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.

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.

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.

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.

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.

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.

Genetic improvement is easy when selecting for one heritable and well-recorded trait at a time. Many industrialised national dairy herds have overall breeding indices that incorporate a range of traits balanced by their known or estimated economic value. Future breeding goals will contain more non-production traits and, in the context of this paper, traits associated with human health and cow robustness. The definition of Robustness and the traits used to predict it are currently fluid; however, the use of mid-infrared reflectance spectroscopic analysis of milk will help to create new phenotypes on a large scale that can be used to improve the human health characteristics of milk and the robustness of cows producing it. This paper describes the state-of-the-art in breeding strategies that include animal robustness (mainly energy status) and milk quality (as described by milk fatty acid profile), with particular emphasis on the research results generated by the FP7-funded RobustMilk project

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.