931 resultados para Spatial variability.


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The rain occurrence is one of the most important phenomena to determinate the climate and, as most of the other climatic phenomena, shows a continuous spatial variability that can be detected through special geostatistical methods. Besides the great influence of the topographic relief on the specific climate of each region, it is normal to expect spatial correlations of this variable with the precipitations; the determinism of this correlation may help to elaborate more precise conclusions involving these phenomena. In this paper, the spatial variability of altitude and of the pluviometric precipitations was rigorously analyzed, besides the existing correlation between these variables. It was concluded that these variables show strong spatial dependence and they are directly correlated. The mapping of the occurrence of both the phenomena was done by geostatistical methods pased on the infomiation concerning the spatial variability of each one.

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There are many methods used to estimate values in places no sampled for construction of contours maps. The aim of this study was to use the methods of interpolation kriging, inverse of the square of the distance and polynomial in the representation of the spatial variability of the pH of the soil in the organic and conventional management in the culture of the coffee plantation. For that, irregular meshes were built for soil sampling in the depth of 0-0,10 meters, totaling 40 points sampling in each area. For gauging of the interpolation methods they were solitary 10% of the total of points, for each area. Initially, the data were appraised through the classic statistics (descriptive and exploratory) and spatial analysis. The method inverse square of the distance and kriging has low error in estimating dados. The method of kriging presented low variation around the average in different managements.

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The objective of this work was to study spatial variability of some chemical soil attributes and lettuce production (total shoot fresh matter mass - MF; commercial shoot fresh matter mass - MFC; commercial shoot dry matter mass - MCS; and head commercial diameter - DCC) offering subsidies to the protected environment mapping in nutrients management areas in lettuce culture aiming for a higher productivity with application of fertilizers. The experiment was conducted in a protective environment (greenhouse) with lettuce irrigated by drip irrigation and sampling grid with 152 points. The special dependence analysis, determined by the variogram, was obtained with the aid of the GS+ Program. Considering the need for crop nutrients through the map obtained for element P (phosphorus) it was possible to establish two distinct areas for the application of this element in plantation fertilization. Through the lettuce yield maps obtained with MFC and DCC attributes was difficult to establish distinct areas for its management with data observed in only one crop cycle. Krigagem has proved useful for mapping the attributes studied.

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Mapping the plant nutritional condition allows viewing different regions in a cropping area, providing the producers with different criteria to use foliar and soil fertilization. The aim of this study was to evaluate the spatial variability of the nutritional condition of canephora coffee (Coffea canephora Pierre ex Froehner) regarding the site specific management of foliar and soil fertilization. In a one hectare area 60 georeferenced points were sampled at irregular intervals. There were five plants in each sampled point; two pairs of leaves were removed from the lateral branches (3 rd and 4 th pairs from extremity to the basis) in the cardinal points of each plant, counting up 40 leaves per point. The foliar samples were chemically analyzed for the following nutrients: N, P, K, Ca, Mg, S, B, Cu and Zn. The same pattern of spatial dependence was presented with adjustment for K and B. Except for N and P, which presented random distribution, the other nutrients presented mild to severe spatial dependence justifying the geostatistical data analysis for making maps for differential and located, foliar and soil fertilizer application in coffee crop.

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The objective of this work was to assess the spatial variability of the chemical attributes of two coffee areas, managed in conventional and organic crop systems, and to calculate the percent of variation between them. In each area, a 40-point-mesh was sampled at 0-0.10 m and 0.10-0.20 m layers, within the crown projection, for pH, SB, K, P, Ca and Mg analysis. The data were analyzed through descriptive statistics and geostatistics. From the soil chemical attributes map, the percent of variation between the systems' chemical attributes was determined by GIS algebraic operations. The results show that the soil chemical attributes present a spatial dependence in both systems and layers. Analysis of the soil chemical attributes showed less spatial variability in the organic system, in relation to the conventional, indicating homogeneous zones for different fertilizer applications. The percent of variation of the chemical attributes in the conventional system, in relation to the organic, at 0-0.10 m and 0.10-0.20m layers are 54.80% and 35.61%, respectively.

