Physical quality of a yellow latossol under integrated crop-livestock system

Soil physical quality is essential to global sustainability of agroecosystems, once it is related to processes that are essential to agricultural crop development. This study aimed to evaluate physical attributes of a Yellow Latossol under different management systems in the savanna area in the state of Piaui. This study was developed in Uruçuí southwest of the state of Piauí. Three systems of soil management were studied: an area under conventional tillage (CT) with disk plowi and heavy harrow and soybean crop; an area under no-tillage with soybean-maize rotation and millet as cover crop (NT + M); two areas under Integrated Crop-Livestock System, with five-month pasture grazing and soybean cultivation and then continuous pasture grazing (ICL + S and ICL + P, respectively). Also, an area under Native Forest (NF) was studied. The soil depths studied were 0.00-0.05, 0.05-0.10 and 0.10-0.20 m. Soil bulk density, as well as porosity and stability of soil aggregates were analyzed as physical attributes. Anthropic action has changed the soil physical attributes, in depth, in most systems studied, in comparison to NF. In the 0.00 to 0.05 m depth, ICL + P showed higher soil bulk density value. As to macroporosity, there was no difference between the management systems studied and NF. The management systems studied changed the soil structure, having, as a result, a small proportion of soil in great aggregate classes (MWD). Converting native forest into agricultural production systems changes the soil physical quality. The Integrated Crop-Livestock System did not promote the improvement in soil physical quality.

Soil spectral library and its use in soil classification

Soil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a Fieldspec sensor, 350-2,500 nm) for the horizons of 223 soil profiles from the regions of Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracajú, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarênicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litólicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350-400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.

Ex vivo characterization of allo-MHC-restricted T cells specific for a single MHC-peptide complex.

Alloreactive T cells are thought to be a potentially rich source of high-avidity T cells with therapeutic potential since tolerance to self-Ags is restricted to self-MHC recognition. Given the particularly high frequency of alloreactive T cells in the peripheral immune system, we used numerous MHC class I multimers to directly visualize and isolate viral and tumor Ag-specific alloreactive CD8 T cells. In fact, all but one specificities screened were undetectable in ex vivo labeling. In this study, we report the occurrence of CD8 T cells specifically labeled with allo-HLA-A*0201/Melan-A/MART-1(26-35) multimers at frequencies that are in the range of 10(-4) CD8 T cells and are thus detectable ex vivo by flow cytometry. We report the thymic generation and shaping of tumor Ag-specific, alloreactive T cells as well as their fate once seeded in the periphery. We show that these cells resemble their counterparts in HLA-A*0201-positive individuals, based on their structural and functional attributes.

Computer-assisted cartography using topographic properties: precision and accuracy of local soil maps in central Mexico

Map units directly related to properties of soil-landscape are generated by local soil classes. Therefore to take into consideration the knowledge of farmers is essential to automate the procedure. The aim of this study was to map local soil classes by computer-assisted cartography (CAC), using several combinations of topographic properties produced by GIS (digital elevation model, aspect, slope, and profile curvature). A decision tree was used to find the number of topographic properties required for digital cartography of the local soil classes. The maps produced were evaluated based on the attributes of map quality defined as precision and accuracy of the CAC-based maps. The evaluation was carried out in Central Mexico using three maps of local soil classes with contrasting landscape and climatic conditions (desert, temperate, and tropical). In the three areas the precision (56 %) of the CAC maps based on elevation as topographical feature was higher than when based on slope, aspect and profile curvature. The accuracy of the maps (boundary locations) was however low (33 %), in other words, further research is required to improve this indicator.

Inter-relation between soybean yield and soil compaction under degraded pasture in Brazilian Savannah

The Cerrado (Brazilian Savannah) plays an important economic and financial role in the nation, since the pastures of this biome feed cattle for half of the domestic bovine meat productivity, and its agricultural fields produce a third of the country's grain. The variability and spatial dependence between the soil physical attributes and soybean yield were evaluated in a crop rotation planted on a degraded brachiaria pasture, on a dystroferric Red Latosol of an experimental farm of the State University of São Paulo (UNESP), in the 2005/2006 growing season. The linear and spatial correlations between these attributes were also studied, to determine conditions that would allow increased agricultural productivity. In the above pasture area, a grid was installed with 124 plots, spaced 10.0 x 10.0 m and 5.0 x 5.0 m apart, in a total area of 7,500 m². From the linear and spatial point of view, the high grain yield can be explained by the number of grains per plant and soil macroporosity. The high variability observed for most soil properties indicated that the crop - livestock integration system results in environmental heterogeneity of the soil.

Runoff and sediment transport in a degraded area

Gully erosion occurs by the combined action of splash, sheetwash and rill-wash (interrill and rill erosion). These erosion processes have a great capacity for both sediment production and sediment transport. The objectives of this experiment were to evaluate hydrological and sediment transport in a degraded area, severely dissected by gullies; to assess the hydraulic flow characteristics and their aggregate transport capacity; and to measure the initial splash erosion rate. In the study area in Guarapuava, State of Paraná, Brazil (lat 25º 24' S; long 51º24' W; 1034 m asl), the soil was classified as Cambissolo Húmico alumínico, with the following particle-size composition: sand 0.116 kg kg-1; silt 0.180 kg kg-1; and clay 0.704 kg kg-1. The approach of this research was based on microcatchments formed in the ground, to study the hydrological response and sediment transport. A total of eight rill systems were simulated with dry and wet soil. An average rainfall of 33.7 ± 4.0 mm was produced for 35 to 54 min by a rainfall simulator. The equipment was installed, and a trough was placed at the end of the rill to collect sediments and water. During the simulation, the following variables were measured: time to runoff, time to ponding, time of recession, flow velocity, depth, ratio of the initial splash and grain size. The rainsplash of dry topsoil was more than twice as high as under moist conditions (5 g m-2 min-1 and 2 g m-2 min-1, respectively). The characteristics of the flow hydraulics indicate transition from laminar to turbulent flow [Re (Reynolds number) 1000-2000]. In addition, it was observed that a flow velocity of 0.12 m s-1 was the threshold for turbulent flow (Re > 2000), especially at the end of the rainfall simulation. The rill flow tended to be subcritical [Fr (Froude Number) < 1.0]. The variation in hydrological attributes (infiltration and runoff) was lower, while the sediment yield was variable. The erosion in the rill systems was characterized as limited transport, although the degraded area generated an average of 394 g m-2 of sediment in each simulation.

Guide to Cement-Based Integrated Pavement Solutions, August 2011

This guide provides a clear, concise, and cohesive presentation of cement-bound materials options for 10 specific engineering pavement applications: new concrete pavements, concrete overlays, previous concrete, precast pavements, roller-compacted concrete, cement-treated base, full-depth reclamation with cement, cement-modified soils, recycled concrete aggregates, and repair and restoration. Each application is presented as a method for meeting specific design and construction objectives that today’s pavement practitioners must accomplish. The benefits, considerations, brief description, and summary of materials, design, and construction requirements, as well as a list of sustainable attributes, are provided for every solution. This guide is intended to be short, simple, and easy to understand. It was designed so that the most up-to-date and relevant information is easily extractable. It is not intended to be used as a design guide for any of the applications identified herein. Recommendations for additional information that can provide such details are given at the end of each solution discussion. The intended audience is practitioners, including engineers and managers who face decisions regarding what materials to specify in the pavement systems they design or manage. The audience also includes city and county engineers, along with the A/E firms that often represent them, and state DOT engineers at all levels who are seeking alternatives in this era of changing markets.

Spatial and linear correlations between soil and corn

The Technologies setting at Agricultural production system have the main characteristics the vertical productivity, reduced costs, soil physical, chemical and biological improvement to promote production sustainable growth. Thus, the study aimed to determine the variability and the linear and special correlations between the plant and soil attributes in order to select and indicate good representation of soil physical quality for forage productivity. In the growing season of 2006, on the Fazenda Bonança in Pereira Barreto (SP), the productivity of autumn corn forage (FDM) in an irrigated no-tillage system and the soil physical properties were analyzed. The purpose was to study the variability and the linear and spatial correlations between the plant and soil properties, to select an indicator of soil physical quality related to corn forage yield. A geostatistical grid was installed to collect soil and plant data, with 125 sampling points in an area of 2,500 m². The results show that the studied properties did not vary randomly and that data variability was low to very high, with well-defined spatial patterns, ranging from 7.8 to 38.0 m. On the other hand, the linear correlation between the plant and the soil properties was low and highly significant. The pairs forage dry matter versus microporosity and stem diameter versus bulk density were best correlated in the 0-0.10 m layer, while the other pairs - forage dry matter versus macro - and total porosity - were inversely correlated in the same layer. However, from the spatial point of view, there was a high inverse correlation between forage dry matter with microporosity, so that microporosity in the 0-0.10 m layer can be considered a good indicator of soil physical quality, with a view to corn forage yield.

