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.

Important public and private initiatives to map agricultural lands and natural resources have been carried out in Brazil to support land use planning. Some studies indicate that Brazil still has up to 109.7 million hectares of cultivated pastures with some level of degradation, representing around 60% of the total pasturelands, estimated at 177 million hectares. This study aimed to gather, process, and analyze publicly available databases to generate quantitative and spatial information about the potential of Brazilian degraded pastures for agricultural expansion. We considered data related to the natural agricultural potential, restrictions imposed by special areas (indigenous lands and Afro-Brazilian “quilombola” settlements), areas with high biodiversity conservation priorities, infrastructure such as distance between major highways and availability of warehouses, current agricultural areas, and the information made available by Agricultural Climate Risk Zoning. The results indicated the existence of approximately 28 million hectares of planted pastures with intermediate and severe levels of degradation that show high potential for agricultural crops. These areas could increase the planted areas with grains in Brazil by approximately 35% in relation to the total area used in the 2022/23 crop season.

The objective of this work was to analyze land use dynamics in the Brazilian Cerrado region from 2002 to 2013. This analysis was based on the interpretation of Landsat satellite images carried out by the projects Projeto de Conservação e Utilização Sustentável da Diversidade Biológica Brasileira (Probio) and TerraClass Cerrado 2013, both coordinated by Ministério do Meio Ambiente. In 2002, 38.9% of the Cerrado was covered by some type of anthropic activity. In 2013, this percentage increased to 43.4%. One of the main highlights is the emergence of a new agricultural frontier in the northern region of the study area, known as Matopiba.

Mapping cropland distribution over large areas has attracted great attention in recent years, however, traditional pixel-based classification approaches produce high uncertainty in cropland area statistics. This study proposes a new approach to map fractional cropland distribution in Mato Grosso, Brazil using time series MODIS enhanced vegetation index (EVI) and Landsat Thematic Mapper (TM) data. The major steps include: (1) remove noise and clouds/shadows contamination using the Savizky–Gloay filter and temporal resampling algorithm based on the time series MODIS EVI data; (2) identify the best periods to extract croplands through crop phenology analysis; (3) develop a seasonal dynamic index (SDI) from the time series MODIS EVI data based on three key stages: sowing, growing, and harvest; and (4) develop a regression model to estimate cropland fraction based on the relationship between SDI and Landsat-derived fractional cropland data. The root mean squared error of 0.14 was obtained based on the analysis of randomly selected 500 sample plots. This research shows that the proposed approach is promising for rapidly mapping fractional cropland distribution in Mato Grosso, Brazil.

The Brazilian Cerrado is one of the world´s biodiversity hotspot and hosts some of the most intensive agricultural activities for food production in the world. The objective of this study was to produce a land-use and land-cover (LULC) map of the Cerrado based on Landsat-8 Operational Land Imager (OLI) images. A set of 121 scenes from 2013 was processed using the image segmentation technique. The segments were exported in the shapefi le format and interpreted visually in a geographical information system software using RGB/564 color composites. The following LULC classes were considered: annual croplands, perennial croplands, cultivated pasturelands, reforestation, mosaic of occupation, urban areas, mining areas, bare soil, forestlands, non-forestlands, water bodies, and non-identifi ed (clouds and burned areas). The overall accuracy was estimated by an independent scientist with large experience in Cerrado´s image interpretation. The results showed that 43.4% of the study area (88.5 million hectares) were already converted into agricultural, urban and mining areas, 54.6% (111 million hectares) were still natural areas, and 1.9% (3.9 million hectares) was classified as non-identified. Cultivated pasturelands were the most representative land-use type (29.5%), followed by annual croplands (8.5%) and perennial croplands (3.1%). The overall accuracy of the final map was 80.2%.

Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021–2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture.