Industrial and urban organic wastes increase soil microbial activity and biomass

Microbial processes have been used as indicators of soil quality, due to the high sensitivity to small changes in management to evaluate, e.g., the impact of applying organic residues to the soil. In an experiment in a completely randomized factorial design 6 x 13 + 4, (pot without soil and residue or absolute control) the effect of following organic wastes was evaluated: pulp mill sludge, petrochemical complex sludge, municipal sewage sludge, dairy factory sewage sludge, waste from pulp industry and control (soil without organic waste) after 2, 4, 6, 12, 14, 20, 28, 36, 44, 60, 74, 86, and 98 days of incubation on some soil microbial properties, with four replications. The soil microbial activity was highly sensitive to the carbon/nitrogen ratio of the organic wastes. The amount of mineralized carbon was proportional to the quantity of soil-applied carbon. The average carbon dioxide emanating from the soil with pulp mill sludge, corresponding to soil basal respiration, was 0.141 mg C-CO2 100 g-1 soil h-1. This value is 6.4 times higher than in the control, resulting in a significant increase in the metabolic quotient from 0.005 in the control to 0.025 mg C-CO2 g-1 Cmic h-1 in the soil with pulp mill sludge. The metabolic quotient in the other treatments did not differ from the control (p < 0.01), demonstrating that these organic wastes cause no disturbance in the microbial community.

Microbiological and biochemical properties of an agricultural Mexican soil amended with sewage sludge

The application of sewage sludge is a concern because it may affect the quality of organic matter and microbiological and biochemical soil properties. The effects of surface application of sewage sludge to an agricultural soil (at 18 and 36 t ha-1 dry basis) were assessed in one maize (Zea mays L.) growing season. The study evaluated microbial biomass, basal respiration and selected enzymatic activities (catalase, urease, acid and alkaline phosphatase, and β-glucosidase) 230 days after sewage sludge application and infrared spectroscopy was used to assess the quality of dissolved organic matter and humic acids. Sewage sludge applications increased the band intensity assigned to polysaccharides, carboxylic acids, amides and lignin groups in the soil. The organic matter from the sewage sludge had a significant influence on the soil microbial biomass; nevertheless, at the end of the experiment the equilibrium of the soil microbial biomass (defined as microbial metabolic quotient, qCO2) was recovered. Soil urease, acid and alkaline phosphatase activity were strongly influenced by sewage sludge applications.

Correction of resistance to penetration by pedofunctions and a reference soil water content

The soil penetration resistance is an important indicator of soil compaction and is strongly influenced by soil water content. The objective of this study was to develop mathematical models to normalize soil penetration resistance (SPR), using a reference value of gravimetric soil water content (U). For this purpose, SPR was determined with an impact penetrometer, in an experiment on a Dystroferric Red Latossol (Rhodic Eutrudox), at six levels of soil compaction, induced by mechanical chiseling and additional compaction by the traffic of a harvester (four, eight, 10, and 20 passes); in addition to a control treatment under no-tillage, without chiseling or additional compaction. To broaden the range of U values, SPR was evaluated in different periods. Undisturbed soil cores were sampled to quantify the soil bulk density (BD). Pedotransfer functions were generated correlating the values of U and BD to the SPR values. By these functions, the SPR was adequately corrected for all U and BD data ranges. The method requires only SPR and U as input variables in the models. However, different pedofunctions are needed according to the soil layer evaluated. After adjusting the pedotransfer functions, the differences in the soil compaction levels among the treatments, previously masked by variations of U, became detectable.

Isotope determination of sulfur by mass spectrometry in soil samples

Sulphur plays an essential role in plants and is one of the main nutrients in several metabolic processes. It has four stable isotopes (32S, 33S, 34S, and 36S) with a natural abundance of 95.00, 0.76, 4.22, and 0.014 in atom %, respectively. A method for isotopic determination of S by isotope-ratio mass spectrometry (IRMS) in soil samples is proposed. The procedure involves the oxidation of organic S to sulphate (S-SO4(2-)), which was determined by dry combustion with alkaline oxidizing agents. The total S-SO4(2-) concentration was determined by turbidimetry and the results showed that the conversion process was adequate. To produce gaseous SO2 gas, BaSO4 was thermally decomposed in a vacuum system at 900 ºC in the presence of NaPO3. The isotope determination of S (atom % 34S atoms) was carried out by isotope ratio mass spectrometry (IRMS). In this work, the labeled material (K2(34)SO4) was used to validate the method of isotopic determination of S; the results were precise and accurate, showing the viability of the proposed method.

Characterization of the soil fertility and root system of restinga forests

The Restinga vegetation consists of a mosaic of plant communities, which are defined by the characteristics of the substrates, resulting from the type and age of the depositional processes. This mosaic complex of vegetation types comprises restinga forest in advanced (high restinga) and medium regeneration stages (low restinga), each with particular differentiating vegetation characteristics. The climate along the coast is tropical (Köppen). Of all ecosystems of the Atlantic Forest, Restinga is the most fragile and susceptible to anthropic disturbances. Plants respond to soil characteristics with physiological and morphological modifications, resulting in changes in the architecture (spatial configuration) of the root system. The purpose of this study was to characterize the soil fertility of high and low restinga forests, by chemical and physical parameters, and its relation to the root system distribution in the soil profile. Four locations were studied: (1) Ilha Anchieta State Park, Ubatuba; (2) two Ecological Stations of Jureia-Itatins and of Chauás, in the municipality of Iguape; (3) Vila de Pedrinhas in the municipality of Ilha Comprida; and (4) Ilha do Cardoso State Park, Cananeia. The soil fertility (chemical and physical properties) was analyzed in the layers 0-5, 0-10, 0-20, 20-40 and 40-60 cm. In addition, the distribution of the root system in the soil profile was evaluated, using digital images and the Spring program. It was concluded that the root system of all vegetation types studied is restricted to the surface layers, 0-10 and 10-20 cm, but occupies mainly the 0-10 cm layer (70 %); that soil fertility is low in all environments studied, with base saturation values below 16 %, since most exchange sites are occupied by aluminum; and that restinga vegetation is edaphic.

N2O emissions from a cultivated mollisol: optimal time of day for sampling and the role of soil temperature

The correct use of closed field chambers to determine N2O emissions requires defining the time of day that best represents the daily mean N2O flux. A short-term field experiment was carried out on a Mollisol soil, on which annual crops were grown under no-till management in the Pampa Ondulada of Argentina. The N2O emission rates were measured every 3 h for three consecutive days. Fluxes ranged from 62.58 to 145.99 ∝g N-N2O m-2 h-1 (average of five field chambers) and were negatively related (R² = 0.34, p < 0.01) to topsoil temperature (14 - 20 ºC). N2O emission rates measured between 9:00 and 12:00 am presented a high relationship to daily mean N2O flux (R² = 0.87, p < 0.01), showing that, in the study region, sampling in the mornings is preferable for GHG.

Soil aggregation under different management systems

Considering that the soil aggregation reflects the interaction of chemical, physical and biological soil factors, the aim of this study was evaluate alterations in aggregation, in an Oxisol under no-tillage (NT) and conventional tillage (CT), since over 20 years, using as reference a native forest soil in natural state. After analysis of the soil profile (cultural profile) in areas under forest management, samples were collected from the layers 0-5, 5-10, 10-20 and 20-40 cm, with six repetitions. These samples were analyzed for the aggregate stability index (ASI), mean weighted diameter (MWD), mean geometric diameter (MGD) in the classes > 8, 8-4, 4-2, 2-1, 1-0.5, 0.5-0.25, and < 0.25 mm, and for physical properties (soil texture, water dispersible clay (WDC), flocculation index (FI) and bulk density (Bd)) and chemical properties (total organic carbon - COT, total nitrogen - N, exchangeable calcium - Ca2+, and pH). The results indicated that more intense soil preparation (M < NT < PC) resulted in a decrease in soil stability, confirmed by all stability indicators analyzed: MWD, MGD, ASI, aggregate class distribution, WDC and FI, indicating the validity of these indicators in aggregation analyses of the studied soil.

Strategy of specification of management areas: rice grain yield as related to soil fertility

It is well-known nowadays that soil variability can influence crop yields. Therefore, to determine specific areas of soil management, we studied the Pearson and spatial correlations of rice grain yield with organic matter content and pH of an Oxisol (Typic Acrustox) under no- tillage, in the 2009/10 growing season, in Selvíria, State of Mato Grosso do Sul, in the Brazilian Cerrado (longitude 51º24' 21'' W, latitude 20º20' 56'' S). The upland rice cultivar IAC 202 was used as test plant. A geostatistical grid was installed for soil and plant data collection, with 120 sampling points in an area of 3.0 ha with a homogeneous slope of 0.055 m m-1. The properties rice grain yield and organic matter content, pH and potential acidity and aluminum content were analyzed in the 0-0.10 and 0.10-0.20 m soil layers. Spatially, two specific areas of agricultural land management were discriminated, differing in the value of organic matter and rice grain yield, respectively with fertilization at variable rates in the second zone, a substantial increase in agricultural productivity can be obtained. The organic matter content was confirmed as a good indicator of soil quality, when spatially correlated with rice grain yield.

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 thermal diffusivity estimated from data of soil temperature and single soil component properties

Under field conditions, thermal diffusivity can be estimated from soil temperature data but also from the properties of soil components together with their spatial organization. We aimed to determine soil thermal diffusivity from half-hourly temperature measurements in a Rhodic Kanhapludalf, using three calculation procedures (the amplitude ratio, phase lag and Seemann procedures), as well as from soil component properties, for a comparison of procedures and methods. To determine thermal conductivity for short wave periods (one day), the phase lag method was more reliable than the amplitude ratio or the Seemann method, especially in deeper layers, where temperature variations are small. The phase lag method resulted in coherent values of thermal diffusivity. The method using properties of single soil components with the values of thermal conductivity for sandstone and kaolinite resulted in thermal diffusivity values of the same order. In the observed water content range (0.26-0.34 m³ m-3), the average thermal diffusivity was 0.034 m² d-1 in the top layer (0.05-0.15 m) and 0.027 m² d-1 in the subsurface layer (0.15-0.30 m).