Mineralogy of the clay fraction of Alfisols in two slope curvatures: III - spatial variability

A good knowledge of the spatial distribution of clay minerals in the landscape facilitates the understanding of the influence of relief on the content and crystallographic attributes of soil minerals such as goethite, hematite, kaolinite and gibbsite. This study aimed at describing the relationships between the mineral properties of the clay fraction and landscape shapes by determining the mineral properties of goethite, hematite, kaolinite and gibbsite, and assessing their dependence and spatial variability, in two slope curvatures. To this end, two 100 × 100 m grids were used to establish a total of 121 regularly spaced georeferenced sampling nodes 10 m apart. Samples were collected from the layer 0.0-0.2 m and analysed for iron oxides, and kaolinite and gibbsite in the clay fraction. Minerals in the clay fraction were characterized from their X-ray diffraction (XRD) patterns, which were interpreted and used to calculate the width at half height (WHH) and mean crystallite dimension (MCD) of iron oxides, kaolinite, and gibbsite, as well as aluminium substitution and specific surface area (SSA) in hematite and goethite. Additional calculations included the goethite and hematite contents, and the goethite/(goethite+hematite) [Gt/(Gt+Hm)] and kaolinite/(kaolinite+gibbsite) [Kt/(Kt+Gb)] ratios. Mineral properties were established by statistical analysis of the XRD data, and spatial dependence was assessed geostatistically. Mineralogical properties differed significantly between the convex area and concave area. The geostatistical analysis showed a greater number of mineralogical properties with spatial dependence and a higher range in the convex than in the concave area.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Building predictive models of soil particle-size distribution

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Comparison between detailed digital and conventional soil maps of an area with complex geology

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

Physical quality of an Oxisol after different periods of management systems

Management systems may lead to a loss of soil physical quality as a result of removal of the plant cover and excessive agricultural mechanization. The hypothesis of this study was that the soil aggregate stability, bulk density, macro- and microporosity, and the S index and saturated hydraulic conductivity may be used as indicators of the soil physical quality. The aim was to study the effects of different periods and managements on the physical attributes of a medium-textured Red Oxisol under soybean and corn for two growing seasons, and determine which layers are most susceptible to variations. A completely randomized experimental design was used with split plots (five treatments and four layers), with four replications. The treatments in 2008/09 consisted of: five years of no-tillage (NTS5), seven years of no-tillage (NTS7), nine years of no-tillage (NTS9), conventional tillage (CTS) and an adjacent area of native forest (NF). The treatments were extended for another year, identified in 2009/10 as: NTS6, NTS8, NTS10, CTS and NF. The soil layers 0-0.05, 0.05-0.10, 0.10-0.20 and 0.20-0.30 m were sampled. The highest S index values were observed in the treatment CTS in the 0-0.05 m layer (0.106) and the 0.05-0.10 m layer (0.099) in 2008/09, and in the 0-0.05 m layer (0.066) in 2009/10. This fact may be associated with soil turnover, resulting in high macroporosity in this treatment. In contrast, in the NTS, limiting macroporosity values were observed in some layers (below 0.10 m³ m-3). Highest aggregate stability as well as the highest saturated hydraulic conductivity (Kθ) values were observed in NF in relation to the other treatments. In 2009/10, the Kθ in NF differed only from NTS10. This study showed that the use of the S index alone cannot be recommended as an absolute indicator of the soil physical quality, even at values greater than 0.035.

Quality of surface water related to land use: a case study in a catchment with small farms and intensive vegetable crop production in southern Brazil

Water degradation is strongly related to agricultural activity. The aim of this study was to evaluate the influence of land use and some environmental components on surface water quality in the Campestre catchment, located in Colombo, state of Parana, Brazil. Physical and chemical attributes were analyzed (total nitrogen, ammonium, nitrate, total phosphorus, electrical conductivity, pH, temperature, turbidity, total solids, biological oxygen demand, chemical oxygen demand and dissolved oxygen). Monthly samples of the river water were taken over one year at eight monitoring sites, distributed over three sub-basins. Overall, water quality was worse in the sub-basin with a higher percentage of agriculture, and was also affected by a lower percentage of native forest and permanent preservation area, and a larger drainage area. Water quality was also negatively affected by the presence of agriculture in the riparian zone. In the summer season, probably due to higher rainfall and intensive soil use, a higher concentration of total nitrogen and particulate nitrogen was observed, as well as higher electrical conductivity, pH and turbidity. All attributes, except for total phosphorus, were in compliance with Brazilian Conama Resolution Nº 357/2005 for freshwater class 1. However, it should be noted that these results referred to the base flow and did not represent a discharge condition since most of the water samples were not collected at or near the rainfall event.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.