Browsing by Author "Oyebanjo, M. O."

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Application of multivariate statistical analysis in characterising the phenotypic variability of locally adapted Muscovy ducks (Cairina moschata) in Nigeria
(Lužianky: National Agricultural and Food Center, 2023) Osaiyuwu, O. H.; Adeyinka, O. A.; Coker, O. M.; Oyebanjo, M. O.; Akinyemi, M. O.
While there are studies that describe the biometric traits and phenotypic variations in Muscovy ducks in Nigeria, there are limited studies that employ a multivariate approach to depict the phenotypic variability of Muscovy ducks in Nigeria. Therefore, this study aimed to explicate genetic variabilities within C. moschata using qualitative and biometric traits. This study used a multivariate statistical method to phenotypically characterise locally adapted Muscovy duck populations from seven ecogeographical locations in Ibadan, Nigeria. Four qualitative traits (eye colour, bill colour, bean colour and shank colour) and eleven biometric traits (head length, neck length, body length, wings length, shank length, toe length, thigh length, bill length, breast length, breast width and bodyweight) were evaluated in 201 ducks (109 males and 92 females). To study the possible effects of geographical locations on selected phenotypes, frequency distribution, univariate analysis, stepwise and canonical discriminant analyses and cluster analysis were performed. The association between body weight (BWT) and other biometric traits was assessed using the Pearson product-moment correlation coefficient. Male ducks (drakes) were generally more abundant than female ducks (hens). The most prevalent colour traits of locally adapted Muscovy ducks were brown eye colour (70.65 %), black bean colour (62.69 %), pinkish white bill colour (45.27%) and grey shank colour (56.22 %). Overall, ecogeographical location did not significantly affect (p < 0.05) the measured biometric traits. However, across all locations, the sexual dimorphism was favourable in male ducks, with respect to biometric traits. Stepwise-canonical discriminant analysis revealed a substantial intermixing of biometric traits, especially in Molete, Oje, Adogba and Ajibode ducks. Similarly, the cluster analysis, although it separated the birds into different clusters, showed some level of admixture. The small Mahalanobis distance (0.61-3.88) suggested that, with respect to location, there was more morphological similarity than dissimilarity between ducks. The correlation analysis revealed that the body weight of ducks can be fairly estimated from other biometric traits due to their positive, statistically significant correlation. In general, the ducks from all seven ecogeographical locations were rather homogeneous than heterogeneous.
Comparison of mathematical models describing the growth of tropically adapted Ross 308 commercial broiler chickens
(University of Nigeria, Nsukka, 2024) Osaiyuwu, O. H.; Oyebanjo, M. O.; Coker, O. M.; Akinyemi, M. O.
Mathematical growth models are useful in describing the growth of livestock. The study was done to assess the predictive ability and accuracy of four three-parameter nonlinear mathematical models (namely: Gompertz, Gompertz-Laird, Logistic, and von Bertalanffy) and one four-parameter (namely: Richards) nonlinear mathematical model. Models were used to predict the body weight (BW) of commercial Ross broiler chickens adapted to tropical conditions (n = 1,286). Age-weight data were collected once every week for 6 weeks. The Gauss-Newton iterative process of the nonlinear procedure in SAS was employed to obtain the parameters for each model. In addition, each model's goodness- of-fit, residuals, and computational difficulty were estimated. Model parameters were evaluated using Akaike's information criterion (AIC), Bayesian information criterion (BIC), adjusted coefficient of determination (AdjR2) and root mean square error (RMSE). The AdjR2 value for all five models was high; however, the highest value was observed in the Gompertz and Gompertz-Laird models. Furthermore, the lowest AIC, BIC and RMSE values were observed in the Gompertz models. Using a complimentary method (involving a subjective pairwise comparison of the observed and predicted BWs), the Logistic, Gompertz-Laird, von Bertalanffy, and Richards models fitted well for the data used. However, the best fitting was obtained in the Gompertz model. Some similarities were observed between the Logistic and Richards models. In conclusion, all five nonlinear mathematical models fitted the age weight data used in this study well, with the Gompertz model being the best.
Predicting the body weight of indigenous goat breeds from morphological measurements using the Classification and Regression Tree (CART) data mining algorithm
(Institute for Animal Husbandry, Belgrade, 2023) Oyebanjo, M. O.; Coker, O. M.; Osaiyuwu, O. H.
Classification and regression tree (CART) is a tree-based data mining algorithm that develops a model to predict an outcome. This study purposed to create a model to predict the body weight (BWT) of Red Sokoto (RS), Sahel (SH), and West African Dwarf (WAD) goats using morphological measurements (such as body length, BL; head girth, HG; head width, HDW; face length, FAL; height at wither, HTW; rump length, RL; shoulder width, SW; rump width, RW; and rump height, RH). In total, 600 goats were used for this study (200 each of RS, SH, and WAD goats). Pearson’s Moment Correlation was used to evaluate the degree of association between BWT and each morphological measurement. Concomitantly, CART analysis was performed to estimate which independent variable (morphological measurements) played a considerable role in the BWT (dependent variable) prediction. In RS and WAD goats, a positive and statistically significant (p < 0.0001) correlation was observed between BWT and each morphological measurement. However, in SH goats, both positive and negative statistically significant correlations were observed between BWT and morphological measurements. The CART analysis indicated that in RS and WAD goats, HG played a considerable role in BWT prediction, while, in SH goats, BL was considered the most critical independent variable in BWT prediction. Therefore, this study suggests that HG can be used as a prognostic index for BWT estimation in Red Sokoto and West African Dwarf, while BL can be used for Sahel goats.