Browsing by Author "DINESH KUMAR SUNWAS"

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    STUDIES ON GROWTH CURVE PARAMETERS OF SIROHI GOAT UNDER FIELD CONDITION
    (RAJASTHAN UNIVERSITY OF VETERINARY AND ANIMAL SCIENCES, BIKANER (Rajasthan), 2021) DINESH KUMAR SUNWAS; Dr. Lokesh Gautam
    The present study was assumed to estimate growth curve parameters. The detailed information of all the animals regarding growth trait of Sirohi goat was composed over the period from year 2009 to 2017 of the database, maintained at All India Co-ordinated Research Project (AICRP) on goat improvement, Livestock Research Station, Vallabhnagar, Udaipur district of Rajasthan. The average value of body weight for both sexes at birth to 360 Days as 2.54±0.01, 5.60±0.03, 8.52±0.06, 11.81±0.09, 13.13±0.10, 14.44±0.10, 15.80±0.12, 17.50±0.15, 18.00±0.16, 18.50±0.16, 20.50±0.20, 21.30±0.20 and 22.10±0.20 Kg, respectively. There were 32.33 per cent male and 67.77 per cent female used for present investigation of data from at birth to 12th months of age. The coefficient of variation (CV) for both sexes was observed for body weights at birth to 360 Days of age were ranged from 19.99 to 32.50 per cent. The range of CV was observed for body weight for high range (19.72-36.59 per cent) for male kids and (18.11-29.76 per cent) for female kids, this may be due to different environmental factor causes variation in body weight of individual. Descriptive statistics of live body weights of Sirohi goat ranged from minimum 1.10 Kg to maximum 60.00 Kg for male kids. 170 The four growth curve models (Brody, Gompertz, Logistic and Bertalanffy) were fitted to the body weight data of 1015 kids of Sirohi goat form birth to 360 days of age. The best non-linear regression growth curve model was determined by considering the following goodness of fit statistics viz., R2, Radj. 2 , MAE, MAPE, MSE, RMSE, AICC and chi-square (X2). The Brody non-linear growth curve model gave values for parameter A (26.66±1.15, 24.81±1.16 and 25.38±1.14Kg), B (0.88±0.01, 0.89±0.01 and 0.89±0.01) and K (0.14±0.01, 0.14±0.01 and 0.14±0.01) for male, female and both sexes, respectively. The goodness of fit statistics was revealed as R2 (99.46, 99.35 and 99.35 per cent), ����. 2 (99.35, 99.22 and 99.26 per cent), MAE (0.449, 0.506 and 0.501), MAPE (0.040, 0.042 and 0.040), MSE (0.26, 0.27 and 0.27), RMSE (0.147, 0.168 and 0.168), AICC (-7.89, -7.13 and -7.41) and Chi-square (0.300, 0.376 and 0.360) for male, female and both sexes, respectively. The Gompertz non-linear growth curve model provided values for parameter A (23.48±0.91, 21.90±0.91 and 22.39±0.91Kg), B (1.79±0.12, 1.80±0.12 and 1.80±0.12) and K (0.28±0.03, 0.28±0.03 and 0.29±0.03) for male, female and both sexes, respectively. The goodness of fit statistics was revealed as R2 (98.62, 98.39 and 98.47 per cent), ����. 2 (98.34, 98.06 and 98.16 per cent), MAE (0.614, 0.622 and 0.621), MAPE (0.067, 0.075 and 0.071), MSE (0.66, 0.68 and 0.68), RMSE (0.205, 0.208 and 0.200), AICC (4.35, 4.60 and 4.60) and Chi-square (0.765, 0.852 and 0.740) for male, female and both sexes, respectively. The Logistic non-linear growth curve model gave values for parameter A (22.49±0.88, 20.99±0.88 and 21.46±0.87Kg), B (3.84±0.58, 3.87±0.63 and 3.89±0.62) and K (0.41±0.05, 0.41±0.05 and 0.42±0.05) for male, female and both sexes, respectively. The goodness of fit statistics was revealed as R2 (97.58, 97.28 and 97.37 171 per cent), ����. 2 (97.09, 96.73 and 96.84 per cent), MAE (0.762, 0.716 and 0.799), MAPE (0.097, 0.103 and 0.103), MSE (1.17, 1.15 and 1.16), RMSE (0.265, 0.264 and 0.261), AICC (11.63, 11.47 and 11.62) and Chi-square (1.389, 1.420 and 1.387) for male, female and both sexes, respectively. The Bertalanffy non-linear growth curve model provided values for parameter A (24.41±0.93, 22.47±0.94 and 22.97±0.93Kg), B (0.46±0.02, 0.47±0.02 and 0.47±0.02) and K (0.24±0.02, 0.24±0.02 and 0.24±0.02) for male, female and both sexes, respectively. The goodness of fit statistics was revealed as R2 (98.95, 98.76 and 98.82 per cent), ����. 