Soil-applied silicon decreases severity of wheat spot blotch on silicon-deficient soils

Spot bloth caused by Bipolaris sorokiniana is an important wheat desease mainly in hot and humid regions. The aim of this study was to evaluate the response of wheat to different sources and modes of Si application, as related to the severity of wheat spot blotch and plant growth, in two Si-deficient Latosols (Oxisols). An greenhouse experiment was arranged in a 2 x 5 factorial completely randomized design, with eight replications. The treatments consisted of two soils (Yellow Latosol and Red Latosol) and five Si supply modes (no Si application; Si applied as calcium silicate and monosilicic acid to the soil; and Si applied as potassium silicate or monosilicic acid to wheat leaves). No significant differences were observed between the two soils. When Si was applied to the soil, regardless the Si source, the disease incubation period, the shoot dry matter yield and the Si content in leaves were greater. Additionally, the final spot blotch severity was lower and the area under the spot blotch disease progress curve and the leaf insertion angle in the plant were smaller. Results of Si foliar application were similar to those observed in the control plants.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Availability of a soluble phosphorus source applied to soil samples with different acidicity levels

Considerations on the interactions of P in the soil-plant system have a long history, but are still topical and not yet satisfactorily understood. One concern is the effect of liming before or after application of soluble sources on the crop yield and efficiency of available P under these conditions. The aim of this study was to evaluate the effect of soil acidity on availability of P from a soluble source, based on plant growth and chemical extractants. Nine soil samples were incubated with a dose of 200 mg kg-1 P in soil with different levels of previously adjusted acidity (pH H2O 4.5; 5.0; 5.5; 6.0 and 6.5) and compared to soils without P application. After 40 days of soil incubation with a P source, each treatment was limed again so that all pH values were adjusted to 6.5 and then sorghum was planted. After the first and second liming the P levels were determined by the extractants Mehlich-1, Bray-1 and Resin, and the fractionated inorganic P forms. In general, the different acidity levels did not influence the P availability measured by plant growth and P uptake at the studied P dose. For some soils however these values increased or decreased according to the initial soil pH (from 4.5 to 6.5). Plant growth, P uptake and P extractable by Mehlich-1 and Bray-1 were significantly correlated, unlike resin-extractable P, at pH values raised to 6.5. These latter correlations were however significant before the second liming. The P contents extracted by Mehlich-1 and Bray-1 were significantly correlated with each other in the entire test range of soil acidity, even after adjusting pH to 6.5, besides depending on the soil buffering capacity for P. Resin was also sensitive to the properties that express the soil buffering capacity for P, but less clearly than Mehlich-1 and Bray-1. The application of triple superphosphate tended to increase the levels of P-Al, P-Fe and P-Ca and the highest P levels extracted by Bray-1 were due to a higher occurrence of P-Al and P-Fe in the soils.

Ammonia losses estimated by an open collector from urea applied to sugarcane straw

The quantification of ammonia (NH3) losses from sugarcane straw fertilized with urea can be performed with collectors that recover the NH3 in acid-treated absorbers. Thus, the use of an open NH3 collector with a polytetrafluoroethylene (PTFE)-wrapped absorber is an interesting option since its cost is low, handling easy and microclimatic conditions irrelevant. The aim of this study was to evaluate the efficiency of an open collector for quantifying NH3-N volatilized from urea applied over the sugarcane straw. The experiment was carried out in a sugarcane field located near Piracicaba, São Paulo, Brazil. The NH3-N losses were estimated using a semi-open static collector calibrated with 15N (reference method) and an open collector with an absorber wrapped in PTFE film. Urea was applied to the soil surface in treatments corresponding to rates of 50, 100, 150 and 200 kg ha-1 N. Applying urea-N fertilizer on sugarcane straw resulted in losses NH3-N up to 24 % of the applied rate. The amount of volatile NH3-N measured in the open and the semi-open static collector did not differ. The effectiveness of the collection system varied non-linearly, with an average value of 58.4 % for the range of 100 to 200 kg ha-1 of urea-N. The open collector showed significant potential for use; however, further research is needed to verify the suitability of the proposed method.

Loss of ammonia from nitrogen fertilizers applied to maize and soybean straw

In Brazilian agriculture, urea is the most commonly used nitrogen (N) source, in spite of having the disadvantage of losing considerable amounts of N by ammonia-N volatilization. The objectives of this study were to evaluate: N lossby ammonia volatilization from: [urea coated with copper sulfate and boric acid], [urea coated with zeolite], [urea+ammonium sulfate], [urea coated with copper sulfate and boric acid+ammonium sulfate], [common urea] and [ammonium nitrate]; and the effect of these N source son the maize yield in terms of amount and quality. The treatments were applied to the surface of a soil under no-tillage maize, in two growing seasons. The first season (2009/2010) was after a maize crop (maize straw left on the soil surface) and the second cycle (2012/2011) after a soybean crop. Due to the weather conditions during the experiments, the volatilization of ammonia-N was highest in the first four days after application of the N sources. Of all urea sources, under volatilization-favorable conditions, the loss of ammonia from urea coated with copper sulfate and boric acid was lowest, while under high rainfall, the losses from the different urea sources was similar, i.e., an adequate rainfall was favorablet o reduce volatilization. The ammonia volatilization losses were greatest in the first four days after application. Maize grain yield differed due to N application and in the treatments, but this was only observed with cultivation of maize crop residues in 2009/2010. The combination of ammonium+urea coated with copper sulfate and boric acid optimized grain yield compared to the other urea treatments. The crude protein concentration in maize was not influenced by the technologies of urea coating.

