Tag Archives: Markov-Chain

Sensitivity Analysis and Future Farm Size Projection of Bio-Fortified Cassava Production in Oyo State, Nigeria (Published)

The study examined the costs and returns to bio-fortified cassava production and forecast the future farm size of bio-fortified cassava production in the study area. A multistage sampling technique was used to select 150 respondents in the study area. Primary data were used for the study which were collected through a well-structured questionnaire. Data collected were analyzed using descriptive, Markov chain, and budgetary analysis. The result of the study showed that the mean age of the respondents were 47(±13.77) with a mean years of experience of 14.62(±6.92). the result of the study showed that TMS 01/0593, TMS 01/0539 and TMS 01/0220 were the mostly grown varies of bio-fortified cassava varieties in the study area. The result of the budgetary analysis showed that the average net return (net farm income) from the production of bio-fortified cassava was ₦196710.95 with RORI of 224.95%. The result revealed that at 35% increase in cost of production, the rate of return on investment dropped to 140.70% in which the investment will not be viable. The bio-fortified cassava farmers have a great potential to boost production through increases in farm sizes of the bio-fortified cassava famers until the year 2026 when equilibrium would be attained at about 2.85ha. in other to adequately achieve these goals, more improved varieties of bio-fortified cassava should be provided, and also, infrastructures should be put in place to help boost farmers moral in their cause of production in the study area.

Keywords: Markov-Chain, Oyo State, Sensitivity Analysis, bio-fortified cassava, farm size

On the use of Markov Analysis in Marketing of Telecommunication Product in Nigeria (Published)

This paper examined the application of Markov Chain in marketing three competitive networks that provides the same services. Markov analysis has been used in the last few years mainly as marketing, examining and predicting the behaviour of customers in terms of their brand loyalty and their switching from one brand to another. The three networks are Airtel, MTN and Globacom are used as a case study. With the application problem, we examine and answer the question on the proportion of the subscribers that each network have at the end of each month when we assumed the same pattern of gains and losses. We observed that, Airtel has the largest proportion of retaining their subscriber followed by MTN and Globacom in that order. Finally, mean recurrence for each network were also determined.

Keywords: Equilibrium, Markov- Property, Markov-Chain, Networks and Subscribers, Transition Probability