scholarly journals SPATIAL DEPENDENCE INDEX FOR CUBIC, PENTASPHERICAL AND WAVE SEMIVARIOGRAM MODELS

2018 ◽  
Vol 24 (1) ◽  
pp. 142-151
Author(s):  
Edemar Appel Neto ◽  
Ismael Canabarro Barbosa ◽  
Enio Júnior Seidel ◽  
Marcelo Silva de Oliveira
Keyword(s):  
Spatial Correlation ◽  
Spatial Dependence ◽  
Maximum Distance ◽  
Nugget Effect ◽  
Asymmetric Behavior ◽  
Range Parameter ◽  
Wave Models

Abstract: This study aims to propose a spatial dependence index (and its classification), from the concept of spatial correlation areas, for the Cubic, Pentaspherical and Wave models. The index, called Spatial Dependence Index (SDI), covers the following parameters: the range (a), the nugget effect (C 0 ) and the contribution (C 1 ), beyond considering the maximum distance (MD) between sampled points and the model factor (MF). The proposed index, unlike the most used in the literature, considers the influence of the range parameter to describe the spatial dependence, highlighting the importance of this formulation. The spatial dependence classification, based on the observed asymmetric behavior in the SDI, was performed considering categorizations from the median and the 3rd quartile of the index. We obtain the spatial dependence classification in terms of weak, moderate, and strong, just as it is usually described in literature.

Effect of Savanna windrow wood burning on the spatial variability of soil properties

2021 ◽  
Vol 51 ◽  
Author(s):  
Diogo Neia Eberhardt ◽  
Robélio Leandro Marchão ◽  
Pedro Rodolfo Siqueira Vendrame ◽  
Marc Corbeels ◽  
Osvaldo Guedes Filho ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Spatial Dependence ◽  
Chemical Properties ◽  
Spatial Arrangement ◽  
Nugget Effect ◽  
Tropical Savannas ◽  
Geostatistical Methods ◽  
Exchangeable Ca ◽  
Spherical Models

ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.

scholarly journals PROPOSAL OF THE SPATIAL DEPENDENCE EVALUATION FROM THE POWER SEMIVARIOGRAM MODEL

2017 ◽  
Vol 23 (3) ◽  
pp. 461-475 ◽  
Author(s):  
Ismael Canabarro Barbosa ◽  
Edemar Appel Neto ◽  
Enio Júnior Seidel ◽  
Marcelo Silva de Oliveira
Keyword(s):  
Spatial Correlation ◽  
Spatial Dependence ◽  
Second Order ◽  
Power Model ◽  
Semivariogram Model ◽  
Model Based ◽  
Symmetrical Distribution ◽  
Model Adjustment ◽  
Equivalent Factor

Abstract: In Geostatistics, the use of measurement to describe the spatial dependence of the attribute is of great importance, but only some models (which have second-order stationarity) are considered with such measurement. Thus, this paper aims to propose measurements to assess the degree of spatial dependence in power model adjustment phenomena. From a premise that considers the equivalent sill as the estimated semivariance value that matches the point where the adjusted power model curves intersect, it is possible to build two indexes to evaluate such dependence. The first one, SPD * , is obtained from the relation between the equivalent contribution (α) and the equivalent sill (C * = C 0 + α), and varies from 0 to 100% (based on the calculation of spatial dependence areas). The second one, SDI * , beyond the previous relation, considers the equivalent factor of model (FM * ), which depends on the exponent β that describes the force of spatial dependence in the power model (based on spatial correlation areas). The SDI * ,for β close to 2, assumes its larger scale, varying from 0 to 66.67%. Both indexes have symmetrical distribution, and allow the classification of spatial dependence in weak, moderate and strong.

