Digital soil mapping using reference area and artificial neural networks.

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

X-ray absorption and phase contrast imaging to study the interplay between plant roots and soil structure

Plant performance is, at least partly, linked to the location of roots with respect to soil structure features and the micro-environment surrounding roots. Measurements of root distributions from intact samples, using optical microscopy and field tracings have been partially successful but are imprecise and labour-intensive. Theoretically, X-ray computed micro-tomography represents an ideal solution for non-invasive imaging of plant roots and soil structure. However, before it becomes fast enough and affordable or easily accessible, there is still a need for a diagnostic tool to investigate root/soil interplay. Here, a method for detection of undisturbed plant roots and their immediate physical environment is presented. X-ray absorption and phase contrast imaging are combined to produce projection images of soil sections from which root distributions and soil structure can be analyzed. The clarity of roots on the X-ray film is sufficient to allow manual tracing on an acetate sheet fixed over the film. In its current version, the method suffers limitations mainly related to (i) the degree of subjectivity associated with manual tracing and (ii) the difficulty of separating live and dead roots. The method represents a simple and relatively inexpensive way to detect and quantify roots from intact samples and has scope for further improvements. In this paper, the main steps of the method, sampling, image acquisition and image processing are documented. The potential use of the method in an agronomic perspective is illustrated using surface and sub-surface soil samples from a controlled wheat trial. Quantitative characterization of root attributes, e.g. radius, length density, branching intensity and the complex interplay between roots and soil structure, is presented and discussed.

Role of compaction versus aggregate disruption on slumping and shrinking of repacked hardsetting seedbeds

Slumping of hardsetting seedbeds upon wetting is likely to determine the shrinking and development of strength on drying. Different processes have been invoked, including aggregate disruption, material relocation, and compaction. To gain a better understanding of the role played by compaction compared with aggregate disruption in seedbed slumping and shrinking, mechanical analysis was combined with previous morphogenetical description. The global structural behavior of repacked seedbeds of a hardsetting sandy loam soil was studied after wetting and again after subsequent drying. Bulk density was measured in 5-mm-depth increments using gamma attenuation, and water content was determined at 10-mm-depth increments. Various wetting conditions were used to simulate a range of climatic and management conditions, including flood irrigation, furrow irrigation of a formed seedbed, drip irrigation, and rainfall. Aggregate coalescence under overburden pressure played the main role in slumping, even though microcracking enhanced coalescence. Most of the slumping occurred at calculated effective stress > 1.1 kPa. Intense aggregate breakdown at the top of seedbeds under fast wetting led to slight slumping because the resulting clogging of the initial interaggregate packing voids was balanced, in part, by the increase in microporosity resulting from aggregate disruption. However, aggregate coalescence induced by overburden pressure developing at the seedbed bottom often resulted in a strong decrease in total porosity. The effect of rainfall kinetic energy on crust bulk density was strong compared with the effect of fast wetting (bulk density increase of about 0.07 Mg m(-3) and 0.03 Mg m(-3), respectively) and could be ascribed to compaction rather than to aggregate breakdown. Shrinking on drying was related to the continuity of the microstructure resulting from wetting rather than to the intensity of slumping. Aggregate breakdown led to more shrinking than did aggregate coalescence.

Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo

O conhecimento do valor da erosividade da chuva (R) de determinada localidade é fundamental para a estimativa das perdas de solo feitas a partir da Equação Universal de Perdas de Solo, sendo, portanto, de grande importância no planejamento conservacionista. A fim de obter estimativas do valor de R para localidades onde este é desconhecido, desenvolveu-se uma rede neural artificial (RNA) e analisou-se a acurácia desta com o método de interpolação "Inverso de uma Potência da Distância" (ID). Comparando a RNA desenvolvida com o método de interpolação ID, verificou-se que a primeira apresentou menor erro relativo médio na estimativa de R e melhor índice de confiança, classificado como "Ótimo", podendo, portanto, ser utilizada no planejamento de uso, manejo e conservação do solo no Estado de São Paulo.

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.

Earthwork optimization system for sustainable highway construction

In highway construction, earthworks refer to the tasks of excavation, transportation, spreading and compaction of geomaterial (e.g. soil, rockfill and soil-rockfill mixture). Whereas relying heavily on machinery and repetitive processes, these tasks are highly susceptible to optimization. In this context Artificial Intelligent techniques, such as Data Mining and modern optimization can be applied for earthworks. A survey of these applications shows that they focus on the optimization of specific objectives and/or construction phases being possible to identify the capabilities and limitations of the analyzed techniques. Thus, according to the pinpointed drawbacks of these techniques, this paper describes a novel intelligent earthwork optimization system, capable of integrating DM, modern optimization and GIS technologies in order to optimize the earthwork processes throughout all phases of design and construction work. This integration system allows significant savings in time, cost and gas emissions contributing for a more sustainable construction.

