cryptoisaactheeconomist-alt/Hybrid-Econometric-SEM-Machine-Learning-Framework-for-Agricultural-Technology-Adoption-in-Nigeria

This study develops a hybrid econometric–SEM–machine learning framework to analyze adoption of indigenous agricultural machinery among smallholder farmers in Niger State, Nigeria. Results show adoption is driven by awareness, perceived benefits, ease of use, and farm size, supporting scalable financing strategies. # Hybrid Econometric–SEM–Machine Learning Framework for Agricultural Technology Adoption in Nigeria ## Overview This study develops a hybrid analytical framework that integrates Econometric modeling, Structural Equation Modeling (SEM), and Machine Learning to examine agricultural technology adoption among smallholder farmers in Nigeria. The focus is on indigenous agricultural machinery, financing constraints, institutional support, and behavioural determinants of adoption. ## Research Problem Agricultural productivity in Nigeria remains low due to limited mechanization and slow adoption of improved technologies. Although indigenous agricultural machinery exists, many remain at prototype or small-scale production stages due to financing and institutional constraints. ## Theoretical Foundations The study is grounded in: - Innovation Diffusion Theory (Rogers, 2003) - Technology Acceptance Model (Davis, 1989) - Financial Intermediation Theory - Innovation Systems Theory ## Methodological Framework The study integrates: - Econometric modeling (OLS, diagnostic tests) - Structural Equation Modeling (SEM) - Machine Learning models (Random Forest and XGBoost) ## Key Findings - Adoption is driven by awareness, relative advantage, perceived usefulness, perceived ease of use, and farm size. - Financial and institutional constraints significantly influence adoption outcomes. - Machine learning shows moderate predictive performance with nonlinear adoption patterns. - SEM confirms strong validity of behavioural constructs. ## Contribution This study provides a unified hybrid framework that combines causal inference, latent variable modelling, and predictive analytics to better understand agricultural technology adoption. ## Keywords Agricultural adoption, SEM, Machine learning, Econometrics, Innovation diffusion, Nigeria, Financing architecture