficiency and its impact on the management of dairy herds.
- Develop phenotypic measures that will also impact the advisory capacities on farms and control of livestock, which are also levers of action on the scale of the farm.
- Implement simulation tools to predict the consequences of different selection strategies in different environments. The results will contribute to the definition of selection strategies to combine efficiency and robustness.
- Create a coherent framework to undertake a balanced genetic selection of these traits, and make a significant and lasting contribution to the objectives of the selection.

In progress - to complete

The livestock sector is highly concerned by the global food system, both by an expected increase in the demand for animal products such as milk, and by the ecological footprint of animal production, which must be minimized. Increasing feed efficiency (FE) in dairy cows would reduce some of the direct emissions (methane and ammonia) from livestock production but would also have a substantial positive impact on the induced emissions associated with crop production, due to the better feed conversion. Genetic improvement of FE is a particularly attractive strategy because it would impact most of the dairy farms for a limited cost. The decreased use of feed inputs implied by such an efficiency gain would give a competitive advantage to dairy production, but will also contribute to reducing environmental impacts. Thus, this project is expected to provide the essential elements needed for genetic selection strategies to improve FE in dairy cows. It fits with the fifth of the major societal challenges of ANR and the first research theme of the APIS-GENE consortium on FE and limitation of N pollution and methane emission by ruminants to improve the overall efficiency of ruminants.

Selecting for FE is not as straight forwards as it might first seem, there is evidence to suggest that robustness and adaptive capacity, especially for reproductive females, can be adversely affected by short-sighted strategies to improve efficiency. Thus, the choice of indicators used to assess FE is of great important, and it is essential to verify and validate the anticipated benefits of any such strategies to improve efficiency for their long-term consequences. Another key issue is to be able to better exploit new possibilities to target specific characteristics that contribute in part to FE. Such characters have rarely been studied because they have been very difficult to phenotype. The project will use new phenotyping technologies and the newly available information from them to develop selection for efficient use of body reserves whilst limiting the risks of undesirable trade-offs with other life functions that have been associated with high levels of production in dairy cows.

DeffiLait aims to elucidate ways by which to improve the FE of dairy cows without decreasing their robustness, to build strategies for doing this, and models to predict the future increases in FE attainable by selection programs, and directly on farm. The project will first involve developing new tools for large-scale phenotyping of the major biological characteristics that are directly involved in FE. The project will produce new tools to better estimate body condition, morphology, and digestive efficiency in large scale studies. These phenotypic measures will also impact on our capabilities for on-farm advising, and monitoring in livestock, which are also levers for improving efficiency at farm level. Then, to study the major determinants of FE, the project will also build an original database of dairy cow lactations with a large set of phenotypes to describe the main sources of energy transformation, thus explaining the observed between-animal variability in FE. This dataset will then be used to quantify the contribution of the different mechanisms to the variability in FE, and to test different indicators and strategies to improve FE. A specific focus will be made on body reserves mobilization in early lactation to assess its genetic components and correlation with other traits with a larger dataset involving commercial farms. The project will then develop simulation tools to predict the short- and long-term consequences of different selection strategies in different environments. The expected results will contribute to the definition of strategies of selection to combine efficiency and robustness. The project will provide a coherent framework to undertake a balanced genetic selection on these traits, and thereby make a significant - and lasting – contribution to improving FE.