The four scenarios lead to differences in the size of the equine population: maybe twice as many horses as today, in the ‘Everybody on Horseback’ scenario, or half as much in the ‘High-Society Horse’ scenario. Businesses, facilities, as well as the types of employment would also be very different (cf. those in ‘The civic horse’ scenario). Likewise, the location of horses in the French regions or the categories of rural areas would differ to a great extent… They all raise shared concerns in terms of (i) the relationship between man and horse, (ii) economic efficiency, (iii) environmental issues, (iv) the preservation of breeds and pressure on surfaces and (v) health, welfare and caring for animals up to and beyond death. These questions call for research development in the fields of animal behaviour, economic and social sciences, breeding and genetic improvement. They also emphasize the need to enhance knowledge and innovation transfer.

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.

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.

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.

Phosphorus (P) is an essential mineral that needs to be supplied in sufficient quantities for maintenance and growth and milk production in dairy cattle. However, over 60% of the P consumed can be excreted in faeces with a potential to cause environmental pollution. Concern over higher levels of P in intensively managed livestock systems has led to legislation such as the Water Framework Directive in the European Union. In this manuscript, several methods of reducing P pollution are discussed. A major source of environmental P pollution has been overfeeding P mainly due to addition of ‘safety margin’ over the animal's requirement and concerns related to fertility. Matching the animal's requirement and feeding in groups so that animals at the same physiological status are fed according to their requirement has a potential to reduce P excretion significantly. P can also be reduced by matching available P with the metabolizable energy content of the diet because more P can be incorporated into milk when P is utilized by rumen microbes, which are limited by energy. Plants contain phytate bound P that need to be broken up before they can be absorbed by the animal. Although ruminants can digest phytate, use of phytase enzyme could help either directly by acting on phytate P or improvement of feed digestibility. Pasture management can lead to improved nutrient cycling, particularly if the soil is deficient in P. However, overfertilizing pasture could result is higher runoff of dissolved reactive P. Management practices that leave adequate forage residue on the surface such as rotational grazing will improve infiltration and decrease runoff, reducing nutrient losses.

Next to dry matter (DM) intake, nutritional factors cause considerable variation in methane (CH4) emitted and nitrogen (N) excreted per kg of DM intake or per kg of milk. Rumen function in particular determines CH4 emission and concomitant (amount and site) of N excretion, including the trade-offs between them with changes in nutrition and cow characteristics. Quantification of the interaction between CH4 and N emission hence requires quantification of effects on rumen function in particular. The models available to quantify CH4 emission require the same types of input. The detail of questions posed determines the choice of model and the required level of detail of model inputs needed to investigate mitigation measures and the interaction between CH4 and N emission for a specific farming case. Simulation results with a mechanistic model of enteric fermentation confirmed a profound impact of nutritional measures on both CH4 and N emission, but also demonstrated that nutritional measures to mitigate N excretion can be associated with an increase in CH4 emission. This result demonstrates the need to consider details on the rumen level when the aim is to quantify accurately the net effect on greenhouse gas emission for a specific case studied, which contrasts with applying generic values. As an alternative to models of quantification, on-farm measurement of emission might be pursued by sampling of excreta and air. The principle problem is that concentrations are measured which not necessarily reflect daily rates. Milk production rate is recorded on-farm however, which makes indicators based on milk composition just as promising candidates to estimate CH4 (milk fat) or N (milk urea) emission, provided bias by variation in milk composition unrelated to CH4 and N emission rate can be prevented.

Nitrous oxide reductase (N2OR), the enzyme responsible for the reduction of N2O to N2 in denitrification, uses copper (Cu) as its cofactor. Its activity is lowered under conditions of Cu deficiency. In general, high organic matter (OM) soil decreases Cu availability. The present study investigated different Cu forms, namely organic (ORG) v. inorganic (INO), and associated concentrations (750, 550, 125, 60 μg Cu/g soil) for their efficacy in affecting denitrification and especially N2OR activity in high OM peat soil in a water saturated anaerobic condition for 24 h. Gas and liquid samples were taken every 8 h and analyzed for NO3−, NO2−, N2O and N2. Inorganic Cu treatments did not affect N transformation rates and N2OR activity among the different treatments (P > 0.05) throughout the incubation compared with the control (CON). The ORG Cu treatments increased NO3− (P < 0.05), NO2− (P < 0.05) and N2O (P < 0.05) transformation rates compared with CON. These changes were ORG Cu dose dependent. N2OR activity increased first in the 750 μg ORG Cu treatment (P < 0.05) during 8 to 16 h followed by the other ORG Cu treatments (P < 0.05) during 16 to 24 h compared with CON. These results highlight the importance of Cu form and concentration on N transformation rate during denitrification. The findings can potentially be applied to systems like soil, wastewater, constructed wetlands, etc., in which reactions of the denitrification pathway are manipulated.

