Estimate of Growth and Carcass Traits from Principal Body Components in Two-Chicken Breeds Infected with Coccidial Oocysts Using Different Functions

• K.F. Ahmed

  Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

  Author

• A.J. Atansuyi

  Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

  Author

• A.I. Fadahunsi

  Biotechnology Unit, Department of Biological Sciences, Elizade University, Ilara-Mokin, Ondo State, Nigeria .

  Author

• K.O. Olayiwola

  Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

  Author

• D.J. Olusola

  Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

  Author

• I.B. Osho

  Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

  Author

• C.A. Chineke

  Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

  Author

Weight is the most important economic trait guiding the production and profitability of farmers. Ceteris paribus, farmers can determine the body weight of their stocks at any given age to maximize profit. This study was conducted to model the growth and carcass traits of Arbor-Acre and α-FUNAAB chickens infected with coccidial oocysts using different functions. Twohundred day-old chicks at 100 for each breed were raised for 8 weeks, each breed was further divided into two based on their sexes and replicated 5 times with 10 chicks. During the experimental period, data were generated on linear body growth traits of economic importance at specific intervals using standard procedures. At 8th week, 40 birds at 20 per breed of 10 males to females were randomly selected for carcass and organ measurements. The birds were fed ad-libitum throughout the period of the study. Statistical analysis was performed on the data collected using SPSS (V.25). Estimate of parameters for the fitted functions in the chicken breeds showed that coefficient of determination (R2) for linear body traits from the models ranged from 1.0 – 98.8%, 12.5 – 95.7% for carcass and 1.0 – 94.1% for organs in the four fitted models. Relationship between live body weight and linear body measurements were well described by all functions in the two chicken breeds. Body weightlinear relationships were best fitted with quadratic (AA=0.988) and cubic (FA=0.986) functions. Similarly, these two functions best predict body-carcass weights of the birds than the allometry function. Chest circumference (R2=95.70%) and drumstick (R2=95.30%) were better predictors of carcass weight in the two chicken breeds while gizzard (R2=94.10%) was the best amongst the other internal organs. Conclusively, predictions of body weight from linear body traits in coccidial infected chickens were best described by both quadratic and cubic models. Quadratic function was recommended for use in estimating body weights of poultry birds.

1. K.F. Ahmed, Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

   Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

2. A.J. Atansuyi, Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

   Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

3. A.I. Fadahunsi, Biotechnology Unit, Department of Biological Sciences, Elizade University, Ilara-Mokin, Ondo State, Nigeria .

   Biotechnology Unit, Department of Biological Sciences, Elizade University, Ilara-Mokin, Ondo State, Nigeria .

4. K.O. Olayiwola, Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

   Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

5. D.J. Olusola, Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

   Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

6. I.B. Osho, Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

   Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

7. C.A. Chineke, Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

   Breeding and Genetics Unit, Department of Animal Production and Health, The Federal University of Technology, Akure, Ondo State, Nigeria

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