Hematologic RIs for healthy water buffaloes (Bubalus bubalis) in southern Mexico

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.

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 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.

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.

Blood lymphocyte subpopulations in healthy water buffaloes (Bubalus bubalis, Mediterranean lineage): Reference intervals and influence of age and reproductive history

There is an increasing interest toward infectious diseases and mechanisms of immune response of water buffaloes, mainly because of the growing economic impact of this species and of its high-quality milk. However, little is known about the immune system of these animals in physiological conditions. Recently, a wide number of antibodies cross reacting with buffalo antigens has been validated for use in flow cytometry (FC), allowing detailed characterization of the lymphocytic population in this species. The aim of the present study was to describe the lymphocyte subpopulations in a large number of healthy water buffaloes, providing reference intervals (RIs), and to assess whether the composition of blood lymphocyte population significantly varied with age and reproductive history. Our final aim was to lay the ground for future studies evaluating the role of host immune response in water buffaloes. One-hundred-twelve healthy buffaloes from four different herds in the South of Italy were included in the study. All animals had been vaccinated for Infectious Bovine Rhinotracheitis (IBR), Salmonellosis, Colibacillosis and Clostridiosis, and all herds were certified Brucellosis- and Tuberculosis-free. Venous blood collected into EDTA tubes was processed for FC, and the percentage of cells staining positive for the following antibodies was recorded: CD3, CD4, CD8, CD21, TCR-δ-N24, WC1-N2, WC1-N3 and WC1-N4. Absolute concentration of each lymphoid subclass was then calculated, based on automated White Blood Cell (WBC) Count. Reference Intervals were calculated according to official guidelines and are listed in the manuscript. The composition of the lymphocyte population varied with age and reproductive history, with animals <2-years-old and heifers having higher concentration of most of the subclasses. The present study provides RIs for the main lymphocytic subclasses in healthy water buffaloes, highlighting gross differences between young and old animals. Establishment of age-specific RIs is recommended in water buffaloes. The data we present may be useful as a basis for further studies concerning mechanisms of immune response toward infectious agents in water buffaloes.