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.

Animal breeding should be considered as a permanent and cumulative approach to reducing the environmental footprint of dairy cattle production systems within an overall national and global mitigation strategy. Current international dairy cattle breeding goals do not explicitly include environmental traits, but observed improvements in milk production and both fertility and longevity contribute substantially to improving the environmental footprint relative to output. Ideally, however, environmental related traits, most notably greenhouse gas emissions and nitrogen excretion, should be explicitly included in national breeding goals with their own economic weight. Access to routine phenotypic observations for the environmental traits or other information including genomic information or information on heritable correlated traits is required for inclusion in the selection index. There is, however, a considerable paucity of information on the genetic parameters for, in particular, greenhouse gas emissions in dairy cattle; these parameters include genetic variance estimates, as well as genetic and phenotypic (co)variances with other performance traits. Large studies with well phenotyped animals across a range of environments are needed to estimate such parameters and also investigate the extent, if any, of genotype-by-environment interactions across contrasting environments. Considerable genetic variation in milk urea nitrogen, as a proxy for nitrogen excretion in the urine, exist and suggest that breeding programmes to improve nitrogen use efficiency will be fruitful. However, because of the antagonistic genetic correlations between milk urea nitrogen and milk production, genetic gain in milk yield is expected to be compromised within a breeding goal that includes milk urea nitrogen.

Genomic selection is rapidly becoming the state-of-the-art genetic selection methodology in dairy cattle breeding schemes around the world. The objective of this paper was to explore possibilities to apply genomic selection for traits related to dairy cow robustness. Deterministic simulations indicate that replacing progeny testing with genomic selection may favour genetic response for production traits at the expense of robustness traits, owing to a disproportional change in accuracies obtained across trait groups. Nevertheless, several options are available to improve the accuracy of genomic selection for robustness traits. Moreover, genomic selection opens up the opportunity to begin selection for new traits using specialised reference populations of limited size where phenotyping of large populations of animals is currently prohibitive. Reference populations for such traits may be nucleus-type herds, research herds or pooled data from (international) research experiments or research herds. The RobustMilk project has set an example for the latter approach, by collating international data for progesterone-based traits, feed intake and energy balance-related traits. Reference population design, both in terms of relatedness of the animals and variability in phenotypic performance, is important to optimise the accuracy of genomic selection. Use of indicator traits, combined with multi-trait genomic prediction models, can further contribute to improved accuracy of genomic prediction for robustness traits. Experience to date indicates that for newly recorded robustness traits that are negatively correlated with the main breeding goal, cow reference populations of ⩾10 000 are required when genotyping is based on medium- or high-density single-nucleotide polymorphism arrays. Further genotyping advances (e.g. sequencing) combined with post-genomics technologies will enhance the opportunities for (genomic) selection to improve cow robustness.

The suitability of milk for specific dairy products might be improved by changing milk fat or milk protein composition. In the RobustMilk project, we showed that milk fat composition is determined by genetic factors. In addition, recent studies indicate that milk protein composition is strongly affected by genetic factors. This suggests that there are opportunities to change milk composition by means of selective breeding. Traditional selection is based on large-scale phenotyping and not all analytical methods are suited for this purpose. The RobustMilk project team has shown that several fatty acids can be predicted on the basis of IR spectra. Accuracy of predicting individual milk proteins based on IR spectra is low. In addition to phenotypic records, selection might be based on genotypic information. DGAT1 and SCD1 genotypes are strongly associated with fat composition. β-Lactoglobulin, β-casein and κ-casein protein variants are strongly associated with protein composition. We conclude that tools are now available for changing detailed milk fat or milk protein composition by means of selective breeding.

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.

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.