Naslov
Modeling the growth pattern of in-season and off-season Ross 308 broiler breeder flocks

Autori
Tompić, Teuta ; Dobša, Jasminka ; Legen, Saša ; Tompić, Nevio ; Medić, Helga

Izvornik
Poultry science (0032-5791) 90 (2011); 2879-2887

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
body weight; growth model; nonlinear function; polynomial function; chicken

Sažetak
Growing the Ross broiler parent according to the target growth curve ensures that males and females achieve optimum lifetime performance and well-being. Accurate control of growth will lead to uniformity and sexual maturity, which are of crucial importance for the production of hygienic, healthy, and fertile eggs of high quality. This study examined the growth of Ross 308 broiler breeder flocks from hatch to 35 wk of age to identify which growth model would describe the growth of these animals most accurately. Growth was measured and modeled using linear and nonlinear functions, and the experimental growth curves were compared with target curves from the Parent Stock Management Manual for Ross 308 (Aviagen). Broiler breeder flock R6 (in-season from February until October) and flock R7 (off-season from August until April) were kept in an environmentally controlled breeder house from hatch until 35 wk of age. Three nonlinear growth functions (logistic, Gompertz, and Richards) and 3 polynomial functions (linear, second-order, and third-order) were applied. Parameters of the models were estimated by the least squares procedure. The fit of growth curves to experimental data was assessed using R2. A t-test was used to identify significant differences in the goodness of fit of the model to the different data sets (breeder manual, R6, and R7). The third-order polynomial gave the best fit to the Ross 308 parent broiler BW data, with R2 ranging from 0.992 to 0.998. Among the nonlinear growth functions, the Richards model gave the best fit to the data, with R2 ranging from 0.992 to 0.995. The advantage of second- and higher-order polynomial models is that they can be linearized and their parameters estimated by linear regression.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Prehrambena tehnologija, Informacijske i komunikacijske znanosti

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