The rapid population growth has driven the demand for more food, fiber, energy, and water, which is associated to an increase in the need to use natural resources in a more sustainable way. The use of precision agriculture machinery and equipment since the 1990s has provided important productive gains and maximized the use of agricultural inputs. The growing connectivity in the rural environment, in addition to its greater integration with data from sensor systems, remote sensors, equipment, and smartphones have paved the way for new concepts from the so-called Agriculture 4.0 or Digital Agriculture. This article presents the results of a survey carried out with 504 Brazilian farmers about the digital technologies in use, as well as current and future applications, perceived benefits, and challenges. The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey and was available from 17 April to 2 June 2020. The primary data obtained for each question previously defined were consolidated and analyzed statistically. The results indicate that 84% of the interviewed farmers use at least one digital technology in their production system that differs according to technological complexity level. The main perceived benefit refers to the perception of increased productivity and the main challenges are the acquisition costs of machines, equipment, software, and connectivity. It is also noteworthy that 95% of farmers would like to learn more about new technologies to strengthen the agricultural development in their properties.

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.

Agricultura de precisão e agricultura digital

The knowledge of soil property spatial variability is useful for determining the rational use of inputs, such as the site-specific application of lime and fertilizer. The objective of this study was to evaluate the vegetation index and spatial variability of physical and chemical soil properties in an integrated crop-livestock system (ICLS). Soil samples were taken from a 6.9 ha area in a regular hexagon grid at 0-0.20 m depths. Soil P, K, Ca, Mg, and cation exchange capacity - CEC; base saturation; clay and sand were analyzed. Soil electrical conductivity (ECa) was measured with a contact sensor. The site was evaluated at the end of the corn season (April) and during forage production (October) using Landsat 5 images, remote sensing techniques and a geographic information system (GIS). Results showed that the normalized difference vegetation index (NDVI) was associated with ECa and soil parameters, indicating crop and pasture variations in the ICLS. Geostatistics and GIS were effective tools for collecting data regarding the spatial variability of soil and crop indicators, identifying variation trends in the data, and assisting data interpretation to determine adequate management strategies.

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations.

Cultural diversity often complicates the understanding of sustainability, sometimes making its concepts seem vague. This issue is particularly evident in food systems, which rely on both renewable and nonrenewable resources and drive significant environmental changes. The widespread impacts of climate change, aggravated by the overuse of natural resources, have highlighted the urgency of balancing food production with environmental preservation. Society faces a pivotal challenge: ensuring that food systems produce ample, accessible, and nutritious food while also reducing their carbon footprint and protecting ecosystems. Agriculture 5.0, an innovative approach, combines digital advancements with sustainability principles. This study reviews current knowledge on digital agriculture, analyzing scientific data through an undirected bipartite network that links journals and author keywords from articles retrieved from Clarivate Web of Science. The main goal is to outline a framework that integrates various sustainability concepts, emphasizing both well-studied (economic) and underexplored (socioenvironmental) aspects of Agriculture 5.0. This framework categorizes sustainability concepts into material (tangible) and immaterial (intangible) values based on their supporting or influencing roles within the agriculture domain, as documented in the scientific literature.