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ABSTRACT Bulk density (BD) is a soil physical property used as a soil quality indicator and variations in this measurement influence soil water content and carbon stock estimates. This study aims to compile a database of samples of bulk density, textural fractions, and organic carbon values, as well as evaluate the accuracy of published pedotransfer functions (PTF) that predict bulk density, and propose a hierarchical PTF to predict the bulk density of Brazilian Soils. The performance of eleven PTFs and the newly proposed PTFs were evaluated and compared using the root mean square error (RMSE) and coefficient of determination (R2) based on a testing soil database collected from the literature. We noticed a slight improvement in accuracy when organic carbon and coarse and fine sand fractions were included as predictors alongside silt and clay. The best results with existing PTFs were obtained by PTF-A in Tomasella and Hodnett (1998) (RMSE = 0.20 g cm–3) and PTF-F in Benites et al. (2007) (RMSE = 0.17 g cm–3). Our proposed PTFs use textural fractions and organic carbon as predictors in a hierarchical form. The proposed PTF-4, which uses fine sand, coarse sand, clay, and organic carbon, presented the lowest value for RMSE (0.14 g cm–3) for BD prediction.

Abstract Private lands are important for conservation worldwide, but knowledge about their effectiveness is still insufficient. To help fill this important knowledge gap, we analyzed the impacts of a national policy for conservation on private lands in Brazil, a global biodiversity hotspot with high potential for nature-based climate solutions. Through the evaluation of over 4 million private rural properties from the Rural Environmental Cadastre, we found that the last policy review in 2012 mainly affected the Amazon Forest. The amnesty granted to 80% of landowners of small properties prevented the restoration of 14.6 million hectares of agricultural land with a carbon sequestration potential of 2.4 gigatonnes. We found that private lands exist within the limits of public conservation areas and that between 2003 and 2020 deforestation rates in these private lands were higher than those across all conservation areas. The Rural Environmental Cadastre can be an effective tool for managing forests within private lands, with potential to integrate governance approaches to control deforestation and mitigate climate change.

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.

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.

Chronic hepatitis C virus infection (HCV) causes liver inflammation and fibrosis, leading to the development of severe liver disease, such as cirrhosis or hepatocellular carcinoma (HCC). Approval of direct‐acting antiviral drug combinations has revolutionized chronic HCV therapy, with virus eradication in >98% of the treated patients. The efficacy of these treatments is such that it is formally possible for cured patients to carry formerly infected cells that display irreversible transcriptional alterations directly caused by chronic HCV Infection. Combining differential transcriptomes from two different persistent infection models, we observed a major reversion of infection‐related transcripts after complete infection elimination. However, a small number of transcripts were abnormally expressed in formerly infected cells. Comparison of the results obtained in proliferating and growth‐arrested cell culture models suggest that permanent transcriptional alterations may be established by several mechanisms. Interestingly, some of these alterations were also observed in the liver biopsies of virologically cured patients. Overall, our data suggest a direct and permanent impact of persistent HCV infection on the host cell transcriptome even after virus elimination, possibly contributing to the development of HCC. [ABSTRACT FROM AUTHOR]

Farmers in the Brazilian Cerrado are increasing grain production by cultivating second crops during the same crop growing season. The release of PlanetScope (PS) satellite images represents an innovative opportunity to monitor double cropping production. In this study, we analyzed the potential of six PS monthly mosaics from the 2021/2022 crop growing season to discriminate double cropping areas in the municipality of Goiatuba, Goiás State, Brazil. The four multispectral bands of the PS images were converted into normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red normalized difference index (GRNDI), and textural features derived from the gray-level co-occurrence matrix (GLCM). The ten most important combinations of these attributes were used to map double cropping systems and other land use and land cover classes (cultivated pasture, sugarcane, and native vegetation) of the municipality through the Random Forest classifier. Training and validation samples were obtained from field campaigns conducted in October 2021 and April 2022. PS mosaic from February 2022 was the most relevant data. The overall accuracy and Kappa index of the final map were 92.2% and 0.892, respectively, with an accuracy confidence of 81%. This approach can be expanded for mapping and monitoring other agricultural frontiers in the Cerrado biome.

Transport costs can play a significant role in agricultural exports and businesses profitability. It can also influence farmers’ decisions regarding cropland expansion, intensification or land abandonment. Thus, transport is useful to take into account when modeling and evaluating land use and cover change related to agriculture production. The dataset described in this article represents the Infrastructure Capital in the work presented by Millington et al. (2021) [1], in which the CRAFTY-Brazil model is used to evaluate the impacts of changing global demand for agricultural products on land use and cover change. This modeling required a transport cost dataset that spanned the model calibration period, was consistent through time and covered the entire study area. The most recent federal road network (for year 2017) obtained in vector format (shapefile) was joined to road section surface status tables for past years (2000, 2005 and 2010) in order to reconstruct the historic road network. Export ports handling agricultural commodities, and their years of operation, were identified. Both datasets were used to derive the relative transport cost to the nearest port for Brazil, for years 2000, 2005, 2010 and 2017.

ABSTRACT The upper Teles Pires River basin is located in the Brazilian agriculture frontier in the north of Mato Grosso state and has experienced significant changes in land use and cover, which can cause major changes in its hydrological dynamics. Climatic and hydrologic data are scarce in the region, which poses uncertainties in the decision-making process aiming at the sustainable management of water resources in this strategic area. The aim of this study was to evaluate the performance of the Large-Scale Distributed Hydrological Model (MGB-IPH) to assess water availability of the upper Teles Pires basin and support water resource management in the Amazon-Cerrado ecotone. The MGB-IPH model was calibrated and validated using data from three streamflow stations available in the basin. In order to verify the model performance, the Nash-Sutcliffe (NS) and the PBIAS statistical parameters were applied. Our results show that, by using the MGB-IPH model with generally available data, the maximum and minimum flow regimes can be successfully assessed in the upper Teles Pires basin. The continuity curves of daily flow simulated by the model showed a good fit with the observed flow. Overall, the results demonstrated the applicability of the MGB-IPH model for water resource assessment and management in the basin.

The Brazilian Savanna presents a complex agricultural dynamic and cloud cover issues; therefore, there is a need for new strategies for more detailed agricultural monitoring. Using a hierarchical classification system, we explored the Harmonized Landsat Sentinel-2 (HLS) dataset to detect soybean in western Bahia, Brazil. Multispectral bands (MS) and vegetation indices (VIs) from October 2021 to March 2022 were used as variables to feed Random Forest models, and the performances of the complete HLS time-series, HLSS30 (harmonized Sentinel), HLSL30 (harmonized Landsat), and Landsat 8 OLI (L8) were compared. At Level 1 (agricultural areas × native vegetation), HLS, HLSS30, and L8 produced identical models using MS + VIs, with 0.959 overall accuracies (OA) and Kappa of 0.917. At Level 2 (annual crops × perennial crops × pasturelands), HLS and L8 achieved an OA of 0.935 and Kappa > 0.89 using only VIs. At Level 3 (soybean × other annual crops), the HLS MS + VIs model achieved the best performance, with OA of 0.913 and Kappa of 0.808. Our results demonstrated the potential of the new HLS dataset for medium-resolution mapping initiatives at the crop level, which can impact decision-making processes involving large-scale soybean production and agricultural sustainability.