Milk coagulation, especially in small ruminant species, is often hard to evaluate, as coagulation traits are normally considered individually and several factors related to udder health might distort yield calculation. Due to the lack of studies about these factors, our objective was to determine milk coagulation efficiency (CE) and its determinants using a deterministic technical efficiency approach, an ordinary least square regression model, and ANOVA. Milk from 300 Manchega ewes was collected and analyzed for composition, milk coagulation properties, and hygienic quality. The study results indicate that the estimated CE in Manchega ewes was 0.69, implying an important proportion of the animals produce poorly coagulating milk. The results of the ordinary least square regression model and ANOVA revealed that the main factor causing inefficiency was the initial pH of milk. Crude protein, casein and plasmin activity had moderate effects on CE, and, finally, other factors such as freezing point depression, somatic cell count, colony-forming units, and fat concentration had minor effects.

This study explores the relationships between composition, hygienic quality, and color values of milk to evaluate whether colorimetry could be used as a valid predictor of the quality of raw milk and its coagulation. For this purpose, we performed analyses in 1,200 individual samples of Manchega sheep milk from 4 flocks from the region of Castilla-La Mancha, Spain. Based on the measured variables, we determined the differences and similarities between coagulating and noncoagulating samples using discriminant analysis techniques. The variables with a higher discriminant ability were lactose content, somatic cell score, pH, and the color values lightness (L*) and red/green value (a*). The model based on color values showed a predictive ability similar to that found in the model based on milk composition and hygienic quality. Canonical correlation analysis allowed us to explore the relationships between both sets of variables. Canonical correlations for the first and second pair of canonical values were 0.794 and 0.438, respectively. Both values were significant and represented 92.82% of the observed variability. The correlation structure showed that color values had a strong correlation with fat and protein content and with total solids, and they had a weak correlation with lactose content and somatic cell score. The 2 first combinations of standardized canonical variability could be considered a predictable measure of the composition and, to a lesser extent, the hygiene of milk. Measurement of color values could be a rapid and effective means of supplementing standard analyses when determining the coagulation ability of Manchega sheep milk.

Conjoint analysis was used to estimate the relative importance of some of the main extrinsic attributes and quality labels of beef in three Spanish cities (Córdoba, Marbella, and Santa Pola) in a study performed with 300 individuals. Consumers were segmented according to their frequency of consumption. Willingness to pay for different meats was also calculated from the conjoint analysis results. Consumer liking of beef that had been finished with an alternative concentrate rich in agro-industrial by-products and aged for three different durations as compared to conventionally finished beef was also evaluated using the same consumers. The most important attribute for Spanish consumers was the price (28%), followed by origin (25%), animal welfare certification (19%), protected geographical indication (14%), and organic agriculture certification (14%). Most consumers preferred beef from Spain at the lowest possible price and with the highest number of quality labels. Consumers were willing to pay a premium of 1.49, 3.61, and 5.53 EUR over 14 EUR/kg for organic certification, protected geographical indication, and animal welfare certification, respectively. Sensory analysis revealed that, for regular consumers, beef finished with an alternative concentrate rich in agro-industrial by-products offered several hedonic advantages (color, flavor, and tenderness) when compared to beef finished using a conventional diet, while occasional consumers did not find any difference between the two kinds of meat.

This study approaches the interrelation patterns between composition of milk and whey, curd yield, chromaticity, syneresis, and technological quality of Manchega sheep milk using multivariate factor analysis. In addition, the effect of the main husbandry components (flock, prolificacy, season of the year, stage of lactation, and parity) on the common latent factors that define the pattern of variation of Manchega milk was assessed. For this purpose, 1,200 individual Manchega ewe milk samples from 4 different flocks registered under the Protected Designation of Origin Queso Manchego were analyzed (50 ewes/flock). Samples were collected in 2 different seasons of the year (spring and autumn) and at 3 time points per season: early, mid-, and late lactation. The obtained results suggested that curd yield mainly depends on milk composition, and the retention of water in the curd is related to coagulation traits. Thus, composition and moisture content could be useful indicators to assess the efficiency and quality of milk intended for cheesemaking, regardless of the analysis of coagulation properties. Finally, in terms of husbandry, a direct effect of flock and stage of lactation was observed on all analyzed factors, with a lower influence of season and parity.

The venereal diseases bovine trichomoniasis (BT) and bovine genital campylobacteriosis (BGC) cause economic losses in endemic areas like La Pampa province in Argentina where beef cattle are usually extensively managed. This study used data compiled between 2007 and 2014 by a Provincial Program for the Control and Eradication of venereal diseases in order to develop and analyze retrospective models of time series for BT and BGC. Seasonality and long-term trend were explored with decomposition and simple regression methods. Autoregressive Integrated Moving Average models (ARIMA) were used to fit univariate models for the prevalence and persistence of BT and BGC. Autoregressive Integrated Moving Average with Explanatory Variable models (ARIMAX) were used to analyze the association between different time series, replacement entries and herd samplings. The prevalence and persistence of BT and BGC have decreased from 2007 to 2014. All the BT and BGC time series are seasonal and their long-term trend is decreasing. Seasonality of BT and BGC is similar, with higher rates of detection in autumn-winter than is spring-summer. Prevalence and persistence time series are correlated, indicating their changes are synchronic and follow a similar time pattern. Prevalence of BT and BGC showed the best fitting with the ARIMA (0,0,1)(0,1,1)₁₂ model. While for persistence of BT and BGC, the best adjustment was with the same model with no seasonal difference where the current number of cases depends on the moving averages of the month and the previous season. Including covariates improve the fitting of univariate models, in addition, estimations using ARIMAX models are more precise than using ARIMA models. The time distribution of the samplings could be increasing the false negative ratio. According to the obtained results, the ARIMA and ARIMAX models can be considered an option to predict the BT and BGC prevalence and persistence in La Pampa (Argentina).