931 resultados para sampling grids


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In soil surveys, several sampling systems can be used to define the most representative sites for sample collection and description of soil profiles. In recent years, the conditioned Latin hypercube sampling system has gained prominence for soil surveys. In Brazil, most of the soil maps are at small scales and in paper format, which hinders their refinement. The objectives of this work include: (i) to compare two sampling systems by conditioned Latin hypercube to map soil classes and soil properties; (II) to retrieve information from a detailed scale soil map of a pilot watershed for its refinement, comparing two data mining tools, and validation of the new soil map; and (III) to create and validate a soil map of a much larger and similar area from the extrapolation of information extracted from the existing soil map. Two sampling systems were created by conditioned Latin hypercube and by the cost-constrained conditioned Latin hypercube. At each prospection place, soil classification and measurement of the A horizon thickness were performed. Maps were generated and validated for each sampling system, comparing the efficiency of these methods. The conditioned Latin hypercube captured greater variability of soils and properties than the cost-constrained conditioned Latin hypercube, despite the former provided greater difficulty in field work. The conditioned Latin hypercube can capture greater soil variability and the cost-constrained conditioned Latin hypercube presents great potential for use in soil surveys, especially in areas of difficult access. From an existing detailed scale soil map of a pilot watershed, topographical information for each soil class was extracted from a Digital Elevation Model and its derivatives, by two data mining tools. Maps were generated using each tool. The more accurate of these tools was used for extrapolation of soil information for a much larger and similar area and the generated map was validated. It was possible to retrieve the existing soil map information and apply it on a larger area containing similar soil forming factors, at much low financial cost. The KnowledgeMiner tool for data mining, and ArcSIE, used to create the soil map, presented better results and enabled the use of existing soil map to extract soil information and its application in similar larger areas at reduced costs, which is especially important in development countries with limited financial resources for such activities, such as Brazil.

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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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In Smart Grids, a variety of new applications are available to users of the electrical system (from consumers to the electric system operators and market operators). Some applications such as the SCADA systems, which control generators or substations, have consequences, for example, with a communication delay. The result of a failure to deliver a control message due to noncompliance of the time constraint can be catastrophic. On the other hand, applications such as smart metering of consumption have fewer restrictions. Since each type of application has different quality of service requirements (importance, delay, and amount of data to transmit) to transmit its messages, the policy to control and share the resources of the data communication network must consider them. In this paper Markov Decision Process Theory is employed to determine optimal policies to explore as much as possible the availability of throughput in order to transmit all kinds of messages, considering the quality of service requirements defined to each kind of message. First a non-preemptive model is formulated and after that a preemptive model is derived. Numerical results are used to compare FIFO, non-preemptive and preemptive policies.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

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The knowledge on spatial distribution of soil properties by means of geostatistics is important as an indicator for a better soil use and management. This study aimed at evaluating the spatial distribution of soil chemical properties, in a forest and pasture area in Manicoré, Amazonas State, Brazil. Grids with 70.00 m x 70.00 m, with regular spacing of 10.00 m x 10.00 m, totaling 64 points, were established, and then soil samples were collected at the depths of 0.0-0.20 m and 0.40-0.60 m and had their chemical properties determined. Data were analyzed by using descriptive statistics and geostatistics, and the sampling density analysis was based on the coefficient of variation and semivariograms range. The mean and median values were adjusted to the closest values, indicating normal distribution, while the spherical, exponential and gaussian models were adjusted to the soil chemical properties. It was concluded that the geostatistics provided adequate information for understanding the spatial distribution. The forest area showed a higher spatial continuity and the pasture area a lower sampling density. The chemical properties showed differences in the spatial variability, while the range represented better the estimates for sampling density and spacing, in the forest and pasture area.

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The use of geostatistical techniques allows detection of the existence of dependence and the spatial distribution of soil properties, thus constituting an important tool in the analysis and detailed description of the behavior of soil physical properties. The aim of the present study was to use geostatistics in assessment of physical properties in a Latossolo (Oxisol) dystrophic under native forest and pasture in the Amazon region of Manicore. Grids with of 70 x 70 m were established in native forest and pasture, and points were marked in these grids spaced at every 10 m, for a total of 64 points. These points were then georeferenced and in each one, soil samples (128) were collected at the depths of 0.00-0.20 and 0.40-0.60 m for a survey of their physical properties. These grids are parallel at a distance of 100 m from one another. The following determinations were made: texture, bulk density and particle density, macroporosity, microporosity, total porosity and aggregate stability in water. After tabulating the data, descriptive statistical analysis and geostatistical analysis were performed. The pasture had a slight variation in its physical properties in relation to native forest, with a high coefficient of variation and weak spatial dependence. The scaled semivariograms were able to satisfactorily reproduce the spatial behavior of the properties in the same pattern as the individual semivariograms, and the use of the parameter range of the semivariogram was efficient for determining the optimal sampling density for the environments under study. The geostatistical results indicate that the removal of native forest for establishing pasture altered the natural variability of the physical properties.

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The objective of this study was to analyze different intensities of soil sampling for accuracy in geostatistical analysis and interpolation maps for precision agriculture in the sugarcane area. Soil samples were collected at two regular grids at a depth of 0.00 to 0.20m for granulometric analysis (area 1) and soil fertility (area 2). We compared soil sampling intensities: 208, 105, 58 and 24 points in Area 1 and 206, 102 and 53 points in Area 2. The data were submitted to descriptive analysis and geostatistics. The variograms constructed with 105 points didn't differ from variograms with 208 points, which doesn't occur for 58 and 24 points. The increase of sampling interval and reducing the number of points promote greater error in kriging. Samples with more than 100 points per area did not result in significant improvements in the error of kriging, or differed in the amount of fertilizer applied to the field.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)