Ovary follicular development is a progressive system from the beginning of small cortical follicles to the ovulation of hierarchical follicles. The review was conducted to provide information on the indigenous chickens commonly used for egg production, chicken ovarian follicles morphology and expression of growth differentiation factor 9 (GDF9) gene in ovarian follicles and its relationship with egg production. The research databases used in the study include google scholar, Science Direct, PubMed, JSTOR and Cambridge Core. Google, Yahoo and Baidu search engines were used to search the information. In this study, the papers selected for use were original research articles and reviews to ensure that the information used was from research results. Besides, only recent English papers, 2010-2021, were used. The keywords used to search for articles were chicken ovarian follicles, ovarian morphology and GDF9 gene expression. The documents showed that pre-hierarchical follicles include many small and large white follicles, which are about 2-5mm in diameter and 5 to 6 small yellow follicles (SYF) that are about 5-10mm in diameter. Preovulatory follicles are about five to six in number and above 10mm in diameter, with the sizes from F6 to F1, with F1 as the largest follicle. Further, the studies revealed that GDF9 gene mRNA is expressed in the highest concentration in small yellow follicles and other studies reported that the expression of GDF9 gene has been found in follicles of the primary to preovulatory stages in chickens. This review concludes that the GDF9 gene expression is mainly throughout follicular growth and it stimulates the proliferation of pre-hierarchical granulosa cells. The increased egg production in chickens depends on progressive developmental stages and the growth of ovarian follicles.
Regression analysis is used for estimating the relationship between the dependent variable and one or more independent variables. The current study was carried out to determine the relationship among measured body measurement traits such as heart girth (HG), body weight (BW), rump width (RW), body length (BL), head length (HL), withers height (WH), ear length (EL), rump height (RH), head width (HW) and sternum height (SH), and to detect the best-fitted regression model for the prediction of BW of Nguni cattle. A total of 70 Nguni cattle (59 females and 11 males) aged 2-4 years were employed in the current study. Pearson's correlation and stepwise regression techniques were employed for the analysis of data. The association findings showed that BW had a positively high significant association (P < 0.01) with WH (r = 0.94), HG (r = 0.91), RH (r = 0.88), SH (r = 0.90), RW (r = 0.73), and a positive statistical significant correlation (P < 0.05) with EL (r = 0.47), and BL (r = 0.46) in males. On the other hand, BW had a positively high remarkable relationship (P < 0.01) with HG (r = 0.75), RH (r = 0.69) and WH (r = 0.57), and a positive statistical significant correlation (P < 0.05) with BL (r = 0.43), SH (r = 0.38) and HW (r = 0.28) in females. Stepwise regression results showed that the model, including WH, HG, SH, RH, RW, HL, EL, and BL was the best-fitted model (R 2 = 0.95, MSE = 817.51) for the estimation of BW in males, and the model with parameters of HG, RH and WH was the best-fitted model (R 2 = 0.62, MSE = 4887.31) for the estimation of BW in female Nguni cattle. Correlation outcomes suggest that enhancement of WH, HG, SH, RH, RW, EL, BL, and HW might improve the BW of Nguni cattle. The regression results suggested that BW could be estimated accurately by the combination of two or more biometric traits. The findings might help cattle farmers to predict body weight using biometric traits.
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