Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis).

The objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, 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 assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: -176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.

Compilations and updates on residual feed intake in sheep.

The increasing global demand for food and the strong effect of climate change have forced animal science to advance regarding new methods of selection in search of more efficient animals in production systems. Feed consumption represents more than 70% of the costs of sheep farms, and more efficient animals can increase the farmers' profitability. One of the main measures of feed efficiency is estimated residual feed intake (RFI), created in 1963 by Robert Koch for estimation in cattle and later adapted for sheep. Animals with negative RFI values (RFI-) are more efficient than animals with positive values (RFI+), with influence on the variables of performance, carcass quality and production of enteric gases. The RFI is the most common and accepted metric of the feed efficiency trait for genetic selection, since it is independent of growth traits, unlike the feed conversion ratio. The purpose of this review article was to present updated literature information on the relationship of RFI estimates with performance measures, molecular markers, greenhouse gas production and feed efficiency, the technical aspects and physiological basis of metabolic in sheep.

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.

Predicting live weight using body volume formula in lactating water buffalo.

Live weight (LW) is an important piece of information within production systems, as it is related to several other economic characteristics. However, in the main buffalo-producing regions in the world, it is not common to periodically weigh the animals. We develop and evaluate linear, quadratic, and allometric mathematical models to predict LW using the body volume (BV) formula in lactating water buffalo (Bubalus bubalis) reared in southeastern Mexico. The LW (391.5 ± 138.9 kg) and BV (333.62 ± 58.51 dm3) were measured in 165 lactating Murrah buffalo aged between 3 and 10 years. The goodness-of-fit of the models was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), coefficient of determination (R2), mean-squared error (MSE) and root MSE (RMSE). In addition, the developed models were evaluated through cross-validation (k-folds). The ability of the fitted models to predict the observed values was evaluated based on the RMSEP, R2, and mean absolute error (MAE). LW and BV were significantly positively and strongly correlated (r = 0.81; P < 0.001). The quadratic model had the lowest values of MSE (2788.12) and RMSE (52.80). On the other hand, the allometric model showed the lowest values of BIC (1319.24) and AIC (1313.07). The Quadratic and allometric models had lower values of MSEP and MAE. We recommend the quadratic and allometric models to predict the LW of lactating Murrah buffalo using BV as a predictor.

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.

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.

Prediction models of intake and productive performance of non-castrated Nellore cattle finished in the feedlot system under tropical conditions.

The objective of this study was to understand and predict the intake and performance of Nellore cattle finished in the feedlot. Individual data from 144 non-castrated male Nellore steers finished in the feedlot between the years 2016 and 2020 were used. Descriptive statistical analyses and Pearson's correlation were performed. The outliers were tested by evaluating the studentized residuals in relation to the values predicted by the equations. Residues that were outside the range of - 2.5 to 2.5 were removed. The goodness of fit of the developed equations was evaluated by the coefficients of determination (r2) and root mean square error (RMSE). The mean dry matter intake (DMI) was 10.2 kg/day, neutral detergent fiber intake (NDFI) was 3.4 kg/day, corresponding to 33.3% of DMI, crude protein intake (CPI) was 1.6 kg/day, and total digestible nutrient intake (TDNI) was 7.1 kg/day. The CPI to ADG ratio was 1.3 kg CPI/kg ADG and the TDNI to CPI ratio was 4.5 kg TDNI/kg CPI. The averages of productive performance were 1.3 kg/day for average daily gain (ADG), 152.6 kg for total weight gain (TWG), and 497.8 kg for final body weight (FBW) in average days in the confinement of 115.7 days. The intake measures correlated significantly with the performance measures, except for carcass yield and days in the feedlot. TWG had a high positive correlation with ADG (r = 0.84), while FBW had a positive correlation (r = 0.86) with hot carcass weight (HCW). Measures of intake, performance, and days in the feedlot can be used as predictors of DMI, FBW, HCW, TWG, and ADG. The prediction equations had satisfactory precision and accuracy for non-castrated Nellore cattle finished in feedlot systems under tropical conditions.

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.

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.

Prediction models of carcass characteristics from non­castrated Nellore cattle finished in the feedlot system under tropical conditions.

