Constraints to Effective Pesticide Utilization Among Vegetable Growers in Jaipur District

A Multivariate and Machine Learning Approach

Abstract

Pesticide misuse in vegetable farming poses significant environmental and health risks in Jaipur District, Rajasthan, driven by constraints such as limited knowledge, high costs, and inadequate administrative support. This study employed a multivariate and machine learning approach to investigate constraints among 120 vegetable growers, to identify and rank technological, economic, environmental, and administrative constraints to pesticide use; to examine their variation by socio-economic characteristics; and cluster farmers by constraint profiles. A structured questionnaire assessed 28 constraint items, analyzed using descriptive statistics, MANOVA, K-Means Clustering, and Exploratory Factor Analysis (EFA). Results revealed severe knowledge gaps and environmental concerns. Education significantly influenced economic constraints with Middle-level educated farmers reporting the highest barriers. Clustering identified three groups i.e., a small outlier group with low soil and market concerns, a moderate group focused on pest resistance, and a large group emphasizing administrative and health issues. EFA extracted five constraint dimensions, explaining 68.23% of variance. Findings underscore the need for targeted interventions, including enhanced extension services, subsidies for eco-friendly pesticides, and education on sustainable pest management, to promote safe pesticide practices and mitigate risks in Rajasthan’s vegetable sector.