Producción Científica

Smart farming (SF) involves the incorporation of information and communication technologies into machinery, equipment, and sensors for use in agricultural production systems. New technologies such as the internet of things and cloud computing are expected to advance this development, introducing more robots and artificial intelligence into farming. Therefore, the aims of this paper are twofold: (i) to characterize the scientific knowledge about SF that is available in the worldwide scientific literature based on the main factors of development by country and over time and (ii) to describe current SF prospects in Brazil from the perspective of experts in this field. The research involved conducting semi-structured interviews with market and researcher experts in Brazil and using a bibliometric survey by means of data mining software. Integration between the different available systems on the market was identified as one of the main limiting factors to SF evolution. Another limiting factor is the education, ability, and skills of farmers to understand and handle SF tools. These limitations revealed a market opportunity for enterprises to explore and help solve these problems, and science can contribute to this process. China, the United States, South Korea, Germany, and Japan contribute the largest number of scientific studies to the field. Countries that invest more in R&D generate the most publications; this could indicate which countries will be leaders in smart farming. The use of both research methods in a complementary manner allowed to understand how science frame the SF and the mains barriers to adopt it in Brazil.

Agricultural Innovation Systems (AIS) approach, arise as tool for better understanding dynamics and complexity of agricultural innovation. The objective of this article is present a framework for AIS capability development, taking as a scientific reference, emerging economy countries experiences. A multi-dimensional methodology of literature review and content analysis is implemented, supported in bibliometric and data mining techniques. The development of capacities in the context of agricultural innovation systems is mediated by the existence of links between actors, which enable social learning processes through networking. AIS, as an approach for the improvement of innovation capacities, emerge in developed countries, with consolidated institutional capacities and constitute an opportunity for the strengthening of the agricultural sector in countries with emerging economies. The analysis of networks, the non-linear perspective of the innovation process and the initiation of research, training, policy and intermediation are the main common themes between AIS and capability building.

Waste is a problem in agriculture, however, there is the possibility of adding value to the management of this waste. The literature explores the potential of agricultural waste treatment technologies, but, it is necessary to systematize the knowledge on the subject. It is necessary to understand which solutions are being developed and which technologies are more consolidated to motivate the development of these solutions. Thus, this study aims to present a panoramic view of research in agricultural waste treatment technologies. The method adopted was bibliometric analysis. We show which technologies are more consolidated in the scientific community. Several biological and thermal solutions were found. Heavy metals, chemical and biological contaminants are major concerns in treatment processes. Anaerobic digestion and composting are the technologies most explored in the scientific community. Other technologies are microalgae, pyrolysis, algae, biorefineries, combustion, incineration, gasification, anaerobic co-digestion, hydrothermal carbonization, vermicomposting, biosynthesis process, dry anaerobic digestion and photobioreactors. This article systematizes and consolidates the knowledge about the technologies that can be applied to the management of agricultural residues, which is important for the consolidation of the field of research and strategic development of these solutions. The adoption of technologies is a significant step towards a circular economy model.

In this current and global context led by scarcity of resources, environmental degradation, global climate change, and a progressive demand for food, the circular economy (CE) represents a key economic model or framework for sustainable, restorative, and regenerative agriculture. Hence, CE applied to agriculture seeks to close the life cycle of products, services, waste, water, and energy to obtain a better use of resources and a reduction of the ecological impact. An initial review of the literature corroborates the hypothesis that the CE framework has not yet been comprehensively adapted to the field of agriculture. This research seeks to overcome this gap in relation to the performance of the circularity of agricultural production systems in support of decision-making processes. A bibliometric analysis of 1060 documents was carried to synthesize the knowledge base on this topic. The results show recent studies that identify weaknesses derived from food production, such as waste generation, biomass, water pollution, and greenhouse gas emissions. It has been identified how their analysis has developed to date and what terms allow us to visualize new approaches; consequently, it is a useful tool for researchers and sponsors who provide financial resources for the development of new lines of research.

Precision Agriculture (PA) and Agriculture 4.0 (A4.0) have been widely discussed as a medium to address the challenges related to agricultural production. In this research, we present a Systematic Literature Review (SLR) supported by a Bibliometric Performance and Network Analysis (BPNA) of the use of A4.0 technologies and PA techniques in the coffee sector. To perform the SLR, 87 documents published since 2011 were extracted from the Scopus and Web of Science databases and processed through the Preferred Reporting Items for Systematic reviews and Meta-Analyzes (PRISMA) protocol. The BPNA was carried out to identify the strategic themes in the field of study. The results present 23 clusters with different levels of development and maturity. We also discovered and presented the thematic network structure of the most used A4.0 technologies in the coffee sector. Our findings shows that Internet of Things, Machine Learning and geostatistics are the most used technologies in the coffee sector, we also present the main challenges and trends related to technological adoption in coffee systems. We believe that the demonstrated results have the potential to be considered by researchers in future works and decision making related to the field of study.