Stratigraphical discontinuities, tropical landscape evolution and soil distribution relationships in a case study in SE-Brazil

On a regional summit surface in the county of Piracicaba (SP) within the Peripheric Depression of São Paulo, formed of discontinued flattened tops, there is an abrupt transition between a Typic Hapludox and a Kandiudalfic Eutrudox, together with two stoneline layers. Using stratigraphical, mineralogical, and cartographic studies, this transition and the soil distribution of this surface were studied, correlating them with the different parent materials and the morphoclimatic model of landscape evolution in Southeastern Brazil. The Typic Hapludox was formed on a sandy Cenozoic deposit (Q) that overlies a pellitic deposit of the Iratí formation (Pi), representing a regional erosive discordance. Westwards to the Piracicaba River, this sequence is interrupted by a diabase sill overlain by a red clayey material which gave origin to the Kandiudalfic Eutrudox. Two post-Permian depositional events were identified by the two stonelines and stratigraphical discontinuities. The first event generated the deposition of a sandy sediment in the form of levelled alveoluses on regional barriers, most of these formed by dikes and diabase sills, probably during a drier phase. The second depositional event, leading to the deposition of the red clay was probably the dissection of the previously formed pediplane during a humid climate, followed by another pedimentation process during a later, drier period.

Properties of top soil and the relationship between soil and trees in a boreal Scots pine stand

Summary

Relationship between soil oxidizable carbon and physical, chemical and mineralogical properties of umbric ferralsols

The occurrence of Umbric Ferralsols with thick umbric epipedons (> 100 cm thickness) in humid Tropical and Subtropical areas is a paradox since the processes of organic matter decomposition in these environments are very efficient. Nevertheless, this soil type has been reported in areas in the Southeast and South of Brazil, and at some places in the Northeast. Aspects of the genesis and paleoenvironmental significance of these Ferralsols still need a better understanding. The processes that made the umbric horizons so thick and dark and contributed to the preservation of organic carbon (OC) at considerable depths in these soils are of special interest. In this study, eight Ferralsols with a thick umbric horizon (UF) under different vegetation types were sampled (tropical rain forest, tropical seasonal forest and savanna woodland) and their macromorphological, physical, chemical and mineralogical properties studied to detect soil characteristics that could explain the preservation of high carbon amounts at considerable depths. The studied UF are clayey to very clayey, strongly acidic, dystrophic, and Al-saturated and charcoal fragments are often scattered in the soil matrix. Kaolinites are the main clay minerals in the A and B horizons, followed by abundant gibbsite and hydroxyl-interlayered vermiculite. The latter was only found in UFs derived from basalt rock in the South of the country. Total carbon (TC) ranged from 5 to 101 g kg-1 in the umbric epipedon. Dichromate-oxidizable organic carbon represented nearly 75 % of TC in the thick A horizons, while non-oxidizable C, which includes recalcitrant C (e.g., charcoal), contributed to the remaining 25 % of TC. Carbon contents were not related to most of the inorganic soil variables studied, except for oxalate-extractable Al, which individually explained 69 % (P < 0.001) of the variability of TC in the umbric epipedon. Clay content was not suited as predictor of TC or of the other studied C forms. Bulk density, exchangeable Al3+, Al saturation, ECEC and other parameters obtained by selective extraction were not suitable as predictors of TC and other C forms. Interactions between organic matter and poorly crystalline minerals, as indicated by oxalate-extractable Al, appear to be one of the possible organic matter protection mechanisms of these soils.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Estimation of soil moisture in the root-zone from remote sensing data

Field-based soil moisture measurements are cumbersome. Thus, remote sensing techniques are needed because allows field and landscape-scale mapping of soil moisture depth-averaged through the root zone of existing vegetation. The objective of the study was to evaluate the accuracy of an empirical relationship to calculate soil moisture from remote sensing data of irrigated soils of the Apodi Plateau, in the Brazilian semiarid region. The empirical relationship had previously been tested for irrigated soils in Mexico, Egypt, and Pakistan, with promising results. In this study, the relationship was evaluated from experimental data collected from a cotton field. The experiment was carried out in an area of 5 ha with irrigated cotton. The energy balance and evaporative fraction (Λ) were measured by the Bowen ratio method. Soil moisture (θ) data were collected using a PR2 - Profile Probe (Delta-T Devices Ltd). The empirical relationship was tested using experimentally collected Λ and θ values and was applied using the Λ values obtained from the Surface Energy Balance Algorithm for Land (SEBAL) and three TM - Landsat 5 images. There was a close correlation between measured and estimated θ values (p<0.05, R² = 0.84) and there were no significant differences according to the Student t-test (p<0.01). The statistical analyses showed that the empirical relationship can be applied to estimate the root-zone soil moisture of irrigated soils, i.e. when the evaporative fraction is greater than 0.45.

Multivariate analysis applied to the study of the relationship between soil and plant properties in a peach orchard

In the State of Rio Grande do Sul, the municipality of Pelotas is responsible for 90 % of peach production due to its suitable climate and soil conditions. However, there is the need for new studies that aim at improved fruit quality and increased yield. The aim of this study was to evaluate the relationship that exists between soil physical properties and properties in the peach plant in the years 2010 and 2011 by the technique of multivariate canonical correlation. The experiment was conducted in a peach orchard located in the municipality of Morro Redondo, RS, Brazil, where an experimental grid of 101 plants was established. In a trench dug beside each one of the 101 plants, soil samples were collected to determine silt, clay, and sand contents, soil density, total porosity, macroporosity, microporosity, and volumetric water content in the 0.00-0.10 and 0.10-0.20 m layers, as well as the depth of the A horizon. In each plant and in each year, the following properties were assessed: trunk diameter, fruit size and number of fruits per plant, average weight of the fruit per plant, fruit pulp firmness, Brix content, and yield from the orchard. Exploratory analysis of the data was undertaken by descriptive statistics, and the relationships between the physical properties of the soil and of the plant were assessed by canonical correlation analysis. The results showed that the clay and microporosity variables were those that exhibited the highest coefficients of canonical cross-loading with the plant properties in the soil layers assessed, and that the variable of mean weight of the fruit per plant was that which had the highest coefficients of canonical loading within the plant group for the two years assessed.

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.

LOSS OF EXTRACTION CAPACITY OF MEHLICH-1 AND MONOCALCIUM PHOSPHATE AS A VARIABLE OF REMAINING P AND ITS RELATIONSHIP TO CRITICAL LEVELS OF SOIL PHOSPHORUS AND SULFUR

The Mehlich-1 (M-1) extractant and Monocalcium Phosphate in acetic acid (MCPa) have mechanisms for extraction of available P and S in acidity and in ligand exchange, whether of the sulfate of the extractant by the phosphate of the soil, or of the phosphate of the extractant by the sulfate of the soil. In clayey soils, with greater P adsorption capacity, or lower remaining P (Rem-P) value, which corresponds to soils with greater Phosphate Buffer Capacity (PBC), more buffered for acidity, the initially low pH of the extractants increases over their time of contact with the soil in the direction of the pH of the soil; and the sulfate of the M-1 or the phosphate of the MCPa is adsorbed by adsorption sites occupied by these anions or not. This situation makes the extractant lose its extraction capacity, a phenomenon known as loss of extraction capacity or consumption of the extractant, the object of this study. Twenty soil samples were chosen so as to cover the range of Rem-P (0 to 60 mg L-1). Rem-P was used as a measure of the PBC. The P and S contents available from the soil samples through M-1 and MCPa, and the contents of other nutrients and of organic matter were determined. For determination of loss of extraction capacity, after the rest period, the pH and the P and S contents were measured in both the extracts-soils. Although significant, the loss of extraction capacity of the acidity of the M-1 and MCPa extractants with reduction in the Rem-P value did not have a very expressive effect. A “linear plateau” model was observed for the M-1 for discontinuous loss of extraction capacity of the P content in accordance with reduction in the concentration of the Rem-P or increase in the PBC, suggesting that a discontinuous model should also be adopted for interpretation of available P of soils with different Rem-P values. In contrast, a continuous linear response was observed between the P variables in the extract-soil and Rem-P for the MCPa extractor, which shows increasing loss of extraction capacity of this extractor with an increase in the PBC of the soil, indicating the validity of the linear relationship between the available S of the soil and the PBC, estimated by Rem-P, as currently adopted.

