A Bayesian confirmatory factor analysis of precision agricultural challenges

Abstract

Precision agriculture (PA) is designed to provide data to assist farmers when making site-specific 
management decisions. By making more informed management decisions, farmers can become more 
efficient, spend less and make more profit. Such benefits may lead to a sustainable agriculture. In 
implementation of PA, farmers encountered several challenges; therefore it is necessary to identify such 
challenges. A survey questionnaire was developed and mailed to a group of 40 experts in Qazvin province. 
The results showed that the challenges can be classified into nine latent variables namely: educational, 
economic, operator demographic, technical, data quality, high risk, time, institution-education and 
incompatibility challenges. The results suggested educational and economic challenges as the two most 
important challenges in the application of PA. Among the variables which build the educational challenges, 
lack of local experts and lack of a knowledgeable research and extension personnel provides more impact 
when compared to others, while lack of allocation funds to performance PA and Initial cost provides more 
impact in the economic challenges, among other variables.