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

Alvar Alonzo Cruz-TamayoMarco Antonio Ramírez-BautistaDaniel Mota-RojasJosé Carlos Escobar-EspañaRicardo García-HerreraAntonio Leandro Chaves GurgelTairon Pannunzio Dias-SilvaMarcos Jácome de AraújoJuliana Caroline Santos SantanaIsadora Osorio Maciel AguiarLuís Carlos Vinhas ÍtavoAlfonso Juventino Chay-Canul
Published in: The Journal of dairy research (2024)
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 ( R 2 ), 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), R 2 , 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*) × HW 2 , 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.
Keyphrases
  • body weight
  • total hip arthroplasty
  • diffusion weighted imaging
  • magnetic resonance imaging
  • machine learning
  • mass spectrometry
  • deep learning
  • social media
  • artificial intelligence
  • neural network
  • data analysis