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Single-step genomic best linear unbiased predictor (ssGBLUP) is a method for jointly estimating breeding values (BV) for genotyped and non-genotyped animals. Genomic information in the Italian Mediterranean Buffalo (IMB) is now available and its inclusion in the genetic evaluation system could increase both accuracy and genetic progress of the traits of interest of the breed. The aim of this study was to test the feasibility of ssGBLUP and to present the first results of the implementation of a genomic evaluation for production and type traits in the IMB. Phenotypic information on production (270-day milk, mozzarella yield (MY), protein and fat kg and %, respectively) and morphology: feet and legs (FL) and mammary system (MS) were used for this study. Production records included 743,904 lactations from 276,451 buffalo cows born from 1984 to 2019. Morphological traits were from 91,966 buffalo cows from 2004 to 2022. Regarding the genotypes, a total of 2,017 buffalo cows and 133 bulls were used. Data were analysed fitting two multitrait animal models, a 6-trait model for production data and a 2-trait model for morphology data. According to the relationship matrix used, two models were fitted: (i) the BLUP with the numerator relationship matrix (A); (ii) the ssGBLUP where A and the genomic relationship matrix (G) are blended into H. BV were estimated with BLUP and ssGBLUP models. Three different scenarios were used, according to the cut-off year used to create the partial datasets, namely 2012, 2015 and 2017. In each scenario, correlation, accuracy, dispersion, and bias statistics were calculated (LR method). Both bulls (N=49) and cows (N=1288) were used for validations. On average, correlation between EBVs from partial and whole datasets estimated with BLUP and ssGBLUP increased from 6 to 49% and from 14 to 17% for production and type traits, respectively. Among the traits analysed, the ones most affected by the change were protein/fat content, MY, and AM. The accuracy increase for these traits was above 20% when using the ssGBLUP. All LR statistics improved also for non-genotyped females. These results showed that implementing ssGBLUP in the breeding program can generate more accurate predictions for important traits in dairy IMB than traditional BLUP.

The Italian Mediterranean Buffalo is the most important buffalo’s breeds for milk production and its derivatives, which has a selection program implemented more than 20 years ago. The selection is based on an aggregate index that includes dairy, mozzarella yield, and functional traits. The improvement of udder conformation (UC) traits is an aspect that plays an essential role in udder health and longevity; it is of great interest due to its direct relationship with economic losses caused by mastitis or poor milkability. The aim of this study was the development of an aggregate selection index (ASI) considering MP (milk production), MSCS150 (geometric mean of somatic cell score at 150 days in milk), and UC traits (fore udder attachment– FUA; rear udder width– RUW; rear legs height– RLH; teat direction – TD; teat length – TL and teat position – TP). The first step was to estimate the genetic (co)variances between the UC, MP, and MSCS150 traits and to estimate the relative weight for each UC trait. The phenotypic records of 15,275 females and a pedigree with 43,395 animals were used. A multitrait animal model that included CG (herd-year-calving season), calving month, and parity as fixed effects, and the animal as a random effect was used. The estimated heritability for MP and MSCS150 were 0.391 and 0.134, respectively, and varied between 0.11 and 0.23 for UC traits. Low negative genetic correlations were observed between MP and RLH (−0.119), the same pattern between MSCS150 and RLH (−0.350) and MSCS150 and FUA (−0.220). A positive correlation was found between FUA and MP (0.4272) and MP and RUW (0.645). Using ASI as selection criteria, an annual reduction of −0.03 is expected for MSCS150 and an increase of 0.26 kg for MP. During a selection period of ten years, the expected cumulative genetic gains would be -0.32 for MSCS150 and an increase of MP in 2.57 kg. Selective breeding programs aim to improve specific traits over time by selecting individuals with desirable genetic characteristics as parents of the next generation. According to the results, by focusing on RUW, RLH, and TD traits, genetic improvements are expected to lead to reduced severity of MSCS150 while maintaining or increasing milk production.

