This paper reviews animal-based welfare indicators to develop a valid, reliable, and feasible on-farm welfare assessment protocol for dairy goats. The indicators were considered in the light of the 4 accepted principles (good feeding, good housing, good health, appropriate behavior) subdivided into 12 criteria developed by the European Welfare Quality program. We will only examine the practical indicators to be used on-farm, excluding those requiring the use of specific instruments or laboratory analysis and those that are recorded at the slaughterhouse. Body condition score, hair coat condition, and queuing at the feed barrier or at the drinker seem the most promising indicators for the assessment of the "good feeding" principle. As to "good housing," some indicators were considered promising for assessing "comfort around resting" (e.g., resting in contact with a wall) or "thermal comfort" (e.g., panting score for the detection of heat stress and shivering score for the detection of cold stress). Several indicators related to "good health," such as lameness, claw overgrowth, presence of external abscesses, and hair coat condition, were identified. As to the "appropriate behavior" principle, different criteria have been identified: agonistic behavior is largely used as the "expression of social behavior" criterion, but it is often not feasible for on-farm assessment. Latency to first contact and the avoidance distance test can be used as criteria for assessing the quality of the human-animal relationship. Qualitative behavior assessment seems to be a promising indicator for addressing the "positive emotional state" criterion. Promising indicators were identified for most of the considered criteria; however, no valid indicator has been identified for "expression of other behaviors." Interobserver reliability has rarely been assessed and warrants further attention; in contrast, short-term intraobserver reliability is frequently assessed and some studies consider mid- and long-term reliability. The feasibility of most of the reviewed indicators in commercial farms still needs to be carefully evaluated, as several studies were performed under experimental conditions. Our review highlights some aspects of goat welfare that have been widely studied, but some indicators need to be investigated further and drafted before being included in a valid, reliable, and feasible welfare assessment protocol. The indicators selected and examined may be an invaluable starting point for the development of an on-farm welfare assessment protocol for dairy goats.

The AWIN project aimed at developing an on-farm welfare assessment protocol for adult dairy goats. A prototype protocol was tested in 30 intensive dairy goat farms to evaluate its feasibility in farms of different size. Time for applying the prototype was recorded and any other constraint was taken into account. Moreover, data collected during the prototype testing provided information on the prevalence of welfare issues in intensive dairy goat farms in Northern Italy. The prototype included 25 animal-based indicators (14 group- and 11 individual-level indicators). The prototype showed a good on-farm feasibility and it was highly accepted among stakeholders, as its application did not interfere with the daily routine. Approximately 2 h were required for the application of the prototype. When feeding racks were available, using them for locking the animals during the individual assessment resulted advantageous to speed the data collection and to reduce handling stress to the goats and disturbance to the farmers. Farm size and different management systems influenced the prevalence of some indicators, with small farms in general better welfare conditions compared to larger farms. The results of the present study represent an important starting point to set up an epidemiological database that may lead to improve the welfare status of goats.

Within the European AWIN project, a protocol for assessing dairy goats’ welfareon the farm was developed. Starting from a literature review, a prototype includinganimal-based indicators covering four welfare principles and 12 welfare criteria was set up.The prototype was tested in 60 farms for validity, reliability, and feasibility. After testing theprototype, a two-level assessment protocol was proposed in order to increase acceptabilityamong stakeholders. The first level offers a more general overview of the welfare status,based on group assessment of a few indicators (e.g., hair coat condition, latency to thefirst contact test, severe lameness, Qualitative Behavior Assessment), with no or minimalhandling of goats and short assessment time required. The second level starts if welfareAnimals 2015, 5 935problems are encountered in the first level and adds a comprehensive and detailed individualevaluation (e.g., Body Condition Score, udder asymmetry, overgrown claws), supported byan effective sampling strategy. The assessment can be carried out using the AWIN Goatapp. The app results in a clear visual output, which provides positive feedback on welfareconditions in comparison with a benchmark of a reference population. The protocol maybe a valuable tool for both veterinarians and technicians and a self-assessment instrumentfor farmers.

Body condition scoring (BCS) is the most widely used method to assess changes in body fat reserves, which reflects its high potential to be included in on-farm welfare assessment protocols. Currently used scoring systems in dairy goats require animal restraint for body palpation. In this study, the Animal Welfare Indicators project (AWIN) proposes to overcome this constraint by developing a scoring system based only on visual assessment. The AWIN visual body condition scoring system highlights representative animals from 3 categories: very thin, normal, and very fat, and was built from data sets with photographs of animals scored by a commonly used 6-point scoring system that requires palpation in 2 anatomical regions. Development of the AWIN scoring system required 3 steps: (1) identification and validation of a body region of interest; (2) sketching the region from photographs; and (3) creation of training material. The scoring system's reliability was statistically confirmed. An initial study identified features in the rump region from which we could compute a set of body measurements (i.e., measures based on anatomical references of the rump region) that showed a strong correlation with the assigned BCS. To validate the result, we collected a final data set from 171 goats. To account for variability in animal size and camera position, we mapped a subset of features to a standard template and aligned all the rump images before computing the body measurements. Scientific illustrations were created from the aligned images of animals identified as representative of each category to increase clarity and reproducibility. For training material, we created sketches representing the threshold between consecutive categories. Finally, we conducted 2 field reliability studies. In the first test, no training was given to 4 observers, whereas in the second, training using the threshold images was delivered to the same observers. In the first experiment, interobserver results was substantial, showing that the visual scoring system is clear and unambiguous. Moreover, results improved after training, reaching almost perfect agreement for the very fat category. The visual body condition scoring system is not only a practical tool for BCS in dairy goats but also shows potential to be fully automated, which would enhance its use in welfare assessment schemes and farm management.

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.