The body condition score (BCS) of a dairy cow is an assessment of the proportion of body fat that it possesses, and it is recognized by animal scientists and producers as being an important factor in dairy cattle management. The scale used to measure BCS differs between countries, but low values always reflect emaciation and high values equate to obesity. The intercalving profile of BCS is a mirror image of the milk lactation profile. Cows lose condition for 50 to 100 d postcalving, because of homeorhetic changes that occur in the somatotropic axis and the sensitivity of peripheral tissues to insulin, and the upregulation of lipolytic pathways in adipose tissue. Management and feeding have little effect on early postcalving BCS loss (wk 1 to 4 postcalving) until the natural period of insulin resistance has passed and the somatotropic axis has recoupled. There is evidence, however, that management and diet can influence the timing of recoupling of the somatotropic axis and the sensitivity of peripheral tissues to insulin, and gene expression differences in adipose tissue 30 d in milk confirm an effect of energy intake on lipogenic enzymes. The BCS in which a cow calves, nadir BCS, and the amount of BCS she loses postcalving are associated with milk production, reproduction, and health. Body condition score may also be a valid indicator of animal welfare, but further research is required to determine the effect of BCS and BCS change on how a cow "feels." Although the actual strength of the association may vary, there is relative consistency in the associations among calving and nadir BCS, and BCS change on milk production, postpartum anestrous, the likelihood of a successful pregnancy and days open, the risk of uterine infection, and the risk of metabolic disorders. For many production and health variables, the association with BCS is nonlinear, with an optimum calving BCS of 3.0 to 3.25 (5-point scale); lower calving BCS is associated with reduced production and reproduction, whereas calving BCS >/=3.5 (5-point scale) is associated with a reduction in early lactation dry matter intake and milk production and an increased risk of metabolic disorders. Ongoing research into the automation of body condition scoring suggests that it is a likely candidate to be incorporated into decision support systems in the near future to aid producers in making operational and tactical decisions.

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As the environments in which livestock are reared become more variable, animal robustness becomes an increasingly valuable attribute. Consequently, there is increasing focus on managing and breeding for it. However, robustness is a difficult phenotype to properly characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. In this review, the following definition of robustness is used: the ability, in the face of environmental constraints, to carry on doing the various things that the animal needs to do to favour its future ability to reproduce. The different elements of this definition are discussed to provide a clearer understanding of the components of robustness. The implications for quantifying robustness are that there is no single measure of robustness but rather that it is the combination of multiple and interacting component mechanisms whose relative value is context dependent. This context encompasses both the prevailing environment and the prevailing selection pressure. One key issue for measuring robustness is to be clear on the use to which the robustness measurements will employed. If the purpose is to identify biomarkers that may be useful for molecular phenotyping or genotyping, the measurements should focus on the physiological mechanisms underlying robustness. However, if the purpose of measuring robustness is to quantify the extent to which animals can adapt to limiting conditions then the measurements should focus on the life functions, the trade-offs between them and the animal's capacity to increase resource acquisition. The time-related aspect of robustness also has important implications. Single time-point measurements are of limited value because they do not permit measurement of responses to (and recovery from) environmental perturbations. The exception being single measurements of the accumulated consequence of a good (or bad) adaptive capacity, such as productive longevity and lifetime efficiency. In contrast, repeated measurements over time have a high potential for quantification of the animal's ability to cope with environmental challenges. Thus, we should be able to quantify differences in adaptive capacity from the data that are increasingly becoming available with the deployment of automated monitoring technology on farm. The challenge for future management and breeding will be how to combine various proxy measures to obtain reliable estimates of robustness components in large populations. A key aspect for achieving this is to define phenotypes from consideration of their biological properties and not just from available measures.