Productive performance and milk composition of dairy ewes supplemented with corn silage (Zea mays L.), sunflower (Helianthus annuus) silage and their mixture.

The work reported in the Research Communication investigated in vitro rumen gas kinetics and fermentation profile as well as in vivo performance of lactating ewes fed corn silage (CS), sunflower silage (SFS) and their 50 : 50 mixture (CS-SFS). For the in vivo experiment, nine early-lactation Suffolk × Texel ewes were grouped in a replicated 3 × 3 Latin square design of three 21-d periods. Treatments were based on ad libitum CS, SFS, and CS-SFS supplemented with concentrate at 48 g/kg LW0.75. In vitro results showed that the CS had the highest dry matter degraded substrate and microbial crude protein production followed by CS-SFS. The in vivo data showed that animals fed on CS had higher digestibility of dry matter and organic matter than CS-SFS, while SFS were intermediate. Nitrogen (N) intake, fecal N excretion, and urine N excretion were similar between groups, however, milk N excretion was lower in SFS than CS. Milk yield was higher for CS and CS-SFS than SFS group, however, SFS-fed ewes had higher milk fat content than either CS or CS-SFS (all differences reported here were significant, P < 0.05 or better). Overall, CS-SFS could be used as dietary roughage for dairy ewes without deleterious effects on nutrient intake, N-balance and milk yield whilst potentially offering a more sustainable alternative to CS.

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.

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.

Comparison of mathematical models to estimate live weight from heart girth in growing Pelibuey sheep / Comparación de modelos matemáticos para estimar el peso vivo mediante el perímetro torácico en ovinos Pelibuey en crecimiento / Comparação de modelos matemáticos para estimar o peso vivo por meio do perímetro torácico em ovinos Pelibuey em crescimento

Abstract Background: Assessment of animal growth based on live weight (LW) in traditional sheep production systems is limited by the high cost of purchase and maintenance of livestock scales. Objective: To develop and evaluate equations for LW prediction using heart girth (HG) in growing Pelibuey sheep. Methods: A dataset (n=415) of clinically healthy male Pelibuey sheep from two months to one year of age, with an average LW of 25.96 ± 10.25 kg and HG of 68.31 ± 10.53 cm, were used. Three equations were evaluated: LW (kg) = −37.70 + 0.93 × HG (Eq. 1); LW (kg) = −1.74 + 0.19 × HG + 0.008 × HG2 (Eq. 2); and LW (kg) = 0.003 × HG2.68 (Eq. 3). Results: The correlation coefficient between LW and HG was r = 0.94 (p<0.001). The three equations showed a high concordance correlation coefficient (CCCs≥0.97). However, the random error was the main component of the mean square partition of the prediction error (≥82.78%) only for Eqs. 1 and 2. The test for parameter identity (intercept=0; slope=1) was accepted only for Eq. 2 (p>0.05). On the other hand, for Eqs. 1 and 3 the intercept was different from zero and the slope was different from one (p<0.05). Conclusion: The second-degree equation accurately and precisely estimated body weight of growing Pelibuey sheep using the HG as a sole predictor variable.

Resumen Antecedentes : Debido a las condiciones de los sistemas tradicionales de producción ovina, la evaluación del crecimiento animal en función del peso vivo (PV) está limitada por el alto costo de la báscula ganadera y su mantenimiento. Objetivo: Desarrollar y evaluar ecuaciones para predecir el peso corporal utilizando el perímetro torácico (PT) en ovinos Pelibuey en crecimiento. Métodos : Se utilizó un conjunto de datos (n=415) de ovinos Pelibuey machos clínicamente sanos, de dos meses a un año de edad y peso promedio de 25,96 ± 10,25 kg y PT de 68,31 ± 10,53 cm. Se evaluaron tres ecuaciones: PV (kg) = −37,70 + 0,93 × PT (Ec. 1), PV (kg) = −1,74 + 0,19 × PT + 0,008 × PT2 (Ec. 2) y PV (kg) = 0,003 × PT2,68 (Ec. 3). Resultados: El coeficiente de correlación entre PV y PT fue r=0,94 (p<0,001). Las tres ecuaciones mostraron alto coeficiente de correlación de concordancia (CCCs≥0,97). Sin embargo, el error aleatorio fue el componente principal de la partición cuadrática media del error de predicción (≥82,78%) solo para las Ecs. 1 y 2. Sin embargo, la prueba de identidad de parámetros (intersección = 0; pendiente = 1) solo se aceptó para la ecuación 2 (p>0,05). Por otro lado, el intercepto fue diferente de cero y la pendiente fue diferente de uno (p<0.05) para las Ecs. 1 y 3. Conclusión: La ecuación de segundo grado estima con exactitud y precisión el peso corporal de ovinos Pelibuey en crecimiento utilizando la PT como única variable predictora.

Resumo Antecedentes: Devido às condições dos sistemas tradicionais de produção de ovinos, a avaliação do crescimento animal com base no peso corporal (PV) é limitada pelo alto custo da balança pecuária, bem como pela manutenção sofisticada necessária. Objetivo: Desenvolver e avaliar equações para predizer o PV usando o perímetro torácico (PT) em ovinos Pelibuey em crescimento. Métodos: Um conjunto de dados (n=415) de ovinos Pelibuey machos clinicamente saudáveis de dois meses a um ano de idade, com peso médio de 25,96 ± 10,25 kg e PT de 68,31 ± 10,53 cm foi utilizado para o desenvolvimento das equações. Três equações foram avaliadas: PV (kg) = -37,70 + 0,93 × PT (Eq. 1), PV (kg) = -1,74 + 0,19 × PT + 0,008 × PT2 (Eq. 2) e PV (kg) = 0,003 × PT2,68 (Eq. 3). Resultados: O coeficiente de correlação entre PV e PT foi r = 0,94 (P < 0,001). As três equações apresentaram alto coeficiente de correlação e concordância (CCCs≥0,97). No entanto, o erro aleatório foi o principal componente da partição do quadrado médio do erro de predição (≥82,78%) apenas para as Eqs. 1 e 2. No entanto, o teste de identidade dos parâmetros (intercepto = 0; inclinação = 1) foi aceito apenas para a Eq. 2 (p>0,05). Por outro lado, para a Eq. 1 e 3, o intercepto foi diferente de zero e a inclinação foi diferente de um (p<0,05). Conclusões: A equação de segundo grau estima com precisão e acurácia o peso corporal de ovinos Pelibuey em crescimento usando o PT como única variável preditora.

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.

Relationships between body mass index, ultrasound measurements and, internal body fat depots in pelibuey sheep

The objective of this study was to evaluate the relationship between body mass index (BMI), ultrasound measurements (USM), and internal fat (IF) in hair sheep. The BMI and USM were determined 24 hours before laughter in 35 animals finished in intensive fattening. At slaughter, the IF (inner adipose tissue) was dissected, weighed, and grouped as mesenteric, omental, or perirenal fat; these three depots were summed to obtain their total weight. The relationship of the variables was evaluated through Pearson's correlation coefficient (r) using the CORR procedure and by regression models using the REG procedure of SAS ver. 9.3. The IF and BMI presented r= 0.75 (P <.0001), while the IF and USM presented an r that varied from 0.70 to 0.80 (P <.0001). Among the internal fat deposits, the perirenal was the one that presented minor correlation coefficients with BMI (r = 0.55), subcutaneous fat thickness (0.55), and thoracic Longissimus dorsi area) (r = 0.33), and for this, the last correlation was not significant (P>0.05). The regression equation between IF and BMI had a coefficient of determination (r2) of 0.67 (P <.0001). The inclusion of the subcutaneous fat thickness together with the BMI improved the r2 with a range of 0.77. The BMI and the subcutaneous fat thickness could be used to predict the weight of the internal fat in hair sheep.

Effect of the Inclusion of Bacillus spp. in Growing-Finishing Pigs' Diets: A Meta-Analysis.

This meta-analysis determined the effect of Bacillus spp. on growth performance of growing−finishing pigs and then assessed causes for the heterogeneity of responses detected using meta-regression. A database of 22 articles published from 2000 to 2020 was identified, and 9 articles fitted the selection criteria and were integrated in the final database. Statistical analysis was performed to analyze the effect size for ADG, average daily feed intake (ADFI), and F:G ratio using a standardized means difference (SMD) at a 95% confidence interval. A meta-regression analysis was used to investigate the cause of heterogeneity, using the individual SMD for each study assessment as the outcome and the associated SE as the measure of variance. Dietary Bacillus spp. supplementation had no effect on ADFI (SMD: −0.052, p = 0.138) and numerically increased ADG (SMD: 0.113, p = 0.081) and reduced the F:G ratio SMD: −0.127, p < 0.001). Meta-regression outcomes suggested that the number of animals per group was an essential component promoting heterogeneity in ADG. Overall, the inclusion of Bacillus spp. (median 486 mg/d) in growing−finishing pigs can increase ADG and can decrease the F:G ratio.

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.

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.