Soil quality assessment through a multi-approach analysis in soils of abandoned terraced land in NE Spain

The abandonment of agricultural land in mountainous areas has been an outstanding problem along the last century and has captured the attention of scientists, technicians and administrations, for the dramatic consequences sometimes occurred due to soil instability, steep slopes, rainfall regimes and wildfires. Hidromorfological and pedological alterations causing exceptional floods and accelerated erosion processes has therefore been studied, identifying the cause in the loss of landscape heterogeneity. Through the disappearance of agricultural works and drainage maintenance, slope stability has resulted severely affected. The mechanization of agriculture has caused the displacement of vines, olives and corks trees cultivation in terraced areas along the Mediterranean catchment towards more economically suitable areas. On the one hand, land use and management changes have implicated sociological changes as well, transforming areas inhabited by agricultural communities into deserted areas where the colonization of disorganized spontaneous vegetation has buried a valuable rural patrimony. On the other hand, lacking of planning and management of the abandoned areas has produced badlands and infertile soils due to wildfire and high erosion rates strongly degrading the whole ecosystems. In other cases, after land abandonment a process of soil regeneration has been recorded. Investigations have been conducted in a part of NE Spain where extended areas of terraced soils previously cultivated have been abandoned in the last century. The selected environments were semi-abandoned vineyards, semi-abandoned olive groves, abandoned stands of cork trees, abandoned stands of pine trees, scrubland of Cistaceaea, scrubland of Ericaceaea, and pasture. The research work was focused on the study of most relevant physical, chemical and biological soil properties, as well as runoff and erosion under soils with different plant cover to establish the abandonment effect on soil quality, due to the peculiarity and vulnerability of these soils with a much reduced depth. The period of observation was carried out from autumn 2009 to autumn 2010. The sediment concentration of soil erosion under vines was recorded as 34.52 g/l while under pasture it was 4.66 g/l. In addition, the soil under vines showed the least amount of organic matter, which was 12 times lower than all other soil environments. The carbon dioxide (CO2) and total glomalin (TG) ratio to soil organic carbon (SOC) in this soil was 0.11 and 0.31 respectively. However, the soil under pasture contained a higher amount of organic matter and showed that the CO2 and TG ratio to SOC was 0.02 and 0.11 respectively indicating that the soil under pasture better preserves the soil carbon pool. A similar trend was found in the intermediate soils in the sequence of land use change and abandonment. Soil structural stability increased in the two soil fractions investigated (0.25-2.00 mm, 2.0-5.6 mm) especially in those soils that did not undergo periodical perturbations like wildfires. Soil quality indexes were obtained by using relevant physical and chemical soil parameters. Factor analysis carried out to study the relationship between all soil parameters allowed to related variables and environments and identify those areas that better contribute to soil quality towards others that may need more attention to avoid further degradation processes

Management zones in agriculture acording to the soil and landscape variables

The study of spatial variability of soil and plants attributes, or precision agriculture, a technique that aims the rational use of natural resources, is expanding commercially in Brazil. Nevertheless, there is a lack of mathematical analysis that supports the correlation of these independent variables and their interactions with the productivity, identifying scientific standards technologically applicable. The aim of this study was to identify patterns of soil variability according to the eleven physical and seven chemical indicators in an agricultural area. It was used two multivariate techniques: the hierarchical cluster analysis (HCA) and the principal component analysis (PCA). According to the HCA, the area was divided into five management zones: zone 1 with 2.87ha, zone 2 with 0.8ha, zone 3 with 1.84ha, zone 4 with 1.33ha and zone 5 with 2.76ha. By the PCA, it was identified the most important variables within each zone: V% for the zone 1, CTC in the zone 2, levels of H+Al in the zone 4 and sand content and altitude in the zone 5. The zone 3 was classified as an intermediate zone with characteristics of all others. According to the results it is concluded that it is possible to separate into groups (management zones) samples with the same patterns of variability by the multivariate statistical techniques.

