Proposal of a Model of Irrigation Operations Management for Exploring the Factors That Can Affect the Adoption of Precision Agriculture in the Context of Agriculture 4.0

Sergio Monteleone (), Edmilson Alves de Moraes, Roberto Max Protil, Brenno Tondato de Faria and Rodrigo Filev Maia
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Sergio Monteleone: School of Business Administration, Centro Universitário FEI, São Paulo 01525-000, SP, Brazil
Edmilson Alves de Moraes: School of Business Administration, Centro Universitário FEI, São Paulo 01525-000, SP, Brazil
Roberto Max Protil: Department of Agricultural Economics, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, MG, Brazil
Brenno Tondato de Faria: School of Electrical Engineering, Centro Universitário FEI, São Bernardo do Campo 09850-901, SP, Brazil
Rodrigo Filev Maia: Centre of Regional and Rural Futures, Deakin University, Hanwood 2680, Australia

Agriculture, 2024, vol. 14, issue 1, 1-33

Abstract: Agriculture is undergoing a profound change related to Agriculture 4.0 development and Precision Agriculture adoption, which is occurring at a slower pace than expected despite the abundant literature on the factors explaining this adoption. This work explores the factors related to agricultural Operations Management, farmer behavior, and the farmer mental model, topics little explored in the literature, by applying the Theory of Planned Behavior. Considering the exploratory nature of this work, an exploratory multi-method is applied, consisting of expert interviews, case studies, and modeling. This study’s contributions are a list of factors that can affect this adoption, which complements previous studies, theoretical propositions on the relationships between these factors and this adoption, and a model of irrigation Operations Management built based on these factors and these propositions. This model provides a theoretical framework to study the identified factors, the relationships between them, the theoretical propositions, and the adoption of Precision Agriculture. Furthermore, the results of case studies allow us to explore the relationships between adoption, educational level, and training. The identified factors and the model contribute to broadening the understanding of Precision Agriculture adoption, adding Operations Management and the farmer mental model to previous studies. A future research agenda is formulated to direct future studies.

Keywords: precision agriculture; adoption; sensing technologies; irrigation; Agriculture 4.0; operations management; farmer mental model; center pivot; model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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