2 (98.74, 98.51 and 98.58 per cent), MAE (0.544, 0.540 and 0.536), MAPE (0.059, 0.062 and 0.058), MSE (0.50, 0.52 and 0.52), RMSE (0.175, 0.178 and 0.177), AICC (0.80, 1.25 and 1.17) and Chi-square (0.548, 0.585 and 0.553) for male, female and both sexes, respectively. Four non-linear growth curve models viz., Brody, Gompertz, Logistic and Bertalanffy fitted to body weights data of male, female and both sexes of Sirohi goat. All the four models were used for estimate growth curve parameters (A, B and K). The growth curve models were compared by using goodness of fit statistics viz., R2, Radj. 2 , MAE, MAPE, MSE, RMSE, AICC and X2 (Chi-Square) values to identify the best growth curve model in explaining the body weights of male, female and both sexes of Sirohi goat. The best growth curve model was defined which had properties viz. highest values for growth curve parameters (A, B, and K), highest R2 value, highest ����. 2 value , lowest MAE value, lowest MAPE value, lowest MSE value, lowest RMSE value and lowest AICC value. Thus, Brody growth curve model was found to be best model for body weight of male, female and both sexes of Sirohi goat due to the highest R2 value, highest ����. 2 value, lowest MAPE value, lowest MSE value, lowest RMSE value and lowest AICC value. 172 The least-squares analysis of variance observed that the random effect of sire was highly significant (P≤0.01) effect on parameters (A) and (B) however, significant (P≤0.05) was revealed on parameter (K). The least-squares analysis of variance observed that effect of year of birth had highly significant (P≤0.01) effect on A, B and K parameters. The least-squares analysis of variance revealed that effect of season of birth was non-significant effect on parameters (A) and (B) of growth curve however, significant (P≤0.05) effect was on parameter (K). The least-squares analysis of variance revealed that effect of type of birth was significant (P≤0.05) effect on parameter (A) and highly significant (P≤0.01) effect was on parameter (B). In present study nonsignificant effect of type of birth was revealed on parameter (K). The least-squares analysis of variance observed that effect of parity was non-significant effect on parameters (A), (B) and (K) of growth curve model. The least-squares analysis of variance revealed that effect of sex of kid was highly significant (P≤0.01) on parameter (A) while the effect of sex of kid was revealed non-significant on parameter (B) and (K) of growth curve. The least-squares analysis of variance observed that highly significant (P≤0.01) effect of cluster was on parameter (A) and (B) of growth curve whereas effect of cluster was revealed non-significant effect on parameter (K). The least-squares analysis of variance observed that effect regression of dam’s weight at kidding was non-significant for parameters (A), (B) and (K) of growth curve model. 173 The heritability estimates of parameters (A), (B) and (C) in the present study was estimated as 0.40±0.05, 0.13±0.03 and 0.02±0.01, respectively. Estimates of genetic correlations between parameters of growth curve of Sirohi goat in present investigation ranged from -0.67±0.33 between parameter (B) and (K) to 1.00±0.04 between parameter (A) and (B). The genetic correlations between parameter (A) and parameter (B) were estimated high value as 1.00±0.04. The genetic correlations between parameters (A) and parameter (K) were valued highly negative value as -0.96±0.31. The genetic correlation between parameters (B) and parameter (K) were estimated moderately negative value as -0.67±0.33. The negative correlation between parameter (B, related to initial weight) and parameter (K) indicate that selection of higher maturity rate could result lower birth weight. Estimates of phenotypic correlations between parameters of growth curve of Sirohi goat in present investigation ranged from - 0.14±0.00 between parameter (B) and parameter (K) to 0.45±0.00 between parameter (A) and parameter (B). The phenotypic correlations between parameter (A) and parameter (B) were estimated moderately positive value as 0.45±0.00. The phenotypic correlations between parameters (A) and parameter (K) were estimated moderately negative value as -0.43 ±0.00. The negative phenotypic correlation between parameters (A) and (K) indicates that selection was used for increase asymptotic body weight could leads to decrease maturity and growth rate. The phenotypic correlation between parameters (B) and parameter (K) were estimated negative value as -0.14±0.00.