Precision Agriculture: Strategies and Technology Adoption

Rodrigues, G. C.

doi:10.3390/agriculture12091474

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…Agriculture is also experiencing a technological transformation related to the adoption of Precision Agriculture (PA), which, after 20 years of research, is not consolidated [6]. Despite the expected economic gains [7], PA adoption is often perceived as not occurring immediately [8], and it is progressing at a slower speed than predicted [9] due to several factors; among them, the lack of farmer knowledge about the use of PA technologies plays an important role in business outcomes [10]. There is a lack of consensus on whether PA improves farm profitability, in addition to the little attention paid to perceptions of PA profitability from the farmer's perspective [11].…”

Section: Introductionmentioning

confidence: 99%

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

Monteleone,

Alves de Moraes,

Protil

et al. 2024

Agriculture

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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.

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