Nitrate retention under sugarcane in wet tropical Queensland deep soil profiles

Nitrate leaching below the crop root-zone in variable charge soils may be adsorbed at anion exchange sites, thereby temporarily reducing the risk of contamination of water bodies. The objectives of this study were (i) to investigate whether nitrate adsorption, accumulation, and retention in the Johnstone River Catchment of Far North Queensland wet tropics is widespread; (ii) to assess the capacity of soil in the Johnstone River Catchment to retain nitrate; and (iii) to deduce the consequences of nitrate adsorption/desorption on contamination of water bodies. Soil cores ranging from 8 to 12.5 m depth were taken from 28 sites across the catchment, representing 9 Ferrosol soil types under sugarcane (Saccharum officinarum-S) cultivation for at least 50 years and from rainforest. The cores were segmented at 0.5-m depth increments and subsamples were analysed for nitrate-N, cation and anion exchange capacities, pH, exchangeable cations (Ca, Mg, K, Na), soil organic C, electrical conductivity, sulfate-S, and chloride. Nitrate-N concentration under sugarcane ranged from 0 to 72.5 mg/kg, compared with 0 to 0.31 mg/kg under rainforest, both Pin Gin soils. The average N load in 1-12 m depth across 19 highly oxidic profiles of the Pin Gin soil series was 1550 kg/ha, compared with 185 kg/ha under 8 non-Pin Gin soils and 11 kg/ha in rainforest on a Pin Gin soil. Most of the nitrate retention was observed at depth of 2-12 m, particularly at 4-10 m, indicating that the accumulation was well below the crop root-zone. The average maximum potential nitrate retention capacity was 10.8 t/ha for the Pin Gin and 4.7 t/ha for the non-Pin Gin soil. Compared with the current N load, the soils still possess a large capacity to adsorb and retain nitrate in profiles. Retention of large quantities of the leached nitrate deep in most of the profiles has reduced the risk of contamination of water bodies. However, computations show that substantial quantities of the nitrate leached below the root-zone were not adsorbed and remain unaccounted for. This unaccounted nitrate might have entered both on- and off-site water bodies and/or have been denitrified.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Increasing the efficiency of utilization of soil and fertilizer phosphorus in the subtropical and tropical agricultural systems C

As a rule, soils of the subtropical and tropical regions, in which rainfall is not limiting, are acidic, and low in phosphorus, and, to a less extent, in other macro and micronutrients as well, such a sulfur, boron and zinc. The establishment of a permanent agricultural prac. tice therefore, demands relatively high usage of liming and phosphatic fertilization, to begin with. Several approaches, not mutually exclusive, could be used in order to increase the efficiency of utilization of soil and fertilizer phosphorus so that, goal of diminishing costs of production is reached. The use of liming materials bringing up pH to 6.0-6.5 causes the conversion of iron and aluminum phosphates to more available calcium phosphates; on the other hand, by raising calcium saturation in the exchange complex, it improves the development and operation if the root system which allows c or a higher utilization of all soil nutrients, including phosphorus, and helps of stand water deficits which may occur. The role of mycorrhizal fungi should be considered as a way of increasing soil and fertilizer P utilization, as well as the limitations thereof. Screening of and breeding for varieties with higher efficiency of uptake and utilization of soil and fertilizer phosphorus leads to a reduction in cost of inputs and to higher benefit/cost ratios. Corrective fertilization using ground rock phosphate helps to saturate the fixation power of the soil thereby reducing, as a consequence, the need for phosphorus in the maintenance fertilization. Maintenance fertilization, in which soluble phos-phatic sources are used, could be improved by several means whose performance has been proved: limimg, granula tion, placement, use of magnesium salts. Last, cost of phosphate fertilization could be further reduced, without impairing yields, through impairing yields, through changes in technology designed to obtain products better adapted to local conditions and to the availability or raw materials and energy sources.

