The utilization of Normalized Difference Vegetation Index (NDVI) data obtained through satellite images can technically improve the process of delimiting management zones (MZ) for annual crops, resulting in socioeconomic and environmental benefits. The aim of this study was to compare delimited MZ, using crop productivity data, with delimited MZ using the NDVI obtained from satellite images in areas under a no-tillage system. The study was carried out in three areas located in the state of Rio Grande do Sul, Brazil. Three crop productivity maps, from 2009 to 2015, were used for each area, whereby the NDVI was calculated for each crop productivity map using images from the Landsat series of satellites. Descriptive and geostatistical analysis were conducted to determine the productivity and NDVI data. The MZ were then delimited using the fuzzy c-means algorithm. Spearman's correlation matrix was used to compare the methodologies used for delimiting the MZ. The MZ based on NDVI calculated from the satellite images correlated with the MZ based on crop productivity data (0.48 < r < 0.61), suggesting that the NDVI can replace or be complementary to productivity data in delimiting MZ for annual cropping systems.

The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in São Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.

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.

Agroforestry systems have been promoted as a solution to address trade-offs between environmental conservation efforts and the need for increased agricultural productivity on smallholder farms in Brazil. However, the impact of land use change from degraded pasture to agroforestry on soil properties remains unclear. The objectives of this research were to: (1) assess soil chemical, physical and biological properties across distinct land uses (degraded pasture, agroforestry and secondary forest); and (2) understand relationships between litter quality, soil organic matter (SOM) and key soil quality parameters in the Brazilian Atlantic Rainforest. Soils, macroinvertebrates and litter were collected in April and September of 2018 under five land uses, including: three types of agroforestry systems, a degraded pasture and a secondary forest in Sapucaia, Rio de Janeiro, Brazil. Our results showed that soil properties clearly separated the three agroforestry systems plots (AS1, AS2, AS3) from the forest and pasture plots. Moreover, litter quality and SOM likely influence multiple biological and physiochemical soil properties under agroforestry systems and secondary forest. Our findings suggest that agroforestry systems can help support soil biological, chemical and physical properties and that the litter quality may be an important driver of their effects and potential contributions to soil restoration in the region.