Our objective was to use measures of intake and productive performance to adjust prediction models for the carcass traits of non-castrated Nellore cattle finished in a feedlot. Individual data from 168 non-castrated male Nellore steers finished in feedlot between the years 2016-2021 were used. Descriptive statistical analyzes and Pearson correlation coefficients were performed. The outliers were tested by evaluating the studentized residuals in relation to the values predicted by the equations. Residues that were outside the range of -2.5 to 2.5 were removed. The goodness of fit of the developed equations was evaluated by the coefficients of determination (R2) and root mean square error (RMSE). Models for carcass yield, subcutaneous fat thickness, ribeye area, and shear force were adjusted. Means of 53.5% carcass yield, 4.8 mm subcutaneous fat thickness, 73 cm2 loin eye area, and 8.1 kg shear force were observed. The observed average intakes were 9.9 kg/day of dry matter, 3.3 kg/day of neutral detergent fiber content, 1.5 kg/day of crude protein, and 7.1 kg/day of total digestible nutrients. The average confinement time was 113 days, the average total weight gain was 152.2 kg and the average daily gain was 1.35 kg/day. Intake measures significantly correlated with shear force and subcutaneous fat thickness and ribeye area. Carcass yield was significantly correlated with total weight gain, feedlot time, and hot carcass weight. Measures of nutrient intake, performance, and confinement time can be used as predictors of carcass yield, ribeye area, fat thickness, and shear force of non-castrated Nellore cattle finished in a feedlot. The prediction equations for ribeye area, carcass yield, subcutaneous fat thickness, and shear force showed sufficient precision and accuracy for non-castrated Nellore cattle finished in confinement systems under tropical conditions. All equations can be used with caution to estimate carcass traits of cattle finished in a feedlot using measures of intake and productive performance.

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.

Prediction of weaning weight in Santa Inês lambs using the body volume formula.

Weaning weight (WW) is one of the most important information within production systems, as it is a reflection of management during the breastfeeding phase and will influence the performance of animals in subsequent phases. This study aimed to develop and evaluate linear, quadratic, and exponential models to predict WW using the body volume (BV) formula in Santa Inês lambs for meat. Eighty-five lambs at 90 days of age with WW 17.52 ± 3.79 kg and BV 13.29 ± 2.86 dm3 were evaluated. The quality of fit of the models was evaluated using the coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). For the external evaluation of the models, an independent dataset from 43 lambs at 90 days of age was used. The first-degree linear model showed the lowest values of MSE (1.02) and RMSE (1.01). In the external evaluation, all models exhibited estimates of mean WW and standard deviation of this weight similar to the external dataset, as well as high values (above 0.89) for the R2 of predicted vs. observed data. Concordance correlation coefficient (CCC) analysis also revealed that all models showed accuracy and precision (CCC > 0.90). There was no difference between the models in terms of accuracy (P > 0.05). The comparison in terms of precision indicated that the linear model is more precise than the exponential model and that the quadratic model is as precise as the linear model. The first-degree linear model should be used due to its simplicity of interpretation and ease of estimation.

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.

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.

Predicting milk yield in Pelibuey ewes from the udder volume measurement with a simple method.

In this research communication we describe the creation of an equation for the prediction of milk yield (MY) from udder volume (UV). A total of 280 measurements were collected between 5 and 15 d postpartum (pp) from 36 multiparous Pelibuey ewes. Study variables were measured between 2 and 9 weeks pp and MY was measured by manual milking, UV prior to and following milking was measured using the technique of making moulds from aluminium foil. The MY ranged from 0.09 to 0.83 kg/d, meanwhile UV prior and following milking ranged from 155 to 1940 and 90 to 1520 cm3, respectively. Measurements of UV had a moderate to high (P < 0.01; 0.58 ≤ r ≤ 0.78) correlation with MY. The UV prior to milking was the best prediction model for MY, which explained 62% of the variation in MY. This equation presented moderate precision (r2 = 0.61) and high accuracy (bias correction factor = 0.94), confirming a good reproducibility index (concordance correlation coefficient = 0.73). Modelling efficiency (MEF = 0.59) showed moderate concordance between observed and predicted values. In conclusion, MY in lactating Pelibuey ewes could be predicted in a moderate way using the predictor variable UV measured with the technique of moulds made with aluminium foil.

Ovarian dynamics of buffalo (Bubalus bubalis) synchronized with different hormonal protocols.

The study evaluates the effect of three hormonal protocols on ovarian dynamics and progesterone (P4) secretion of buffalo (Bubalus bubalis). Twenty-nine pluriparous Murrah buffaloes were used. The protocols were as follows: OVSYNCH (n = 10): 100 µg of gonadorelin (day 0), 500 µg of cloprostenol (day 7), and 100 µg of gonadorelin (day 9). CIDR+EB (intravaginal device (CIDR®) + estradiol benzoate; n = 10): CIDR plus 2 mg of EB (day 0), withdrew of CIDR, 500 µg of cloprostenol (day 7) and 1 mg of EB (day 8). CIDR+eCG (n = 9): CIDR plus 2 mg of EB (day 0), withdrew of CIDR, 500 µg of cloprostenol and 400 IU of eCG (day 7). Follicles were counted with an ultrasound and measured at 0, 24, and 54 h. The maximum follicle diameter and ovulation were evaluated at 70, 80, and 94 h after CIDR withdrew. Estrous was detected per 1 h three times daily. Blood samples were collected on days 0, 7, 10, 15, and 22 to determine P4 concentration. In CIDR+EB protocol, 50% of buffaloes presented estrous, at 69.6 h. All buffaloes ovulated. CIDR+eCG group had the shortest (69 h) ovulation time. No treatment differences for follicular population, maximum follicle diameter, and P4 concentration on days 7 and 10 (P > 0.05) were found. The P4 concentration in OVSYNCH and CIDR+eCG protocols were > 1 ng/ml, on days 15 and 22 (P < 0.05). There was no difference in ovarian activity; however, the P4 secretion was normal in the OVSYNCH and CIDR+eCG protocols compared to the CIDR+EB protocol.