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In the present work we use an asymptotic approach to obtain the long wave equations. The shallow water equation is put as a function of an external parameter that is a measure of both the spatial scales anisotropy and the fast to slow time ratio. The values given to the external parameters are consistent with those computed using typical values of the perturbations in tropical dynamics. Asymptotically, the model converge toward the long wave model. Thus, it is possible to go toward the long wave approximation through intermediate realizable states. With this approach, the resonant nonlinear wave interactions are studied. To simplify, the reduced dynamics of a single resonant triad is used for some selected equatorial trios. It was verified by both theoretical and numerical results that the nonlinear energy exchange period increases smoothly as we move toward the long wave approach. The magnitude of the energy exchanges is also modified, but in this case depends on the particular triad used and also on the initial energy partition among the triad components. Some implications of the results for the tropical dynamics are disccussed. In particular, we discuss the implications of the results for El Nĩo and the Madden-Julian in connection with other scales of time and spatial variability. © Published under licence by IOP Publishing Ltd.

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The purpose of this study was to estimate the varieties of coffee Arabic Catucáand Catuaí productivity, in Zona da Mata, Minas Gerais, through agrometeorological mathematical models, considering the spatial variability of productivity in the area. The samplings were georrefered building an irregular grid, totalizing 50 samplings per area. After that, geostatic analysis was made to quantify spatial dependence degree of the real values and the estimated productivity. According to the classification, the models superestimated the productivity for the two varieties.

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The spatial variability of several soil attributes (bulk density, penetration resistance, water content, organic matter content and pH) as well as soybean yield have been assessed during the 2007/08 growing season, in Selviria (MS) in a Hapludox (Typic Acrustox), under no tillage. The objectives were to assess the spatial variability of soil and plant parameters at the small plot scale and to select the best soil attribute explaining most the variability of agricultural productivity. Soil and plant were sampled on a grid with 121 points within a plot of 25,600 m 2 in area and slope of 0.025 mm -1 slope. Medium and low coefficients of variation were obtained for most of the studied soil attributes as expected, due to the homogenizing effect of the no-till system on the soil physical environment. From the standpoint of linear regression and spatial pattern of variability, productivity of soybeans could be explained according to the hydrogen potential (pH). Results are discussed taken into account that the soybean crop in no-tillage is widely used in crop-livestock integration on the national scene.

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Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31. 7 ha from which samples were collected at depths of 0. 00-0. 20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30. 4l ha-1) and best coffee beverage quality (61 sacks ha-1). Based on the results, we believe that multivariate analysis, geostatistics and the soil-relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production. © 2012 Springer Science+Business Media New York.

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The characterization of soil CO2 emissions (FCO2) is important for the study of the global carbon cycle. This phenomenon presents great variability in space and time, a characteristic that makes attempts at modeling and forecasting FCO2 challenging. Although spatial estimates have been performed in several studies, the association of these estimates with the uncertainties inherent in the estimation procedures is not considered. This study aimed to evaluate the local, spatial, local-temporal and spatial-temporal uncertainties of short-term FCO2 after harvest period in a sugar cane area. The FCO2 was featured in a sampling grid of 60m×60m containing 127 points with minimum separation distances from 0.5 to 10m between points. The FCO2 was evaluated 7 times within a total period of 10 days. The variability of FCO2 was described by descriptive statistics and variogram modeling. To calculate the uncertainties, 300 realizations made by sequential Gaussian simulation were considered. Local uncertainties were evaluated using the probability values exceeding certain critical thresholds, while the spatial uncertainties considering the probability of regions with high probability values together exceed the adopted limits. Using the daily uncertainties, the local-spatial and spatial-temporal uncertainty (Ftemp) was obtained. The daily and mean emissions showed a variability structure that was described by spherical and Gaussian models. The differences between the daily maps were related to variations in the magnitude of FCO2, covering mean values ranging from 1.28±0.11μmolm-2s-1 (F197) to 1.82±0.07μmolm-2s-1 (F195). The Ftemp showed low spatial uncertainty coupled with high local uncertainty estimates. The average emission showed great spatial uncertainty of the simulated values. The evaluation of uncertainties associated with the knowledge of temporal and spatial variability is an important tool for understanding many phenomena over time, such as the quantification of greenhouse gases or the identification of areas with high crop productivity. © 2013 Elsevier B.V.

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The precision agriculture technologies such as the spatial variability of soil attributes have been widely studied mostly with sugarcane. Among these technologies have been recently highlighted the use of the vegetation index derived from remote sensing products, such as powerful tools indicating the development of vegetation. This study aimed to analyze the spatial variability of clay content, pH and phosphorus in an Oxisol in an area with sugarcane production, and correlate with the Normalized Difference Vegetation Index (NDVI). The georeferenced grid was created for the soil properties (clay, phosphorus and pH) and generated the maps of spatial variability. For these same sites were calculated the NDVI, in addition to mapping of this ratio, the evaluation of the spatial correlation between this and other studied properties. The clay and phosphorus content showed positive spatial correlation with the NDVI, while no spatial correlation was observed between NDVI and pH. The satellite images from the sensor ETM + Landsat were used to correlate to NDVI to observe the spatial variability of the studied attributes.

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Considering the importance of knowledge of the spatial distribution of soil properties, the purpose of this study was to evaluate the spatial variability of physical properties in a Cambisolunder different land uses in the southern Amazon region. The study was conducted on three farms with cassava, sugarcane, and agroforestry, in the region of Humaitá, in the south of the State of Amazonas. In these areas, 70 x 70 m grids were established, with a regular spacing of 10 x 10 m and a total of 64 points, where soils were sampled at 0.0-0.10 m depth. Texture (sand, silt, and clay), macroporosity, microporosity, total porosity, bulk density, and aggregate stabilitywere determined. The data were analyzed using descriptive statistics and geostatistics. It was found that the propertiesvaried spatially and that the range of these variations between land uses was different, with the highest variability for the sugarcane management.

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This study aim to evaluate the spatial variability of soil physical attributes in of Alfissol forest in Manicoré region, AM. The mapping of an agroforestry growing area of 70×70m was made using a regular grid sampling of 10 × 10m. In each grid, soil samples were collected at 0.0-0.2 and 0,40-0,60 m depth, with a total of 128 sample points. Physical analyses were made (texture, soil bulk and particles density, macro and microporosity, total porosity and aggregates stability in water). With the exception of DMG, DMP and class aggregates <1.00 mm had all the physical attributes spatial dependence structure ranging from moderate to weak. Values were above the range established in the mesh (12.00 to 45.56 m), enabling to make a basis for future studies in forest area. The physical attributes presented in kriging maps different spatial behavior, however there are relationships between these attributes shown by geostatistics proving that this is effective tool for studies in forest area.

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The mechanical resistance to penetration (RP) is an attribute indicator of compaction was influenced by soil moisture. The objective of this study was to evaluate the spatial variability of soil resistance to penetration and soil moisture on area under cultivation of sugar cane in the region of Humaitá, Amazonas, Brazil. We conducted a sampling grid of 70x70 m where points were scored at regular intervals of 10 m, a total of 64 points. Soil samples were collected at depths of 0.00 to 0.15 m, 0.15 to 0.30 I 0.30 to 0.45 m for determination of soil moisture and soil resistance to penetration in the field. After analysis of these data analyzes were descriptive statistics and geostatistics. We conclude that all the variables studied showed spatial dependence and range values were higher than stipulated by the sampling grid and the RP and soil moisture showed a spatial relationship where lower values of PR concentrated on smaller values of soil moisture.