Impact of diversified grazing systems on milk production, nutrient use and enteric methane emissions in dual-purpose cows.

This study evaluated three continuous grazing systems: Brachiaria Brizantha, Clitoria ternatea and naturalized pastures, complemented with commercial concentrate and C. ternatea silage on milk yield, nutrient use and enteric methane (CH4) emissions. Nine multiparous cows of local Zebu breeds, with an average weight of 448 ± 87 kg, were used. The chemical composition of the food was determined. Live weight, milk production, and quality were assessed. Furthermore, serum urea, urea nitrogen, creatinine and glucose in blood were monitored, and nitrogen use efficiency were calculated. Enteric methane (CH4) emissions were estimated using Tier-2 methodology. A 3 × 3 latin square experimental design was applied. The grazing systems of B. brizantha and C. ternatea had the greater live weights of 465.8 and 453.3 kg/cow, although the latter is similar to naturalized pasture. Milk production and quality were not affected by grazing system, with the exception of the non-fat solids, where the C. ternatea system was lower (102.2 g/kg) than the other grazing systems. The crude protein and N intake, and N excretion in feces and urine were lower in naturalized pasture systems (1139.0 g/day). N outputs in milk was high in the C. ternatea system (56.3 g/cow/day). The naturalized pastures systems showed the better feed use efficiency (25.7%) compared to others. Serum urea and blood urea nitrogen were greater in B. brizantha followed by C. ternatea. Enteric CH4 emissions were indifferent among grazing systems when expressed as a percentage of greenhouse gases (7.1%). In conclusion, the grazing C. ternatea supplemented with commercial concentrate and C. ternatea silage maintains milk production and quality, reduced cow/day emissions (by 2.5%) and lowered energy losses as methane.

Inclusion of chia seeds (Salvia hispanica L.) and pumpkin seeds (Cucurbita moschata) in dairy sheep diets.

Chia (Salvia hispanica L.) seed (CS) and Pumpkin (Cucurbita moschata) seed (PS) are used in ruminant diets as energy sources. The current experiment studied the impact of dietary inclusion of CS and PS on nutrient intake and digestibility, milk yield, and milk composition of dairy sheep. Twelve primiparous Texel × Suffolk ewes [70 ± 5 days in milk (DIM); 0.320 ± 0.029 kg milk yield] were distributed in a 4 × 3 Latin square design and fed either a butter-based control diet [CON; 13 g/kg dry matter] or two diets with 61 g/kg DM of either CS or PS. Dietary inclusion of CS and PS did not alter live weight (p >0.1) and DM intake (p >0.1). However, compared to the CON, dietary inclusion of both CS and PS increased the digestibility of neutral detergent fiber (p <0.001) and acid detergent lignin (p < 0.001). Milk production (p = 0.001), fat-corrected milk (p < 0.001), and feed efficiency (p < 0.001) were enhanced with PS, while the highest milk protein yield (p < 0.05) and lactose yield (p < 0.001) were for CS-fed ewes. Compared to the CON diet, the ingestion of either CS and/or PS decreased (p < 0.001) the C16:0 in milk. Moreover, both CS and PS tended to enhance the content of C18:3n6 (p > 0.05) and C18:3n3 (p > 0.05). Overall short-term feeding of CS and/or PS (up to 6.1% DM of diet) not only maintains the production performance and digestibility of nutrients but also positively modifies the milk FA composition.

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico.

This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R2, RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R2 value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.

Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms.

Determination of live weight, which is one of the most important features that determine meat production, is a very important issue for herd management and sustainable livestock. In this context, the necessity of finding alternative methods has emerged, especially in rural conditions, due to the difficulties to be experienced in finding the weighing tool. Especially for conditions with no weighing tool, it has been tried to establish relations between the information obtained from body measurements and live weight. Since these studies will differ from species to species and breed to breed, the need for new studies is extremely high. For this aim, it is to evaluate the body measurement information obtained with the present study using several statistical approaches. To implement this aim, several data mining and machine learning algorithms such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), and support vector machine regression (SVR) algorithms were used for training (70%) and test (30%) sets. To predict final body weight, 280 hair sheep breeds (162 female and 118 male) ranging from 2 months to 3 years were used with different data mining and machine learning approaches. Various goodness-of-fit criteria were used to evaluate the performances of the aforementioned algorithms. Although the MARS and SVR algorithms gave the same and highest results in terms of R2 and r values for both the train and the test sets, the SVR algorithm is one of the methods to be recommended as a result of this study, especially when other goodness-of-fit criteria are evaluated. In conclusion, the usage of SVR algorithms may be a useful tool of machine learning approaches for detecting the hair sheep breed standards and may contribute to increasing the sheep meat quality in Mexico.

Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods.

This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluated were decision trees, random forests, support vector machines, and multi-layer perceptrons and were used to predict the total carcass bone (TCB), total carcass fat (TCF), and total carcass muscle (TCM). The best model for predicting the three parameters, TCB, TCF, and TCM was random forests, with mean squared error (MSE) of 0.31, 0.33, and 0.53; mean absolute error (MAE) of 0.26, 0.29, and 0.53; and the coefficient of determination (R2) of 0.67, 0.69, and 0.76, respectively. The results showed that machine learning methods from in vivo ultrasound measurements can be used as determinants of carcass tissue composition, resulting in reliable results.

Haematological values in cattle reared in humid and subhumid tropics of Mexico.

The objective of this study was to determine some factors that influence the haematological values of cattle reared in the humid and subhumid tropics of Mexico. Whole blood samples were taken from 1355 crossbred cattle in the years 2017 to 2019. Haematocrit (HTC, %), total plasma protein (TPP, g/dL) and peripheral eosinophils count (PEOS, × 103/µL) were determined manually, and the main haematological variables were recorded with an automatic analyser. The statistical analysis considered as classification variables age, sex, season (cold, dry and rainy), year (2017, 2018 and 2019) and origin of the cattle. The mean of the haematological parameters was determined along with the confidence limits (CL) of the different categories of animals according to age. Calves younger than 1-year-old presented higher levels of HTC, red blood cell count (RBC), haemoglobin (HGB), red blood cell distribution width (RDW), platelet number (PLT), white blood cell count (WBC) and lymphocyte count (LYMF) than animals older than 2 years of age. However, they showed the lowest mean cell volume (MCV) and TPP values. In cows, the highest levels of PEOS, granulocytes (GRAN), MCV and medium cells (MID) were observed and the lowest HTC, RBC, RDW and WBC levels. Intervals were determined with the 1st quartile (Q1) or lower confidence interval (90% CI) as the minimum values and the 3rd quartile (Q3) or upper confidence interval (90% CI) as the maximum values. The haematological parameters of cattle reared in the Southeast of Mexico are significantly affected by age, sex and environmental conditions.

Estimation of body weight using body measurements in female water buffaloes reared in southeastern Mexico.

Buffalo farming is an important livestock activity in Mexico. However, the low technological level of the farms makes it difficult to monitor the growth rates of the animals. The objectives of this study were to analyse the body measurements of 107 adult female Murrah buffaloes, to estimate the interrelationships between those measurements and body weight, and to develop equations to predict body weight (BW) using body measurements including withers at height (WH), rump height (RH), body height (BH), heart girth (HG), abdominal girth (AG), pelvic girth (PG), body length (BL), girth circumference (GC), diagonal body length (DBL), pelvic circumference (PC), and abdomen circumference (AC). The study was conducted on two commercial farms in southern Mexico. Pearson correlation and stepwise regression techniques were used for the data analysis. To find out the best regression models, we used model quality criteria such as coefficient of determination (R2), adjusted R2 (Adj.R2), root mean square error (RMSE), Mallow's Cp, Akaike's information criteria (AIC), Bayesian information criteria (BIC), and coefficient of variation (CV). Correlation results indicated that BW had a positive high correlation (P < 0.01) of all the measured traits. Model 4 (-780.56 + 311.76GC + 383.51DBL + 51.82PC + 47.65AC-106.78BL) was the best regression model with a higher R2 (0.87), Adj. R2 (0.86) smaller Cp (4.24), AIC (749.19), BIC (752.16), and RMSE (36.91). The current study suggests that GC, DBL, PC, AC, and BL might be used in combination to estimate BW of adult female Murrah buffaloes.

Effects of the Inclusion of Ground Pouteria sapota Kernel on Intake, Digestibility, and Growth Performance in Lambs.

This study determined the effect of replacing ground corn and soybean meal with ground Pouteria sapota kernel (PSSM) in lamb diets on nutrient intake and digestibility, performance, and carcass traits. Twenty-one male hair sheep lambs with an average body weight of 22 ± 3.5 kg were randomly assigned to three treatment diets containing PSSM at 0, 10, and 20% of the total dry matter (DM) inclusion. The study lasted 60 days, which included 15 days for adaption and 45 days for sample collection. The PSSM inclusion did not affect intake or performance (p > 0.05). However, ether extract (EE) digestibility linearly increased (p < 0.0001), while crude protein (CP) and acid detergent fiber (ADF) linearly decreased. Final body weight, total weight gain, average daily weight gain, feeding efficiency, and carcass traits were not affected by PSSM inclusion. In conclusion, these results suggest that PSSM can replace up to 200 g/kg DM of ground corn and soybean meal without affecting intake or animal performance.

