Nicolo Pietro Paolo Macciotta

In this work intergenerational and transgenerational effects of calving months on dairy performances of Italian Mediterranean river buffaloes were investigated. The EBVs for milk, fat, and protein yields, as well as fat and protein contents of 112,625 females (F₃) born between 1985 and 2022 were analyzed with a linear model that included the fixed effects of the calving month of their dams (F₂, 63,442 cows), granddams (F₁, 44,015 cows), and great-granddams (F₀, 33,452), the covariable of F₀ calving date, and the covariable of the F₁ EBV. For a subset of 53,706 F₃ buffaloes, further analyses were run including in the model the number of days with a temperature-humidity index larger than 70 during the last 100 d of their F₀ pregnancy. All the 5 considered traits were affected by the ancestor month of calving. The effect tended to decrease as the distance between generations increased. Buffaloes whose great-granddams calved in September showed the lowest LSM of EBV for dairy traits, whereas the largest values were exhibited by buffaloes whose F₀ had calvings in December. Similar pattern was observed for the F₁ calving month effect. An exception was found for the effect of F₂ calving month. The F₃ cows whose dams calved in December showed the largest average EBV for yield traits. This result seems to indicate that for adjacent generations, environmental conditions in the periconceptional period may be more important than in late pregnancy. Results of the present work confirmed previous reports of inter- and transgenerational effects of calving months of female ancestors in dairy cattle, suggesting the existence also for buffalo of mechanisms of epigenetic inheritance related to environmental conditions during pregnancy. A deeper understanding of the role of transgenerational and intergenerational epigenetic inheritance in the expression of phenotypes of economic interest would provide useful insights for the management and the breeding of river buffaloes.

Selection for increased milk yield in dairy cattle has led to a decline in fertility due to unfavorable genetic correlation. However, fertility remains crucial in dairy farming for maintaining a regular productive cycle, ensuring continuous milk supply, and reducing costs related to nonproductive cows. For this reason, the aim of this study was to estimate genetic parameters for fertility traits and to assess their genetic correlation with production traits in the Italian Jersey cattle breed. We considered days open (DO) as a fertility trait, and kg/305-d milk (MY), protein (PY), and fat yields (FY) as production traits, respectively. After data editing, the final dataset included 33,053 DO records from 16,667 cows, restricted to values between 25 and 400 d. The mean (± SD) phenotypic value for DO was 121 ± 71 d. Variance components and h² estimates for DO were obtained using a single-trait animal model, whereas genetic correlations were estimated with a multiple-trait animal model. The models accounted for herd, permanent environment, and animal as random effects, whereas parity and month-year were considered as fixed effects. A pedigree-based relationship matrix was used in the model. Estimated h² (± SE) for DO was low (0.04 ± 0.01), and unfavorable genetic correlations (± SE) were observed with MY (0.16 ± 0.08), PY (0.12 ± 0.09), and FY (0.23 ± 0.09). Genetic trends revealed a consistent increase in production traits, whereas the trend of DO showed a greater variability over time. Despite the low h², these results highlight the potential to improve fertility through selection and support the inclusion of reproductive traits in breeding programs aimed at achieving a more balanced and sustainable genetic progress in Italian Jersey cattle.

