The aim of this study was to compare the diversity and composition of fecal bacteria in goats and cows offered the same diet and to evaluate the influence of animal species on the gut microbiome. A total of 17 female goats (Blond Adamellan) and 16 female cows (Brown Swiss) kept on an organic farm were fed pasture and hay. Bacterial structure in feces was examined by high-throughput sequencing using the V4–V5 region of the 16S rRNA gene. The Alpha diversity measurements of the bacterial community showed no statistical differences in species richness and diversity between the two groups of ruminants. However, the Pielou evenness index revealed a significant difference and showed higher species evenness in cows compared to goats. Beta diversity measurements showed statistical dissimilarities and significant clustering of bacterial composition between goats and cows. Firmicutes were the dominant phylum in both goats and cows, followed by Bacteroidetes, Proteobacteria, and Spirochaetes. Linear discriminant analysis with effect size (LEfSe) showed a total of 36 significantly different taxa between goats and cows. Notably, the relative abundance of Ruminococcaceae UCG-005, Christensenellaceae R-7 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-009, Ruminococcaceae UCG-013, Ruminococcaceae UCG-014, Ruminococcus 1, Ruminococcaceae UCG-002, Lachnospiraceae NK4A136 group, Treponema 2, Lachnospiraceae AC2044 group, and Bacillus was higher in goats compared to cows. In contrast, the relative abundance of Turicibacter, Solibacillus, Alloprevotella, Prevotellaceae UCG-001, Negativibacillus, Lachnospiraceae UCG-006, and Eubacterium hallii group was higher in cows compared with goats. Our results suggest that diet shapes the bacterial community in feces, but the host species has a significant impact on community structure, as reflected primarily in the relative abundance of certain taxa.

There is consensus that the quality of the human–animal relationship (HAR) is relevant to guarantee appropriate levels of animal welfare. Given the impact that HAR may have on both goats and human beings, the aim of the present review is to elucidate: (1) how humans and goats communicate; (2) which are the factors affecting human–goat interactions; (3) how we can measure the quality of this relationship. The systematic review led to the selection of 58 relevant articles. Effective human–goat communication takes place by means of visual, tactile and auditory stimuli and, to a less extent, via olfactory and gustative stimuli. Goats have well-developed socio-cognitive abilities and rely on humans to get relevant information. A deep knowledge of goats’ communication means and socio-cognitive abilities may greatly help improving the human–goat relationship. Management practices (e.g., rearing methods, amount and quality of interactions), as well as genetic selection for suitable individual traits, may contribute to improving HAR. Several measures to assess the quality of HAR have been validated, including avoidance in the pen and at the feeding rack and latency to first contact. Finally, farmers’ attitudes and empathy with goats, as well as their motivation to work with animals, should be improved through appropriate training.

Introduction: Following the increasing interest about the development of indicators of positive welfare and affective state in farm animals, the aim of this research is to present some preliminary results on the application of a prototype protocol based exclusively on positive welfare measures and to suggest potential benefits that can promote positive welfare.Methods: The protocol was applied in 20 loose housing dairy cattle farms (6 on deep litter with straw, 14 in cubicles) and included only indicators of positive welfare and emotional states: feeding and resting synchronization, rumination during resting, comfortable lying postures, no visible eye white, relaxed ear postures, percentage of cow contacts with humans in the Avoidance Distance test. Potential benefits in terms of housing, feeding and management were then related to these variables (Mann-Whitney U test). Qualitative Behavior Assessment (QBA) was also carried out and analyzed by Principal Component Analysis to explore the effect of factors that were not evenly distributed in our sample (number of feed distributions, access to pasture, presence of paddock or environmental enrichments, automatic milking systems).Results: When hay was included in the diet, higher feeding synchronization (93.7 ± 1.6 vs. 52.2 ± 4.7%; p < 0.01), percentage of cows with relaxed ear postures (35.8 ± 5.4 vs. 15.5 ± 2.1%; p < 0.01) and percentage of cows with no visible eye white (55.9 ± 17.0 vs. 36.6 ± 4.1%; n.s.) were recorded. A higher level of feeding synchronization was observed also when the feeding places/cow ratio was > 1 (72.1 ± 9.9 vs. 53.8 ± 5.8%), although differences were not significant (p = 0.14). Deep litter had a more positive effect than cubicles on comfort at resting, with a significantly higher percentage of ruminating cows (65.8 ± 10.2 vs. 34.2 ± 3.7%; p < 0.01), a higher percentage of cows with no visible eye white (55.6 ± 9.9 vs. 33.1 ± 3.7%; p < 0.05) and a higher percentage of cows in a more comfortable posture, with stretched legs (14.3 ± 5.1 vs. 5.6 ± 1.6%; p = 0.09). QBA highlighted the most positive emotional state in the only farm that allowed access to pasture.Conclusions: This study represents a first attempt to apply a protocol for on-farm welfare evaluation based exclusively on the use of positive welfare indicators and provides suggestions on possible benefits (e.g., deep litter, feeding places/cow ratio > 1, hay in the diet and access to pasture) to enhance dairy cattle welfare.

The present study aims to understand the influence of a reduced amount of straw litter on goats’ lying comfort and hygienic characteristics of milk and straw. Lying behaviour (frequency, lying posture, location and social context) of 24 Alpine goats subjected to two different straw treatments (Regular: 2 kg/straw/head + 1 kg straw/head/day, n = 12; Scarce: 1 kg straw/head + 0.5 kg straw/head/day, n = 12) was observed from video recordings using a scan sampling method at 15-min scan intervals in March 2017. Moreover, straw and milk samples were submitted to microbiological analysis for the detection of total bacteria (SPC) and Enterobacteriaceae count. No significant differences in the duration of lying were observed between treatments, except at the beginning of the experimental period (Day 1: Regular: 26.0%; Scarce: 20.3% of scans; p < .05). In both treatments lying behaviour followed the normal lying patterns for goats, with a preference for sternal lying (99.4% of scans), in contact with a pen wall (78.2% of scans) and not in contact with other goats (alone: 79.7% of scans). Milk and straw SPC and Enterobacteriaceae count were not affected by the treatment. This preliminary study suggests the possibility to reduce the amount of straw as bedding material in goat farms, without affecting animal welfare and microbiological characteristics of milk and straw litter. However, further studies are required to confirm these results under different climatic conditions.

Air quality in livestock housings impacts animal welfare; however, information on air quality in dairy goat barns is still limited. The air quality and environmental conditions of two farms in northern Italy were monitored for seven days after litter renewal in both summer and winter. The farms had different barn designs and litter replacement frequencies. Internet of Things (IoT) sensors were used to measure CO₂, NH₃, PM_2.5, temperature and humidity. Results suggest that building structure, particularly the management of openings, along with litter replacement frequency, and season, significantly affect gas concentrations, while PM_2.5 seems to be more related to the external conditions. The recommended thresholds for goat health and welfare were all met (537 vs. 778 ppm for CO₂, 1.78 vs. 3.29 ppm for NH₃ and 3.4 vs. 12.7 µg m⁻³ for PM_2.5 in Farms A and B, respectively). The low average temperature humidity index values recorded in winter at Farm A (45.9) suggest potential cold stress, which could be mitigated through improved barn opening management. Our research introduces a novel use of IoT sensors in the frame of precision livestock farming to monitor air quality in goat barns, allowing data-driven interventions to improve animal welfare.