Browsing by Author "Oludayo Michael Akinsola"

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    Effect of Tier Level, Exposure and Period on Egg Production and Grade of Eggs
    (2018-10-23) Adekola Omololu Atanda; Okeke Rufina Obioma; Balami Samuel Paul; Louis Ugwu; Abdullahi Idris; Oludayo Michael Akinsola
    A total of 230 Nera birds were studied in an open and close-ended layer house for 6 weeks to determine the effect of cage location, tier level, and exposure of bird on egg production. Birds were supplied ad Libitum with feed and water. Eggs were collected twice daily at 11.00 am, and 4.00 p.m. counted, weighed and classified into sizes. The production of the egg was found to be significantly influenced by the location of laying hen. The upper tier recorded 29.17% superiority over the lower tier. This showed that birds laid more eggs in the upper tier. Besides, tier did not significantly (P < 0.05) affect the sizes of the egg laid. In this study, the birds used were exposed to light and dark conditions. The result obtained showed that more eggs were produced at the better lit area than the more shaded area. It is therefore economically viable to have more light in the
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    Genetic Parameter Estimates for Milk and Conformation Traits of Multi-genotype Cattle
    (Asian Journal of Advances in Agricultural Research, 2018-03-05) Oludayo Michael Akinsola; Ishaya Bawa Atang; Adekola Omololu Atanda; Louis Ugwu; Danladi Shuaibu Bunjah; Dorcas John Jirgi; Muizzat Onozasi Bello
    The objectives of this research were to estimate the genetic parameters of milk and conformation traits under environmental sensitivity of multi-genotype cattle. The data from the study came from two genotypes of cows (Holstein and 3/4HolsteinX1/4Bunaji). Milk yield, six body traits (stature, ST; chest width, CW; body depth, BD; heart girth, HG; rump width, RW, chest ligament, CL) and four udder traits (rear udder height, RUH; rear udder width, RUW; udder clearance, UC; teat length, TL). Multi-trait animal models were used to estimate the (co)variance components based on average information restricted maximum likelihood method (AIREML) using R 3.0.3 software.
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    Neural Network and Regression Based Model for Cows’ Milk Yield Prediction in Different Climatic Gradients
    (2018-08-17) Bosede Oyegbile; Oludayo Michael Akinsola; Okeke Rufina Obioma; Adekola Omololu Atanda; Balami Samuel Paul; Mary Foluke Oladipo; Zulfat Suleiman Abba
    The present study was designed to develop the prediction equations for 305 days fat corrected milk yield on the basis of part periods milk yield, milk component and conformation traits of multi genotype cows. Artificial Neural Network model had the best prediction accuracy across varying environments, though Genetic Function Algorithm had the overall best adequacy for fat corrected milk yield predictions (FCM305d=1036.1-98.3RP+22FY+15.92UC-0.07RUH; Adj R2=0.997; RMSE=30.07; BIC=1997.28).