Hydrographic and hydrogeological basin of Entre-Ribeiros: probable recharge zone delimitation and environmental impact assessment

The probable recharge zone delimitation of Entre-Ribeiros Basin (Northwest of the state of Minas Gerais / Brazil) is proposed in this study. The delimitation is based upon stratigraphy, geomorphology, geo-environmental domains and hydrogeology studies. Combining the recharge zone map with the land use variation between 1975 and 2008, the occupation trends of possible recharge zones are identified. Concluding, the environmental impacts for this basin are discussed.

Development of an automated bench top electronic penetrometer

The aim of this study was to develop a an automated bench top electronic penetrometer (ABEP) that allows performing tests with high rate of data acquisition (up to 19,600 Hz) and with variation of the displacement velocity and of the base area of cone penetration. The mechanical components of the ABEP are: a supporting structure, stepper motor, velocity reducer, double nut ball screw and six penetration probes. The electronic components of ABEP are: a "driver" to control rotation and displacement, power supply, three load cells, two software programs for running and storing data, and a data acquisition module. This penetrometer presented in compact size, portable and in 32 validation tests it proved easy to operate, and showed high resolution, high velocity in reliability in data collection. During the validation tests the equipment met the objectives, because the test results showed that the ABEP could use different sizes of cones, allowed work at different velocities, showed for velocity and displacement, were only 1.3% and 0.7%, respectively, at the highest velocity (30 mm s-1) and 1% and 0.9%, respectively for the lowest velocity (0.1 mm s-1).

Use of penetrometers in agriculture: a review

Soil physical quality can be easily and quickly evaluated by using simple equipment to identify levels of soil compaction. Hence, it is necessary to know the variables responsible for changes in the soil penetration resistance (SPR). The aim of this review is to identify the main factors related to the various equipment used for assessing SPR as a soil physical quality indicator in agriculture. This literature review describes the different types of equipment used and its relationship with SPR. A wide range of procedures, devices, and equipments are available. Much of variability in SPR results is related to the equipment model, cone angle and diameter, and penetration rate. Usually, restrictions to root growth are correlated with SPR values above 2-3 MPa. However, comparisons of SPR values obtained under different soil moisture regimes in the same soil type have provided conflicting results of difficult interpretation. In order to minimize these problems, there is a need for standardization of measurement procedures and interpretation, and/or correction of SPR values according to a soil water content of reference.

Spatial variability of mechanical resistance to penetration evaluated with mobile unit on haplic vertisol cultivated with mango

Since the advent of mechanized farming and intensive use of agricultural machinery and implements on the properties, the soil began to receive greater load of machinery traffic, which can cause increased soil compaction. The aim of this study was to evaluate the spatial variability of soil mechanical resistance to penetration (RP) in the layers of 0.00-0.10, 0.10-0.20, 0.20-0.30 and 0.30-0.40m, using geostatistics in an area cultivated with mango in Haplic Vertisol of the northeastern semi-arid, with mobile unit equipped with electronic penetrometer. The RP data was collected in 56 points from an area of 3 ha, and random soil samples were collected to determine the soil moisture and texture. For RP data analysis we used descriptive statistics and geostatistics. The soil mechanical resistance to penetration presented increased variability, with adjustment of the spherical and exponential semivariograms in the layers. We found that 42% of the area in the layer of 0.10-0.20m showed RP values above 2.70 MPa. Maximum values of RP were found in the layer of 0.19-0.27m, predominantly in 56% of the 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.

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.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Absorption of primary macronutrients and soybean growth at different compactation densities and moisture levels in a silt loam soil

ABSTRACT Soybean cultivation is increasing rapidly in the region of Alto Vale do Itajaí, State of Santa Catarina, where there is a predominance of silt soils. The objective of this work was to evaluate the content of primary macronutrients in shoots and shoot and root vegetative growth of soybean (Glicine max L. Merrill) grown in a silt-loam soil under different compactation densities and moisture levels. A randomized block design in a 4x4 factorial arrangement was used, with four compactation densities: 1.00; 1.20; 1.40 and 1.60 Mg m-3, and four soil moisture levels: 0.130; 0.160; 0.190 and 0.220 kg kg-1 and four replications. Each pot consisted of the overlapping of three 150-mm PVC rings, where soil was maintained in the higher and lower part of the pot with a density of 1.00 Mg m-3 and in the intermediate ring, the compactation densities were increased. Values of soil density higher than 120 Mg m-3 negatively affected N, P and K uptake by soybean plants, as well as the plant mass of the shoots and roots. The higher levels of soil moisture reduced the compaction effect and promoted better absorption of P and K.

