Maria Luisa Dettori

Extensive and semi-extensive dairy sheep farming are commonly regarded, from a societal perspective, as positive systems. However, animal well-being and productive performance may be compromised by specific methods adopted within those systems. In the present study, we investigated the effects of farming, pasture and grazing descriptors on sheep milk composition, coagulation properties, and cheese-making efficiency, as assessed from sheep bulk milk collected in semi-extensive farms. Bulk milk samples were collected from 47 commercial farms located in Sardinia, Italy, rearing flocks of the local Sarda breed. Sampling was conducted at 4 consecutive stages of lactation (months of January, March, May, and July) to account for seasonal variation in milk production. A total of 188 samples were analyzed for milk composition, milk coagulation properties, cheese yield (CY%) and nutrient recoveries in the curd (REC). Farms were scored using 9 descriptors related to farming, pasture and grazing conditions. These descriptors were submitted to principal components (PCA) and cluster analyses to investigate their interrelationships. Subsequently, ANOVA was performed to assess the effects of individual descriptors and descriptor-based clusters on milk traits. As expected, most milk traits were significantly affected by the stage of lactation, and a substantial proportion of total variance was attributable to the individual farm effect. Among individual descriptors, flock size significantly influenced protein-related traits: farms with small and medium flock size (fewer than 400 ewes in production) exhibited greater percentages of milk protein, casein and REC_PROTEIN. The average load at pasture, calculated as the ratio of flock size to total pasture area, significantly affected milk traits: high load at pasture (≥10.1 sheep/ha) was associated with increased milk pH, which in turns is related to longer rennet coagulation time and reduced curd firmness, indicating impaired coagulation aptitude. Farm management score significantly affected udder health markers, with lower bacterial cell score in farms with the best management. In addition, the presence of competitors on grazing areas (wild boars and deer) was associated with increased somatic cell score, suggesting that interspecific feeding competition may negatively influence milk quality. Pasture botanical composition also significantly affected cheesemaking efficiency. Flocks grazing predominantly on leguminous species (e.g., sulla and clover) exhibited greater protein and energy recovery in the curd compared with those grazing mainly on graminaceous. The PCA identified 2 main farm typologies, classified as Cluster 1 "Extensive-large flock" and Cluster 2 "Intensive-small flock." However, ANOVA revealed limited differences in milk traits, with the only significant effect observed for protein recovery, which was higher in Intensive-small flock farms. In conclusion, farming, pasture and grazing descriptors emerged as relevant tools to investigate the effects of semi-extensive systems on sheep milk traits, with direct implications for cheesemaking efficiency.

The protein profile of milk is an increasingly important research topic in relation to the technological properties of milk for cheese-making and value of it for human nutrition. Goats are a valuable source of milk worldwide, but there is no precise and rapid method for the simultaneous identification and quantification of the genetic variants of the major protein fractions. In this study we devised a method for the rapid identification and quantification of the main genetic variants of α_S1-, α_S2-, β- and κ- caseins, and of α-lactalbumin and β-lactoglobulin (whey proteins) in caprine milk using an HPLC-UV reverse-phase C8 column. The R² of the calibration equations was > 0.99 for all 15 protein fractions identified. A total of 520 milk samples were taken from 260 goats (in duplicates) of 6 different breeds reared on 22 farms operating different farming systems, ranging from traditional/extensive to modern/intensive, and analyzed them to externally validate the method. Milk samples from DNA-genotyped goats were used to identify the genetic variants of the caseins. Compared with other studies, with our method we were able to separate the main genetic variant of caseins within a total run time of 24 min and with a repeatability > 92% for all fractions except for the C variant of κ-casein. Farms were found to be the major source of the variability in the contents of true proteins (56.1% of total variance), true caseins (48.6%), κ-casein C (50.6%) and whey proteins (52.9-62.9%) in goats' milk, whereas individual goat within farm was the most important source of variability in the contents of individual caseins and their genetic variants (53.9% to 94.7%).

