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Predictive Models Developed For Body Weight Estimation In Poultry And Livestock Animals

Predictive Models Developed For Body Weight Estimation In Poultry And Livestock Animals, The body linear measurement is determined using the tailors’ tape rule (in centimeters).

The measurements are as follows:

  • Bodyweight: This is done with the use of weighing balance calibrate in grams.
  • Body length: measured from the neck region to the tail region.
  • Shank length: Measured from the lock joint to the terms metatarsus.
  • Breast girth: measured as the circumference of the breast around the deepest region of the breast.
  • Thigh-length: measured from the lock joint to the line joints.
  • Keel length: measured from the chest bone to the end towards the abdomen region, etc. (Ojedapo 2013).

Pelvis Width, Shank Length, Keel Length, Wing Length, and Breast Width

Data collected are subject to statistical analysis of variance (ANOVA) using the general Linear mode of SAS (2013). The mathematical equation can be developed based on a large number of real bodies Linear measurement data. The equation change via a constructed table. Individual equations usually derived based on the following:

The  condition of the animal

Predictive Models

After the body measurement, the data could be grouped on the basis of sex, age is determined by counting the number of permanent incisors and breeds than depending on the design, different statistical methods could be used to analyze the relationship between the live body weight and the body Linear measurements.

In most of the literature, however, the relationships between the body weight and Linear measurement, and among the Linear measurements themselves and determined by the use of Pearson’s correlation coefficient.

The body weight would then be regressed on body Linear measurements using the general linear model and regression analysis to generate prediction models.

To find the best-fitted regression model, coefficient multiple determination (R2) residual mean square (MSC) error standard deviation (SDE) and range observed in the predicted weight could be used to check and compare different regression models (Snedecor and Cochran 1989).

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