Quantifying the Indirect Economic Costs of Cassava Mosaic Disease in Nigeria: A Computable General Equilibrium Analysis

📖 ABSTRACT/OVERVIEW

Published estimates of Cassava Mosaic Disease (CMD) economic impact in Nigeria rely predominantly on direct yield loss measurements and partial budget analyses, which capture only a fraction of the true economic burden imposed by persistent epidemic disease pressure across the national cassava system. Indirect costs operating through rural labour markets, food price transmission, nutritional outcomes, and import substitution dynamics remain unquantified at the national level. This doctoral research develops the first computable general equilibrium model calibrated to the Nigerian economy for quantifying the indirect and economy-wide economic costs of CMD. The model integrates: a national CMD burden module incorporating geo-referenced disease incidence data from 15-year surveillance records; a calibrated social accounting matrix for Nigeria's 36 states; cassava sector disaggregation into subsistence, commercial, and processing production archetypes; and dynamic general equilibrium simulation of CMD-induced productivity shocks propagating through rural-urban linkages, food commodity prices, household nutrition indicators, and agricultural trade balances. Shock scenarios simulate counterfactual outcomes under three disease management investment levels: status quo, moderate intensification through certified planting material expansion, and full containment through a national CMD control programme. The research generates the first economy-wide benefit-cost analysis of national CMD management investment for Nigeria, with disaggregated impacts across income quintiles and geopolitical zones. Original theoretical contributions include a novel pathosystem-general equilibrium integration methodology applicable to other crop disease systems in developing economies. Keywords: Cassava Mosaic Disease, economic modelling, computable general equilibrium, Nigeria, indirect costs.

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Departments# Plant Pathology