Authors : Moluno Anthony Ndidi; Eme Luke Chika; Ezeugwu, N.C; Ohaji Evans .C

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/s76zhw42

DOI : https://doi.org/10.38124/ijisrt/25jun1086

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This research is aimed at Applying Bayesian decision model in Agribusiness value chain intervention project in Niger Delta. The objectives are: to determine Prior (Prototype) and Posterior (Model) Probability, Expected Monetary Value (EMV), Marginal Probability, Expected Value of Perfect Information (EVPI), Expected Profit in Perfect Information (EPPI), The problems the study solve were: inadequate funding of multipurpose scheme, inefficient economic benefits and losses. The methodology applied involves data which were collected from the beneficiaries (Incubators and Incubatess) in selected beneficiaries of LIFE-ND agribusiness cluster across the 98 selected local government across the Nine states , Nine LIFE-ND mandate State Offices of Abia, Bayelsa, Cross River, Akwa Ibom, Edo, Delta, Rivers, Ondo, Imo, and the National Coordinating Office in Port Harcourt and Federal Ministry of Agriculture and Rural Development. The methods used in this research for the Agri-business intervention projects were as follows: estimating the performance of economic efficiency of the multipurpose projects, estimating performance of the net benefits of the interaction between multi-purpose and the multi-objective, assembling the total net benefits of the interaction between multipurpose and the multi-objective, analyzing the data obtained as the total net benefits to ascertain the reliability and validation of the sources of data by using: Contingency coefficient and association, Pearson moment correlation coefficient and T- distribution test. The results of Bayesian model of expected monetary values of the Agribusiness multipurpose project are as follows: Economic efficiency which produces the Maximum Expected Monetary Value (EMV*) ₦8.16 Billion, Expected Profit in perfect information is (EPPI) is ₦20.34 Billion. Expected Value of Perfect Information (EVPI) ₦12.17Billion.

Keywords : Modeling, Prior-Posterior, Probability, Value Chain, Agribusiness.

References :

1. Moluno A.N and Eme L.C. (2025) Efficiency and Profitability Analysis Agro-production in  the Niger Delta : A data Envelopment and Tobit Regression Approach. Malaysian Journal of Sustainable Agriculture (MJSA) 9(2) (2025) 115-120.
2. (2)Moluno A.N and Eme L.C. (2025 Optimizing Agri-business Commodity Value chain for sustainable Agriculture in the Niger Delta Using Game Theory. International Journal of Engineering and Modern Technology(IJMT) E-ISSN 2504-8848 P-ISSN 2695-2149 Vol 11 No. 4  2025  www.iirdjournals.org
3. Ohaji E, Mahmud H (2024) Optimizing Watershed Management : An Integrated Approach with Game Theory-Linear programming, and Dorminance Property. https:// www.researchgate.net/publicati on/381108022
4. Eme L.C and Ohaji E (2019) Bayesian Decision Modeling in Watershed Management- Cross     River Basin, Nigeria, Civil and Environmental Research Vol 11, No 2, 2019.
5. Jittima S et al (2021) The Optimization of Bayesian Extreme Value: Experimental Evidence for the Agricultural Commodities in the US. https:// www.mdpi.com/journal/economics,2021,9,30.
6. Qian, S.S., Stow, C.A., Borsuk, M.E., 2003. On Monte Carlo methods for Bayesian inference. Ecol. Model. 159, 269–277.
7. Kattwinkel, M., Reichert, P., 2017. Bayesian parameter inference for individual-based models using a Particle Markov chain Monte Carlo method. Environ. Model. Softw 87, 110–119
8. Zheng, Y., Keller, A.A., 2007. Uncertainty assessment in watershed-scale water quality modeling and management: 1. Framework and application of generalized likelihood uncertainty estimation (GLUE) approach. Water Resour. Res. 43, W08407.
9. Fonseca, A., Ames, D.P., Yang, P., Botelho, C., Boaventura, R., Vilar, V., 2014. Watershed model parameter estimation and uncertainty in data-limited environments. Environ. Model. Softw 51, 84–93.
10. Pang A and Sun T (2014) Bayesian network for environmental flow decision-making and an  application in the Yellow river estuary, China.Hydrol. Earth Syst. Sci., 18, 1641–1651, 2014
11. www.hydrol-earth-syst-sci.net/18/1641/2014/ doi:10.5194/hess-18-1641-2014
12. Sanjay S et al (2023) Predicting monthly river discharge using Bayesian Optimization- Based SVR model. HYDRO 2023 INTERNATIONAL 28th International Conference on Hydraulics,Water Resources, River and Coastal Engineering 21-23 December 2023