Evidence for conformal invariance of crop yields

2006 ◽  
Vol 462 (2071) ◽  
pp. 2119-2143 ◽  
Author(s):  
Peter McCullagh ◽  
David Clifford
Keyword(s):  
Spatial Correlation ◽  
Conformal Invariance ◽  
Crop Yields ◽  
Gaussian Model ◽  
Agricultural Practices ◽  
Field Trials ◽  
Spatial Correlations ◽  
Range Parameter ◽  
Smoothness Parameter ◽  
Rate Of Decay

The aim of this paper is to study the nature of spatial correlation of yields of agricultural crops. The focus is primarily on natural or non-anthropogenic spatial variation, patterns that cannot be explained by topography, by variety or treatment effects, or by agricultural practices. Conformal invariance implies stationarity and isotropy, and also determines the rate of decay of spatial correlations. The resulting Gaussian model is studied empirically to see whether it describes satisfactorily the pattern of spatial correlations observed in field trials of various crops. By embedding the law in a larger statistical model, a convolution of white noise and the Matérn class having a range parameter λ −1 and a smoothness parameter ν , and by gathering data of sufficient range and quantity, the model predictions were tested. Twenty-five examples of crop yields are studied, including cereals, root crops and other vegetables, nut, citrus and alfalfa yields. At the scale of typical field trials, we find that non-anthropogenic variation is reasonably close to isotropic. Furthermore, we find consistent evidence that the range parameter tends to be large and the smoothness parameter small. The large value of the range parameter confirms Fairfield Smith (Fairfield Smith 1938 J. Agric. Sci. 28 , 1–23), who found that spatial correlation in agricultural processes decreases with distance, but at a slower rate than exponential. The small value of the smoothness parameter means that, by Matérn standards, agricultural processes are rough. For each of the examples studied, the limiting model fits the data just as well as the full model, in reasonable agreement with the hypothesis of the conformal model that ( λ ,  ν )=(0, 0) for all crops in all seasons.

scholarly journals Effects of vinasse application under the physical attributes of soil covered with sugarcane straw

2013 ◽  
Vol 33 (4) ◽  
pp. 636-646 ◽  
Author(s):  
Cicero da S. Costa ◽  
Elvira M. R. Pedrosa ◽  
Mario M. Rolim ◽  
Hugo R. B. Santos ◽  
Aluízio T. Cordeiro Neto
Keyword(s):  
Spatial Dependence ◽  
Repeated Measures ◽  
Water Movement ◽  
Soil Layer ◽  
Total Porosity ◽  
Moderate Degree ◽  
Nugget Effect ◽  
Physical Conditions ◽  
Sugarcane Straw ◽  
Physical Attributes

Areas under vinasse application have been associated to favorable physical conditions for root development, aeration, infiltration and water movement in soil profile. This study aimed to evaluate changes on physical attributes of soil under sugarcane straw after vinasse application in two sugarcane growing areas (Area 1 and Area 2) under mechanized management in the state of Paraíba, Brazil. In each area, the samples were collected in the 0-0.20, 0.20-0.40 and 0.40-0.60m layers of the soil, in 36 points, distributed in a 10×10m mesh, one day before and 40 days after vinasse application. The data were submitted to multivariate analysis with repeated measures and geostatistics. The vinasse application decreased soil density and increased total porosity in both Areas and increased organic matter in Area 2. In Area 1 occurred pure nugget effect for the fractions of sand, silt and clay, independent of soil layer. In Area 2, this effect was verified mostly at superficial layers, except for the fraction of clay that presented a moderate degree of spatial dependence.

scholarly journals Mapping of soil micronutrients in an european atlantic agricultural landscape using ordinary kriging and indicator approach

Bragantia ◽  
2010 ◽  
Vol 69 (suppl) ◽  
pp. 175-186 ◽  
Author(s):  
Jorge Dafonte Dafonte ◽  
Montserrat Ulloa Guitián ◽  
Jorge Paz-Ferreiro ◽  
Glécio Machado Siqueira ◽  
Eva Vidal Vázquez
Keyword(s):  
Spatial Distribution ◽  
Spatial Dependence ◽  
Ordinary Kriging ◽  
Management Practices ◽  
Agricultural Landscape ◽  
Interpolation Method ◽  
Indicator Kriging ◽  
Climatic Conditions ◽  
Nugget Effect ◽  
Indicator Approach

