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Soil nutrient content varies spatially across agricultural fields in hard-to-predict ways, particularly in floodplains with complex fluvial depositional history. Satellite reflectance data from the Sentinel-2 (S2) mission provides spatially continuous land reflectance data that can aid model development when used with point observations of nutrients. Reflectance from vegetation is assumed to obstruct land reflectance of bare soil, such that researchers have masked vegetation in models. We developed a routine for masking vegetation within Google Earth Engine (GEE) using Random Forest classification for iterative application to libraries of S2-images. Using gradient boosting, we then developed soil nutrient models for surface soils at a 250-ha agricultural site using S2 images. Soils were sampled at 2145 point locations to a 23-cm depth and analyzed for Ca, K, Mg, P, pH, S, and Zn. Results showed that masking vegetation improved model performance for models from subsets of the data (80% of samples used for model development, 20% validation), but full data sets did not require masking to achieve accuracy. Models of Ca, K, Mg, and S were successful (validation R2 > 0.60 to 0.96), but models for pH, P, and Zn failed. Bare soil composite images from S2 data are helpful in predicting soil fertility in low-relief floodplains. [ABSTRACT FROM AUTHOR]

Novel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets. Core Ideas: Soil properties can be predicted via proximal sensors using multinational datasets.This study encompassed soil samples from Brazil, France, Mozambique, India, and United States.Soil organic carbon and texture were accurately predicted via proximal sensors.The broad application of proximal sensors to aid soil characterization is encouraged. [ABSTRACT FROM AUTHOR]

Quartz is one of the most common minerals in soils, mostly present in sand and silt fractions. Although quartz is basically formed of SiO2, other elements can be easily detected and assessed nowadays using a portable X-ray fluorescence (pXRF) spectrometer. Our study aims to evaluate the chemical composition of different quartz varieties, identifying their main elements, and relating them to soil attributes. Six quartz varieties (hyaline, amethyst, milky, rose, smoky, and ferriferous) were analyzed via pXRF and 13 oxides/trace elements were identified and used for quartz discrimination (Al2O3, CaO, P2O5, SiO2, Cl, Cr, Fe, K2O, Mn, Rb, S, Ti, and V). Hyaline quartz was characterized by the highest SiO2 and the lowest contents of other elements. Al2O3 was the second-highest compound present in all varieties of quartz, reaching 21,547 mg kg−1 in the smoky variety. S, P2O5, Cl, SiO2, and K2O were the main components determined by Random Forest algorithm for discriminating quartz varieties. Some elements detected may serve as a reserve of nutrients to plants to be released in soils along weathering, depending on quartz particle size, soil texture, leaching, and associated attributes. pXRF enhanced the information on chemical characterization of quartz varieties, without the generation of chemical pollutants. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the differential response to land use changes between native forest and croplands regarding the quantitative soil variables of aggregate weight classes and different carbon pools in extremely kaolinitic soils from the east coast of Brazil. In the soil A horizon, the total (TOC) and dissolved (DOC) organic carbon contents were analyzed. In the 0–0.08 m soil layer, the weight and the organic carbon content (Cag) were determined for six size aggregate classes. The mean differential (Δ) of each property for each area was calculated. Overall, the TOC and DOC were greater in the native forest sites over the counterpart cultivated sites within each area. The ΔDOC of all the five areas were negative. The ΔCag of the 1–2 mm and 0.053–0.105 mm soil aggregate classes of Sooretama were the only ones with mean positive values. The ordination of the five areas by the ΔCag in the six soil aggregate size classes isolated Coruripe as the area with the most negative differentials, because of the forest conservation and management of the cropland. The differentials of organic carbon between forest and agricultural use of the analyzed properties did not reveal a possible effect of soil texture. [ABSTRACT FROM AUTHOR]

Choosing the appropriate management system is essential for sustainable agricultural practices. Yet, soil-specific properties at the subsurface are seldom considered when choosing the appropriate tillage system. This study assessed the effect of tillage depth on physical–hydraulic properties in three contrasting soil classes in the establishment of perennial crops. Tillage practices were evaluated in soils with natural dense layers (Inceptisols and Ultisols), and soils with very small and stable granular structure (Oxisols). From least to most aggressive, tested tillage systems included surface furrowing + plant holes (MT); plowing followed by two diskings + furrowing (CT); plowing followed by two diskings + subsoiling (SB); and plowing followed by two diskings + rotary hoeing (DM). Physical indicators with the greatest explanatory power were relative field capacity (RFC, 97%), aeration capacity (AC, 95%), macroporosity (Pmac, 95%), the S index (Sgi, 89%), and bulk density (Bd, 81%). DM caused the greatest modification in soil structure, especially at the surface. It increased values of AC, Pmac, and Sgi, and reduced Bd values. Only deep tillage systems (DM and SB) improved soil structure in deeper layers. Highest Bd values were observed for MT (1.47 g cm−3), and lowest for DM (1.21 g cm−3). Soil classes responded differently to soil tillage systems. DM was most effective in soils with densified layers (Inceptisol and Ultisol). Effects were less expressive in the studied Oxisol. Comparing MT and DM, Pmac increased by more than 100% in the studied Ultisol, but by less than 20% in the Oxisol. No tillage system affected the Oxisol's soil structure in deeper layers, due to its small and stable granular structure. The choice of optimal tillage strategies should consider soil-specific properties, especially at greater depths, to guarantee more productive and sustainable crop systems. [ABSTRACT FROM AUTHOR]