Trace element differentiation in ferruginous accumulation soil patterns under tropical rainforest of southern Cameroon, the role of climatic change

Regions under tropical rainforest cover, such as central Africa and Brazil are characterised by degradation and dismantling of old ferricrete structures. In southern Cameroon, these processes are relayed by present-day ferruginous accumulation soil facies, situated on the middle and the lower part of hill slopes. These facies become progressively harder towards the surface, containing from bottom to top, mainly kaolinite, kaolinite-goethite and Al-rich goethite-hematite, and are discontinuous to the relictic hematite-dominated ferricrete that exist in the upper part of the hill slope. These features were investigated in terms of geochemical differentiation of trace elements. It appears that, in contrast to the old ferricrete facies, the current ferruginous accumulations are enriched in transitional trace elements (V, Cr, Co, Y, Sc) and Ph, while alkali-earth elements are less differentiated. This recent chemical accumulation is controlled both by intense weathering of the granodiorite bedrock and by mobilisation of elements previously accumulated in the old ferricrete. The observed processes are clearly linked to the present-day humid climate with rising groundwater tables. They slowly replace the old ferricretes formed during Cretaceous time under more seasonal climatic conditions, representing an instructive case of continuos global change. (C) 2002 Elsevier Science B.V. All rights reserved.

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.

Aplicabilidade do lisem (limburg soil erosion) para simulação hidrológica em uma bacia hidrográfica tropical

As variáveis de entrada do modelo LISEM, mais importantes do ponto de vista da gênese do escoamento superficial direto (umidade do solo antecedente aos eventos de precipitação pluvial (θap) e espessura da camada de solo considerada no balanço hídrico) foram calibrados para eventos individuais de precipitação pluvial ocorridos na bacia hidrográfica do ribeirão Marcela, da região Alto Rio Grande, MG. Avaliou-se, também, a influência de diferentes cenários nas variáveis do escoamento superficial direto. O modelo apresentou elevada sensibilidade à umidade do solo, sendo este sua principal variável de calibração. A influência da camada de balanço hídrico esteve associada à umidade de calibração, observando-se que, quanto maior a profundidade, maior a umidade requerida para calibrar o modelo. Os cenários mata natural, eucalipto e café, aos 35 meses após o plantio, atenuaram as vazões máximas de 37,6; 42,2; e 28,0 %, cuja intensidade de precipitação pluvial foi de 48,0 mm h-1. O modelo hidrológico LISEM apresentou resultados satisfatórios na simulação hidrológica do escoamento superficial direto, mostrando que pode ser aplicado para previsão do comportamento hidrológico de bacias hidrográficas tropicais, desde que devidamente calibrado.

The combined use of reflectance, emissivity and elevation Aster/Terra data for tropical soil studies

Reflectance, emissivity and elevation data of the sensor ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)/Terra were used to characterize soil composition variations according to the toposequence position. Normalized data of SWIR (shortwave infrared) reflectance and TIR (thermal infrared) emissivity, coupled to a soil-fraction image from a spectral mixture model, were evaluated to separate bare soils from nonphotosynthetic vegetation. Regression relationships of some soil properties with reflectance and emissivity data were then applied on the exposed soil pixels. The resulting estimated values were plotted on the ASTER-derived digital elevation model. Results showed that the SWIR bands 5 and 6 and the TIR bands 10 and 14 measured the clay mineral absorption band and the quartz emissivity feature, respectively. These bands improved also the discrimination between nonphotosynthetic vegetation and soils. Despite the differences in pixel size and field sampling size, some soil properties were correlated with reflectance (R² of 0.65 for Al2O3 in band 6; 0.61 for Fe2O3 in band 3) and emissivity (R² of 0.65 for total sand fraction in the 10/14 band ratio). The combined use of reflectance, emissivity and elevation data revealed variations in soil composition with topography in specific parts of the landscape. From higher to lower slope positions, a general decrease in Al2O3 and increase in total sand fraction was observed, due to the prevalence of Rhodic Acrustox at the top and its gradual transition to Typic Acrustox at the bottom.

