Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable.

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.

Data from 113 Dutch organic farms were analysed to determine the effect of cross-breeding on production and functional traits. In total, data on 33 788 lactations between January 2003 and February 2009 from 15 015 cows were available. Holstein–Friesian pure-bred cows produced most kg of milk in 305 days, but with the lowest percentages of fat and protein of all pure-bred cows in the data set. Cross-breeding Holstein dairy cows with other breeds (Brown Swiss, Dutch Friesian, Groningen White Headed, Jersey, Meuse Rhine Yssel, Montbéliarde or Fleckvieh) decreased milk production, but improved fertility and udder health in most cross-bred animals. In most breeds, heterosis had a significant effect (P < 0.05) on milk (kg in 305 days), fat and protein-corrected milk production (kg in 305 days) and calving interval (CI) in the favourable direction (i.e. more milk, shorter CI), but unfavourably for somatic cell count (higher cell count). Recombination was unfavourable for the milk production traits, but favourable for the functional traits (fertility and udder health). Farm characteristics, like soil type or housing system, affected the regression coefficients on breed components significantly. The effect of the Holstein breed on milk yield was twice as large in cubicle housing as in other housing systems. Jerseys had a negative effect on fertility only on farms on sandy soils. Hence, breed effects differ across farming systems in the organic farming and farmers can use such information to dovetail their farming system with the type of cow they use.

The aim of this study was to describe a systematic process of record-linkage, cross-validation, case-ascertainment and capture–recapture analysis to assess the quality of tuberculosis registers and to estimate the completeness of notification of incident tuberculosis cases in The Netherlands in 1998. After record-linkage and cross-validation 1499 tuberculosis patients were identified, of whom 1298 were notified, resulting in an observed under-notification of 13·4%. After adjustment for possible imperfect record-linkage and remaining false-positive hospital cases observed under-notification was 7·3%. Log-linear capture–recapture analysis initially estimated a total number of 2053 (95% CI 1871–2443) tuberculosis cases, resulting in an estimated under-notification of 36·8%. After adjustment for possible imperfect record-linkage and remaining false-positive hospital cases various capture–recapture models estimated under-notification at 13·6%. One of the reasons for the higher than expected estimated under-notification in a country with a well-organized system of tuberculosis control might be that some tuberculosis cases, e.g. extrapulmonary tuberculosis, are managed by clinicians less familiar with notification of infectious diseases. This study demonstrates the possible impact of violation of assumptions underlying capture–recapture analysis, especially the perfect record-linkage, perfect positive predictive value and absent three-way interaction assumptions.