Analysis of the Direct Payment Scheme and farm incomes by DG-AGRI shows that on average about 50 % of the European livestock farms are reliant on subsidies. Currently, precision farming is only profitable for big farms and the investment is always difficult to pay back. Besides, there is an increasing demand on more information on food quality and safety and traceability. Thus, the EU livestock sector is in need of innovative tools to (i) increase its efficiency and become an attractive activity for the younger generations and (ii) assure full transparency and traceability along the supply chain. In this context, CATTLECHAIN 4.0 project aims to develop an integral smart solution to support the EU livestock sector to overcome the aforementioned challenges. By recording different parameters from the cows with IoT wearables, and analysing this data with Artificial Intelligence algorithms, farmers get a decision support tool about their farm condition, so they can take immediate action if any issue arises and prevent losses/health issues. Besides, by aggregating the data from multiple cows and farms in a blockchain based cloud platform, consumers and public authorities can access a traceability tool with truthful and secure data about the meat/dairy supply chain. Finally, aligned with consumers’ demands and thanks to the information gather by the IoT wearables, an Animal Welfare Seal will be created that guarantees that the meat and dairy products meet the highest animal welfare standards. This ground-breaking technologies and services will be validated in ambitious trial scenarios with 5.000 cows. The project will boost the growth of the two industries involved, firstly of the coordinator SENSO a tech-startup that will exponentially increase its turnover, and secondly of NATRUS, company specialised in premium hamburguers, who will gain a competitive advantage by being able to completely trace back their products.

This project aims at determining the main genetic and environmental factors determining fine milk composition in fat and protein. It includes 3 tasks but is a part of a larger programme with six tasks, three of them being supported by non-ANR fundings : (1) adapt Mid Infra Red (MIR) spectrometers of the different milk analysis laboratories to export spectra, develop a dedicated data base ; (2) develop calibration equations to predict fatty acid composition of milk in the three dairy species through MIR and chromatography data, from samples collected in four INRA farms; (3=task1) develop a reference method for protein measurement and derive calibration equations by the analysis of both reference and MIR data ; (4=task2) collect MIR spectra, milk and blood samples, and diet and management information from 12,000 cows and 3,000 ewes, as well as complementary information from the national database ; predict fatty acid and protein concentration from MIR spectra ; (5=task3) genotype a part of these samples with a SNP chip and perform QTL fine mapping, determine the effect of management and feeding policies on fatty acids and protein profiles in milk. (6) develop management and feeding strategies for general recommendation. Steps (3), (4) and (5) are included in this Genomic project and are denoted TASK1, TASK2, and TASK3, respectively. Specific aims of TASK1 is first to establish a detailed qualitative and quantitative profile of the 12 main milk proteins: caseins, alpha-lactalbumin, beta-lactoglobulin, lactoferrin, lactoperoxydase, using highly resolvent reference steadfast (RP-HPLC) and innovative (UPLC) techniques combined with Mass Spectrometry (MS). The challenge will be to identify the known (and eventually unknown) genetic variants and different isoforms and to determine their relative proportions. The second aim will be to derive from the detailed phenotype descriptions a calibration equations allowing the prediction of the milk protein fraction composition on a large number of milk samples by MIR. TASK2 aims at collecting all the samples and informations required by the project in a large number of commercial farms, in cattle as well as in sheep. 12,000 Holstein, Normande and Montbéliarde cows and 3,000 Lacaune ewes are targeted, based on prior information in the national data base. In cattle, herds are defined according to their size, their geographic concentration around labs participating to the project, their breeds, and their genetic diversity. Four kinds of information are collected: (1) three milk samples per lactation, (2) MIR spectra from these samples, (3) blood samples from the corresponding cows, for DNA extraction, (4) diet and management information for a good interpretation of the results. TASK3 is the genotyping step. About two thirds of the blood samples are selected for DNA extraction and genotyping, on the basis of MIR results. Depending on the cost of the chips in 2010, two options are possible. The most conservative is proposed as a minimum. A dedicated 1536-SNP chip will be developed to target all regions of interest, defined on the basis of external knowledge (results from Le Pin QTL experiment, Sarde data, other literature data, QTL known to affect total fat or protein). The chip will be completed by additional markers from the remaining part of the genome at a lower density. Data will be analysed by combining linkage and linkage disequilibrium. The effect of all environmental and diet factors will be estimated.

Adapting selection tools and objectives to efficiently manage French cattle meat and milk productions is a major challenge of the next years. Genomic selection provides a fantastic opportunity to reorient bovine selection towards a more sustainable breeding. The Gembal project aims at developing a multi-breed genomic selection to extend its use to all beef and dairy breeds, including the small ones. Special attention will be paid for functional traits and maternal traits: calving ease, fertility and longevity of cows in both beef and dairy breeds. At national level, this project should be a common foundation for all breeding schemes, thus avoiding a multiplication of too small and inefficient initiatives. The core of the project is the making-up of the technical basis for the development of multi-breed genomic selection in beef and dairy cattle. The basic idea is that a sample - so-called imputation population - will be genotyped with a high density chip in each breed, whereas most other individuals will be genotyped at a lower cost for a medium density chip. The condition required to build the imputation populations is an extensive use of a new molecular tool, a high density chip with 800,000 SNP developed by Illumina with a consortium including INRA and UNCEIA. Task 2 is dedicated to this technical part of the project. In Task 3, the large multi-breed resource cattle population generated in Task 2 will be the basis for academic researches aimed at characterizing the genetic diversity across breeds and the history of each population submitted to its own context, i.e. drift and selection. This task will also be useful to detect the conserved chromosomal segments across breeds that can be used in multi-breed genomic selection as it will be envisioned in Task 5. Task 4 corresponds to imputation, i.e the statistical procedure to infer missing genotypes in most individuals from the complete genotype information in a limited imputation sample. We will study the quality of the imputation according to breed effective and imputation sample size. We will also develop more computationally efficient algorithms, as imputation will be very demanding with the fast development of genomic selection. Then, a genomic prediction model, using linkage disequilibrium information across breeds, will be developed in Task 5. The methodological challenges are the development of powerful and robust statistical approaches as well as and computing tools for the prediction in a multi-breed context, especially for functional traits with correlated direct and maternal genetic effects. The applications regarding functional traits will be carried out in Task 6 and Task 7 for dairy and beef breeds, respectively. In Task 6, the existence of three breeds in France for which reference populations of reasonable to very large size are available and for which genomic selection programs are already implemented will allow us to undertake reliable comparisons of within vs multi-breed genomic evaluations, hopefully revealing what are the underlying conditions for a successful implementation of multi-breed evaluation. An alternative strategy will consist in checking whether the conserved genome fragments corresponding to favourable haplotypes of QTL detected in any large breed are also segregating in the smaller breeds. Then a genomic evaluation based on these haplotypes could be implemented for the smaller breeds. In Task 7, the multi-beef breed reference population will be composed of the 2,300 bulls that also constitute the beef imputation populations. If a sufficient number of QTL are commonly detected across beef and dairy breeds, a QTL detection and a computation of prediction equations from the beef and dairy pooled reference populations will be undertaken for maternal functional traits.