RESUMO Devido às condições dos sistemas tradicionais de produção de ovinos, a avaliação do crescimento animal a partir do peso vivo (PV) é limitada pelo alto custo da balança pecuária, bem como pela sofisticada manutenção necessária. Assim, nos últimos anos, as medidas biométricas (MB) têm sido avaliadas como um método indireto e preciso para predizer o PV de animais de criação. Portanto, o objetivo desta pesquisa foi avaliar diferentes modelos de predição do PV de cordeiros em crescimento utilizando-se a fórmula do volume corporal (VC). Foram registrados dados de PV, perímetro torácico (PT) e comprimento corporal (CC) de 290 cordeiros entre dois e oito meses de idade. O VC foi calculado com base nos dados PT e CC, sendo usada uma fórmula que calcula o volume de um cilindro. A previsão do PV a partir da fórmula VC foi estimada por meio de equações de regressão, utilizando-se três modelos matemáticos (linear, quadrático e exponencial). Os valores médios do PV, PT, CC e VC dos cordeiros foram 29,12±12,04kg, 70,00±11,69cm, 38,40±6,43cm e 23,93±9,90 (dm3), respectivamente. O coeficiente de correlação entre PV e VC foi r=0,96 (P<0,001). O modelo quadrático apresentou o maior coeficiente de determinação (0,93) e o menor erro de predição (3,29kg) Nas condições do presente estudo, conclui-se que é possível predizer o peso vivo de cordeiros em crescimento por meio da fórmula de volume corporal.

ABSTRACT Due to the conditions in which traditional sheep production systems operate, the evaluation of animal growth from live weight (LW) is limited by the high cost of the livestock scale as well as the sophisticated maintenance required. In this scenario, in recent years, biometric measurements have been investigated as an accurate indirect method to predict the LW of farm animals. Therefore, the present study was undertaken to examine different models for predicting the body weight of growing lambs using the body volume (BV) formula. Body volume, heart girth (HG) and body length (BL) data of 290 lambs aged between two and eight months were recorded. Body volume was calculated from HG and BL data using a formula that calculates the volume of a cylinder. The estimation of LW from the BV formula was achieved through regression equations using three mathematical models (linear, quadratic and exponential). The mean values of LW, HG, BL and BV of the lambs were 29.12±12.04kg, 70.00±11.69cm, 38.40±6.43cm and 23.93±9.90dm3, respectively. The correlation coefficient between LW and BV was r = 0.96 (P<0.001). The quadratic model showed the highest coefficient of determination (0.93) and the lowest prediction error (3.29kg). Under the experimental conditions adopted in this study, it is possible to predict the live weight of growing lambs using the body volume formula.

ABSTRACT This study was conducted to develop predictive equations for carcass characteristics and primal cut weights of native Mexican guajolotes using body measurements (BM). For this study, a total of 36 male guajolotes (Meleagris gallopavogallopavo), aged 6 to 10 months, and mean slaughter body weight (SBW) of 4543.14 ± 656.60 g, were used. The birds were kept under traditional extensive conditions. ThefollowingBMswererecorded24 h before slaughter: thoracicperimeter (TP), body circumference (BC), body length (BL), wing length (WL), keel length (KL), shank length (SL) and shank diameter (SD). After slaughter, hot carcass weight (HCW), cold carcass weight (CCW), hot dressing percentage (HDP), cold dressing percentage (CDP), organs and viscera weight (VIS) and abdominal fat weight (AFW) were recorded. The carcasses were dissected in to five primal cut (breast, thigh, drumstick, back and wing). The SBW and BMs showed moderate to high positive correlations (p<0.01; 0.34≤r<0.97) with carcass characteristics and primal cut weights. In the equations generated to predict HCW, CCW, HDP, CDP, VIS and AFW, the R2 ranged from 0.40 to 0.96, and the predictor variables were SBW, KL, BC, WL and SL. Regarding the equations developed to predict the primal cut weights, R2 ranged from 0.58 to 0.91. In these models, SBW, BC, SD, WL and KL explained most of the observed variation. The prediction equations obtained in the study had moderate to high accuracy; therefore, they can be used by researchers, technicians and poultry producers to obtain information on the carcass composition of native Mexican guajolotes.

ABSTRACT The study was done to predict egg weight from the external traits of the Guinea fowl egg using the statistical methods of multiple linear regression (MLR) and regression tree analysis (RTA). A total of 110 eggs from a flock of 23-week-old Guinea fowl were evaluated. Egg weight (EW) and external traits: eggshell weight (ESW), egg polar diameter (EPD), egg equatorial diameter (EED), egg shape index (ESI), and egg surface area (ESA) were measured. Descriptive statistics, Pearson correlation coefficients, and regression equations using the MLR were obtained; additionally, a RTA was done using the CHAID algorithm with the SPSS software (IBM ver. 22). EW presented positive correlations (p<0.0001) with ESA (r = 0.72), EPD (r = 0.65), and EED (r = 0.49). EW can be predicted through MLR using ESA as a predictor variable (R2 = 72%). Predictive accuracy improves when adding EPD and EED traits to the model (R2 = 75%). The RTA built a diagram using ESA, EED, and EPD as significant independent variables; of these, the most important variable was ESA (F = 50,295, df1 = 4, and df2 = 105; Adj. p<0.000) and the variation explained for EW was 74%. Likewise, the RTA showed that the highest egg weight (41.818 g) is obtained from eggs with a surface area > 59.03 cm2 and a polar diameter > 5.10 cm. The proposed statistical methods can be used to reliably predict the egg weight of Guinea fowl.