Sustainable Farming: Insights from Data Clustering

Abstract

This study delves into the perceptions and practices of the agricultural community regarding eco-friendly technologies and air pollution through a detailed clustering analysis of survey data. The primary objective is to identify distinct groups within the agricultural sector based on their responses to various factors, including demographic information, types of crops grown, perceptions of air pollution, and attitudes toward sustainable practices. The analysis employs K-Means clustering to categorize respondents into three distinct clusters, each representing a unique combination of views and practices. The findings are visualized using scatter plots and box plots, offering a clear depiction of the variations and commonalities within each cluster. The study reveals significant diversity in the adoption and perception of eco-friendly practices in agriculture. Some groups demonstrate high satisfaction and effectiveness, indicating successful integration of sustainable methods, while others show skepticism and challenges, possibly due to economic constraints or lack of access to resources and knowledge. The economic interpretation of these clusters suggests that varying levels of resource availability, technological access, and knowledge dissemination influence differences in the adoption of sustainable practices. The study concludes with recommendations for targeted policy-making, educational initiatives, and resource allocation to support and enhance the adoption of eco-friendly practices across different segments of the agricultural community. This tailored approach can significantly contribute to the broader objective of promoting sustainable agriculture and environmental stewardship.

Keywords

• economy
• economic development
• sustainable agriculture
• farming systems
• data clustering
• farmer perception
• air pollution
• emission