POSSIBILITIES OF PREDICTING MEAT QUANTITY AND BONE FROM BY SOME LIVE BODY MEASUREMENTS IN RABBITS | ||
| Egyptian Poultry Science Journal | ||
| Volume 45, Issue 3, September 2025, Pages 203-221 PDF (967.47 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/epsj.2025.398905.1330 | ||
| Author | ||
| Mahmoud Gharib Ghraib* | ||
| Anim. Prod. Res. Inst.(APRI), Agric. Res. Center (ARC), Egypt. | ||
| Abstract | ||
| Possibilities of predicting meat quantity and with little of bone from non-offensive and non-exhaustive in vivo Linear Measures were examined using 103 male produced from a population of endogenous Egyptian Baladi (Black and Red), and their reciprocal crosses with the acclimatized New-Zealand White rabbits. Linear non-offensive, morphometric measures included chest width (CW); thigh circumference (TC); Hind foot length (FL) and Body length (BL) at 12 Wks. Beside tow weights; body weight ate 12 wks. Of age, (BW12) and pre_slaughter weight at 16 wks. (Pre_S) were done. The model of multiple regression analysis for meat quantity and bone weight based on non-offensive in vivo linear measures recorded a significant (P< 0.001) for Meat Weight meaning that the model of (MW) is generally efficient from the statistical point of view on doing the desirable prediction , while it seemed to be non significant in case of predicting (BW). The scatter plot of the data reveals that the relationship between the dependent and the independent variables is (as presumably assumed); tend to be linear with a tendency of the dependent variables to generally increase as the bulk values of the regressor increase (positive). When considering the ratio of variability of the model (regressors) is responsible for (R-square = 70 % for MW), prediction of MW % based on non- invasive in vivo Linear Measures individually (R2 = 66.8 to 67.8 %); or simultaneously (R2 = 70 %) was likewise reasonably possible, a low estimate was recorded for BW. The Mallows C(p) statistic, Models 4, 5, and 6 for meat weight showed the most balanced performance, with R² values of approximately 0.689 and C(p) values ranging from 3 :7. Notably, these models consistently included key predictors such as (femur length), (body length), and Lbw (live body weight), highlighting their significant contribution to prediction accuracy. | ||
| Keywords | ||
| body; chest; loin; – Meat and Bone weight- backward multiple regression | ||
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