Effect of Adding Extra Virgin Olive Oil to Hair Sheep Lambs' Diets on Productive Performance, Ruminal Fermentation Kinetics and Rumen Ciliate Protozoa.

This study determined productive performance, ruminal fermentation kinetics and rumen ciliate protozoa in hair sheep lambs fed different levels of olive oil. Twenty-four growing lambs were used, with an initial live weight of 10.5 ± 2.9 kg, and randomly assigned into four treatments (six animals per treatment) containing increasing levels of extra virgin olive oil (0, 2, 4 and 6% of dry matter). Animals were fed for 80 days, and sampling was carried out weekly. Intake of dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and metabolizable energy (ME) differed between treatments (p < 0.05), with a linear and cubic tendency to decrease when oil concentrations were increased. Digestibility coefficients of OM, CP and NDF were not affected; however, the relationship between total intake and nutrient digestibility (DM, OM, NDF, ADF) increased with 2% DM olive oil. Compared with all treatments, the concentration of propionic acid increased by 16% with 4% olive oil. The intake of olive oil did not affect the protozoa population and live weight gain. Overall, the inclusion of olive oil in low concentrations (2% of DM) positively influences feed intake and nutrient digestibility in hair sheep lambs.

Effect of different dietary inclusion levels of whole plant green tomato (Physalis philadelphica) silage on nutrient intake and digestibility, and in vitro rumen fermentation kinetics in sheep.

Mexico has many agricultural by-products that can be used for animal feed, and green tomatoes are produced throughout the country and can be an alternative to overcome the high prices of cereal-based feeds. This study determined in vitro fermentation kinetics, production performance, nutrient intake, digestibility, and nitrogen balance from sheep supplemented with whole plant green tomato (GT) on corn silage (CS) based diets. For 21 days, eighteen Suffolk lambs (38 ± 4 kg of live weight) were grouped into three dietary GT inclusion levels to replace CS: a control diet based on 100% CS (GT0, 570 g /kg dry matter, DM), while 100 g/kg DM (GT100) and 200 g/kg DM (GT200) of GT were included as a replacement for CS. A completely randomized design was used to measure in vitro gas production, in vitro rumen fermentation, chemical composition, and in vivo parameters. In vitro gas production, "A" (ml/g DM), fermentation rates "B," (h-1), and "C" (h-½), were lower for GT200, while DM disappearance (mg/100mg) was lower for GT100 compared with GT0. Compared to GT0, GT100 and GT200 did not affect (P > 0.05) DM and organic matter (OM) intake (g/kgLW0.75). Ether extract intake was higher for GT0 and GT100 (P < 0.001) compared to GT200. Neutral detergent fiber (NDF) intake was higher (P < 0.05) for GT200 compared with GT0. Intake of lignin was higher (P < 0.001) for GT200 than that of GT0 and GT100. Digestibility coefficients for DM, OM, NDF, and Acid detergent fiber (ADF) were lower (P < 0.05) in GT100 than in the rest of the treatments. Nitrogen intake and N excreted in feces and urine were lower (P < 0.001) for GT0. N balance was negative for all treatments, being higher for GT200 (P < 0.05). Overall, the addition of GT at 100 or 200 g/kg DM in sheep diets negatively affects nutrient digestibility and N balance, so their dietary inclusion is not recommended.

Models to predict live weight from heart girth in crossbred beef heifers.

The objective of this study was to develop and evaluate linear, quadratic, and exponential mathematical models to predict live weight (LW) from heart girth (HG) in crossbred heifers raised in tropical humid conditions in Mexico. Live weight (363.32 ± 150.88 kg) and HG (166.83 ± 24.88 cm) were measured in 400 heifers aged between 3 and 24 months. Linear and non-linear regression was used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), the Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). In addition, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was evaluated based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The correlation coefficient between LW and HG was r = 0.98 (P < 0.001). The quadratic model showed the lowest values of MAE (736.57), RMSEP (27.13), AIC (3783.95), and BIC (3799.91). Additionally, this model exhibited better goodness-of-fit values regarding external and internal validation criteria (higher R2 and lower RMSEP and MAE), thus having better predictive performance. The RMSE represented about 8% of the observed LW. Heart girth is highly correlated (r = 0.98) with LW. The quadratic model showed a high predictive capacity for crossbred heifers kept in tropical conditions.

Herbal Vitamin C Prevents DNA Oxidation and Modifies the Metabolomic Water Profile of Tilapia (Oreochromis spp.).

This experiment aimed to evaluate the effects of herbal vitamin C at different levels on tilapia (Oreochromis spp.) growth, potential DNA damage, and the metabolomic profile of water effluent. Forty-five tilapias were housed in separate plastic tanks (80 L), and these were randomly assigned to three treatments: (a) a commercial diet (CD) only; (Nutripec Purina®); (b) the commercial diet plus 250 mg of herbal vitamin C (HVC)/kg (CD250); and (c) the commercial diet plus 500 mg of HVC/kg (CD500). Biometric measurements were taken each week, blood samples were collected from the caudal vein on the final day, and water effluent was taken each week and immediately frozen (-80 °C) until further analysis (gas chromatography/mass spectrometry (GC/MS) systems). Data were completely randomized with a 2 × 2 factorial arrangement of treatments. Upon including herbal vitamin C, the final BW (p = 0.05) and BWG (p = 0.06) increased linearly. Herbal vitamin C decreases DNA damage (p ≥ 0.05). PLS-DA showed a 41.6% variation between treatments in the water samples. Fifteen metabolites had the best association between treatments, with a stronger correlation with CD500. Herbal vitamin C could improve fish performance, prevent DNA damage, and influence changes in the metabolomic profile of the water.

Estimation of Carcass Tissue Composition from the Neck and Shoulder Composition in Growing Blackbelly Male Lambs.

This study was designed to develop predictive equations estimating carcass tissue composition in growing Blackbelly male lambs using as predictor variables for tissue composition of wholesale cuts of low economic value (i.e., neck and shoulder). For that, 40 lambs with 29.9 ± 3.18 kg of body weight were slaughtered and then the left half carcasses were weighed and divided in wholesale cuts, which were dissected to record weights of fat, muscle, and bone from leg, loin, neck, rib, and shoulder. Total weights of muscle (CM), bone (CB) and fat (CF) in carcass were recorded by adding the weights of each tissue from cuts. The CM, CF and CB positively correlated (p < 0.05; 0.36 ≤ r ≤ 0.86), from moderate to high, with most of the shoulder tissue components, but it was less evident (p ≤ 0.05; 0.32≤ r ≤0.63) with the neck tissue composition. In fact, CM did not correlate with neck fat and bone weights. Final models explained (p < 0.01) 94, 92 and 88% of the variation observed for CM, CF and CB, respectively. Overall, results showed that prediction of carcass composition from shoulder (shoulder) tissue composition is a viable option over the more accurate method of analyzing the whole carcass.

Using Post-Mortem Measurements to Predict Carcass Tissue Composition in Growing Rabbits.

The objective of this study was to determine post-mortem measurements for predicting carcass traits in growing rabbits. A total of 50 clinically healthy New Zealand White × Californian male rabbits with a body weight (BW) of 1351 ± 347 g between 60 to 80 days of age were used. Body weight was recorded 12 h before slaughtering. Data recorded at slaughtering included carcass weights (HCW). After cooling at 4 °C for 24 h, carcasses were weighed (CCW) and then were carefully split longitudinally with a band saw to obtain left and right halves. In the right half carcass, the following measurements were recorded using a tape measure: dorsal length (DL), thoracic depth (TD), thigh length (TL), carcass length (CL), lumbar circumference (LC). The compactness index (CCI) was calculated as the CCW divided by the CL. Thereafter, the right half carcass was weighed and manually deboned to record weights of muscle (TCM), and bone (TCB). The CCI explained of 93% of variation for TCM (R2 = 0.93 and a CV = 9.30%). In addition, the DL was the best predictor (p < 0.001) for TCB (R2 = 0.60 and a CV = 18.9%). Our results indicated that the use of carcass measurements could accurately and precisely (R2 = ≥ 0.60 and ≤0.95) be used as alternatives to predict the carcass tissues composition in growing rabbits.

Study of racial profile of the native Guajolote (Meleagris gallopavo gallopavo) in two regions of Mexico: morphometric characterization.

The present study aimed at the morphometric characterization of the native Guajolote reared in two regions of Mexico using multivariate analysis techniques. Data from a total of 362 unrelated native Guajolotes (257 males and 105 females) were used: of these, 160 were from the Central region and 202 were from the Southeast region. The birds were also grouped according to age, as youngs (≤ 8 months; n = 150) and adults (≥ 9 months; n = 212). The body weight (BW) and nine morphometric measurements-chest circumference (CC), body length (BL), body height (BH), neck length (NL), peak length (PL), shank length (SL), shank diameter (SD), wing length (WL) and wing width (WW)-were measured. There were significant differences (p < 0.05) due to the effect of region, sex and age for most of the morphometric measurements evaluated. There was a high percentage of positive and significant correlations (p < 0.001; p < 0.01) between the variables. In each region, three principal components were extracted that represented more than 75% of the accumulated variation among the variables. The most discriminating morphometric measurements between populations were WW, PL and NL. The Mahalanobis distance between the males and females of the two populations was 37.457 and 29.310 (p < 0.001), respectively. This differentiation can contribute to the definition of the phenotypic standard of this poultry genetic resource for its official recognition as a breed, as well as in the orientation of its genetic improvement programs in the future.

Physicochemical Characteristics of Yogurt from Sheep Fed with Moringa oleifera Leaf Extracts.

This study determined the effect of feeding Moringa oleifera (MO) leaf extracts to lactating ewes on the physicochemical composition of their milk and yogurt during storage (4 °C for 14 days) and the sensory acceptance of the yogurt. Over 45 days, 24 multiparous lactating Pelibuey and Katahdin ewes (two days in lactation) were randomly assigned to four groups: MO-0, basal diet (BD) + 0 mL MO; MO-20, BD + 20 mL MO; MO-40, BD + 40 mL MO; and MO-60, BD + 60 mL MO. In the milk, an increase of 6% in protein, 26% in leucine, 14% in ash, and 1% in the pH (6.71) was observed with MO-60. The density values decreased by 0.3% at a higher dose of MO compared to MO-0, while the nonfat solids (NFS) in the milk were similar between the treatments. In the yogurt, an increase of 5% in protein, 113% in leucine (MO-20), 9% in NFS, and a reduction of 2% in moisture with MO-60 was observed. The acidity reflected an inverse relationship to the pH, as did the moisture and NFS with MO-60. In conclusion, dietary supplementation with MO in lactating ewes did not have negative effects on the chemical composition of their yogurt during storage (14 days). Overall, feeding sheep with 20 mL of MO positively influenced the physicochemical composition of their milk and yogurt during storage.

Do Live Weight, Body Condition Score, Back Muscle or Back-Fat Reserves Create the Suspicion of Goats Infected with Eimeria or Trichostrongylids?

Thirty goats of the breeds Czech Brown Shorthaired and Czech White Shorthaired and their crosses were randomly selected from a flock at a farm in the Czech Republic. All animals were monitored for one year at monthly intervals for their nutritional status (live weight, LW; body-condition score, BCS; depth of musculus longissimus thoracis et lumborum, MLTL; back-fat thickness, BT) and infection intensity with Eimeria sp. (EIM) and strongylid nematodes (STR). Regression-correlation analysis showed a possible interrelation of BCS with EIM infection. Analysis of muscle and fat reserves indicated that BT was better than MLTL in identifying EIM infection. Goat nutritional status was not significantly correlated with STR infection. A linear tendency (p = 0.092), however, was detected for the response of MLTL to STR infection. Results of this study indicated theoretical use of BCS for Eimeria identification and suggested some perspective of BCS for targeting animals infected by strongylid nematode. Validity of our results, however, was limited by number of observed animals managed under specific breeding conditions.

Chemical and fatty acid composition of Manchego type and Panela cheeses manufactured from either hair sheep milk or cow milk.

This study compared the chemical composition and fatty acid (FA) profile of Manchego type cheese and Panela cheese made from hair sheep milk and compared these with both types of cheese manufactured with cow milk as a reference. In addition, this study aimed to determine differences in sensory characteristics between Manchego type cheeses manufactured with either hair sheep milk or cow milk. A total of 25 and 14 Manchego type cheeses from hair sheep milk and cow milk were manufactured, respectively. In addition, 30 and 15 Panela cheeses from hair sheep milk and cow milk were manufactured, respectively. The chemical composition and FA profile were determined in all cheeses. In addition, a sensory analysis was performed in Manchego type cheeses manufactured from either hair sheep milk or cow milk. Moisture content was lower in Manchego type cheeses (37.5 ± 1.26 and 37.5 ± 1.26 g/100 g in cheeses manufactured from hair sheep milk and cow milk, respectively) than in Panela cheeses (54.0 ± 1.26 and 56.1 ± 1.26 g/100 g in cheeses manufactured from hair sheep milk and cow milk, respectively). Ash, protein, and sodium contents were higher in Manchego type cheeses than in Panela cheeses. Manchego type cheese manufactured from hair sheep milk contained more C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C18:2 cis-9,cis-12, total saturated FA, total short-chain FA, total medium-chain FA, total polyunsaturated FA, and de novo FA than Manchego type cheeses from cow milk. Total content of short-chain FA was higher in hair sheep cheeses (24.4 ± 1.30 and 19.6 ± 1.30 g/100 g in Manchego type and Panela cheeses, respectively) than in cow cheeses (8.89 ± 1.30 and 8.26 ± 1.30 g/100 g in Manchego type and Panela cheeses, respectively). Manchego type cheeses from hair sheep milk obtained higher scores for odor (7.05), texture (6.82), flavor (7.16), and overall acceptance (7.16) compared with those made from cow milk (6.37, 6.12, 6.17, and 6.83, respectively). In conclusion, both Manchego type cheese and Panela cheese manufactured with hair sheep milk had a similar chemical composition and contained higher levels of short-chain FA, total polyunsaturated FA, and de novo FA than those manufactured with cow milk.

Physico-Chemical, Sensory and Texture Properties of an Aged Mexican Manchego-Style Cheese Produced from Hair Sheep Milk.

The objective of this study was to evaluate the physicochemical and texture changes of the Manchego-style cheese produced from hair sheep (Pelibuey) throughout 180 days of ripening, as well as consumer's acceptance. Cheese pH was constant from 1 to 180 days of ripening. Moisture, water activity, fat, elasticity and hardness decreased from day 1 to day 180, while protein, trichloroacetic acid-soluble N and free amino acid increased. Cheese lightness decreased as ripening time increased, while elasticity and hardness decreased. Principal Component Analysis was useful in discriminating cheeses according to their physicochemical composition and that allowed cheeses to be classified in two groups according to their ripening time and this resulted in those with less than 60 days and those with more than 90 days of ripening. Compared with cheeses ripened at 1 and 90 days, aged cheeses at 180 days reduced scores for appearance, color, odor, taste, texture and overall acceptance. Overall, Manchego-style cheeses from hair sheep had the usual ripened-cheese physicochemical changes.

Estimation of body weight in hair ewes using an indirect measurement method.

The aim of the present study was to develop and evaluate an equation to predict body weight (BW) using hip width (HW) in Pelibuey ewe lambs and ewes. Five hundred seventy-seven 2-month-old to 3-year-old, non-pregnant, non-lactating, clinically healthy ewe lambs and adult ewes with a mean BW of 34.7 ± 12.4 kg and HW of 15.6 ± 3.4 cm were considered. Three equations were evaluated: BW (kg): - 19.17 + 3.46 × HW (Eq. 1), BW (kg): - 17.79 + 3.25 × HW + 0.007 × HW2 (Eq. 2) and BW (kg): 0.39 × HW1.63 (Eq. 3). Independent data from 80 animals with similar characteristics (BW of 23.4 ± 10.9 kg and HW of 12 ± 3.1 cm) were also considered to evaluate the developed equations. The evaluation was based on the relationship between the observed and predicted values of BW analysed using a linear regression, the mean squared error of prediction (MSEP), the root MSEP (RMSEP) and the concordance correlation coefficients (CCCs). Additionally, cross-validation analyses were performed using the k-folds validation (k = 10) procedure. The correlation coefficient (r) between BW and HW was 0.94 (P < 0.001). The parameters for precision and accuracy showed that the proposed equations had high precision (R2 > 0.95%), accuracy (Cb > 0.98) and reproducibility (CCC > 0.96) in predicting the BW of ewe lambs and adult ewes. Equation (1) accurately predicted observed BW, with a bias (observed - predicted) of 4.3 kg and RMSEP of 9.68% with respect to the observed BW (random error of 84.23%); it also generated the best prediction according to the residual mean squared prediction error, coefficient of determination and mean absolute error. In conclusion, the highly correlated relationship between BW and HW in Pelibuey ewe lambs and adult ewes under humid tropic conditions enabled the development of mathematical models herein to estimate BW with an adequate goodness of fit. The linear model showed the best performance according to the goodness-of-fit evaluation and internal and external validation; hence, this model is proposed for use in both the experimental and commercial farms.