Spatial variability of soil fertility and its relationship with seed physiological potential in a soybean production area

The correlation of soil fertility x seed physiological potential is very important in the area of seed technology but results published with that theme are contradictory. For this reason, this study to evaluate the correlations between soil chemical properties and physiological potential of soybean seeds. On georeferenced points, both soil and seeds were sampled for analysis of soil fertility and seed physiological potential. Data were assessed by the following analyses: descriptive statistics; Pearson's linear correlation; and geostatistics. The adjusted parameters of the semivariograms were used to produce maps of spatial distribution for each variable. Organic matter content, Mn and Cu showed significant effects on seed germination. Most variables studied presented moderate to high spatial dependence. Germination and accelerated aging of seeds, and P, Ca, Mg, Mn, Cu and Zn showed a better fit to spherical semivariogram: organic matter, pH and K had a better fit to Gaussian model; and V% and Fe showed a better fit to the linear model. The values for range of spatial dependence varied from 89.9 m for P until 651.4 m for Fe. These values should be considered when new samples are collected for assessing soil fertility in this production area.

The relationship between diffuse spectral reflectance of the soil and its cation exchange capacity is scale-dependent

Diffuse reflectance spectroscopy (DRS) is increasingly being used to predict numerous soil physical, chemical and biochemical properties. However, soil properties and processes vary at different scales and, as a result, relationships between soil properties often depend on scale. In this paper we report on how the relationship between one such property, cation exchange capacity (CEC), and the DRS of the soil depends on spatial scale. We show this by means of a nested analysis of covariance of soils sampled on a balanced nested design in a 16 km × 16 km area in eastern England. We used principal components analysis on the DRS to obtain a reduced number of variables while retaining key variation. The first principal component accounted for 99.8% of the total variance, the second for 0.14%. Nested analysis of the variation in the CEC and the two principal components showed that the substantial variance components are at the > 2000-m scale. This is probably the result of differences in soil composition due to parent material. We then developed a model to predict CEC from the DRS and used partial least squares (PLS) regression do to so. Leave-one-out cross-validation results suggested a reasonable predictive capability (R2 = 0.71 and RMSE = 0.048 molc kg− 1). However, the results from the independent validation were not as good, with R2 = 0.27, RMSE = 0.056 molc kg− 1 and an overall correlation of 0.52. This would indicate that DRS may not be useful for predictions of CEC. When we applied the analysis of covariance between predicted and observed we found significant scale-dependent correlations at scales of 50 and 500 m (0.82 and 0.73 respectively). DRS measurements can therefore be useful to predict CEC if predictions are required, for example, at the field scale (50 m). This study illustrates that the relationship between DRS and soil properties is scale-dependent and that this scale dependency has important consequences for prediction of soil properties from DRS data

Biodegradation of the herbicide mecoprop-p with soil depth and its relationship with class III tfdA genes

Mecoprop-p [(R)-2-(4-chloro-2-methylphenoxy) propanoic acid) is widely used in agriculture and poses an environmental concern because of its susceptibility to leach from soil to water. We investigated the effect of soil depth on mecoprop-p biodegradation and its relationship with the number and diversity of tfdA related genes, which are the most widely known genes involved in degradation of the phenoxyalkanoic acid group of herbicides by bacteria. Mecoprop-p half-life (DT50) was approximately 12 days in soil sampled from <30 cm depth, and increased progressively with soil depth, reaching over 84 days at 70–80 cm. In sub-soil there was a lag period of between 23 and 34 days prior to a phase of rapid degradation. No lag phase occurred in top-soil samples prior to the onset of degradation. The maximum degradation rate was the same in top-soil and sub-soil samples. Although diverse tfdAα and tfdA genes were present prior to mecoprop-p degradation, real time PCR revealed that degradation was associated with proliferation of tfdA genes. The number of tfdA genes and the most probable number of mecoprop-p degrading organisms in soil prior to mecoprop-p addition were below the limit of quantification and detection respectively. Melting curves from the real time PCR analysis showed that prior to mecoprop-p degradation both class I and class III tfdA genes were present in top- and sub-soil samples. However at all soil depths only tfdA class III genes proliferated during degradation. Denaturing gradient gel electrophoresis confirmed that class III tfdA genes were associated with mecoprop-p degradation. Degradation was not associated with the induction of novel tfdA genes in top- or sub-soil samples, and there were no apparent differences in tfdA gene diversity with soil depth prior to or following degradation.