This work aimed to assess the variability of casein genes in a population of 153 bucks and 825 lactating does of the Sarda breed, and to perform association analysis between polymorphic sites and milk yield and composition traits. To genotype the casein genes, we chose an SNP panel including 44 SNPs mapping to the four casein genes CSN1S1, CSN2, CSN1S2, and CSN3. Genotyping (made by KASP™ genotyping assay, based on competitive allele-specific PCR) revealed the high variability of the Sarda goat, and haplotype analysis revealed linkage disequilibrium (LD) between CSN1S1 and CSN2 genes, in addition to two LD blocks within the CSN1S2 and two LD blocks within the CSN3 gene, in bucks and does. Association analysis revealed that variability at all four casein genes was associated with milk protein content, total solids, and milk energy. The three Ca-sensitive casein genes were associated with lipid content, and CSN1S2 showed a unique pattern, with intron variants associated with milk yield, in addition to milk pH, NaCl, and SCS (Somatic Cell Score). This information might prove useful in selection schemes and in future investigations aiming to better understand the biology of lactation, and the direct link between genotype and phenotype.

The objectives of this study were to explore the use of Fourier-transform infrared (FITR) spectroscopy on individual sheep milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: i) a random cross-validation (CV) [80% calibration (CAL); 20% validation (VAL) set], and ii) a leave-one-out validation (3 farms used as CAL, and the remaining one as VAL set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical application of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recovery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation procedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as "water" and "fingerprint" regions. These findings suggest that, according to the traits studied, the inclusion of "water" regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain.

At the global level, the quantity of goat milk produced and its gross production value have increased considerably over the last 2 decades. Although many scientific papers on this topic have been published, few studies have been carried out on bulk goat milk samples. The aim of the present study was to investigate in the field the effects of farming system, breed type, individual flock, and stage of production on the composition, coagulation properties (MCP), curd firming over time parameters (CF_t), predicted cheese yield (CY%), and nutrient recovery traits (REC) of 432 bulk milk samples from 161 commercial goat farms in Sardinia, Italy. We found that the variance due to individual flock was of the same order as the residual variance for almost all composition and cheesemaking traits. With regard to the fixed effects, the effect of farming system on bulk milk variability was not highly significant for the majority of traits (it was lower than individual flock), whereas the effects of breed type and stage of production were much higher. More specifically, the intensive farms produced milk with the best concentrations of almost all constituents, whereas extensive farms exhibited faster rennet coagulation times, a slower rate of curd firming, lower potential curd firmness, and lower percentages of fat and energy recoveries in the fresh curd. Farms rearing the local breed, Sarda, alone or together with the Maltese breed, produced milk with the best concentrations of fat and protein, superior curd firmness, and better predicted percentage of fresh curd (CY_CURD) and recovery traits. The results show the potential of both types of breed, either for their quantitative (specialized breeds) or their qualitative (local breeds) attributes. As expected, the concentrations of fat, protein fractions, and lactose were influenced by the stage of production, with samples collected in the early stage of production (in February and March) having a greater quantity of the main constituents. Somatic cells reached the highest levels in the late stage of production, which corresponds to the goats' advanced stage of lactation (June-July), although no differences were present in the logarithmic bacterial counts between the early and late stages. Regarding cheesemaking potential, bulk milk samples of the late stage were characterized by delayed rennet coagulation and curd firming times, the lowest values of curd firmness, and a general reduction in CY%, and REC traits. In conclusion, we highlight several issues regarding the effects of the most important sources of variation on bulk goat milk, and point to some critical factors relevant for improving dairy goat farming and milk production.

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CY_CURD), total solids (%CY_SOLIDS), and water retained (%CY_WATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R²_VAL) and the root mean square error of validation (RMSE_VAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R²_VAL = 0.65, 0.96, and 0.23 for %CY_CURD, %CY_SOLIDS, and %CY_WATER, respectively). The WAV procedure showed R²_VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CY_CURD and %CY_WATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production.

