Hybrid Econometric-SEM-Machine Learning Framework for Agricultural Technology Adoption Modeling Under IDT Theory&nbsp;

Domaine:

agriculturesocioeconomic

Type de record:

paper

Créateur:

IsaBam

Éditeur:

Hôte:

Agricultural productivity in Nigeria remains constrained by low mechanization and weak adoption of improved indigenous agricultural machinery, despite their potential to reduce production costs and improve farm output. This study develops a hybrid analytical framework that integrates econometric modeling, Structural Equation Modeling (SEM), and Machine Learning (ML) to examine the determinants of adoption and support the design of a scalable financing architecture for indigenous agricultural machinery among smallholder farmers. Data sourced from farm settlements in Batati, Kutigi, Wuya and Bida in Niger state, and were analyzed using robust OLS, SEM, and tree-based ML models (Random Forest and XGBoost), supported by diagnostic tests and post-estimation validation. Adoption is mainly driven by awareness, relative advantage, perceived usefulness, perceived ease of use, and farm size, while age, education, and experience are not significant. SEM confirms strong validity and good model fit, and econometric results support the key behavioral and economic effects. Machine learning shows moderate predictive accuracy, with XGBoost performing better than Random Forest, indicating nonlinear adoption patterns. Overall, the study shows that adoption is shaped by behavioral, economic, and institutional factors, and the integrated econometric-SEM-machine learning framework improves understanding and prediction of adoption behavior.

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<p>Hybrid Econometric-SEM-Machine Learning Framework for Agricultural Technology Adoption Modeling Under IDT Theory&nbsp;</p>

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