One suggested approach to improve the reproductive performance of dairy herds is through the targeted management of subgroups of biologically similar animals, such as those with similar probabilities of becoming pregnant, termed pregnancy risk. We aimed to use readily available farm data to develop predictive models of pregnancy risk in dairy cows.Data from a convenience sample of 108 dairy herds in the UK were collated and each herd was randomly allocated, at a ratio of 80:20, to either training or testing data sets. Following data cleaning, there were a total of 78 herds in the training data set and 20 herds in the testing data set. Data were further split by parity into nulliparous, primiparous, and multiparous subsets. An XGBoost model was trained to predict the insemination outcome in each parity subset, with predictors from farm records of breeding, calving and milk recording. Training data comprised 74,511 inseminations in 45,909 nulliparous animals, 86,420 inseminations in 39,439 primiparous animals, and 158,294 inseminations in 32,520 multiparous animals. The final models were evaluated by predicting with the testing data, comprising 31,740 inseminations in 19,647 nulliparous animals, 38,588 inseminations in 16,215 primiparous animals, and 65,049 inseminations in 12,439 multiparous animals. Model discrimination was assessed by calculating the area under receiver operating characteristic curves (AUC); model calibration was assessed by plotting calibration curves and compared across test herds by calculating the expected calibration error (ECE) in each test herd.The models were unable to discriminate between insemination outcomes with high accuracy, with an AUC of 0.63, 0.59 and 0.62 in the nulliparous, primiparous and multiparous subsets, respectively. The models were generally well-calibrated, meaning the model-predicted pregnancy risks were similar to the observed pregnancy risks. The mean (SD) ECE in the test herds was 0.038 (0.023), 0.028 (0.012) and 0.020 (0.008) in the nulliparous, primiparous and multiparous subsets respectively.The predictive models reported here could theoretically be used to identify subgroups of animals with similar pregnancy risk to facilitate targeted reproductive management; or provide information about cows' relative pregnancy risk compared with the herd average, which may support on-farm decision-making. Further research is needed to evaluate the generalizability of these predictive models and understand the source of variation in ECE between herds; however, this study demonstrates that it is possible to accurately predict pregnancy risk in dairy cows using readily available farm data.

Mobility scoring data can be used to estimate the prevalence, incidence, and duration of lameness in dairy herds. Mobility scoring is often performed infrequently with variable sensitivity, but how this impacts the estimation of lameness parameters is largely unknown. We developed a simulation model to investigate the impact of the frequency and accuracy of mobility scoring on the estimation of lameness parameters for different herd scenarios. Herds with a varying prevalence (10, 30, or 50%) and duration (distributed around median days 18, 36, 54, 72, or 108) of lameness were simulated at daily time steps for five years. The lameness parameters investigated were prevalence, duration, new case rate, time to first lameness, and probability of remaining sound in the first year. True parameters were calculated from daily data and compared to those calculated when replicating different frequencies (weekly, two-weekly, monthly, quarterly), sensitivities (60–100%), and specificities (95–100%) of mobility scoring. Our results showed that over-estimation of incidence and under-estimation of duration can occur when the sensitivity and specificity of mobility scoring are <100%. This effect increases with more frequent scoring. Lameness prevalence was the only parameter that could be estimated with reasonable accuracy when simulating quarterly mobility scoring. These findings can help inform mobility scoring practices and the interpretation of mobility scoring data.

Most consumers expect welfare to be part of the core of animal production and will avoid products which they view as not fulfilling minimum conditions. But even if consumers did not object to poor animal welfare, there is more than enough evidence that promoting welfare corresponds to better performance and higher quality products. Thus, there are plenty of reasons vindicating welfare assessment of farm animals. Welfare is a multidimensional concept enclosing both physical and mental components, so assessment has to address its full complexity to be credible and fair. To achieve this, assessment protocols should include valid and reliable indicators pertaining the real welfare of all the animals in a group. The protocols should also include easily measured indicators to guarantee its feasibility in farm conditions. CIISA’s Animal Behaviour and Welfare Research Lab has been involved in both the developing and the application of welfare assessment protocols in ruminants. As part of the AWIN project, CIISA built, together with the University of Milan research team, an assessment protocol for dairy goats. It was also the first to apply the Welfare Quality® protocol to Portuguese intensive dairy farms and to introduce changes to these protocols so that they could be used in ruminants at pasture.

