Literature DB >> 16673776

Modeling growth characteristics of meat-type guinea fowl.

S N Nahashon1, S E Aggrey, N A Adefope, A Amenyenu.   

Abstract

This study was conducted to describe the growth pattern of the French guinea fowl, a meat-type variety. Using BW data from hatch to 9 wk, 2 nonlinear mathematical functions (Gompertz and logistic) were used to estimate growth patterns of the French guinea fowl. The French guinea fowl did not exhibit sexual dimorphism for growth characteristics. From the Gompertz model, the asymptotic BW, growth rate, and age at maximum growth were 2.05 kg, 0.25 kg/wk, and 5.74 wk in males, respectively, and 2.03 kg, 0.25 kg/wk, and 5.72 wk in females, respectively. The ages at maximum growth were 5.75 and 5.74 wk for males and females, respectively, using the logistic model. Differences in asymptotic BW between males and females were not significant in both Gompertz and logistic models. However, the average asymptotic BW of about 1.50 kg for both sexes predicted by the logistic model was below the average predicted BW from the Gompertz model (2.04 kg) at 9 wk. Also, the logistic model overestimated hatching weight (0.06 kg) more than the Gompertz model (0.03 kg), suggesting that the growth pattern of the French guinea fowl is Gompertz. The inverse relationship between the asymptotic weight and age at maximum growth of the French guinea fowl is similar to that of the pearl gray guinea fowl, chickens, quail, and ducks. Understanding the growth characteristics of French guinea fowl will contribute to the efforts of improving production efficiency of this least studied avian species.

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Year:  2006        PMID: 16673776     DOI: 10.1093/ps/85.5.943

Source DB:  PubMed          Journal:  Poult Sci        ISSN: 0032-5791            Impact factor:   3.352


  4 in total

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Journal:  Poult Sci       Date:  2022-04-06       Impact factor: 4.014

2.  Influence of major genes for crested-head, frizzle-feather and naked-neck on body weights and growth patterns of indigenous chickens reared intensively in Kenya.

Authors:  Thomas M Magothe; William B Muhuyi; Alexander K Kahi
Journal:  Trop Anim Health Prod       Date:  2009-07-05       Impact factor: 1.559

3.  Poultry growth modeling using neural networks and simulated data.

Authors:  H A Ahmad
Journal:  J Appl Poult Res       Date:  2009       Impact factor: 1.178

4.  Sex-specific growth, shape, and their impacts on the life history of a long-lived vertebrate.

Authors:  Luke A Hoekstra; Rachel C Weber; Anne M Bronikowski; Fredric J Janzen
Journal:  Evol Ecol Res       Date:  2018
  4 in total

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