Embrapa Soils

Rio de Janeiro, Brazil

Embrapa Soils

Rio de Janeiro, Brazil
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Bento R.A.,National Institute of Amazonian Research | Saggin-Junior O.J.,Embrapa Agrobiology | Pitard R.M.,Embrapa Agrobiology | Straliotto R.,Embrapa Agrobiology | And 5 more authors.
Water, Air, and Soil Pollution | Year: 2012

Leguminous trees have a potential for phytoremediation of oil-contaminated areas for its symbiotic association with nitrogen-fixing bacteria and arbuscular mycorrhizal fungi (AMF). This study selects leguminous tree associated with symbiotic microorganisms that have the potential to remediate petroleum-contaminated soil. Seven species of trees were tested: Acacia angustissima, Acacia auriculiformis, Acacia holosericea, Acacia mangium, Mimosa artemisiana, Mimosa caesalpiniifolia, and Samanea saman. They were inoculated with AMF mix and nitrogen-fixing bacteria mix and cultivated over five oil levels in soils, with five replicates. The decreasing of total petroleum hydrocarbons (TPH) values occurred especially with S. saman and its symbiotic microorganisms on highest oil soil contamination. Despite the large growth of A. angustissima and M. caesalpiniifolia on the highest level of oil, these species and its inoculated microorganisms did not reduce the soil TPH. Both plants were hydrocarbon tolerant but not able to remediate the polluted soil. In contrast were significative hydrocarbon decrease with M. artemisiana under high oil concentrations, but plant growth was severely affected. Results suggest that the ability of the plants to decrease the soil concentration of TPH is not directly related to its growth and adaptation to conditions of contamination, but the success of the association between plants and its symbionts that seem to play a critical role on remediation efficiency. © Springer Science+Business Media B.V. 2012.

de Albuquerque Nunes W.A.G.,Embrapa Western Region Agriculture | Menezes J.F.S.,University of Rio Verde | de Melo Benites V.,Embrapa Soils | de Unior S.A.,Adecoagro Ivinhema Valley | Dos Oliveira A.S.,CAMDA
Scientia Agricola | Year: 2015

Slaughterhouses generate large amounts of rumen content and its use as a fertilizer may offer an environmentally friendly strategy for its management. The effect of an organic fertilizer produced from slaughterhouse waste on the fertility of sandy and clayey soils as well as soybean yield (Glycine max L.) and corn (Zea mays L.) crops was evaluated. Field experiments were set up and five rates up to 16 t ha−1 of organic fertilizer (293 kg ha−1 N, 334 kg ha−1 P and 27 kg ha−1 K) were applied annually, before sowing in spring/summer, as well as a mineral fertilization of 300 kg ha−1 in the formulation 2-20-20 for soybean and 300 kg ha−1 of 12-15-15 for corn. The organic fertilizer changed soil fertility in the field experiments by increasing pH, Ca, Mg, K, P-Mehlich and P-resin. Such effects were more evident in the sandy than in the clayey soil, and the most superficial layer was affected more. The organic fertilizer rate needed to achieve maximum yield decreased for corn in both soils and for soybean in the sandy soil, although the amounts required may still be regarded as high. © 2015, Scientia Agricola. All rights reserved.

de Arruda G.P.,APagri Agronomic consultancy | Dematte J.A.M.,University of Sao Paulo | Chagas C.S.,Embrapa Soils | Fiorio P.R.,University of Sao Paulo | And 2 more authors.
Scientia Agricola | Year: 2016

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area. © 2016, Scientia Agricola. All rights reserved.

Grunwald S.,University of Florida | Vasques G.M.,Embrapa Soils | Rivero R.G.,University of Georgia
Advances in Agronomy | Year: 2015

Grand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab- and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (. S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contrast the benefits and constraints of proximal and remote sensing, fusion of soil-environmental data, and integration pathways to mash data and methods into complex soil assessments. © 2015 Elsevier Inc.

de Castro R.C.,State University of Rio de Janeiro | de Melo Benites V.,Embrapa Soils | Cesar Teixeira P.,Embrapa Soils | dos Anjos M.J.,State University of Rio de Janeiro | de Oliveira L.F.,State University of Rio de Janeiro
Applied Radiation and Isotopes | Year: 2015

The aim of this study was to evaluate the phosphorus (P) mobility in a tropical Brazilian soil type red Oxisol treated with three different forms of granular fertilizer. Total Reflection X-Ray Fluorescence (TXRF) was applied to determine the concentration of P at different distances from granular fertilizer application point. The results showed that most of the P from fertilizers tends to concentrate in a region of up to 10. mm around the place of the fertilizer deposition. © 2015.

Hong J.,University of Florida | Hong J.,East Tennessee State University | Grunwald S.,University of Florida | Vasques G.M.,Embrapa Soils
Journal of Environmental Quality | Year: 2015

Phosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a "precision soil conservation" approach combining finescale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil MP. Findings from this study contribute to a better understanding of spatially explicit interactions between soil P and other environmental variables, facilitating improved land resource management while minimizing adverse risks to the environment. © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

Maia C.M.B.F.,Embrapa Forestry | Novotny E.H.,Embrapa Soils | Rittl T.F.,Wageningen University | Hayes M.H.B.,University of Limerick
Current Organic Chemistry | Year: 2013

Soil organic matter (SOM) holds a prominent place among the many indicators that are studied in relation to soil function. Different viewpoints are reflected in characterizing SOM, depending on the study procedures used, or the focus of the researchers. There are many possibilities for the isolation and fractionation of SOM and this has led to a plurality of interpretations and conclusions. Transformations to organic materials that lead to the more recalcitrant components of SOM are outlined, and the associations which these materials can have in the soil environment, and aspects of their compositions are referred to. A review is given of the organic matter pools in soils, of their functions, and of the controls which they have in soil systems. A succinct review is given of physical fractionation procedures for SOM. This approach is highly relevant, though rarely used in modern studies of SOM. The merits and demerits of wet oxidation procedures, relative to dry combustion for determining soil organic carbon contents are discussed, and reference is made to the emerging chemometric techniques based on the use of Near (NIR) and Mid (MIR) infrared spectroscopy. © 2013 Bentham Science Publishers.