Efficiency of nitrogen fertilizer applied at corn sowing in contrasting growing seasons in Paraguay

In order to select soil management practices that increase the nitrogen-use efficiency (NUE) in agro-ecosystems, the different indices of agronomic fertilizer efficiency must be evaluated under varied weather conditions. This study assessed the NUE indices in no-till corn in southern Paraguay. Nitrogen fertilizer rates from 0 to 180 kg ha-1 were applied in a single application at corn sowing and the crop response investigated in two growing seasons (2010 and 2011). The experimental design was a randomized block with three replications. Based on the data of grain yield, dry matter, and N uptake, the following fertilizer indices were assessed: agronomic N-use efficiency (ANE), apparent N recovery efficiency (NRE), N physiological efficiency (NPE), partial factor productivity (PFP), and partial nutrient balance (PNB). The weather conditions varied largely during the experimental period; the rainfall distribution was favorable for crop growth in the first season and unfavorable in the second. The PFP and ANE indices, as expected, decreased with increasing N fertilizer rates. A general analysis of the N fertilizer indices in the first season showed that the maximum rate (180 kg ha-1) obtained the highest corn yield and also optimized the efficiency of NPE, NRE and ANE. In the second season, under water stress, the most efficient N fertilizer rate (60 kg ha-1) was three times lower than in the first season, indicating a strong influence of weather conditions on NUE. Considering that weather instability is typical for southern Paraguay, anticipated full N fertilization at corn sowing is not recommended due the temporal variability of the optimum N fertilizer rate needed to achieve high ANE.

Genotypic variability in seed accumulation of foliar-applied molybdenum to common bean

The genotypic variability in molybdenum (Mo) accumulation in common bean seeds has been demonstrated in cases in which soil is the main Mo source, but this variability is yet unknown when Mo is foliar-applied. Therefore, seed Mo concentrations (SMoCc) and seed Mo contents (SMoCt) of 12 genotypes were determined in four experiments in the Zona da Mata, Minas Gerais, Brazil, in which plants were sprayed with 600 g ha-1 Mo. For comparison, two additional experiments without external Mo were conducted. Without Mo application, the average SMoCc was undetectable or 2.83 µg g-1, without significant differences among genotypes. On average, with Mo applications, SMoCc ranged from 14.7 to 25.0 µg g-1 and SMoCt, from 3.94 to 6.84 µg. 'Majestoso' was among the genotypes with the highest SMoCc in the four experiments. However, the large-seeded 'Jalo MG-65' and 'Carnaval' generally had higher SMoCt than the small-seeded 'Majestoso'. 'Ouro Negro' and especially 'Valente' were among the genotypes with the lowest SMoCc and SMoCt. The values of these variables were 61 and 90 %, respectively, higher for 'Majestoso' than those for 'Valente'. Our results suggest that common bean genotypes differ in their capacity to accumulate foliar-applied Mo in the seeds. Mo-rich seeds of large-seeded genotypes or of small-seeded of small-seeded genotypes with good capacity to accumulate Mo in seeds can be produced with relatively less Mo fertilizer.

Fuzzy logic applied to the modeling of water dynamics in an Oxisol in northeastern Brazil

Modeling of water movement in non-saturated soil usually requires a large number of parameters and variables, such as initial soil water content, saturated water content and saturated hydraulic conductivity, which can be assessed relatively easily. Dimensional flow of water in the soil is usually modeled by a nonlinear partial differential equation, known as the Richards equation. Since this equation cannot be solved analytically in certain cases, one way to approach its solution is by numerical algorithms. The success of numerical models in describing the dynamics of water in the soil is closely related to the accuracy with which the water-physical parameters are determined. That has been a big challenge in the use of numerical models because these parameters are generally difficult to determine since they present great spatial variability in the soil. Therefore, it is necessary to develop and use methods that properly incorporate the uncertainties inherent to water displacement in soils. In this paper, a model based on fuzzy logic is used as an alternative to describe water flow in the vadose zone. This fuzzy model was developed to simulate the displacement of water in a non-vegetated crop soil during the period called the emergency phase. The principle of this model consists of a Mamdani fuzzy rule-based system in which the rules are based on the moisture content of adjacent soil layers. The performances of the results modeled by the fuzzy system were evaluated by the evolution of moisture profiles over time as compared to those obtained in the field. The results obtained through use of the fuzzy model provided satisfactory reproduction of soil moisture profiles.

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.

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

ABSTRACT In recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. However, these information sources are rarely used together. Therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, GIS and technical knowledge has been applied and evaluated. Two areas of study, in the State of São Paulo, Brazil, totaling approximately 28.000 ha were used for this work. First, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. Subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). Finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. The proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).