A Methodological Evaluation and Cost-Effectiveness Forecast for Regional Agricultural Monitoring Networks in Ghana

Agricultural monitoring networks are critical for food security and policy planning, yet their methodological rigour and long-term financial sustainability in West Africa are poorly understood. Existing evaluations often lack robust, forward-looking economic analysis. This study aims to methodologically evaluate the design of regional monitoring systems and to forecast their cost-effectiveness, providing an evidence-based framework for sustainable implementation. We conducted a comparative analysis of network architectures, followed by a time-series forecasting model. Cost-effectiveness was projected using a Bayesian state-space model: yₜ = + Cₜ + Nₜ + ₜ, where yₜ is a composite performance metric, Cₜ denotes cumulative costs, and Nₜ represents node density. Forecasts incorporated robust standard errors to account for heteroskedasticity. The centralised hub model demonstrated superior cost-efficiency, with a forecasted 23% reduction in operational expenditure per monitored hectare over a five-year horizon compared to decentralised designs. Model projections indicate a 95% probability that this cost advantage exceeds 15%. Strategic centralisation, rather than maximal spatial coverage, offers the most viable path for financially sustainable agricultural monitoring. The forecasting model provides a replicable tool for pre-implementation planning. Policymakers should prioritise investment in centralised monitoring hubs with integrated data pipelines. Future network expansions must be preceded by formal cost-effectiveness forecasts using the presented methodology. agricultural monitoring, cost-effectiveness, Bayesian forecasting, network design, sustainability This paper provides a novel Bayesian forecasting framework for evaluating the long-term economic viability of environmental monitoring infrastructures, a previously neglected aspect of implementation science in rural development.