Producción Científica

Agriculture has always had a great significance in the civilization development. However, modern agriculture is facing increasing challenges due to population growth and environmental degradation. Commercially, farmers are looking for ways to improve profitability and agricultural efficiency to reduce costs. Smart Farming is enabling the use of detailed digital information to guide decisions along the agricultural value chain. Thus, better decisions and efficient management control are required through generated information and knowledge at any farm. New technologies and solutions have been applied to provide alternatives to assist in information gathering and processing, and thereby contribute to increased agricultural productivity. Therefore, this article aims to gain state-of-art insight and identify proposed solutions, trends and unfilled gaps regarding digitalization and Big Data applications in Smart Farming, through a literature review. The current study accomplished these goals through analyses based on ProKnow-C (Knowledge Development Process – Constructivist) methodology. A total of 2401 articles were found. Then, a quantitative analysis identified the most relevant ones among a total of 39 articles were included in a bibliometric and text mining analysis, which was performed to identify the most relevant journals and authors that stand out in the research area. A systemic analysis was also accomplished from these articles. Finally, research problems, solutions, opportunities, and new trends to be explored were identified.

Purpose: Conduct a bibliometric study on agricultural startups (AgTech) and the main concepts related to them in the literature. Theoretical framework: The agribusiness sector has the challenge of producing food sustainably to ensure food security for the planet’s population by 2050. In this context, there is an exponential growth in investments in agriculture technology (Kakani et al., 2020). Most of these technologies are developed and marketed by AgTechs, the technological startups in agribusiness. AgTechs are expressive in the 4.0 agriculture scenario, where more precise and environmentally sustainable technologies are sought (Dutia, 2014. However, despite the growing number of AgTechs, few studies present their main concepts in the scientific literature. Design/methodology/approach: The Web of Science (WoS) and Scopus databases were used together with softwares: SciMAT and VOSviewer to develop the bibliometric study. The SciMAT was used to clean up raw bibliographic data, analyze, and configure the analysis. The maps generated were produced at VOSviewer and based on co-citation for the periods defined in the SciMAT. Findings: The results showed that the theme is not well consolidated in the literature, but it is in a dizzying growth, with 71.3% of the articles having been published in the last three years in 79 journals and with publications covering 44 countries. Research, Practical & Social implications: the AgTech theme is consolidating in literature where digital and disruptive technologies are concerned, however, the human factors, business models, and management aspects involved in this topic are being neglected, which resulted in the proposal of a Research Agenda that can help both academics and practitioners to analyze AgTechs aspects that appear to not be in focus right now. Originality/value: The study brought important contributions to a better understanding of the term AgTech in the literature and to the improvement of concepts related to this ecosystem.

Pilotless aircraft systems will reshape our critical thinking about agriculture. Furthermore, because they can drive a transformative precision and digital farming, we authoritatively review the contemporary academic literature on UAVs from every angle imaginable for remote sensing and on-field management, particularly for sugarcane. We focus our search on the period of 2016–2021 to refer to the broadest bibliometric collection, from the emergence of the term “UAV” in the typical literature on sugarcane to the latest year of complete publication. UAVs are capable of navigating throughout the field both autonomously and semi-autonomously at the control of an assistant operator. They prove useful to remotely capture the spatial-temporal variability with pinpoint accuracy. Thereby, they can enable the stakeholder to make early-stage decisions at the right time and place, whether for mapping, re-planting, or fertilizing areas producing feedstock for food and bioenergy. Most excitingly, they are flexible. Hence, we can strategically explore them to spray active ingredients and spread entomopathogenic bioagents (e.g., Cotesia flavipes and Thricrogramma spp.) onto the field wherever they need to be in order to suppress economically relevant pests (e.g., Diatraea saccharalis, Mahanarva fimbriolata, sugarcane mosaic virus, and weeds) more precisely and environmentally responsibly than what is possible with traditional approaches (without the need to heavily traffic and touch the object). Plainly, this means that insights into ramifications of our integrative review are timely. They will provide knowledge to progress the field’s prominence in operating flying machines to level up the cost-effectiveness of producing sugarcane towards solving the sector’s greatest challenges ahead, such as achieving food and energy security in order to thrive in an ever-challenging world.

The 2nd International Symposium on Work in Agriculture (March 29–April 1 2021) was a moment for debate on the perspectives of work in agriculture. In this article, we propose to summarize the exchanges that outline the major issues from this event. We made a bibliometric analysis of all the contributions presented and a cross-cutting expert synthesis. Agricultural work and drivers of employment were the main topics of the presentations, with North and Global South countries facing different labor productivity dynamics. Some questions are continent specific: will OECD countries see agriculture without farmers? Will agriculture provide decent work to youth, notably in the Sub-Saharan Africa? With which models of agriculture? Some issues are common globally: the ability of the agroecological transition to improve working conditions and provide new jobs; the recognition of women and ethnic groups’ contributions to work and to decisions. The digital revolution and the migration flows to and from rural areas and cities are also changing agricultural work substantially. Cross-cutting perspectives are introduced such as the potential of the decent work concept to foster the attractiveness of the agricultural professions, the effect of value chains and the potential of the territorial approach to work to foster interdisciplinary and transdisciplinary viewpoints in addressing problems in the domain.

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.

Climate change and global warming interconnected with the new contexts created by the COVID-19 pandemic and the Russia-Ukraine conflict have brought serious challenges to national and international organizations, especially in terms of food security and agricultural planning. These circumstances are of particular concern due to the impacts on food chains and the resulting disruptions in supply and price changes. The digital agricultural transition in Era 4.0 can play a decisive role in dealing with these new agendas, where drones and sensors, big data, the internet of things and machine learning all have their inputs. In this context, the main objective of this study is to highlight insights from the literature on the relationships between machine learning and food security and their contributions to agricultural planning in the context of Agriculture 4.0. For this, a systematic review was carried out based on information from text and bibliographic data. The proposed objectives and methodologies represent an innovative approach, namely, the consideration of bibliometric evaluation as a support for a focused literature review related to the topics addressed here. The results of this research show the importance of the digital transition in agriculture to support better policy and planning design and address imbalances in food chains and agricultural markets. New technologies in Era 4.0 and their application through Climate-Smart Agriculture approaches are crucial for sustainable businesses (economically, socially and environmentally) and the food supply. Furthermore, for the interrelationships between machine learning and food security, the literature highlights the relevance of platforms and methods, such as, for example, Google Earth Engine and Random Forest. These and other approaches have been considered to predict crop yield (wheat, barley, rice, maize and soybean), abiotic stress, field biomass and crop mapping with high accuracy (R2 ≈ 0.99 and RMSE ≈ 1%). © 2022 by the authors.