Relationships among body condition score, FAMACHA© score and haematological parameters in Pelibuey ewes.

The objective of the present study was to determine the associations between the FAMACHA© score, body condition score (BCS) and several haematological parameters of Pelibuey ewes. In total, 52 clinically healthy Pelibuey ewes were evaluated in this study. A blood sample was collected from each animal to determine haematological variables. The BCS was evaluated on a scale of 1 to 5 and subsequently grouped into four categories: (1) < 1.5, (2) 2.0-2.5, (3) 3.0-3.5 and (4) > 4.0. The coloration of conjunctival mucosa was evaluated using the FAMACHA© system. The analysed haematological parameters were red blood cell (RBC) count, haemoglobin (HGB), haematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red blood cell distribution width standard deviation (RDW-SD) and red blood cell distribution width coefficient of variation (RDW-CV). A descriptive statistical analysis was performed, and the Pearson correlation coefficients between variables were estimated. Haematological parameters were analysed with one-way analyses of variance, and the Tukey test was applied when significant differences were detected. The FAMACHA© score was only associated with MCHC (r = - 0.30, P < 0.05). The haematological parameters of RBC count, HCT, RDW-SD and RDW-CV statistically differed among BCS groups. The BCS was associated with RBC count, HCT, RDW-SD and RDW-CV, with r values ranging from 0.35 to 0.48. The haematological values were similar to those reported in ewes from Iran and Iraq. Hence, the optimization of the BCS calculation of Pelibuey ewes can generate association tables with haematological parameters to aid in the sanitary and feeding management of sheep.

Requirements of milk intake and intake of milk components for pre-weaning growth of Pelibuey lambs.

The objective of the present study was to determine the needs of milk intake (MI) and their components (i.e., lactose, fat, protein, and energy) to increase the average daily gain (ADG) of Pelibuey lambs during the pre-weaning period. Data of 62 lambs were used (35 males and 27 females; 32 of single lambing and 30 twins). All lambs were housed in individual pens with their mothers during the first 45 days post-lambing, without access to mother's food. Every 10 d, MI was estimated by the double lamb weighing method, and then, the intake of each milk component was calculated, considering both milk chemical composition and its estimated intake. The ADG was associated with MI and its components through a linear regression analysis. All milk variables were highly (P < 0.05; 0.69 ≤ r ≤ 0.85) correlated with ADG. The ADG explained (P < 0.0001) between 48 and 72% of the variation observed for MI and its components, being lower for fat intake and higher for MI. Given the positive slope parameter (ß1), an increase of one kilogram of body weight required intakes of 3.1 kg of milk, 117 g of fat, 203 g of protein, 187 g of lactose, or 16.8 MJ of energy. In conclusion, Pelibuey lambs to gain one kilogram of live weight during the pre-weaning period need to consume 3.1 kg of milk having an adequate amount of protein, fat, lactose, and energy.

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.

Productive behavior in growing kid goats and methane production with the inclusion of chokecherry leaf (Prunus salicifolia).

Currently for the reduction of methane (CH4) emissions are using fodder rich in condensed tannins, however, not yet known exactly how they act in the rumen is not yet clear. The objective of this study was to evaluate the effect of the inclusion of leaves of Prunus salicifolia (PS, 0%, 15%, and 30%) on the productive behavior of growing kid goats, methane production, nutritional value, fermentation, and ruminal digestibility through an in vivo and in vitro study was performed. Diets were administered ad libitum to 6 Saanen kids with live weight (LW) of 12.25 ± 2.25 kg. Three levels of inclusion were used in a diet based on ground corn grain, soybean meal, corn stover, and oat hay in substitution with P. salicifolia leaves. The in vivo productive behavior was determined, as well as the fermentation kinetics, in vitro gas production, CH4 and hydrogen (H2) in an in vitro system was determined. For the in vivo study, we used a 3 × 3 Latin square design in in vivo study and an analysis of variance with three replications for in vitro gas production. The inclusion of 30% increased (P = 0.0011) dry matter intake (DMI 589.33 g/day) compared to the control group (418.80 g/day). The highest N excretion (feces and urine) (P < 0.001) was for T0, in addition to presenting a negative nitrogen balance compared with T15 and T30. The production of CH4 (mM)/g DM incubated, and CH4 (mM)/g DM fermented, CH4 (mM/day), and H2 was lower (P < 0.05) in T30 than T0 and T15. CH4 (mM)/day was lower (P < 0.036) in T130 (283 mM/day) compared with T0 (407 mM/day) P. salicifolia is a forage that helps to reduce the production of methane and can be included in the diets of growing kid goats in amounts less than 30% without affecting production performance.