The aim of this research was to characterize the genetic diversity of the Sarda (Sa, n = 131), Sardo Bruna (SB, n = 44) and Sardo Modicana (SM, n = 26) cattle breeds, reared in the island of Sardinia (Italy). A portion of the mitochondrial DNA hypervariable region was sequenced, in order to identify a potential signature of African introgression. The F_ST coefficients among populations ranged between 0.056 for Sa vs SB and 0.167 for SB vs SM. AMOVA analysis indicated there was a significant differentiation of the three breeds, although most of diversity was gathered at the within-breed level. The Median Joining Network of the Sardinian sequences showed a potential founder effect signature. A MJ network including Sardinian cattle plus African, Italian, Iberian and Asian sequences, revealed the presence of haplogroup T3, already detected in Sa cattle, and the presence of Hg T1 and Hg T1′2′3, in Sa and SB. The presence of a private haplotype belonging to haplogroup T1, which is characteristic of African taurine breeds, may be due to the introgression of Sardinian breeds with African cattle, either directly (most probable source: North African cattle) or indirectly (through a Mediterranean intermediary already introgressed with African blood).

The aim of the present research was to analyze the variability of 45 SNPs from different genes involved in metabolism and innate immunity to perform an association analysis with the milk yield, composition and milk coagulation traits. A population of 1112 Sarda breed sheep was sampled. Genotyping was generated by a TaqMan Open Array^TM. Thirty out of the 45 SNPs were polymorphic, and 12 displayed a minor allele frequency higher than 0.05. An association analysis showed that the variability at genes PRKAG3 and CD14 was significantly associated with the daily milk yield. The variability at PRKAG3 was also associated with the protein and casein content, somatic cell score and bacterial score. The variation at the PRKAA2 gene was associated with the milk lactose concentration. The SNPs at CD14 were also associated with the traditional milk coagulation properties, while the SNPs at GHR and GHRHR were associated with k_SR, a derived coagulation parameter related to the rate of syneresis. The information provided here is new and increases our knowledge of genotype–phenotype interactions in sheep. Our findings might be useful in appropriate breeding schemes to be set up for the Sarda sheep breed, but these should be confirmed by further studies, possibly performed on independent populations.

The prediction of traditional goat milk coagulation properties (MCP) and curd firmness over time (CFt) parameters via Fourier-transform infrared (FTIR) spectroscopy can be of significant economic interest to the dairy industry and can contribute to the breeding objectives for the genetic improvement of dairy goat breeds. Therefore, the aims of this study were to (1) explore the variability of milk FTIR spectra from 4 goat breeds (Camosciata delle Alpi, Murciano-Granadina, Maltese, and Sarda), and to assess the possible discriminant power of milk FTIR spectra among breeds, (2) assess the viability to predict coagulation traits by using milk FTIR spectra, and (3) quantify the effect of the breed on the prediction accuracy of MCP and CFt parameters. In total, 611 individual goat milk samples were used. Analysis of variance of measured MCP and CFt parameters was carried out using a mixed model including the farm and pendulum as random factors, and breed, parity, and days in milk as fixed factors. Milk spectra for each goat were collected over the spectral range from wavenumber 5,011 to 925 × cm-1. Discriminant analysis of principal components was used to assess the ability of FTIR spectra to identify breed of origin. A Bayesian model was used to calibrate equations for each coagulation trait. The accuracy of the model and the prediction equation was assessed by cross-validation (CRV; 80% training and 20% testing set) and stratified CRV (SCV; 3 breeds in the training set, one breed in the testing set) procedures. Prediction accuracy was assessed by using coefficient of determination of validation (R2VAL), the root mean square error of validation (RMSEVAL), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R2VAL values obtained with the CRV procedure were moderate to high for the majority of coagulation traits, with RMSEVAL and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits.