“…moreover, it'll alter the breeders to expect the burden of animals at a selected age and to discover the stage that related to the reduction in rate of growth. 4 Moreover, growth curve of farm animal has been accustomed observe dynamically of growth course, to forecast the poultry growth law and instruct the feeding and management programs to enhance the choice and breeding effects. 5 The growth curve of livestock can be estimated with several nonlinear regression model of Brody, Bertalanffy, Gompertz, Logistic, Richards and Weibull.…”
This study was aimed to obtain the growth curve of body weight in male layer chickens (Lohman MB 202) based on Logistic and Gompertz models. The frequently data of body weight from one day age to adult age were used for growth curve prediction. Total of one thousand birds from private sector poultry farm in Tasikmalaya Regency, Indonesia were used in this study as the data source. The growth curve estimation was calculated based on Logistic (L) and Gompertz (G) models using Curve Expert 1.4. computer program. The final weight (asymptote) in birds was reached of 1111.27 g (L) and 1685.13 g (G). Therefore, the weight of inflection (Wi) of birds were reached of 555.64 g (L) and 619.53 g (G). The time of inflection (ti) and maximum growth rate (GR) of birds in both models were 6 weeks and 19 g/week respectively. In conclusion, both models had similar coefficient of determination (R2) value. However, the growth curve of Gompertz model was confirmed as better growth curve for body weight of birds than that of Logistic model due to lower of root mean square error (RMSE) value.
“…moreover, it'll alter the breeders to expect the burden of animals at a selected age and to discover the stage that related to the reduction in rate of growth. 4 Moreover, growth curve of farm animal has been accustomed observe dynamically of growth course, to forecast the poultry growth law and instruct the feeding and management programs to enhance the choice and breeding effects. 5 The growth curve of livestock can be estimated with several nonlinear regression model of Brody, Bertalanffy, Gompertz, Logistic, Richards and Weibull.…”
This study was aimed to obtain the growth curve of body weight in male layer chickens (Lohman MB 202) based on Logistic and Gompertz models. The frequently data of body weight from one day age to adult age were used for growth curve prediction. Total of one thousand birds from private sector poultry farm in Tasikmalaya Regency, Indonesia were used in this study as the data source. The growth curve estimation was calculated based on Logistic (L) and Gompertz (G) models using Curve Expert 1.4. computer program. The final weight (asymptote) in birds was reached of 1111.27 g (L) and 1685.13 g (G). Therefore, the weight of inflection (Wi) of birds were reached of 555.64 g (L) and 619.53 g (G). The time of inflection (ti) and maximum growth rate (GR) of birds in both models were 6 weeks and 19 g/week respectively. In conclusion, both models had similar coefficient of determination (R2) value. However, the growth curve of Gompertz model was confirmed as better growth curve for body weight of birds than that of Logistic model due to lower of root mean square error (RMSE) value.
“…2). The Gompertz model has been frequently applied to growth curves in both animals and plants (Winsor, 1932; Fox, 1999; Yakupoglu & Atil, 2001):in which: Y =the length of the waxy caudal filaments at given development time, t ; Y 0 =the initial length at t= 0; B =the growth rate of the waxy caudal filaments; e =the base of the neperian or natural logarithm; and K =maximal length of the waxy caudal filaments. Fig.…”
The physiological age of adult males of seven mealybug species was measured in relation to the elongation of the male pair of the waxy caudal filaments. These filaments begin to emerge after eclosion and reached their maximum length from 29.4-46.6 h. The studied males were divided into three age groups, expressed as percentages of the total waxy caudal filaments length. Attraction to a sex pheromone source was significantly higher in the oldest male group (maximum filaments growth) compared with youngest one. Only the oldest male group copulated successfully; few of the younger males tested displayed 'courtship' behavior towards conspecific virgin females. The calculated duration of the sexually active phase of the adult male life cycle varied among species ranging from 34.4 to 46.6 h. There were marked variations in the strength of attraction to a pheromone source according to time of day. There was a continuous decrease in sexual activity from morning to evening. Our findings reveal clear maturation periods for adult males of the seven studied species. The long immature phase of the adult male mealybug is probably also related to several physiological processes that are needed to complete male maturation. The most noticeable change is the elongation of the waxy caudal filaments. However, mating may be performed at any time ambient conditions are suitable. Whereas male mealybug flight towards a pheromone source is restricted to a few hours, the male may continue mating activity throughout its sexually active period.
“…Growth is explained mathematically by models that have parameters with biological meaning (Selvaggi et al, 2015). These parameters can estimate the expected weight of individuals at a specific age (Yakupoglu & Atil, 2001). Mathematical functions allow animal breeders to obtain unbiased results by assessing the events in summary form and predicting weight at particular ages (Narinç et al, 2017).…”
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.
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