A Classification for a Geostatistical Index of Spatial Dependence

Seidel, Enio Júnior; Oliveira, Marcelo Silva de

doi:10.1590/18069657rbcs20160007

Cited by 34 publications

(49 citation statements)

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“…The ratio between the nugget effect and the sill defines the level of spatial dependence of the data. The dependence can be characterized as strong, moderate or weak (CRESSIE and HARTFIELD, 1966;SEIDEL et al, 2016;SEIDEL and OLIVEIRA, 2014). More information of these geostatistical parameters can be found in Cressie (1993), Isaaks and Srivastava (1989), Govaerts (1997), Chilès and Delfiner (1999) and Journel (1978).…”

Section:  mentioning

confidence: 99%

Autoregressive Analysis of Variance for Experiments With Spatial Dependence Between Plots: A Simulation Study

Rossoni

Lima

2019

Rev. Bras. Biom.

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The analysis of variance remains one of the most appreciated techniques of field experiment, even despite almost a hundred years of its first proposal. However, in many cases, its application can be several impaired due the fact of lack – or even forgotten - of assumptions. In several experiments, the researchers make use of blocks to control the local heterogeneity, nevertheless, in some cases, only this it cannot be enough, especially in experiments where the data have some kind of spatial dependence. Therefore, to increase the accuracy of comparisons between treatments, an alternative is to consider the study of the spatial dependence of the variables in the analysis. With the knowledge of the relative positions of the plots (referenced data), the spatial variability can be used as a positive factor, collaborating with the experimental results. To develop this study we used data generated by simulation. The data was generated according a Randomized Complete Block Design (RCBD), with eighteen and five treatments per block; and several scenarios of spatial dependence in the error. We compared the non-spatial analysis (which considers the errors independent) with spatial analysis (analysis of variance considering the autoregressive model - ANOVA-AR). The use of spatial statistical tools in the analysis of data increased the precision of the analysis, through the reduction of the Mean Squared Error. We also noticed a reduction of Mean Squared Block and Mean Squared Treatment. The greater reduction was notice in ANOVA-AR3 for great part of the simulated scenarios, mainly in those with strong spatial dependence. The experiments with a small number of treatments per block did not present a reduction of Mean Squared Error, however, the reduction of Mean Squared Block and Mean Squared Treatment, ally to the fact that data are spatial dependent justify the use of ANOVA-AR.

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Section:  mentioning

confidence: 99%

Autoregressive Analysis of Variance for Experiments With Spatial Dependence Between Plots: A Simulation Study

Rossoni

Lima

2019

Rev. Bras. Biom.

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“…In the spatial dependence analysis, the spatial dependence index was calculated and classified (Seidel & Oliveira, 2016), in which the spatial dependence for the spherical model is: considered weak for values up to 7%, moderate when between 7% and 15%, and strong when above 15%; for the exponential model it is: considered weak for values up to 6%, moderate between 6% and 13% and strong if above 13%; and for the Gaussian model it is: considered weak for values up to 9%, moderate between 9% and 20% and strong if above 20% (Table 1), by Equation (2):…”

Section: /10mentioning

confidence: 99%

Diagnosis of losses in the mechanized harvest of two cultivars of industrial tomato