Comparison of estimation methods of soil strength in five soils

In agriculture, the soil strength is used to describe the susceptibility to deformation by pressure caused by agricultural machine. The purpose of this study was to compare different methods for estimating the inherent soil strength and to identify their suitability for the evaluation of load support capacity, compaction susceptibility and root growth. The physical, chemical, mineralogical and intrinsic strength properties of seven soil samples, collected from five sampling pits at different locations in Brazil, were measured. Four clay (CS) and three sandy clay loam (SCL) soils were used. The clay soils were collected on a farm in Santo Ângelo, RS (28 º 16 ' 16 '' S; 54 º 13 ' 11 '' W 290 m); A and B horizons at the Universidade Federal de Lavras, Lavras, MG (21 º 13 ' 47 '' S; 44 º 58 ' 6'' W; 918 m) and on the farm Sygenta, in Uberlandia, MG (18 º 58 ' 37 '' S; 48 º 12 ' 05 '' W 866 m). The sandy clay loam soils were collected in Aracruz, ES (19 º 47 ' 10 '' S; 40 º 16 ' 29 '' W 81 m), and on the farm Xavier, Lavras, MG (21 º 13 ' 24 '' S; 45 º 05 ' 00 '' W; 844 m). Soil strength was estimated based on measurements of: (a) a pneumatic consolidometer, (b) manual pocket (non-rotating) penetrometer; and (c) automatic (rotating) penetrometer. The results of soil strength properties were similar by the three methods. The soil structure had a significant influence on soil strength. Results of measurements with both the manual pocket and the electric penetrometer were similar, emphasizing the influence of soil texture. The data showed that, to enhance the reliability of predictions of preconsolidation pressure by penetrometers, it is better to separate the soils into the different classes, rather than analyze them jointly. It can be concluded that the consolidometer method, although expensive, is the best when evaluations of load support capacity and compaction susceptibility of soil samples are desired.

Soil physical properties and sugarcane root growth in a red oxiso

Sugarcane, which involves the use of agricultural machinery in all crop stages, from soil preparation to harvest, is currently one of the most relevant crops for agribusiness in Brazil. The purpose of this study was to investigate soil physical properties and root growth in a eutroferric red Oxisol (Latossolo Vermelho eutroférrico) after different periods under sugarcane. The study was carried out in a cane plantation in Rolândia, Paraná State, where treatments consisted of a number of cuts (1, 3, 8, 10 and 16), harvested as green and burned sugarcane, at which soil bulk density, macro and microporosity, penetration resistance, as well as root length, density and area were determined. Results showed that sugarcane management practices lead to alterations in soil penetration resistance, bulk density and porosity, compared to native forest soil. These alterations in soil physical characteristics impede the full growth of the sugarcane root system beneath 10 cm, in all growing seasons analyzed.

Modelling of soil penetration resistance for an oxisol under no-tillage

Soil penetration resistance is an important property that affects root growth and elongation and water movement in the soil. Since no-till systems tend to increase organic matter in the soil, the purpose of this study was to evaluate the efficiency with which soil penetration resistance is estimated using a proposed model based on moisture content, density and organic matter content in an Oxisol containing 665, 221 and 114 g kg-1 of clay, silt and sand respectively under annual no-till cropping, located in Londrina, Paraná State, Brazil. Penetration resistance was evaluated at random locations continually from May 2008 to February 2011, using an impact penetrometer to obtain a total of 960 replications. For the measurements, soil was sampled at depths of 0 to 20 cm to determine gravimetric moisture (G), bulk density (D) and organic matter content (M). The penetration resistance curve (PR) was adjusted using two non-linear models (PR = a Db Gc and PR' = a Db Gc Md), where a, b, c and d are coefficients of the adjusted model. It was found that the model that included M was the most efficient for estimating PR, explaining 91 % of PR variability, compared to 82 % of the other model.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Soil physical properties and grape yield influenced by cover crops and management systems

