Describing lactation in mammals using a lactation curve aims to provide a concise summary of the pattern of milk yield and valuable information about the biological and economic efficiency of the animal or herd under consideration. A total of 106 581 monthly test-day milk records collected from 12 677 Tehran Province primiparous Holstein cows from 151 herds, were used in this study. Using the General Linear Model procedure in SAS, the effect of herd, calving year, age at calving, season of production, age and days in milk were found to be significant on daily milk yield. The suitability of seven mathematical models (with three, four and five parameters) for describing the 305-day milk yield lactation curve of Holstein cows, were examined in this study. Comparisons of the models were made based on the coefficient of determination, root mean square error, Durbin Watson coefficient and sum of daily deviations, by using nonlinear (NLIN), regression (REG) and autoregression (AUTOREG) procedures in SAS. The best three, four and five parameter functions with respect to these criteria were the Incomplete Gama, Dijkstra and Grossman functions. With regards to the Wilmink model, the best results obtained were from models with the constant of 0.05 and 0.065. The Wood function was selected as the best model for prediction of daily milk yield, due to parameters in the function and less computational limitation. ________________________________________________________________________________

In this research data representing 72,946 primiparous cows from 724 herds with 638,063 total test day records calved between 2001 and 2011. These data were analysed to determine the effect of age at first and season of calving on parameters of the Wood lactation curve. Also, genetic trend of the lactation curve parameters in different calving years were evaluated. The results indicate that the highest rate of atypical lactation curve was related to cows that calved in summer (28.05 %). The maximum phenotypic relationship between initial milk yield and total 305-d milk yield was observed in cows calved in spring (0.40). The role of peak yield is more than peak time on 305-d total milk yield in primiparous Holstein. One month increase in age at first calving from 18 to 26 month raised 305-d milk yield by around 138 kg and from 27 to 32 month decreased by 61 kg. The persistency of lactation between 101 and 200 days is higher than that of 201–305 days. Our results indicate that the shape of lactation curve is largely dependent on the season of calving (higher level of milk production in cows which calved in autumn and winter). The heritabilities of parameters of lactation curve and persistency measures were low. The genetic trends for peak time, peak yield and 305-d milk yields were positive and estimated to be 0.019, 0.021 and 8.13 kg/year respectively. So the range from 24 to 26.5 month of calving is the optimum calving time in primiparous Holstein for maximizing 305-d milk yield.

Lactation persistency (cow’s ability to maintain milk production after reaching its peak) is a very important economic characteristic in the dairy cattle production system. Different definition and functions for describing and measuring of this trait were proposed by researchers. The random regression model using Legendre polynomial was one of the common and effective methodologies for evaluation of persistency in the last decade. Several factors affecting persistency such as different characteristics of lactation curve, environment factors, reproduction traits and health status of the dairy cow. Based on different studies the heritability of this trait was low to medium and negative or positive amount of genetic correlation between persistency and total milk yield in dairy cattle is attributed to persistency measures and method of data analysis. Persistency is related with low and later peak yield and selecting cows for peak yield will improve persistency and lactation curve traits. Analysis of relationships between persistency and other functional traits show signs that genetic improvement for persistency is possible and favorable. Different aspects and relationships of persistency with various lactation and other functional traits in dairy cows are reviewed in this article.

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Iranian native chicken, including Fars indigenous chicken, is an important genetic resource due to its adaptation to stressful environmental conditions, good endurance and resistance to disease. The aim of this research was to determine the genetic infrastructure of Fars indigenous chicken using several nonlinear functions. The dataset included body weight at hatch (BW1), body weight at the 8th week (BW8), body weight at the 12th week (BW12), weight at sexual maturity (WSM), age at sexual maturity (ASM), number of eggs in the first 12 weeks of laying period (EN), egg weight at the first day of laying (EW1), average egg weight at the 28thday of laying (EW28), and average egg weight at weeks 28, 30, and 32 of the laying period (AEW). Growth models were fitted using the NLIN procedure and WOMBAT software was used to predict variance components for the best fit model parameters. Results suggested three‐parameter models, for example, Gompertz, fitted better to the data than others. The maturity weight (A), initial weight (B), and maturity rate (K) parameters in the Gompertz model were 1996.8 ± 6.63, 4.11 ± 0.03, and 0.021 ± 0.0001, respectively. The heritability of A, B, and K parameters were 0.03, 0.05, and 0.12, respectively.