Smart Agriculture and Farmer Well-Being: Examining the Combined Effects of Precision Technology, Environmental Sustainability and Livelihood Security

Abstract

Smart agriculture has emerged as a promising approach for improving agricultural productivity while supporting sustainable resource management. This study examined the relationships between precision technology, environmental sustainability, and agricultural performance using a secondary dataset of 500 farms from multiple agricultural regions. Because the dataset did not include direct indicators of farmer well-being or livelihood security, crop yield was used as a proxy for livelihood-related agricultural performance. A quantitative, cross-sectional research design was employed, and the data were analyzed using Python. The analytical framework included descriptive statistics, Pearson correlation analysis, multiple linear regression, and Random Forest-based feature importance analysis. The descriptive results indicated modest differences in crop yield across irrigation methods, fertilizer types, and disease-status categories. However, regression analysis showed that neither the Precision Technology Index nor the Environmental Sustainability Index had a statistically significant influence on crop yield. Correlation analysis also revealed generally weak relationships among the principal study variables, while the machine learning model demonstrated limited predictive accuracy, with environmental factors contributing more strongly than the constructed technology index. These findings suggest that agricultural productivity is shaped by multiple interacting environmental and management factors and that crop yield alone is insufficient to represent farmer well-being. Future research should integrate socioeconomic, livelihood, and well-being indicators with agronomic data to provide a more comprehensive understanding of the contribution of smart agriculture to sustainable rural development.