Nutrients in soil layers under no-tillage after successive pig slurry applications

Successive applications of pig slurry to soils under no-tillage can increase the nutrient levels in the uppermost soil layers and part of the nutrients may be transferred to deeper layers. The objective was to evaluate the distribution of nutrients in the profile of a soil after 19 pig slurry applications under no-tillage for 93 months. The experiment was conducted from May 2000 to January 2008 in an experimental area of the Federal University of Santa Maria, southern Brazil, on a Typic Hapludalf. The treatments consisted of pig slurry applications (0, 20, 40 and 80 m³ ha-1) and at the end of the experiment, soil samples were collected (layers 0-2, 2-4, 4-6, 6-8, 8-10, 10-12, 12-14, 14-16, 16-18, 18-20, 20-25, 25-30, 30-35, 35-40, 40-50 and 50-60 cm). The levels of mineral N, available P and K and total N, P and K were evaluated. The 19 pig slurry applications in 93 months promoted migration of total N and P down to 30 cm and available P and K to the deepest layer analyzed. At the end of the experiment, no increase was observed in mineral N content in the deeper layers, but increased levels of available P and K, showing a transfer of N, P and K to layers below the sampled. This evidences undesirable environmental and economic consequences of the use of pig slurry and reinforces the need for a more rational use, i.e., applications of lower manure doses, combined with mineral fertilizers.

Characterization of soil chemical properties of strawberry fields using principal component analysis

One of the largest strawberry-producing municipalities of Rio Grande do Sul (RS) is Turuçu, in the South of the State. The strawberry production system adopted by farmers is similar to that used in other regions in Brazil and in the world. The main difference is related to the soil management, which can change the soil chemical properties during the strawberry cycle. This study had the objective of assessing the spatial and temporal distribution of soil fertility parameters using principal component analysis (PCA). Soil sampling was based on topography, dividing the field in three thirds: upper, middle and lower. From each of these thirds, five soil samples were randomly collected in the 0-0.20 m layer, to form a composite sample for each third. Four samples were taken during the strawberry cycle and the following properties were determined: soil organic matter (OM), soil total nitrogen (N), available phosphorus (P) and potassium (K), exchangeable calcium (Ca) and magnesium (Mg), soil pH (pH), cation exchange capacity (CEC) at pH 7.0, soil base (V%) and soil aluminum saturation(m%). No spatial variation was observed for any of the studied soil fertility parameters in the strawberry fields and temporal variation was only detected for available K. Phosphorus and K contents were always high or very high from the beginning of the strawberry cycle, while pH values ranged from very low to very high. Principal component analysis allowed the clustering of all strawberry fields based on variables related to soil acidity and organic matter content.

Soil losses from typic cambisols and red latosol as related to three erosive rainfall patterns

Rainfall erosivity is one of the main factors related to water erosion in the tropics. This work focused on relating soil loss from a typic dystrophic Tb Haplic Cambisol (CXbd) and a typic dystrophic Red Latosol (LVdf) to different patterns of natural erosive rainfall. The experimental plots of approximately 26 m² (3 x 8.67 m) consisted of a CXbd area with a 0.15 m m-1 slope and a LVdf area with 0.12 m m-1 slope, both delimited by galvanized plates. Drainpipes were installed at the lower part of these plots to collect runoff, interconnected with a Geib or multislot divisor. To calculate erosivity (EI30), rainfall data, recorded continuously at a weather station in Lavras, were used. The data of erosive rainfall events were measured (10 mm precipitation intervals, accuracy 0.2 mm, 24 h period, 20 min intervals), characterized as rainfall events with more than 10 mm precipitation, maximum intensity > 24 mm h-1 within 15 min, or kinetic energy > 3.6 MJ, which were used in this study to calculate the rainfall erosivity parameter, were classified according to the moment of peak precipitation intensity in advanced, intermediate and delayed patterns. Among the 139 erosive rainfall events with CXbd soil loss, 60 % were attributed to the advanced pattern, with a loss of 415.9 Mg ha-1, and total losses of 776.0 Mg ha-1. As for the LVdf, of the 93 erosive rainfall events with soil loss, 58 % were listed in the advanced pattern, with 37.8 Mg ha-1 soil loss and 50.9 Mg ha-1 of total soil loss. The greatest soil losses were observed in the advanced rain pattern, especially for the CXbd. From the Cambisol, the soil loss per rainfall event was greatest for the advanced pattern, being influenced by the low soil permeability.

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.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.