Título:	Precision agriculture in Brazil: The trajectory of 25 years of scientific research.
Autoria:	CHERUBIN, M. R. DAMIAN, J. M. TAVARES, T. R. TREVISAN, R. G. COLAÇO, A. F. EITELWEIN, M. T. MARTELLO, M. INAMASU, R. Y. PIAS, O. H. C. MOLIN, J. P.
Afiliação:	RICARDO YASSUSHI INAMASU, CNPDIA.
Ano de publicação:	2022
Referência:	Agriculture, v. 12, a1882, 2022.
Páginas:	29 p.
Conteúdo:	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 (~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.
Palavras-chave:	Precision farming Bibliometrics Scientific network
ISSN:	2077-0472
Digital Object Identifier:	https://doi.org/10.3390/agriculture12111882
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPDIA)