A dairy farm greenhouse gas (GHG) model was applied in this study to compare the Intergovernmental Panel on Climate Change (IPCC) method and the life cycle assessment (LCA) procedure, which are the principal methods for quantifying the carbon footprint of dairy production. The objectives of this paper were to compare the auditing methods in estimating the carbon footprint of grass and confinement dairy systems and to assess the methods in estimating the footprint of grass-based dairy farms varying in cow genetic potential, stocking rate and level of concentrate feeding. The input data used to operate the model was based on published research studies. The results of the study showed that the IPCC and LCA methods ranked the carbon footprint of dairy systems differently. For example, the IPCC method found that the carbon footprint of the confinement dairy system was 8% lower than the grass system, but the LCA results show that the confinement system increased the carbon footprint by 16%. The comparison of grass-based dairy systems, differing in cow genotype, stocking rate and concentrate fed per cow also showed that the methods did not agree on the ranking of dairy systems carbon footprint. The re-ranking of dairy systems carbon footprint occurred because the IPCC method excludes emissions associated with imported goods, for example, concentrate. Thus, it is incorrect to consider only components of the dairy system relevant for policy reporting such as that used by IPCC when estimating the carbon footprint of dairy produce. Instead, holistic approaches, such as LCA, which consider on and off-farm GHG emissions should be used. Therefore, reform of the present policy framework is required to enable quantification of the impact of mitigation strategies on global emissions. The evaluation of the carbon footprint from grass-based systems differing in cow genotype also demonstrated that selecting cows solely for milk production will increase the carbon footprint of grass-based dairy systems relative to cows selected on a combination of traits, because of reduced cow fertility and thus higher emissions from replacement heifers.

To shed some light on possible directions for research and public policies, but also to help equine industry stakeholders to prepare for upcoming changes, the French National Institute for Agricultural Research (INRA), together with the French Institute for Horse and Horse Riding (IFCE), have conducted a collective scenario-building exercise for the French equine industry to 2030. The study is based on “morphological analysis”, a method which explores past and current trends, and potential shifts in order to consider possible future developments.

For centuries, animal breeders have very effectively been selecting livestock species, making use of the natural variation that exists within the population. As part of the developments towards broader breeding goals, the RobustMilk project was designed to develop new practical technologies to allow breeders to re-focus their selection to include milk quality and dairy cow robustness and to evaluate the consequences of selection for these traits taking cognisance of various milk production systems. Here we introduce the background to robustness, the value of expanding milk quality analysis (including the possibility of using milk quality characteristics as proxy measures for robustness traits), interactions between robustness and milk quality traits and the need for different breeding tools to enable delivery of these concepts to the industry. Developing a database with phenotypes from research herds across Europe, phenotyping tools using mid-infrared red spectroscopic analysis of milk, and the development of statistical and genomic tools for robustness and milk quality formed the core of the project. In the following papers you will read the outcomes and developments that happened during the project.

Nitrogen (N) losses from dairy production systems are a cause for environmental concern. Excreted primarily as urea N in the urine, this volatile form of N can be lost as ammonia (NH3) contributing to ground-level ozone, the greenhouse effect and the deterioration of terrestrial and aquatic ecosystems. In addition, the production of urea N places a metabolic demand for energy on the dairy cow and excessively high levels of blood urea N are known to have deleterious effects on reproductive performance. Therefore, it is of interest to develop strategies that reduce N excretion from dairy cows and to this end, dietary manipulation of N efficiency offers great potential. There are a significant number of reports in the literature on N efficiency in the lactating dairy cow, including reducing dietary CP intake, improving the balance of amino acids reaching the small intestine, optimising the forage mix and optimising the energy sources in the diet. Across these experiments, N intake ranged from 0.33 to 0.67 kg/day with N efficiency ranging from 0.21 to 0.42. This paper will report on recent N balance experiments conducted at University College Dublin, as well as reports in the literature on studies aimed at improving N efficiency in the lactating dairy cow.

A phenotype describes the outcome of the interacting development between the genotype of an individual and its specific environment throughout life. Animal breeding currently exploits large data sets of phenotypic and pedigree information to estimate the genetic merit of animals. Here we describe rapid, low-cost phenomic tools for dairy cattle. We give particular emphasis to infrared spectroscopy of milk because the necessary spectral data are already routinely available on milk samples from individual cows and herds, and therefore the operational cost of implementing such a phenotyping strategy is minimal. The accuracy of predicting milk quality traits from mid-infrared spectroscopy (MIR) analysis of milk, although dependent on the trait under investigation, is particularly promising for differentiating between good and poor-quality dairy products. Many fatty acid concentrations in milk, and in particular saturated fatty acid content, can be very accurately predicted from milk MIR. These results have been confirmed in many international populations. Albeit from only two studied populations investigated in the RobustMilk project, milk MIR analysis also appears to be a reasonable predictor of cow energy balance, a measure of animal robustness; high accuracy of prediction was not expected as the gold standard method of measuring energy balance in those populations was likely to contain error. Because phenotypes predicted from milk MIR are available routinely from milk testing, longitudinal data analyses could be useful to identify animals of superior genetic merit for milk quality and robustness, as well as for monitoring changes in milk quality and robustness because of management, while simultaneously accounting for the genetic merit of the animals. These sources of information can be very valuable input parameters in decision-support tools for both milk producers and processors.

In France, the horse population has been expanding since 1995 to reach 950 000 heads in 2010, representing about 15% of the total European horse population today. This growth is the result of the development of pony-riding for children and the increasing interest of French people in recreational riding and horse-betting. These changes offer major advantages to the different horse sectors, especially in the context of declining State support, increasing international competition on the horse market, societal changes regarding animal welfare, decreasing horse-meat consumption and harmonization of regulations at European level.

This work was part of the EU RobustMilk project. In this work package, we have focused on two aspects of robustness, micro- and macro-environmental sensitivity and applied these to somatic cell count (SCC), one aspect of milk quality. We showed that it is possible to combine both categorical and continuous descriptions of the environment in one analysis of genotype by environment interaction. We also developed a method to estimate genetic variation in residual variance and applied it to both simulated and a large field data set of dairy cattle. We showed that it is possible to estimate genetic variation in both micro- and macro-environmental sensitivity in the same data, but that there is a need for good data structure. In a dairy cattle example, this would mean at least 100 bulls with at least 100 daughters each. We also developed methods for improved genetic evaluation of SCC. We estimated genetic variance for some alternative SCC traits, both in an experimental herd data and in field data. Most of them were highly correlated with subclinical mastitis (>0.9) and clinical mastitis (0.7 to 0.8), and were also highly correlated with each other. We studied whether the fact that animals in different herds are differentially exposed to mastitis pathogens could be a reason for the low heritabilities for mastitis, but did not find strong evidence for that. We also created a new model to estimate breeding values not only for the probability of getting mastitis but also for recovering from it. In a progeny-testing situation, this approach resulted in accuracies of 0.75 and 0.4 for these two traits, respectively, which means that it is possible to also select for cows that recover more quickly if they get mastitis.

On the basis of a review of the industry, the dynamics of past trends and current questions about its future, the foresight panel identified around 40 drivers of change likely to impact the future of the horse industry. These were categorised, prioritised and organised into five components that formed the basis of the scenario-building exercise:

• • the economic and societal context;

• • public expectations of horses and of equestrian and horseracing activities;

• • public policy and regulation;

• • equestrianism and horseracing;

• • the organisation and strategies of horse producers.

This chapter presents the main trends and future outlooks for the factors encompassed within these five major thematic areas synthesised in microscenarios.

Nitrogen (N) losses and ammonia emissions from livestock operations represent a significant water and air pollutant. It has been repeatedly demonstrated that reduction in dietary protein input results in improved N utilization efficiency and reduced N losses with manure in dairy cows. Reducing dietary protein can also benefit the producer by reducing feed cost. These interventions, however, have to be balanced with the risk of decreased productivity. If animal requirements for metabolizable protein (MP) are not met, productivity cannot be sustained. Supplementation with rumen-protected (RP) amino acids (AA) limiting milk production and milk protein synthesis may compensate for the lack of MP in dairy cow diets. Long-term experiments at Penn State University demonstrated reduced urinary N losses, blood urea, milk urea N, and ammonia emissions from manure with MP-deficient v. MP-adequate diets. In some trials, however, milk production and milk protein yield were decreased with the MP-deficient diets. Supplementation of these diets with RPAA (Lys, Met and His) increased dry matter intake and milk yield of the cows to levels comparable with MP-adequate diets and can be a successful strategy for reducing N losses and maintaining productivity in lactating dairy cows fed low-protein diets.