Nutrient maps based on intensive soil sampling are useful to develop site-specific management practices. Geostatistical methods have been widely used to determine the spatial correlation and the range of spatial dependence at different sampling scales. If spatial dependence is detected, the modelled semivariograms can then be used to map the interested variable by kriging, an interpolation method that produces unbiased estimates with minimal estimation variance. The objectives of this paper were to examine and to map the spatial distribution of the micronutrients Cu, Zn, Fe and Mn on an agricultural area in Galicia, Spain, under European Atlantic climatic conditions. The ordinary kriging was first used to determine the values for the non-sampled locations, then the indicator approach was used to transform the micronutrient content values into binary values having the mean values of each nutrient as the threshold content. All four elements analyzed showed spatial dependence using the indicator semivariograms. The strength of spatial dependence was assessed using the values of nugget effect and range from the semivariogram, the fitted range values decreased in the order Mn >Fe >Zn >Cu. The spatial dependence of the combination of two or more of the studied micronutrients was also examined using indicator semivariograms. In opposition to spatial analysis of individual microelements, indicator semivariograms obtained for the binary coding of the variables showed a great nugget effect value or a low proportion of sill. The maps for each nutrient obtained using indicator kriging showed some similarity in the spatial distribution, suggesting the delimitation of uniform management areas.

scholarly journals brief discussion on the treatment of spatial correlation in multinomial discrete models

2021 ◽  
Vol 14 (1) ◽  
pp. 521-535
Author(s):  
Francisco Bahamonde-Birke
Keyword(s):  
Spatial Correlation ◽  
Spatial Dependence ◽  
Social Processes ◽  
The Other ◽  
Discrete Models ◽  
Compromise Solution ◽  
Discrete Modeling ◽  
Computational Costs ◽  
Geographic Space ◽  
The Social

Spatial dependence plays a key role in all phenomena involving the geographic space, such as the social processes associated with transport and land use. Nevertheless, spatial dependence in multinomial discrete models has not received the same level of attention as have the other kinds of correlations in the discrete modeling literature, mainly due to the complexity of its treatment. This paper aims at offering a brief discussion on the different kinds of spatial correlation affecting multinomial discrete models and the different ways in which spatial correlation has been addressed in the discrete modeling literature. Furthermore, the paper offers a discussion on the advantages and limitations of the different approaches to treat spatial correlation and it also proposes a compromise solution among complexity, computational costs, and realism that can be useful in some specific situations.

192: Tubularized Urethral Replacement: What is the Maximum Distance for Normal Tissue Regeneration?

2004 ◽  
Vol 171 (4S) ◽  
pp. 51-51
Author(s):  
Roger E. De Filippo ◽  
Hans G. Pohl ◽  
James J. Yoo ◽  
Anthony Atala
Keyword(s):  
Tissue Regeneration ◽  
Normal Tissue ◽  
Maximum Distance

Maximum Distance Separable Hankel Rhotrices over Finite Fields

2018 ◽  
Vol 43 (1-4) ◽  
pp. 13-45
Author(s):  
Prof. P. L. Sharma ◽  
◽  
Mr. Arun Kumar ◽  
Mrs. Shalini Gupta ◽  
◽  
...  
Keyword(s):  
Finite Fields ◽  
Maximum Distance ◽  
Maximum Distance Separable

Convergence of regional population density in Russia over 120 years

2020 ◽  
pp. 133-158
Author(s):  
K. A. Kholodilin ◽  
Y. I. Yanzhimaeva
Keyword(s):  
Population Density ◽  
Spatial Dependence ◽  
Population Distribution ◽  
Regional Level ◽  
Capital City ◽  
County Level ◽  
Spatial Effects ◽  
Russian Regions ◽  
Regional Population ◽  
Regional Population Density

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

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