PubMed | Federal University of Fluminense, Federal University of Rio de Janeiro, Integrated Petroleum Expertise IPEXCo and Embrapa Soils
Type: | Journal: The Science of the total environment | Year: 2016

The determination of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) in raw cow milks have never been reported in Brazil. Since the consumption of food of animal origin, including milk and dairy products, is the major source of human exposure to these compounds, this study aimed to establish the levels and profile of seven PCDDs and ten PCDFs in raw cow milk samples collected in eight Brazilian states which among the major milk producer states. Raw milk samples were collected in 34 different dairy cattle farms during the summer of 2013. All PCDD/Fs congeners were found at least in some of the 34 samples studied. The octa-chlorinated congeners (OCDD and OCDF) were ubiquitous and also present in high concentrations. The mean values of WHO-TEQ

Samuel-Rosa A.,Process 9 | Samuel-Rosa A.,Federal Rural University of Rio de Janeiro | Heuvelink G.B.M.,ISRIC World Soil Information | Vasques G.M.,Embrapa Soils | Anjos L.H.C.,Federal Rural University of Rio de Janeiro
Geoderma | Year: 2015

In this study we evaluated whether investing in more spatially detailed environmental covariates improves the accuracy of digital soil maps. We used a case study from Southern Brazil to map clay content (CLAY), organic carbon content (SOC), and effective cation exchange capacity (ECEC) of the topsoil for a. ~. 2000. ha area located on the edge of the plateau of the Paraná Sedimentary Basin. Five covariates, each with two levels of spatial detail were used: area-class soil maps, digital elevation models (DEM), geologic maps, land use maps, and satellite images. Thirty-two multiple linear regression models were calibrated for each soil property using all spatial detail combinations of the covariates. For each combination, stepwise regression was used to select predictor variables incorporated in the model. Model evaluation was done using the adjusted R-square of the regression. The baseline model, calibrated with the less detailed version of each covariate, and the best performing model were used to calibrate two linear mixed models for each soil property. Model parameters were estimated using restricted maximum likelihood. Spatial prediction was performed using the empirical best linear unbiased predictor. Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation. Results show that for CLAY the prediction accuracy did not considerably improve by using more detailed covariates. The amount of variance explained increased only ~ 2 percentage points (pp), less than that obtained by including the kriging step, which explained 4. pp. On the other hand, prediction of SOC and ECEC improved by ~ 13. pp when the baseline model was replaced by the best performing model. Overall, the increase in prediction performance was modest and may not outweigh the extra costs of using more detailed covariates. It may be more efficient to spend extra resources on collecting more soil observations, or increasing the detail of only those covariates that have the strongest improvement effect. In our case study, the latter would only work for SOC and ECEC, by investing in a more detailed land use map and possibly also a more detailed geologic map and DEM. © 2015 Elsevier B.V.

Chagas C.D.S.,Embrapa Soils | de Carvalho Junior W.,Embrapa Soils | Bhering S.B.,Embrapa Soils | Calderano Filho B.,Embrapa Soils
Catena | Year: 2016

Soil texture is an essential and extremely variable physical property that strongly influences many other soil properties that are highly relevant for agricultural production, e.g., fertility and water retention capacity. In plain areas, terrain properties derived from a digital elevation model are not effective for digital soil mapping, and the variation in the properties of such areas remains a challenge. In this regard, remote sensing can facilitate the mapping of soil properties. The purpose of this study was to evaluate the efficiency of using of data obtained from the Thematic Mapper (TM) sensor of Landsat 5 for digital soil mapping in a semi-arid region, based on multiple linear regression (MLR) and a random forest model (RFM). To this end, 399 samples of the soil surface layer (0-20. cm) were used to predict the sand, silt and clay contents, using the bands 1, 2, 3, 4, 5 and 7, the Normalized Difference Vegetation Index (NDVI), the grain size index (GSI), and the relationships between bands 3 and 2, bands 3 and 7, and bands 5 and 7 (clay index) of the Landsat 5 TM sensor as covariates. Among these covariates, only band 1 (b1), the relationship between bands 5 and 7 (b5/b7) for sand, silt and clay, and band 4 (b4) for silt were not significantly correlated according to Pearson's correlation analysis. The validation of the models showed that the properties were best estimated using the RFM, which explained 63% and 56% of the spatial variability of sand and clay, respectively, whereas the MLR explained 30% of the spatial variation of silt. An analysis of the relevance of the variables predicted by the RFM showed that the covariates b3/b7, b5, NDVI and b2 explained most of the variability of the considered properties. The RFM proved to be more advantageous than the MLR with respect to insensitivity to overfitting and the presence of noise in the data. In addition, the RFM produced more realistic distribution maps of the soil properties than did the MLR, taking into account that the estimated values of the soil attributes were in the same range as the calibration data, while the MLR model estimates were out of the range of the calibration data. © 2016 Elsevier B.V.

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