مقايسه برخي مدل‌هاي رياضي غيرخطي براي توصيف منحني رشد گوساله‌هاي سيستاني

Comparison of some non-linear mathematical models to describe the growth curve of Sistani calves

ﻫﺪف از اﯾﻦ ﭘﮋوﻫﺶ، ﺑﺮازش ﻣﺪلﻫﺎي ﻏﯿﺮﺧﻄﯽ ﻣﺨﺘﻠﻒ ﺑﺮاي ﺗﻮﺻﯿﻒ ﻣﻨﺤﻨﯽ رﺷﺪ و اﻧﺘﺨﺎب ﻣﻨﺎﺳﺐﺗﺮﯾﻦ ﻣﺪل ﺗﻮﺻﯿﻒﮐﻨﻨﺪه ﻣﻨﺤﻨﯽ رﺷﺪ ﺑﺮاي ﮔﻮﺳﺎﻟﻪﻫﺎي ﮔﺎو ﺳﯿﺴﺘﺎﻧﯽ ﺑﻮد. از رﮐﻮردﻫﺎي وزن ﺑﺪن 241 ﮔﻮﺳﺎﻟﻪ )118 رأس ﻧﺮ و 123 رأس ﻣﺎده( ﮐﻪ ﺗﻮﺳﻂ اﯾﺴﺘﮕﺎه ﺗﺤﻘﯿﻘﺎت ﮔﺎو ﺳﯿﺴﺘﺎﻧﯽ زﻫﮏ ﺑﯿﻦ ﺳﺎلﻫﺎي 1389 ﺗﺎ 1396 ﺟﻤﻊآوري ﺷﺪه ﺑﻮد، اﺳﺘﻔﺎده ﺷﺪ. ﭼﻬﺎر ﻣﺪل ﻏﯿﺮﺧﻄﯽ )ﮔﻤﭙﺮﺗﺰ، ﻟﺠﺴﺘﯿﮏ، رﯾﭽﺎردز، و وﯾﺒﻮل( ﺑﺮ روري رﮐﻮردﻫﺎي وزن ﺑﺪن ﺑﺮازش و ﻣﻨﺎﺳﺐﺗﺮﯾﻦ ﻣﺪل ﺗﻮﺳﻂ ﻣﻌﯿﺎرﻫﺎي ﺑﺮازش ﻧﯿﮑﻮﯾﯽ )ﺟﺬر ﻣﯿﺎﻧﮕﯿﻦ ﻣﺮﺑﻌﺎت ﺧﻄﺎ، ﻣﻌﯿﺎر اﻃﻼﻋﺎت ﺑﯿﺰي، ﻣﻌﯿﺎر اﻃﻼﻋﺎت آﮐﺎﺋﯿﮏ و ﺿﺮﯾﺐ ﺗﻌﯿﯿﻦ ﺗﺼﺤﯿﺢ ﺷﺪه( ﻣﻮرد ارزﯾﺎﺑﯽ ﻗﺮار ﮔﺮﻓﺖ. ﺑﺮاﺳﺎس ﻣﻌﯿﺎرﻫﺎي ﺑﺮازش ﻧﮑﻮﯾﯽ، ﻣﺪل رﯾﭽﺎردز ﻣﻨﺎﺳﺐﺗﺮﯾﻦ ﺗﺎﺑﻊ ﺑﺮاي ﺗﻮﺻﯿﻒ ﻣﻨﺤﻨﯽ رﺷﺪ در ﮔﻮﺳﺎﻟﻪﻫﺎي ﻧﺮ و ﻣﺎده ﺑﻮد. اﺛﺮ ﺟﻨﺲ ﺑﺮ روي ﻓﺮاﺳﻨﺠﻪﻫﺎي ﻣﻨﺤﻨﯽﻫﺎ در ﺑﺴﯿﺎري از ﺗﻮاﺑﻊ ﻣﻌﻨﯽدار ﺑﻮد )0/05

The purpose of this study was to fit different nonlinear models to describe growth curve and selection the best model to describe a growth curve for calves of Sistani calves. Body weight records of 241 calves (118 males and 123 females) collected by the Sistani Dairy Cattle Research Station of Zahak from year 2010 to 2017 were used. Four nonlinear models (Gompertz, Logistic, Richards, and Weibull) were fitted to the body weight records and the best model was selected by the goodness-of-fit criteria (Mean square error, Bayesian information criterion, Akaike information criterion and corrected coefficient of determination). According to goodness-of-fit criteria, Richards model was the most appropriate model to describe the growth curve in male and female calves. The effect of sex on curve parameters was significant in many functions (P <0.05). Logistic and Richards models had the highest and the lowest initial weight parameter, respectively. Male calves reached to the inflection point in a higher age and weight compared to female calves. According to the results of this study, a proper model can be used to study the growth pattern of this breed in order to better nutritional management and selection for rapid growth with high accuracy.