Feed restriction has been described as a management tool to limit birds’ access to feed in order to reduce feed cost without compromising their health and meat products. The present study was undertaken to assess the effects of strain of cocks and feed restriction at different ages on carcass characteristics. The study was conducted at Teaching and Research Farm of Ekiti State University, Ado-Ekiti, Nigeria for a period of 16 weeks. A total number of 180 day-old cockerels of two strains comprising Black and White plumage (90 chicks each) were procured to examine and compare their carcass characteristics. At 6th week of age, the birds were randomly distributed into four treatments with three replicates for each strain. The treatments are, T1: control (ad libitum), T2: feed restricted from 6th -7th week, T3: feed restricted from 8th -9th week and T4: feed restricted from 10th -11th week. Data collected at 16th week of age include live body weight, bled weight, dressed weight, eviscerated weight, breast weight, back muscle weight, drumstick and thigh weights. Analyzed results showed that live weight and carcass weights were not significantly (P > 0.05) affected by strain. Both strains recorded similar mean values. With regard to feed restriction, no significant (P > 0.05) effect was found for live weight and most traits with the exception of bled weight, breast muscle weight and thigh weight. However, the control and 6th week restricted birds recorded superior mean values in such cases. The study indicates that the two strains are good for increased meat production and revenue generation, while the 6th week feed restriction is suggested for maximum productivity and profit.
Unnecessary delay on queues at hospitals and lack of access to care are associated to loss of lives. In this study we analyze the flow of inpatients in order to find out the appropriate number of beds needed to realize a smooth flow at points where congestion existed in a Nigerian university teaching hospital. Basic hospital statistics, like percentage occupancy, bed turnover rate, etc., are computed and we use queuing model to analyze patient flow and allocation of beds and demonstrate that patient flow in the hospital can be modeled using Erlang loss model.
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