Despite the increasing adoption of proximal sensors worldwide, rare works have coupled proximal with remotely sensed data to spatially predict soil properties. This study evaluated the contribution of proximal and remotely sensed data to predict soil texture and available contents of micronutrients using portable X-ray fluorescence (pXRF) spectrometry, magnetic susceptibility (MS), and terrain attributes (TA) via random forest algorithm. Samples were collected in Brazil from soils with high, moderate, and low weathering degrees (Oxisols, Ultisols, Inceptisols, respectively), and analyzed by pXRF and MS and for texture and available micronutrients. Seventeen TA were generated from a digital elevation model of 12.5 m spatial resolution. Predictions were made via: (i) TA; (ii) TA + pXRF; (iii) TA + MS; (iv) TA + MS + pXRF; (v) MS + pXRF; and (vi) pXRF; and validated via root mean square error (RMSE) and coefficient of determination (R2). The best predictions were achieved by: pXRF dataset alone for available Cu (R² = 0.80) and clay (R2 = 0.67) content; MS + pXRF dataset for available Fe (R2 = 0.68) and sand (R2 = 0.69) content; TA + pXRF + MS dataset for available Mn (R2 = 0.87) content. PXRF data were key to the best predictions. Soil property maps created from these predictions supported the adoption of sustainable soil management practices. [ABSTRACT FROM AUTHOR]

Precision agriculture provides detailed information on the spatial variability of soil properties, including nutrient content, allowing for local-specific decision making. Recently, proximal sensors have been used to accurately predict soil properties, contributing to reduce costs of conventional wet-chemistry analyses for soil characterization. However, further investigations on this approach in tropical soils are needed. This work aimed to use portable X-ray fluorescence (pXRF) spectrometry data for prediction of exchangeable Ca2+ and available K+ and P contents in soils of a highly heterogeneous tropical area and evaluating its practical applications. 90 samples from soil A horizon were collected in a regular grid design, and analyzed through pXRF and for nutrient contents. Such data were split into modeling (63 samples) and validation (27 samples) datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR) and stepwise multiple linear regression (SMLR) were tested for predictions. The models were used to spatially represent nutrient contents across the area and to compare the practical effects of varying regression models. PXRF elemental data provided reliable predictions of exchangeable Ca2+ and available P via SMLR and PwR, respectively, reaching root mean square errors (RMSE) of 5.66 cmolc dm−3 for Ca2+ and 9.13 mg dm−3 for P. Available K+ predictions were not successful. Different models yielded contrasting maps showing the classes of soil fertility across the area, drawing attention to the importance of testing multiple prediction models and using the best one for precision agriculture. Fusion of data from different proximal sensors may enhance available K+ predictions. [ABSTRACT FROM AUTHOR]

Biofuels are already part of the energy matrix in many countries because of their environmental and economic benefits. Ethyl biodiesel can be produced by various processes, the main one being transesterification of triglyceraldehydes and ethanol. The current technological challenge is to develop efficient heterogeneous catalysts, at low cost, non‐polluting and recyclable to be used in this process. In this context, hydromagnesites Mg5(CO3)4(OH)2⋅4H2O modified with different cations (Ca2+, Zn2+, Cu 2+, Ba2+) and synthesized by co‐precipitation are used as precursors of catalytic oxides calcined at 600 °C. The biodiesel synthesis was conducted at 120 °C for 12 h using 20% (w/w) of catalyst per mass of soybean oil and the ratio 1:20 (mol/mol) of soybean oil/ethanol. The alkyl esters formed were quantified by Gas Chromatography‐Flame Ionization Detection (GC‐FID). The transesterification reaction catalyzed by the reference oxide had a yield of 40%, whereas the best yield (80%) came from the hydromagnesite modified with 20% of Ca2+ which also had the highest basicity/acidity ratio. After use in three reaction cycles, the catalyst with 20% Ca2+ had no significant reduction in efficiency when it was washed with acetone and recalcined at 600 °C, while the untreated catalyst only had a yield of 42%, showing the need for effective reconditioning. [ABSTRACT FROM AUTHOR]