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The present study attempts to develop a systematic and useful way to describe growth quantitatively in reproducing animals. Such a modeling approach would be useful for optimizing production systems involving egg and chick production. For feed-restricted animals, a model with biologically meaningful parameters would facilitate the design and study of alternative growth strategies. Single phase growth models have been used to develop software programs that intend to increase the sustainability of poultry production by minimizing excretions to the environment and maximizing profit. Modified versions of the Gompertz model have been widely adopted by biologists and livestock scientists However, growth occurs in multiple stages, and the Gompertz model in its basic monophasic form only adequately describes a single phase of unrestricted growth. Thus, additional phases should be considered to model restricted growth, and particularly the growth of reproducing animals, where there are at least 3 biologically relevant growth phases: prepubertal, pubertal, and postpubertal. The objective of the current research was to evaluate the suitability of monophasic, diphasic, and triphasic models for various types of poultry grown to reproductive age and to explore their suitability for development of optimal growth recommendations for modern broiler breeders.
No animals were used in this study. Published target BW data for laying hens (Lohmann Brown-lite; Lohmann Tierzucht, 2017), broiler breeders (Ross 308; Aviagen, 2016), and turkey hens (Hybrid Converter; Hendrix Genetics BV, 2017) were used to evaluate the various Gompertz model forms. The lines chosen were arbitrary because once evaluated, coefficients for the most appropriate models can be estimated for the serial BW data of additional lines. All models were estimated using the NLMIXED procedure of SAS. One-, two-, and three-phase models were fit to the published BW data.
For layer line hens, the triphasic model had the best fit, with the lowest RMSE and BIC values and the highest R2 value. The model predicts 1.561 kg of growth during the prepubertal phase, 0.332 kg of growth in the pubertal phase, and 0.122 kg of growth in the postpubertal phase. A pubertal phase rate coefficient (b2) of 0.4964 predicted accumulation of 98% of the total growth for that phase in approximately 12 wk, from 16 to 28 wk of age, peaking at I2 = 18.71 wk of age. For broiler breeder line hens, the triphasic model had the best fit, with the lowest RMSE and BIC values and the highest R2 value. The model predicts 2.118 kg of growth during the prepubertal phase, 1.537 kg of growth in the pubertal phase, and 0.637 kg of growth in the postpubertal phase. A pubertal phase rate coefficient (b2) of 0.2466 predicted accumulation of 98% of the total growth for that phase in approximately 25 wk, from 16 to 40 wk of age, peaking at I2 = 21.45 wk of age. By 38 wk of age, 99% of phase 1 growth was complete, and 72.6% of phase 3 growth was complete by 64 wk of age. For broiler breeder males, the triphasic model had the best fit, with the lowest RMSE and BIC values and the highest R2 value. The model predicts 2.186 kg of growth during the prepubertal phase, 2.214 kg of growth in the pubertal phase, and 1.055 kg of growth in the postpubertal phase. A pubertal phase rate coefficient (b2) of 0.1997 predicted accumulation of 98% of the total growth for that phase in approximately 31 wk, from 12 to 43 wk of age, peaking at I2 = 19.28 wk of age. For turkey hens, the diphasic model had the best fit, with the highest BIC value. In the turkey line, the RMSE and R2 values did not decrease with the addition of a third growth phase. The model predicts 10.352 kg of growth during the prepubertal phase and 2.15 kg of growth in the pubertal phase. A pubertal phase rate coefficient (b2) of 0.3149 predicted accumulation of 98% of the total growth for that phase in approximately 19 wk, from 17 to 36 wk of age, peaking at I2 = 21.02 wk of age.
The present study outlines a method for quantitatively describing growth that occurs in superimposed phases. Turkey hen growth was best described by a diphasic model, and chicken growth (laying hens and broiler breeder hens and roosters) was best described with a triphasic model. The triphasic model with its biologically relevant continuous parameters presents an opportunity to implement a more robust quantitative BW optimization approach, which is imminently needed for broiler breeders. There remain infinite BW trajectories and many optimization hypotheses that could be tested, but the triphasic model provides a way to begin to test key hypotheses in a systematic and strategic manner.