INTRODUCTION Growth is a fundamental property of biological sys- tems and it can be defined as an increase in body size per time unit (Schulze et al., 2001; Lawrence and Fowl- er, 2002). Growth of the fowl is similar to the growth of other animals, and it consists of 3 or 4 cycles, with 2 occurring after hatching (Grossman, 1988). Modeling growth curves of animals is necessary for optimizing the management and efficiency of animal production (Köhn et al., 2007). Many studies have modeled the growth characteristics of cattle (Brown et al., 1976; López de Torre et al., 1992; Menchaca et al., 1996), pigs (Whit- temore, 1986; Krieter and Kalm, 1989; Bastianelli and Sauvant, 1997; Knap et al., 2003; Schinckel et al., 2003; Wellock et al., 2004), and poultry (Knízetová et al., 1991a,b; Darmani Kuhi et al., 2003; Sengül and Kiraz, 2005; Nahashon et al., 2006; Roush et al., 2006). Ross broiler parents exhibit the same inherent rapid growth and feed efficiency as their offspring. Maintain- ing the Ross broiler parent stock at its target BW leads to optimum productivity. To achieve the objectives of the rearing period, such as target BW for age and flock uniformity, weekly weighing of sample chickens from the flock and adjustment of feed allowances are neces- sary. In addition to feed allowance, other factors may influence BW, such as heat conditioning of broilers from younger and older breeder flocks during incuba- tion and the first week post-hatch (Yalçm et al., 2005). Accurate control of growth improves flock uniformity. Good growth models can help ensure that animal pro- duction is efficient and cost-effective. The literature on poultry and other animals tradi- tionally defines the relationship between age and live weight as a nonlinear, S-shaped function (Sengül and Kiraz, 2005). Our preliminary work showed a good fit of second- and third-order polynomial functions to ex- Modeling the growth pattern of in-season and off-season Ross 308 broiler breeder flocks T. Tompić,* J. Dobša,† S. Legen,* N. Tompić,‡ and H. Medić§ 1 *Bioinstitut d.o.o., 40000 Čakovec, Croatia; †Faculty of Organization and Informatics, 42000 Varaždin, Croatia; ‡Podravka d.d., 48000 Koprivnica, Croatia; and §Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia ABSTRACT Growing the Ross broiler parent according to the target growth curve ensures that males and fe- males achieve optimum lifetime performance and well- being. Accurate control of growth will lead to uniformi- ty 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 Man- ual 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 R 2 . 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 R 2 ranging from 0.992 to 0.998. Among the non- linear growth functions, the Richards model gave the best fit to the data, with R 2 ranging from 0.992 to 0.995. The advantage of second- and higher-order poly- nomial models is that they can be linearized and their parameters estimated by linear regression. Key words: body weight, growth model, nonlinear function, polynomial function, chicken 2011 Poultry Science 90:2879–2887 doi:10.3382/ps.2010-01301 PRODUCTION, MODELING, AND EDUCATION 2879 Received December 16, 2010. Accepted August 30, 2011. 1 Corresponding author: hmedic@pbf.hr ©2011 Poultry Science Association Inc. at EIFL Croatia on September 3, 2014 http://ps.oxfordjournals.org/ Downloaded from