The use of cover crops in vineyards is a conservation practice with the purpose of reducing soil erosion and improving the soil physical quality. The objective of this study was to evaluate cover crop species and management systems on soil physical properties and grape yield. The experiment was carried out in Bento Gonçalves, RS, Southern Brazil, on a Haplic Cambisol, in a vineyard established in 1989, using White and Rose Niagara grape (Vitis labrusca L.) in a horizontal, overhead trellis system. The treatments were established in 2002, consisting of three cover crops: spontaneous species (SS), black oat (Avena strigosa Schreb) (BO), and a mixture of white clover (Trifolium repens L.), red clover (Trifolium pratense L.) and annual rye-grass (Lolium multiflorum L.) (MC). Two management systems were applied: desiccation with herbicide (D) and mechanical mowing (M). Soil under a native forest (NF) area was collected as a reference. The experimental design consisted of completely randomized blocks, with three replications. The soil physical properties in the vine rows were not influenced by cover crops and were similar to the native forest, with good quality of the soil structure. In the inter-rows, however, there was a reduction in biopores, macroporosity, total porosity and an increase in soil density, related to the compaction of the surface soil layer. The M system increased soil aggregate stability compared to the D system. The treatments affected grapevine yield only in years with excess or irregular rainfall.

Revisiting the S-index for soil physical quality and its use in Brazil

The S-index was introduced in 2004 in a publication by A.R. Dexter. S was proposed as an indicator of soil physical quality. A critical value delimiting soils with rich and poor physical quality was proposed. At present, Brazil is world leader in citations of Dexter's publication. In this publication the S-theory is mathematically revisited and extended. It is shown that S is mathematically correlated to bulk density and total porosity. As an absolute indicator, the value of S alone has proven to be incapable of predicting soil physical quality. The critical value does not always hold under boundary conditions described in the literature. This is to be expected because S is a static parameter, therefore implicitly unable to describe dynamic processes. As a relative indicator of soil physical quality, the S-index has no additional value over bulk density or total porosity. Therefore, in the opinion of the author, the fact that bulk density or total porosity are much more easily determined than the water retention curve for obtaining S disqualifies S as an advantageous indicator of relative soil physical quality. Among the several equations available for the fitting of water retention curves, the Groenevelt-Grant equation is preferable for use with S since one of its parameters and S are linearly correlated. Since efforts in soil physics research have the purpose of describing dynamic processes, it is the author's opinion that these efforts should shift towards mechanistic soil physics as opposed to the search for empirical correlations like S which, at present, represents far more than its reasonable share of soil physics in Brazil.

Critical limits of soil penetration resistance in a rhodic Eutrudox

Soil penetration resistance is an important indicator of soil physical quality and the critical limit of 2 MPa has been widely used to characterize the soil physical quality, in both no-tillage and conventional systems. The aim of this study was to quantify the influence of different tillage and cropping systems on the soil penetration resistance in a Rhodic Eutrudox. The experiment was carried out in a 5 × 2 factorial, completely randomized block design (tillage systems vs cropping systems), with four replications. The tillage systems consisted of: conventional tillage disk harrow; minimum tillage with annual chiseling; minimum tillage with chiseling every three years; no-tillage for 11 consecutive years; and no-tillage for 24 consecutive years. The factor cropping systems was represented by: crop rotation and crop succession. The soil penetration resistance (SPR) was determined in 20 soil samples per treatment and layer (0.0-0.10; 0.10-0.20 and 0.20-0.30 m) for each soil matric potential: -6, -10, -33, -100, -500 kPa. The SPR was determined at a volumetric soil water content equivalent to the fraction of plant-available water of 0.7. There were no differences of soil penetration resistance between the two cropping systems. Differences in soil penetration resistance among tillage systems were related to the matric potential at which the samples were equilibrated. The critical SPR limit of 2 MPa normally used for conventional tillage should be maintained. However, this value of 2 MPa is inappropriate for the physical quality characterization of Rhodic Eutrudox under no-tillage and/or minimum tillage with chiseling. Regardless of the cropping systems, the critical SPR limit should be raised to 3 MPa for minimum tillage with chiseling and to 3.5 MPa for no-tillage.