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 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.
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.
Vasques G.M.,Embrapa Soils |
Dematte J.A.M.,University of Sao Paulo |
Viscarra Rossel R.A.,CSIRO |
Ramirez Lopez L.,ETH Zurich |
And 3 more authors.
European Journal of Soil Science | Year: 2015
Soil mapping across large areas can be enhanced by integrating different methods and data sources. This study merges laboratory, field and remote sensing data to create digital maps of soil suborders based on the Brazilian Soil Classification System, with and without additional textural classification, in an area of 13000ha in the state of São Paulo, southeastern Brazil. Data from 289 visited soil profiles were used in multinomial logistic regression to predict soil suborders from geospatial data (geology, topography, emissivity and vegetation index) and visible-near infrared (400-2500nm) reflectance of soil samples collected at three depths (0-20, 40-60 and 80-100cm). The derived maps were validated with 47 external observations, and compared with two conventional soil maps at scales of 1:100000 and 1:20000. Soil suborders with and without textural classification were predicted correctly for 44 and 52% of the soil profiles, respectively. The derived suborder maps agreed with the 1:100000 and 1:20000 conventional maps in 20 and 23% (with textural classification) and 41 and 46% (without textural classification) of the area, respectively. Soils that were well defined along relief gradients (Latosols and Argisols) were predicted with up to 91% agreement, whereas soils in complex areas (Cambisols and Neosols) were poorly predicted. Adding textural classification to suborders considerably degraded classification accuracy; thus modelling at the suborder level alone is recommended. Stream density and laboratory soil reflectance improved all classification models, showing their potential to aid digital soil mapping in complex tropical environments. © 2015 British Society of Soil Science.
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.