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 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 nonlinear 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 polynomial models is that they can be linearized and their parameters estimated by linear regression.

In the research, in this paper, we investigate spatial and temporal variations in the composition of wastewater near Croatian highways in three climatic regions (continental, Mediterranean, highland) during three seasons (autumn, winter and spring). In our paper, the spatial division of the investigated areas that pertain to the three aforementioned climatic regions was obtained using the method of hierarchical clustering of monitored locations. One thousand five hundred thirty-three samples from 14 locations along Croatian highways were collected and analysed by methods of multivariate exploratory analysis. By methods of principal components, factor analysis and hierarchical clustering of variables, we grouped the variables into factors. Whereas 60 % of variation in the data was explained by three principal components, six principal components accounted for 88 % of data variation. The key section of our research was conducted by the decision tree method. For the purpose of analysis, we classified 1,533 samples into three classes representing climatic regions separately for each season and obtained the accuracy of 76-90 % on test samples. Finally, using decision trees, we identified the most important variables that differentiate climatic regions by the level of contamination of water along highways in different seasons.

In this paper we have analysed concetrations of heavy metals (lead, copper, nickel, zink, mercury, cadmium, and chromium) in wastewater along highways in Croatia. We have used standard statistical methods: analysis of variance, Kruskal-Wallis test and principal analysis. Analysis of variance and Kruskal-Wallis test were used to detect factors that influence the concentration of lead, copper, nickel, and zink in wastewater. We have investigated the influence of the highway sampling location, the side of a highway, and the influence of the season of the year. Principal components were used to identify groups of elements with similar characteristics in wastewater.