The paper presents an overview of the usage of Instagram as a social media platform for teaching and engagement in the field of dairy cow nutrition and management. Our Instagram content, which includes posts, stories, hashtags, reels, and live videos, aims to educate and engage our followers and covers a wide range of topics, including research updates, student activities, and information on dairy cow health and welfare. This approach to education allows us to reach a larger audience while also providing a forum for interaction and discussion on essential dairy cow nutrition concerns.

Dairy cows are exposed to multiple stressors during the productive cycle, such as metabolic challenges, overcrowding, grouping change, environmental stress and dietary errors. Thus, it is essential to study reliable markers able to detect stress conditions in dairy farms. This study evaluates dairy cows' immunologic and metabolic markers after the sudden and combined exposition to a high‐grain diet (75% concentrates) and the abrupt change of the housing system (from free stall to tie stall). A group of twenty‐four Holstein cows were enrolled in a challenge study of 28 days duration. Several immunological and metabolic blood markers were evaluated over the trial. Blood samples were taken at day 0 (normal value) and day 1, 3, 7, 14, 21, and 28 (challenge). Data were submitted to a mixed model for repeated measures, including time as fixed and cows as random effects. The nutritional and environmental challenge had heavy effects on animal welfare and cows responded with a dramatic rumination drop. Our results suggest that the most responsive markers after abiotic stressors in cows were as follows: Serum Amyloid A and ROM in the acute response; Ceruloplasmin and GGT in the mid acute and Albumin, Paroxonase and FRAP in the chronic phase. Serum Amyloid A, Ceruloplasmin, Paraoxonase, GGT and ROM resulted as positive phase proteins, while, Albumin and FRAP resulted as negative phase proteins. Preliminary obtained results could concur to develop strategies able to mitigate stressor effects; moreover, the proposed design can be used as a model to test stress nutritional modulators.

This study evaluates Instagram's efficacy as an educational tool in veterinary sciences, focusing on dairy cow nutrition and management. Using the Instagram account "Stalla Didattica Unibo," established in December 2020, a survey was conducted with 102 respondents, primarily consisting of university students (44.12%). The respondents were divided into 2 major groups: students and the general population, with the latter comprising professionals and others not currently in educational programs. In the overall population, the age group of 25 to 34 yr accounted for 51%, but among students, it was 73.3%. In terms of gender distribution, there was a female majority in the general population (59.8%) and a male majority among students (73.3%). Most responders were from Italy, with Emilia-Romagna contributing significantly. Engagement was measured not only by the length of time participants followed the account but also by active interactions such as likes, comments, and shares. Among the total population, 47.1% had followed the page for over a year, compared with 37.8% of students. Importantly, engagement metrics showed active participation, with a majority reporting improved knowledge (75.6% of the total population and 74.5% of students) and practical application of content (64.4% of the total population and 67.6% of students). Content preferences varied, with the general population favoring quizzes (33.3%) and stories (17.8%), whereas students showed a stronger preference for standard posts (36.3%). Impact was defined as the perceived influence of the Instagram content on the respondents' learning and professional practices, with 46.7% of the total population and 52.9% of students agreeing or strongly agreeing that the page had a positive impact on their knowledge and practical skills. Furthermore, 84.3% of students and 77.8% of the total population stated they would recommend the page to others, underscoring the account's effectiveness as a digital educational tool. The study highlights Instagram's potential to enhance veterinary education and suggests further research to explore the broader application of these strategies across different educational contexts and platforms.

• Thermoregulatory mineral mixture did not show effect under moderate HS conditions • Other tools to mitigate HS may play a more important role than mineral supplement • Even mild and moderate HS can elevate respiratory rate and rectal temperature

The aim of this study was to evaluate the effects of a complete hay-based total mixed ration (TMR) for calves, focusing on their feed intake, animal growth performance, and fecal output, and to develop a preliminary estimation equation for solid feed intake and body weight in Holstein heifer calves. Twenty female Italian Holstein calves (37.14 ± 2.72 kg) born between February and July were studied from the day of birth until 77 days of age. From the fourth day of life, they were fed 3 L/day of pasteurized milk twice daily and supplemented with the same hay-based TMR. The data on feed intake, fecal characteristics, and growth performances were collected and showed that these calves had adequate parameters. Moreover, the data collected was used to create equations to predict body weight and solid feed intake using a mixed model. The goodness of fit of the developed equations was evaluated by coefficients of determinations (R²). The equation obtained shows high R² (0.98 for solid feed intake and 0.99 for calf weight), indicating the satisfactory precision and accuracy needed to predict female calves’ body weight and solid feed intake.

The aim of this study was to develop near-infrared spectroscopy (NIRS) prediction models for the estimation of chemical components and the fibre undegradable fractions (uNDF) of hay-based total mixed rations (TMR). A total of 205 TMR samples were used for the study. All the chemical components were measured using standard AOAC reference methods and expressed as percentages of dry matter (DM). Prediction models were developed using both cross- and independent validation and different mathematical treatments applied on spectral data. The best spectral treatment was chosen based on the method which simultaneously achieved the lowest root mean square error and the highest explained variance in cross-validation. The coefficient of determination in external validation (R²_P) was the greatest for starch prediction model (R²_P = 0.84), followed by acid detergent fibre (ADF; R²_P = 0.79), and amylase-treated ash-corrected NDF with addition of sodium sulphite (aNDFom) and crude protein prediction models (CP; R²_P = 0.73). The concordance correlation coefficient (CCC) in validation ranged from 0.66 (ash prediction model) to 0.92 (starch prediction model), indicating substantial to accurate models’ predictive ability. This study indicated that NIRS can be a screening method for the prediction of CP, Starch, aNDFom, ADF, acid detergent lignin (ADL), uNDF and Ash. The use of TMR utilised in various herds provided high variability for the NIRS calibration dataset, implying that the developed NIRS pre-diction models could be applicable to TMR collected from herds located in the Parmigiano Reggiano cheese production area.