Contribution of humic substances from different composts to the synthesis of humin in a tropical soil

The contribution of humic substances of different composts to the synthesis of humin in a tropical soil was evaluated. Increasing doses (0, 13, 26, 52, and 104 Mg ha-1) of five different composts consisting of agroinpowderrial residues were applied to a Red-Yellow Latosol. These composts were chemically characterized and 13C NMR determined and the quantity of the functional alkyl groups of humic acids applied to the soil as compost was estimated. Thirty days after application of the treatments, organic matter samples were collected for fractionation of humic acids (HA), fulvic acids (FA) and humin (HU), from which the ratios HA/FA and (HA + FA)/HU were calculated. The application of the composts based on castor cake resulted in the highest HU levels in the soil; alkyl groups of the HA fraction of the composts were predominant in the organic components added to the HU soil fraction.

Determination of hydraulic properties of a tropical soil of Hawaii using column experiments and inverse modeling

A method for determining soil hydraulic properties of a weathered tropical soil (Oxisol) using a medium-sized column with undisturbed soil is presented. The method was used to determine fitting parameters of the water retention curve and hydraulic conductivity functions of a soil column in support of a pesticide leaching study. The soil column was extracted from a continuously-used research plot in Central Oahu (Hawaii, USA) and its internal structure was examined by computed tomography. The experiment was based on tension infiltration into the soil column with free outflow at the lower end. Water flow through the soil core was mathematically modeled using a computer code that numerically solves the one-dimensional Richards equation. Measured soil hydraulic parameters were used for direct simulation, and the retention and soil hydraulic parameters were estimated by inverse modeling. The inverse modeling produced very good agreement between model outputs and measured flux and pressure head data for the relatively homogeneous column. The moisture content at a given pressure from the retention curve measured directly in small soil samples was lower than that obtained through parameter optimization based on experiments using a medium-sized undisturbed soil column.

Influence of liming on residual soil respiration and chemical properties in a tropical no-tillage system

Because of the climate changes occurring across the planet, especially global warming, the different forms of agricultural soil use have attracted researchers´ attention. Changes in soil management may influence soil respiration and, consequently, C sequestration. The objectives of this study were to evaluate the long-term influence of liming on soil respiration and correlate it with soil chemical properties after two years of liming in a no-tillage system. A randomized complete block design was used with six replications. The experimental treatments consisted of four lime rates and a control treatment without lime. Two years after liming, soil CO2 emission was measured and the soil sampled (layers 0-5, 5-10, 10-20, and 20-30 cm). The P, Ca2+ e Mg2+ soil contents and pH and base saturation were determined. CO2 emission from soil limed at the recommended rate was 24.1 % higher, and at twice the recommended rate, 47.4 % higher than from unlimed soil. Liming improved the chemical properties, and the linear increase in soil respiration rate correlated positively with the P, Ca2+ and Mg2+ soil contents, pH and base saturation, and negatively with H + Al and Al3+ contents. The correlation coefficient between soil respiration rate and chemical properties was highest in the 10-20 cm layer.

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.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

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 %.

Transfer of cadmium and barium from soil to crops grown in tropical soils

Phytotoxicity and transfer of potentially toxic elements, such as cadmium (Cd) or barium (Ba), depend on the availability of these elements in soils and on the plant species exposed to them. With this study, we aimed to evaluate the effect of Cd and Ba application rates on yields of pea (Pisum sativum L.), sorghum (Sorghum bicolor L.), soybean (Glycine max L.), and maize (Zea mays L.) grown under greenhouse conditions in an Oxisol and an Entisol with contrasting physical and chemical properties, and to correlate the amount taken up by plants with extractants commonly used in routine soil analysis, along with transfer coefficients (Bioconcentration Factor and Transfer Factor) in different parts of the plants. Plants were harvested at flowering stage and measured for yield and Cd or Ba concentrations in leaves, stems, and roots. The amount of Cd accumulated in the plants was satisfactorily evaluated by both DTPA and Mehlich-3 (M-3). Mehlich-3 did not relate to Ba accumulated in plants, suggesting it should not be used to predict Ba availability. The transfer coefficients were specific to soils and plants and are therefore not recommended for direct use in risk assessment models without taking soil properties and group of plants into account.