The definition of dietary models that meet the animal's nutritional needs, improve its physiological state, and preserve the microbiota equilibrium, along with the challenge of reducing environmental effects is essential for producing sustainable, high-quality milk suitable for dairy production. This study aimed to investigate whether a more sustainable diet, with a higher content of alfalfa hay but equal net energy, protein, and NDF levels, has an effect on rumen fluid, feces, and milk microbiota in Sarda sheep. Additionally, it aimed to determine if microbiota changes across different niches correlated with production and traits. Twenty-four animals were divided into 2 groups and fed different diets. Rumen fluid, feces, and milk samples were collected at the beginning (d 1, T0) and at the end of the trial (d 22, T1). Microbial communities were analyzed using Ion GeneStudio S5 for sequencing the V2, V3, V4, V6-V7, V8, and V9 regions of the 16S rRNA gene. Amplicon sequence variant clustering and taxonomic assignment were performed with QIIME2 v2023.9.2. Differential abundant taxa between the 2 experimental groups (H-alfalfa, fed the high-alfalfa content diet, and L-alfalfa, fed the low-alfalfa content diet) were assessed at the 2 time points using the ANCOM-BC plugin of QIIME2, along with the analysis of the diversity indices. Similarly, milk composition, milk coagulation properties and BW, DMI, production, and energy requirements for milk production traits were analyzed for each time point using a linear model with diet as fixed effect. Results confirmed an overall homogeneity of the samples at T0 for milk composition, milk coagulation properties, and energy requirements for milk production, except for DMI. However, no significant difference in DMI was observed at T1 between experimental groups. Regarding the microbiota composition, analysis revealed minor differences between H-alfalfa and L-alfalfa groups in taxa abundances for rumen fluid and feces, both at T0 and 1. In milk, 8 taxa resulted to be enriched at T1 in the treated group. However, all the affected taxa had a low abundance, and no significant correlation was observed with the phenotypic traits. Diets did not affect the microbial structure of rumen fluid and milk, whereas significant differences in beta diversity (Bray-Curtis and unweighted UniFrac) were observed in feces at T1. Five distinct milk taxa resulted positively correlated with 3 taxa in rumen fluid and 2 in feces, supporting the idea that host microbial niches form a network of interconnected communities, even within isolated environments. In addition, a positive correlation was observed between energy requirements for milk production and feces' Oscillospiraceae family, a taxon typical of healthy gut in ruminants. The higher content of alfalfa hay showed no highly significant effects on microbiota composition, milk composition, milk coagulation traits, and energy requirements for milk production. Similarities in chemical composition and energy content between diets likely masked qualitative differences in the ingredients and resulted in minor differences between diet groups. Nevertheless, no adverse effects of the high-alfalfa hay diet were observed, suggesting its higher value as an effective strategy, particularly advantageous from an environmental perspective.

Heat stress in farmed animals is becoming a problem of great importance even in temperate climates, especially due to the increase in average global temperatures. Previous studies conducted on sheep have reported negative effects of heat stress on milk yield and composition and on the cheesemaking properties. The study of the effect of heat stress in sheep is complicated by the seasonality of calving and the overlap of the effects of the lactation stage with those of the climate. The aim of this work was to study the effects of heat stress in dairy sheep through a repeated measures model, separating the lactation stage and climate effects. The dataset included 2,695 repeated measurements of 555 Sardinian sheep, raised in 34 farms in Sardinia. The analyzed phenotypes were milk yield, fat, protein, lactose and somatic cell contents, milk coagulation properties (rennet clotting time, curd firming time, curd firmness), and individual laboratory cheese yield (ILCY). Temperature and humidity values of the day of the survey and of the 3 previous days were provided by the regional agency for the protection of the environment (Agenzia Regionale per la Protezione dell'Ambiente of the Sardinia Region). Climate data were then used to calculate the respective temperature and humidity indices. The effect of the temperature-humidity index (THI) was evaluated using a linear mixed model that considered stage of lactation (DIM), parity, maximum value of hourly THI calculated over 24 h (THI_max) class (divided into quartiles), DIM × THI interaction, and sampling date; the farm and the animal were considered random effects. The DIM × THI interaction was significant for all parameters except somatic cells and curd firmness. Milk yield showed an increase from the first to the third THI_max class and a reduction in the fourth class (THI > 73); this trend was the same in the 3 lactation stages (early, mid, and late). Fat content showed an opposite trend, independent of the lactation stage. Proteins decreased linearly at the beginning and mid lactation whereas they showed no significant changes at the end of lactation. Lactose was reduced in the fourth class of THI_max, both in mid and late lactation. Rennet clotting time showed different patterns depending on DIM, whereas ILCY showed worse values as THI_max increased, especially in the early and mid lactation. The study of THI in the different phases of lactation made it possible to separate the 2 effects on the parameters studied and highlighted how in dairy sheep, heat stress leads to a reduction in production and a general worsening of the quality of the milk.

Milk yield and its composition show individual variation due to the effects of the environment. Previous studies suggest that meteorological variables exert negative effects on milk yield and composition, especially during summer. This study aimed to examine the effects of meteorological variables on bulk milk composition in the Sardinian sheep production system. In this work, a total of 218,170 records belonging to 4562 dairy sheep farms were merged with the meteorological data provided by 60 meteorological stations located on Sardinia Island (Italy). Milk composition in the late spring and summer recorded during a 5-year period was used to evaluate the impact of climate exposure on bulk milk traits. The milk quality was analyzed using a linear mixed model that included the fixed effects of the year of sampling, the flock size, the temperature humidity index (THI) and the random effect of the flock. The variability of milk composition explained by flock and management ranged from 30 to 64%. The flock size exerted a significant effect on milk composition: large flocks characterized by advanced management and feeding techniques resulted in higher milk quality (e.g., higher protein and fat, lower lactose) compared to traditionally managed small flocks. The impact of THI on milk composition was statistically significant across different milk quality traits (p < 0.001); the effect of thermal stress varied according to the month of lactation. For instance, milk fat content in May increased by +0.4% for THI > 76. In June, no relevant differences were observed, whereas a decrease in fat percentage was observed in July as THI values increased (up to −0.5% for THI > 76). While somatic cell counts remained relatively stable across different conditions, total bacterial count showed greater seasonal variability, peaking during warmer periods. In addition, using factor analysis, we developed a multivariate meteorological index (MMI), which explained 51% of the variance of the original meteorological data. MMI was highly correlated with THI (r = 0.75). The same linear mixed model applied for modeling THI was used to assess the effect of MMI on milk traits. Fat, protein fractions and lactose showed significant variation across MMI classes (p-value < 0.001) in the same direction as those based on THI. Overall, our findings underscore the impact of both flock size and environmental conditions on milk quality, with heat stress and traditional versus modern management practices leading to measurable differences in milk traits.

Livestock expresses complex traits influenced by several factors. The response of animals to variations in climatic factors, such as increases in temperature, may induce heat stress conditions. In this study, animals living at different temperatures were compared using the genome-wide Wright fixation index (F_ST). A total of 825 genotypes of Sarda breed ewes were divided into two groups based on the flocks’ average temperature over a 20-year period to compute the F_ST: 395 and 430 sheep were represented in colder and hotter groups, respectively. After LOWESS regression and CONTROL CHART application, 623 significant markers and 97 selection signatures were found. A total of 280 positional candidate genes were retrieved from a public database. Among these genomic regions, we found 51 annotated genes previously associated with heat stress/tolerance in ruminants (FCGR1A, MDH1, UGP2, MYO1G, and HSPB3), as well as immune response and cellular mechanisms related to how animals cope with thermal stress (RIPK1, SERPINB1, SERPINB9, and PELI1). Moreover, other genes were associated with milk fat (SCD, HERC3, SCFD2, and CHUK), body weight, body fat, and intramuscular fat composition (AGPAT2, ABCD2, MFAP32, YTHDC1, SIRT3, SCD, and RNF121), which might suggest the influence of environmental conditions on the genome of Sarda sheep.

Objectives: The aim of this study was to investigate the genomic structure of the cattle breeds selected for meat and milk production and to identify selection signatures between them. Methods: A total of 391 animals genotyped at 41,258 SNPs and belonging to nine breeds were considered: Angus (N = 62), Charolais (46), Hereford (31), Limousin (44), and Piedmontese (24), clustered in the Meat group, and Brown Swiss (42), Holstein (63), Jersey (49), and Montbéliarde (30), clustered in the Milk group. The population stratification was analyzed by principal component analysis (PCA), whereas selection signatures were identified by univariate (Wright fixation index, F_ST) and multivariate (canonical discriminant analysis, CDA) approaches. Markers with F_ST values larger than three standard deviations from the chromosomal mean were considered interesting. Attention was focused on markers selected by both techniques. Results: A total of 10 SNPs located on seven different chromosomes (7, 10, 14, 16, 17, 18, and 24) were identified. Close to these SNPs (±250 kb), 165 QTL and 51 genes were found. The QTL were grouped in 45 different terms, of which three were significant (Bonferroni correction < 0.05): milk fat content, tenderness score, and length of productive life. Moreover, genes mainly associated with milk production, immunity and environmental adaptation, and reproduction were mapped close to the common SNPs. Conclusions: The results of the present study suggest that the combined use of univariate and multivariate approaches can help to better identify selection signatures due to directional selection.