In Ghana, various agricultural processes are subject to varying degrees of yield variability due to environmental factors and operational inefficiencies. The study employs a multilevel regression model with fixed effects for regional variations, random intercepts for farm-level differences, and controls for socio-economic and climatic variables. The specific model equation is: Y₈₉ = eta₀ + eta₁X₁₈₉ + eta₂X₂₈₉ + bᵢ + u₈₉, where Y represents yield, X₁ and X₂ are process control variables, bᵢ accounts for regional differences, and u₈₉ captures farm-specific variability. A significant proportion (45%) of the variance in crop yields was attributed to regional differences, indicating that uniform process-control measures might not be sufficient across all regions. Farm-level factors accounted for an additional 20% of yield variation, suggesting a need for tailored interventions. The multilevel regression analysis revealed the importance of considering both macro (regional) and micro (farm) levels in optimising process-control systems for agricultural productivity. Farmers are advised to adopt region-specific process-control measures based on regional yield data, while also focusing on improving farm-level efficiency through targeted interventions.
Foster et al. (Thu,) studied this question.