Oxygen-sensing reporter strain of Pseudomonas fluorescens for monitoring the distribution of low-oxygen habitats in soil.

The root-colonizing bacterium Pseudomonas fluorescens CHA0 was used to construct an oxygen-responsive biosensor. An anaerobically inducible promoter of Pseudomonas aeruginosa, which depends on the FNR (fumarate and nitrate reductase regulation)-like transcriptional regulator ANR (anaerobic regulation of arginine deiminase and nitrate reductase pathways), was fused to the structural lacZ gene of Escherichia coli. By inserting the reporter fusion into the chromosomal attTn7 site of P. fluorescens CHA0 by using a mini-Tn7 transposon, the reporter strain, CHA900, was obtained. Grown in glutamate-yeast extract medium in an oxystat at defined oxygen levels, the biosensor CHA900 responded to a decrease in oxygen concentration from 210 x 10(2) Pa to 2 x 10(2) Pa of O(2) by a nearly 100-fold increase in beta-galactosidase activity. Half-maximal induction of the reporter occurred at about 5 x 10(2) Pa. This dose response closely resembles that found for E. coli promoters which are activated by the FNR protein. In a carbon-free buffer or in bulk soil, the biosensor CHA900 still responded to a decrease in oxygen concentration, although here induction was about 10 times lower and the low oxygen response was gradually lost within 3 days. Introduced into a barley-soil microcosm, the biosensor could report decreasing oxygen concentrations in the rhizosphere for a 6-day period. When the water content in the microcosm was raised from 60% to 85% of field capacity, expression of the reporter gene was elevated about twofold above a basal level after 2 days of incubation, suggesting that a water content of 85% caused mild anoxia. Increased compaction of the soil was shown to have a faster and more dramatic effect on the expression of the oxygen reporter than soil water content alone, indicating that factors other than the water-filled pore space influenced the oxygen status of the soil. These experiments illustrate the utility of the biosensor for detecting low oxygen concentrations in the rhizosphere and other soil habitats.

Examining the information content of time-lapse crosshole GPR data collected under different infiltration conditions to estimate unsaturated soil hydraulic properties

Time-lapse geophysical data acquired during transient hydrological experiments are being increasingly employed to estimate subsurface hydraulic properties at the field scale. In particular, crosshole ground-penetrating radar (GPR) data, collected while water infiltrates into the subsurface either by natural or artificial means, have been demonstrated in a number of studies to contain valuable information concerning the hydraulic properties of the unsaturated zone. Previous work in this domain has considered a variety of infiltration conditions and different amounts of time-lapse GPR data in the estimation procedure. However, the particular benefits and drawbacks of these different strategies as well as the impact of a variety of key and common assumptions remain unclear. Using a Bayesian Markov-chain-Monte-Carlo stochastic inversion methodology, we examine in this paper the information content of time-lapse zero-offset-profile (ZOP) GPR traveltime data, collected under three different infiltration conditions, for the estimation of van Genuchten-Mualem (VGM) parameters in a layered subsurface medium. Specifically, we systematically analyze synthetic and field GPR data acquired under natural loading and two rates of forced infiltration, and we consider the value of incorporating different amounts of time-lapse measurements into the estimation procedure. Our results confirm that, for all infiltration scenarios considered, the ZOP GPR traveltime data contain important information about subsurface hydraulic properties as a function of depth, with forced infiltration offering the greatest potential for VGM parameter refinement because of the higher stressing of the hydrological system. Considering greater amounts of time-lapse data in the inversion procedure is also found to help refine VGM parameter estimates. Quite importantly, however, inconsistencies observed in the field results point to the strong possibility that posterior uncertainties are being influenced by model structural errors, which in turn underlines the fundamental importance of a systematic analysis of such errors in future related studies.

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.

Report of the Workshop on Intelligent Compaction for Soils and HMA, April 2-4, 2008

This document summarizes the discussion and findings of a workshop on intelligent compaction for soils and hot-mix asphalt held in West Des Moines, Iowa, on April 2–4, 2008. The objective of the meeting was to provide a collaborative exchange of ideas for developing research initiatives that accelerate implementation of intelligent compaction (IC) technologies for soil, aggregates, and hot mix asphalt. Technical presentations, working breakout sessions, a panel discussion, and a group implementation strategy session comprised the workshop activities. About 100 attendees representing state departments of transportation, Federal Highway Administration, contractors, equipment manufacturers, and researchers participated in the workshop.

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.