Single-step genomic best linear unbiased predictor (ssGBLUP) is a method for jointly estimating breeding values (BV) for genotyped and non-genotyped animals. Genomic information in the Italian Mediterranean Buffalo (IMB) is now available. Its inclusion in the genetic evaluation system could increase both the accuracy and genetic progress of the traits of interest of the breed. The study aimed to test the feasibility of ssGBLUP and show the first results of implementing a genomic evaluation for production and type traits in the IMB. Phenotypic information on production (270-day milk, mozzarella yield (MY), protein and fat kg and %, respectively) and morphology: feet and legs (FL) and mammary system (MS) were used for this study. Production records included 743,904 lactations from 276,451 buffalo cows born from 1984 to 2019. Morphological traits were from 91,966 buffalo cows from 2004 to 2022. Regarding the genotypes, 2,017 buffalo cows and 133 bulls were used. Data were analyzed fitting two multi-trait animal models, a 6-trait model for production data and a 2-trait model for morphology data. According to the relationship matrix used, two models were fitted: (i) the BLUP with the numerator relationship matrix (A) and (ii) the ssGBLUP where A and the genomic relationship matrix (G) are blended into H. BVs were estimated with BLUP and ssGBLUP models. The cutoff year used to create the partial data set was 2012. The correlation, accuracy, dispersion, and bias statistics were calculated (LR method). Both bulls (N=49) and cows (N=1288) were used for validations. On average, the correlation between EBVs from partial and whole datasets estimated with BLUP and ssGBLUP increased from 6 to 49% and from 14 to 17% for production and type traits, respectively. Among the traits analyzed, the most affected by the change were protein/fat content, MY, and AM. The accuracy increase for these traits was above 20% when using the ssGBLUP. All LR statistics also improved for non-genotyped females. These results showed that implementing ssGBLUP in the breeding program can generate more accurate predictions for essential traits in dairy IMB than traditional BLUP.

The growth of the world population that will occur in the next 30 years will be responsible for an increase in animal-derived food and proteins of animal origin. The livestock sector will be obliged to face new challenges, such as the reduction of environmental impact, the improvement of animal-derived food quality and safety, the reduction of antibiotics, and the increase in efficiency. One of the strategies that could be adopted is Precision Livestock Farming (PLF), recognized as the most sustainable tool to improve farm sustainability. It can be defined as “the continuous, automated, and real-time monitoring of production, reproduction, health, and welfare through the application of advanced information and communication technologies (ICT)”. In this new farm concept, animals, environment, machinery, and processes become “information objects” to enhance data; farm management and animals are defined as CITD systems: they are Complex, Individually different, Time- variant, and Dynamic. Several PLF technologies have been recently applied to buffalo species, improving some critical points of the farm, such as milking, nutrition, reproduction, and management. This short review reports some experiences carried out in buffalo species.

Nowadays climate change is affecting the planet’s biodiversity, and livestock practices must adapt themselves to improve production without affecting animal welfare. This work investigates the influence that some climatic parameters such as Environment Temperature, Relative Humidity, Thermal excursion and Temperature–Humidity Index (THI), can have on milk quantity and quality in two different dairy species (buffaloes and cows) raised on the same farm. A further aim was to understand if THI threshold used for cows could also be used for buffaloes. The climatic parameters were recorded daily through a meteorological station located inside the farm. Milk quantity (converted into ECM) and quality (Fat Percentage—FP; Protein Percentage—PP; Somatic Cell Count—SCC) were measured. Data were analyzed with Spearman’s correlation index, separately for buffaloes and cows. The results indicate a greater sensitivity of cows to heat stress and a strong negative correlation of the ECM with meteorological data (p < 0.01). The results of this study may stimulate the use of integrated technologies (sensors, software) in the dairy sector, since the IoT (sensors, software) helps to enhance animal well-being and to optimize process costs, with a precision livestock farming approach.

The measurement of milk electrical conductivity (EC) is a relatively simple and inexpensive technique that has been evaluated as a routine method for the diagnosis of mastitis in dairy farms. The aim of this study was to obtain further knowledge on relationships between EC, production traits and somatic cell count (SCC) in Italian Mediterranean Buffalo. The original dataset included 5411 records collected from 808 buffalo cows. Two mixed models were used to evaluate both the effect of EC on MY, PP and FP and EC at test-day, and the effect of EC on somatic cell score (SCS) by using five different parameters (EC_param), namely: EC collected at the official milk recording test day (EC_day0), EC collected 3 days before official milk recording (EC_day3), and three statistics calculated from EC collected 1, 3 and 5 days before each test-day, respectively. All effects included in the model were significant for all traits, with the only exception of the effect of EC nested within parity for FP. The relationship between EC and SCS was always positive, but of different magnitude according to the parity. The regression of EC on SCS at test-day using different EC parameters was always significant except when the regression parameter was the slope obtained from a linear regression of EC collected over the 5-day period. Moreover, in order to evaluate how well the different models fit the data, three parameters were used: the Average Information Criteria (AIC), the marginal R2 and the conditional R2. According to AIC and to both the Marginal and Conditional R2, the best results were obtained when the regression parameter was the mean EC estimated over the 5-day period.

