Precision Agriculture in Brazil: The Trajectory of 25 Years of Scientific Research

Autor(es)

Cherubin, Mauricio Roberto ; Damian, Junior Melo ; Tavares, Tiago Rodrigues ; Trevisan, Rodrigo Goncalves ; Colaco, Andre Freitas ; Eitelwein, Mateus Tonini ; Martello, Mauricio ; Inamasu, Ricardo Yassushi ; Pias, Osmar Henrique de Castro ; Molin, Jose Paulo

• Tipo de documento: Artigo Científico
• Fonte: AGRICULTURE-BASEL; v. 12, n. 11, p. 29-pg., 2022-11-01.

Resumo

Precision agriculture (PA) stands out as an innovative way to manage production resources, increasing the efficiency and the socioeconomic and environmental sustainability of agricultural systems. In Brazil, the principles and tools of PA started to be adopted in the late 1990s. To reveal the scientific trajectory and advances in PA taken over the past 25 years in Brazil, we conducted a comprehensive and systematic literature review. After searching for available peer-reviewed literature, 442 publications were selected to compose the database. Our bibliometric review showed that the scientific PA network is growing in Brazil, with the number and quality of publications, the number of interactions among research groups, and the number of international collaborations increasing. Soil and plant management are the two main pillars of PA research (similar to 61% of the publications). More recently, research has evolved to include other areas, such as the use of proximal sensors to monitor soil and crop development, remote sensing using images from satellites and remotely piloted aircraft systems, and the development of decision support tools. A substantial part of Brazilian PA research is marked by the evaluation and adaptation of imported technologies, a scenario that is slowly changing with the growth of well-trained human resources and advances in national industry. Based on Brazilian scientific history and remaining challenges, the key potential areas for future research are (i) the development of digitally based decision support systems, i.e., a shift of focus from on-farm data technologies towards effective, site-specific decision making based on digital data and improved analytics; (ii) on-farm precision experimentation to underpin on-farm data collection and the development of new decision tools; and (iii) novel machine learning approaches to promote the implementation of digitally based decision support systems. (AU)

• Processo FAPESP: 20/16670-9 - Modelagem de dados espectrais para análise da fertilidade de solos tropicais: associação das técnicas vis-NIR e XRF para a modernização dos métodos tradicionais de análise
• Beneficiário: Tiago Rodrigues Tavares
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado