Has unsustainable groundwater use induced low flow regimes in the Urucuia Aquifer System? An urgent call for integrated water management.

Groundwater depletion in the Urucuia Aquifer System (UAS) has concerned the Brazilian water agencies since it is the principal water source in the arid length of the São Francisco river. In contrast, the irrigation fields increasing and uncertainty regarding climate variability have impaired the management of this important hydrograph region of Brazil. Therefore, the proper management of the UAS relies on the correct definition of the factors that are impacting most of the ground and surface waters, i.

CA-Markov prediction modeling for the assessment of land use/land cover change in two sub-basins of the Tocantins-Araguaia River Basin.

Due to the anthropogenic pressures of expansion areas for livestock and agricultural production in the Brazilian Cerrado, it is of paramount importance to understand the dynamics of land use/land cover (LULC) changes in this region. Thus, we investigated LULC changes in two sub-basins of the Tocantins-Araguaia River basin from 1997 to 2015 and consequently projected future changes for the timespan between 2030 and 2050. The Formoso sub-basin experienced significant expansion of agricultural and pasture areas, whereas the Sono sub-basin limited farmland expansion (more stable native vegetation) due to substantial protected areas, trends that were also observed for future projections (2030 and 2050).

Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors.

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled.

Interactions between land use and soil type drive soil functions, highlighting water recharge potential, in the Cantareira System, Southeast of Brazil.

Most of the soil quality assessment protocols are focused on crop production and conservation management, while studies on vital soil functions, such as water recharge potential, should be incorporated into the monitoring of impacts on environmental quality. Our objective was to evaluate, through the Nexus approach, how dynamic (land use and management) and inherent (soil type) factors impact soil physical properties and processes that drive water recharge potential, biomass production, and water erosion in the Cantareira System, Brazil. The assessment considered three soils (Typic Hapludult, Typic Dystrudept, and Typic Usthortent) and four land uses (native forest, rotational grazing, extensive grazing, and eucalyptus), which constitute the main soils and land uses in the Cantareira System region.

Proximal sensing provides clean, fast, and accurate quality control of organic and mineral fertilizers.

Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested.

Detailed characterization of iron-rich tailings after the Fundão dam failure, Brazil, with inclusion of proximal sensors data, as a secure basis for environmental and agricultural restoration.

Following the Fundão dam failure in Brazil, 60 million m of iron-rich tailings were released impacting an extensive area. After this catastrophe, a detailed characterization and monitoring of iron-rich tailings is required for agronomic and environmental purposes. This can be facilitated by using proximal sensors which have been an efficient, fast, and cost-effective tool for eco-friendly analysis of soils and sediments.

Clean quality control of agricultural and non-agricultural lime by rapid and accurate assessment of calcium and magnesium contents via proximal sensors.

Ca and Mg are the most important chemical elements in lime. Properly measuring Ca and Mg contents is essential to assess the quality of lime products. Quality control guarantees the adequate use of lime in industrial processes, in soils, and helps avoiding adulteration.

Potentially toxic elements in iron mine tailings: Effects of reducing soil pH on available concentrations of toxic elements.

Tailings from iron mining are characterized by high concentrations of iron and manganese oxides, as well as high pH values. With these characteristics, most of the potentially toxic elements (PTE) contained in the tailings are somewhat unavailable. The aim of the present study was to evaluate how a reduction in the pH of iron mine tailings may affect PTE availabilities.

Deep Tillage Strategies in Perennial Crop Installation: Structural Changes in Contrasting Soil Classes.

Tillage modifies soil structure, which can be demonstrated by changes in the soil’s physical properties, such as penetration resistance (PR) and soil electrical resistivity (ρ). The aim of this study was to evaluate the effect of deep tillage strategies on three morphogenetically contrasting soil classes in the establishment of perennial crops regarding geophysical and physical-hydric properties. The experiment was conducted in the state of Minas Gerais, southeastern Brazil.

Geochemistry signatures of mercury in soils of the Amazon rainforest biome.

Mercury (Hg) toxicity in soils depends on Hg species and other physical and chemical attributes, as selenium (Se) hotspots in soils, particularly relevant in Amazonian soils. The study of Hg species and their relations in representative locations of the Amazon rainforest biome is critical for assessing the potential risks of Hg in this environment. This work aimed to determine the concentration of total Hg and its species (Hg, Hg and Hg), and to correlate Hg concentration with total elemental composition, magnetic susceptibility, and physicochemical attributes of Amazon soils.

Pocket-sized sensor for controlled, quantitative and instantaneous color acquisition of plant leaves.

The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves.

X-ray fluorescence spectrometry applied to digital mapping of soil fertility attributes in tropical region with elevated spatial variability.

Portable X-ray fluorescence (pXRF) spectrometry offers valuable information for prediction models of soil fertility attributes spatial variation, although this approach is yet scarce in tropical regions. This study aims to predict and build spatial variability maps of soil pH, remaining phosphorus (P-Rem), soil organic matter (SOM) and sum of bases (SB) using pXRF results through stepwise multiple linear regression (SMLR) and Random Forest (RF) in a highly variable tropical area. Composite samples from soil A horizon were collected at 90 points throughout the campus of the Federal University of Lavras, Minas Gerais, Brazil, for pH, P-Rem, SOM, SB and pXRF analyses.

Rare earth elements (REEs): geochemical patterns and contamination aspects in Brazilian benchmark soils.

Rare earth elements have been increasingly used in modern societies and soils are likely to be the final destination of several REE-containing (by)products. This study reports REE contents for topsoils (0-20 cm) of 175 locations in reference (n = 68) and cultivated (n = 107) areas in Brazil. Benchmark soil samples were selected accomplishing a variety of environmental conditions, aiming to: i) establishing natural background and anthropogenic concentrations for REE in soils; ii) assessing potential contamination of soils - via application of phosphate fertilizers - with REE; and, iii) predicting soil-REE contents using biomes, soil type, parent material, land use, sand content, and biomes-land use interaction as forecaster variables through generalized least squares multiple regression.

Occasional tillage in no-tillage systems: A global meta-analysis.

No-tillage (NT) is a major component of conservation agricultural systems. Challenges that have arisen with the adoption of NT include soil compaction, weed management, and stratification of organic matter and nutrients. As an attempt to overcome these challenges, occasional tillage (OT) has been used as a soil management practice in NT systems.

Foliar Elemental Analysis of Brazilian Crops via Portable X-ray Fluorescence Spectrometry.

Foliar analysis is very important for the nutritional management of crops and as a supplemental parameter for soil fertilizer recommendation. The elemental composition of plants is traditionally obtained by laboratory-based methods after acid digestion of ground and sieved leaf samples. This analysis is time-consuming and generates toxic waste.

Modeling arsenic content in Brazilian soils: What is relevant?

Arsenic accumulation in the environment poses ecological and human health risks. A greater knowledge about soil total As content variability and its main drivers is strategic for maintaining soil security, helping public policies and environmental surveys. Considering the poor history of As studies in Brazil at the country's geographical scale, this work aimed to generate predictive models of topsoil As content using machine learning (ML) algorithms based on several environmental covariables representing soil forming factors, ranking their importance as explanatory covariables and for feeding group analysis.

Assessing soil contamination in automobile scrap yards by portable X-ray fluorescence spectrometry and magnetic susceptibility.

A by-product of industrialization and population growth, automobile scrap yards are a potential source of metal contamination in soil. This study evaluated the use of portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (χ) analysis in assessing metal soil contamination in scrap yards located in Brazil. Five automobile scrap yards were selected in Curitiba, Paraná State (CB1, CB2, and CB3) and Lavras, Minas Gerais State (LV1 and LV2).

Author Correction: Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil.

This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). A soil survey was carried out and 74 soil profiles were analyzed. The tested models were: Multinomial logistic regression (MLR), C5 decision tree (C5-DT) and Random forest (RF).

Inorganic materials as ameliorants for soil remediation of metal toxicity to wild mustard (Sinapis arvensis L.).

The ameliorating effects of different inorganic materials were investigated on a soil originating from a zinc smelter dumping site contaminated by toxic metals. Wild mustard (Sinapis arvensis L.) was used as a test plant.