The use of genetic evaluations in the Water Buffalo by means of a Best Linear Unbiased Prediction (BLUP) animal model has been increased over the last two-decades across several countries. However, natural mating is still a common reproductive strategy that can increase the proportion of missing pedigree information. The inclusion of genetic groups in variance component (VC) and breeding value (EBV) estimation is a possible solution. The aim of this study was to evaluate two different genetic grouping strategies and their effects on VC and EBV for composite (n = 5) and linear (n = 10) type traits in the Italian Mediterranean Buffalo (IMB) population. Type traits data from 7,714 buffalo cows plus a pedigree file including 18,831 individuals were provided by the Italian National Association of Buffalo Breeders. VCs and EBVs were estimated for each trait fitting a single-trait animal model and using the official DNA-verified pedigree. Successively, EBVs were re-estimated using modified pedigrees with two different proportion of missing genealogies (30 or 60% of buffalo with records), and two different grouping strategies, year of birth (Y30/Y60) or genetic clustering (GC30, GC60). The different set of VCs, estimated EBVs and their standard errors were compared with the results obtained using the original pedigree. Results were also compared in terms of efficiency of selection. Differences among VCs varied according to the trait and the scenario considered. The largest effect was observed for two traits, udder teat and body depth in the GC60 genetic cluster, whose heritability decreased by −0.07 and increased by +0.04, respectively. Considering buffalo cows with record, the average correlation across traits between official EBVs and EBVs from different scenarios was 0.91, 0.88, 0.84, and 0.79 for Y30, CG30, Y60, and CG60, respectively. In bulls the correlations between EBVs ranged from 0.90 for fore udder attachment and udder depth to 0.96 for stature and body length in the GC30 scenario and from 0.75 for udder depth to 0.90 for stature in the GC60 scenario. When a variable proportion of missing pedigree is present using the appropriate strategy to define genetic groups and including them in VC and EBV is a worth-while and low-demanding solution.

Abstract Background Diet supplementation with polyphenols is a novel strategy to improve meat quality in livestock, by preventing oxidative deterioration of lipids and protein. Polyphenols have beneficial effects on both human and animal health and can be obtained from several sources, such as olive mill wastewaters (OMWW). These are severe environmental pollutants and therefore may be recycled and utilized in other sectors. The aim of this study was to evaluate growth performance, meat characteristics, fatty acid composition, antioxidant status, different forms of myoglobin and malondialdehyde formation in kids who received a diet supplemented with polyphenols obtained from OMWW. Weaned goat kids (n = 18) were divided into two homogenous groups: control (C) group (n = 9) received a fattening standard diet while the other group (n = 9) received the same diet, supplemented with 3.2 mg/day of polyphenols powder extract (PE group). Average daily gain (ADG) was calculated 10 days apart throughout the study. After 78 days, the kids were slaughtered and pH and carcass yield were evaluated. Longissimus thoracis et lumborum muscle was collected and utilized for chemical analysis, meat quality evaluation and oxidative stability. Results No differences were recorded in ADG, carcass weight, pH and dressing between the two groups. Furthermore a similar meat proximate composition, texture and color was observed. Dietary polyphenols supplementation significantly (P

The microbiota of the gastrointestinal tract (GIT) are crucial for host health and production efficiency in ruminants. Its microbial composition can be influenced by several endogenous and exogenous factors. In the beef and dairy industry, the possibility to manipulate gut microbiota by diet and management can have important health and economic implications. The aims of this study were to characterize the different GIT site microbiota in water buffalo and evaluate the influence of diet on GIT microbiota in this animal species. We characterized and compared the microbiota of the rumen, large intestine and feces of water buffaloes fed two different diets with different non-structural carbohydrates/crude proteins (NSC/CP) ratios. Our results indicated that Bacteroidetes, Firmicutes and Proteobacteria were the most abundant phyla in all the GIT sites, with significant differences in microbiota composition between body sites both within and between groups. This result was particularly evident in the large intestine, where beta diversity analysis displayed clear clustering of samples depending on the diet. Moreover, we found a difference in diet digestibility linked to microbiota modification at the GIT level conditioned by NSC/CP levels. Diet strongly influences GIT microbiota and can therefore modulate specific GIT microorganisms able to affect the health status and performance efficiency of adult animals.

The aim of this study was to quantify nitrogen (N) and phosphorus (P) utilisation and excretion in intensive dairy buffaloes. Italian Mediterranean buffaloes were divided into 4 groups (n=6/group) as follows: Groups M50, M125 and M225 at 50, 125 and 225 days of lactation, respectively, and Group NL which was non-lactating. Lactating buffaloes had greater (P