This study was conducted within the context of the Animal Welfare Indicators (AWIN) project and the underlying scientific motivation for the development of the study was the scarcity of data regarding inter-observer reliability (IOR) of welfare indicators, particularly given the importance of reliability as a further step for developing on-farm welfare assessment protocols. The objective of this study is therefore to evaluate IOR of animal-based indicators (at group and individual-level) of the AWIN welfare assessment protocol (prototype) for dairy goats. In the design of the study, two pairs of observers, one in Portugal and another in Italy, visited 10 farms each and applied the AWIN prototype protocol. Farms in both countries were visited between January and March 2014, and all the observers received the same training before the farm visits were initiated. Data collected during farm visits, and analysed in this study, include group-level and individual-level observations. The results of our study allow us to conclude that most of the group-level indicators presented the highest IOR level ('substantial', 0.85 to 0.99) in both field studies, pointing to a usable set of animal-based welfare indicators that were therefore included in the first level of the final AWIN welfare assessment protocol for dairy goats. Inter-observer reliability of individual-level indicators was lower, but the majority of them still reached 'fair to good' (0.41 to 0.75) and 'excellent' (0.76 to 1) levels. In the paper we explore reasons for the differences found in IOR between the group and individual-level indicators, including how the number of individual-level indicators to be assessed on each animal and the restraining method may have affected the results. Furthermore, we discuss the differences found in the IOR of individual-level indicators in both countries: the Portuguese pair of observers reached a higher level of IOR, when compared with the Italian observers. We argue how the reasons behind these differences may stem from the restraining method applied, or the different background and experience of the observers. Finally, the discussion of the results emphasizes the importance of considering that reliability is not an absolute attribute of an indicator, but derives from an interaction between the indicators, the observers and the situation in which the assessment is taking place. This highlights the importance of further considering the indicators' reliability while developing welfare assessment protocols.

This research investigated whether using qualitative behaviour assessment (QBA) with a fixed list of descriptors may be related to quantitative animal- (ABM) and resource-based (RBM) measures included in the AWIN (Animal Welfare Indicators) welfare assessment prototype protocol for goats, tested in 60 farms. A principal component analysis (PCA) was conducted on QBA descriptors; then PCs were correlated to some ABMs and RBMs. Subsequently, a combined PCA merged QBA scores, ABMs and RBMs. The study confirms that QBA can identify the differences in goats' emotions, but only few significant correlations were found with ABMs and RBMs. In addition, the combined PCA revealed that goats with a normal hair coat were scored as more relaxed and sociable. A high farm workload was related to bored and suffering goats, probably because farmers that can devote less time to animals may fail to recognise important signals from them. Goats were scored as sociable, but also alert, in response to the presence of an outdoor run, probably because when outdoors they received more stimuli than indoors and were more attentive to the surroundings. Notwithstanding these results, the holistic approach of QBA may allow to register animals' welfare from a different perspective and be complementary to other measures.

Consistency over time (COT) of animal-based indicators is key to a reliable and feasible welfare protocol, indicating that results are representative over long-term situations. High levels of consistency ensure fairness for the farmer and credibility of the system. In addition, indicator COT reduces recording costs, as having indicators that do not change over a long period of time will require less farm visits to achieve reliable estimates. To date, COT of animal-based indicators included in the welfare assessment of dairy goats has never been tested. Therefore, our aim was to investigate COT of animal-based indicators included in the Animal Welfare Indicators (AWIN) welfare assessment prototype protocol for dairy goats. To meet this goal, a study was designed where an average of 3 mo elapsed between 2 sets of visits to the same 20 dairy goat farms (10 in Portugal and 10 in Italy), with no major changes in management routines or housing conditions occurring during this period. Initially, we performed a Wilcoxon signed rank test to investigate whether the results obtained during the 2 visits were significantly different. After this preliminary screening, the indicators presenting nonsignificant differences between visits were submitted to a second step analysis, where discriminative and evaluative analyses were conducted to reach a final indicator lineup. The discriminative approach helped distinguishing among farms, whereas the agreement analysis showed us the range of differences between repeated assessments. Some particular conclusions could be drawn from this combined analysis, helping to the development of the final AWIN welfare assessment protocol for dairy goats and as a further step to develop a welfare assessment monitoring scheme for this and other species. In this sense, the AWIN welfare assessment protocol allows for the quick differentiation between farms based on the identification of persistent welfare problems, by recording highly consistent and feasible indicators. In a second step, a more comprehensive protocol, consisting of indicators more likely to be subject to variations along time, was applied. Repeated assessments and long-term studies of indicator consistency are needed to help determine the frequency of visits required to obtain a consistent and feasible welfare assessment scheme. This paper adds to the literature by providing guidance on the variability of animal-based indicators over time.

Welfare assessment can play multiple roles in the path to welfare improvement. In the dairy goat area, identification of the main welfare problems across countries and different production systems is needed. By the application of a prototype welfare assessment protocol, based on animal-based indicators, we aimed to provide an insight into the main welfare problems affecting intensively kept dairy goats in Portugal. Thirty farms, organised in three size categories, were assessed. The main areas of concern were claw overgrowth, queuing at feeding and hindquarter dirtiness, with larger farms heading higher concerns. Additionally, this paper aimed to investigate indicators' consistency over time. Ten of the 30 farms were revisited four months later, during which no major husbandry changes were made. Our results showed an overall consistency. This study can help define intervention thresholds or minimum legal levels for each indicator, by determining their overall prevalence.