Soares

Alves

Freitas

et al. 2019

Agraria

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The production of industrial tomato in Brazil has been growing in the last years and the form of harvest adopted has been mechanized. The diagnosis of tomato fruit losses is fundamental to the harvesting process. The industrial process was destroyed and the statistical process control. The experiment was carried out in a field of 45 ha in an industrial tomato crop irrigated by central pivot in the municipality of Corumbá de Goiás, GO, Brazil. A sampling grid of 60 x 60 m (120 sample points) and 100 x 50 (84 sample points), respectively, were used as georeferenced samples to quantify weight loss and poisoning. As the main losses, in the mechanized harvest, the fruit of industrial tomato do not occur with mature fruit. A N901 cultivar had a lower quality relation than a HMX7885. The mechanical tomato harvesting process is influenced by the random power medium during a larger part of the operation. However, throughout the year, the tasks are not random, they are involved with the operation and the maintenance and maintenance of the process is necessary.Diagnóstico de perdas na colheita mecanizada de duas cultivares de tomate industrial RESUMO: A produção de tomate industrial no Brasil vem crescendo nos últimos anos e a forma de colheita adotada tem sido a mecanizada. O diagnóstico das perdas de frutos de tomate industrial decorrentes do processo de colheita pode auxiliar na redução de desperdícios desta operação. O objetivo desde trabalho foi estudar as perdas de tomate industrial decorrentes do processo de colheita mecanizada utilizando a ferramenta da geoestatística e o controle estatístico de processo. O experimento foi realizado em um talhão de 45 ha em uma lavoura de tomate industrial irrigada por pivô central no município de Corumbá de Goiás, GO, Brasil. As amostras georreferenciadas para quantificar produtividade e de perdas foram coletadas utilizando uma malha amostral de 60 x 60 m (120 pontos amostrais) e 100x50 (84 pontos amostrais), respectivamente. As principais perdas, na colheita mecanizada, de frutos de tomate industrial ocorrem no solo com frutos maduros. A cultivar N901 apresentou em média uma perda de frutos menor em relação a HMX7885. O processo de colheita mecanizada de tomate industrial é influenciado por meio de causas aleatórias durante a maior parte da operação. Contudo, ao longo da colheita fatores não aleatórios podem causar falhas na operação, sendo necessário o monitoramento e ajustes constantes visando manter o processo sob controle.Palavras-chave: máquinas agrícolas; agricultura de precisão; variabilidade espacial; controle estatístico de processo Diagnosis of losses in the mechanized harvest of two cultivars of industrial tomato Rev. Bras.

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“…The SDI index has a positive asymmetric sampling distribution (SEIDEL & OLIVEIRA, 2016) Results obtained from the correlations indicated that the SDI is able to capture, in an intense way, the behavior of the range and the when evaluating the spatial dependence, evidencing also the behavior in the horizontal sense of the semivariogram. This is an important feature of the SDI index, which differentiates it from other indexes in the literature, since according to FERRAZ et al (2012) the range has a considerable role in determining the limit of spatial dependence.…”

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confidence: 93%

“…The data were obtained from 25 articles, published from 2006 to 2015 and made available on the Scielo Brazil portal, with application of Geostatistics in Soil attributes, used and cited in SEIDEL & OLIVEIRA (2016). From the papers, the following information was collected for each attribute: model of semivariogram adjusted, estimated range (a), estimated nugget effect (C 0 ), estimated contribution (C 1 ), estimated sill (C=C 0 +C 1 ), maximum sample distance (MD), soil type, type of attribute (chemical, physical or mineralogical) and soil layer (depth).…”

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confidence: 99%

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Some aspects about the spatial dependence index for variability of soil attributes

et al. 2018

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The main purpose of this article was to evaluate the behavior and relationship of the range and components of SDI (Spatial Dependence Index) in general and in function of field factors such as soil types, type of attribute and soil layers. This evaluation was based on real data collected in national journals. It was noticed that the parameter range, in general and for different field factors, presented asymmetric positive behavior. The components of the SDI showed approximately symmetrical behavior. The SDI can capture the range behavior more intensely (the spatial variability behavior in the horizontal direction of the semivariogram), and, in a less intense way, the behavior of the contribution and sill parameters (the spatial dependence behavior in the vertical direction of the semivariogram). Thus, the SDI describes the behavior of spatial dependence of the total set of aspects of the semivariogram.

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A Classification for a Geostatistical Index of Spatial Dependence

Cited by 34 publications

References 31 publications

Autoregressive Analysis of Variance for Experiments With Spatial Dependence Between Plots: A Simulation Study

Autoregressive Analysis of Variance for Experiments With Spatial Dependence Between Plots: A Simulation Study

Diagnosis of losses in the mechanized harvest of two cultivars of industrial tomato

Some aspects about the spatial dependence index for variability of soil attributes

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