Assessment of Mechanized Rice Farming in Northwestern Nigeria: Socio-Economic Insights and Predictive Modeling

Nasir Umar Hassan () and Ayse Gozde Karaatmaca
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Nasir Umar Hassan: Department of Business Administration, Graduate School of Social Science, Near East University, Northern Cyprus, Mersin 10, 99138 Nicosia, Turkey
Ayse Gozde Karaatmaca: Department of Business Administration, Graduate School of Social Science, Near East University, Northern Cyprus, Mersin 10, 99138 Nicosia, Turkey

Sustainability, 2025, vol. 17, issue 21, 1-17

Abstract: In Nigeria’s northwestern states of Kano, Katsina, and Kaduna, mechanized rice production is an important contributor to household income and rural economic activity, especially amid a rapidly growing population projected to exceed 400 million by 2050. This study investigates the socio-economic insights of mechanized rice farmers and assesses the impact of mechanization on income, seasonal production, government support, and rural poverty alleviation. Data were collected from 125 respondents across 14 local government areas by using structured questionnaires and analyzed through descriptive statistics and hybrid machine learning models. The findings show that revenue generation significantly influences the adoption of mechanized rice farming, while government involvement is limited and largely ineffective. Advanced predictive modeling revealed that hybrid approaches, particularly those combining regression and Artificial Neural Networks with Bayesian Optimization, outperformed traditional models in forecasting rice yield. Key challenges identified include the high cost of equipment and restricted access to subsidized inputs. This study concludes that income from rice sales drives mechanization and that targeted policy interventions are necessary to overcome socio-economic barriers and improve productivity. These findings highlight the dual importance of economic empowerment and technological innovation in advancing sustainable rice production and improving livelihoods in Nigeria’s rice-growing regions.

Keywords: agricultural sustainability; artificial intelligence (AI); government support; household size; machine learning tools; sustainable rice platform (SRP) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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