Distribuição espacial das frações granulométricas argila e areia total em um Latossolo Vermelho-Amarelo

Lima, J.S.S.; Bona, Diego Antonio Ottonelli; Fiedler, Nilton César; Pereira, Daniel Pena

doi:10.1590/s0100-67622014000300014

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…It was observed that the spherical model fitted the data series from all the particle-size fractions of the soil, as presented in Table 1. Lima et al (2014) set the particle-size fractions of the soil to the spherical model for the construction of thematic maps by ordinary kriging (Figure 2). Considering the indicative semivariograms, the same pattern of spatial distribution of the occurrence probability is observed between the clay and fine sand fractions, with adjustments to the same model and the same ranges (160 m).…”

Section: Resultsmentioning

confidence: 99%

Probability of Particle-Size Fractions Occurrence in Different Landforms

Lima¹,

Silva

Pereira

et al. 2018

Reveng

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Knowing the variation of particle-size fractions, considering the relief forms, contributes for understanding the variation of other soil attributes. This work aimed to study the spatial distribution of the probability of particle-size fractions occurrence (clay, silt, very fine sand, fine sand, coarse sand, and total sand) in a clayey Oxisol with predominance of concave and convex curvatures. A sampling grid with 94 sampling points in 33x33m spacing at a depth of 0–0.20 m was built using a GPS. The spatial analysis was performed through indicator kriging. The spherical model was fit for all soil fractions, with ranges varying from 130 m to 280 m. In the region of convex curvature, the clay fraction presented the highest probability of occurrence (0.75 to 1.00), whereas in the concave region the coarse sand and total sand fractions presented the highest probability of occurrence. The very fine sand fraction and the silt did not present pattern of distribution in relation to the dominant curvatures of the relief.

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Section: Resultsmentioning

confidence: 99%

Probability of Particle-Size Fractions Occurrence in Different Landforms

Lima¹,

Silva

Pereira

et al. 2018

Reveng

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“…The definition of the spatial dependence values allows us to distinguish correlated samples. According to Lima et al (2014), samples that were collected from a distance that is smaller than their range are correlated to each other. That permits the interpolation for spaces that are smaller than the ones adopted in the sampling.…”

Section: Discussionmentioning

confidence: 99%

SPATIAL DISTRIBUTION OF PHYSIOLOGICAL QUALITY OF Joannesia princeps Vell. SEEDS1

et al. 2018

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The objective of this study was to analyze the spatial distribution of the attributes that determine the physiological quality of Joannesia princeps seeds. Seeds were collected, individually from, 40 mother plants in the Horto Florestal Municipal Laerth Paiva Gama, in Alegre-ES, which constituted the georeferenced sampling mesh. For germination, the seeds tegument was cracked and was held the imbibition in gibberellic acid GA3 500 mg L-1 for 24 h in environmental chamber with temperature adjusted to 30 ºC. The seeds were sown in plastic bags of ± 600 cm3 containing soil+sand+manure in the proportion of 1:1:1. On the 65th day after sowing, the following variables were analised: emergence (E), emergence speed index (IVE), root length (CR), diameter of base (DC), shoot length of the aerial part (CPA), fresh mass of the root system (MFSR), fresh mass of the aerial part (MFPA), dry mass of the root system (MSSR) and dry mass of the aerial part (MSPA). Data were subjected to descriptive statistics, geostatistics and kriging. With the exception of CPA and MFSR, it was found spatial dependence for the other studied variables: E, IVE, CR, DC, MFPA, MSSR e MSPA; varying of 14,0 a 47,5 m, so that the lowest range for the CR and longer range and degree of spatial dependence for the IVE. The analysis of the spatial distribution of the physiological quality of seeds of J. princeps can be used as a tool to define the sampling region of seeds with high and low vigor.

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“…The first assumption was inspired by the fact that, in models with sill, there is similarity between the sample variance and the sample sill (Trangmar, Yost, Uehara, 1985;Lima, G. et al, 2014;Nagahama et al, 2014;Lima, J. et al, 2014;Jordão et al, 2015). So that, it was considered as possible to extend this idea to the power model, turning the sample variance approximately equal to the value of sample equivalent sill, in other words, the sample variance could be considered as the estimate of the equivalent sill.…”

Section: Methodsmentioning

confidence: 99%

Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

et al. 2017

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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 * = C0 + 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.Keywords: Geostatistics; Variographic analysis; Semivariogram without sill; Spatial dependence indexes. Resumo:Em geoestatística, a utilização de medidas que descrevam a dependência espacial do atributo é de grande importância, porém apenas alguns modelos (que possuem estacionariedade de segunda ordem) são contemplados com tais medidas. Assim, este trabalho tem como objetivo propor medidas para avaliação do grau de dependência espacial em fenômenos com ajuste de modelo Barbosa, I. C. et al. 462 Bull. Geod. Sci, Articles section, Curitiba, v. 23, n°3, p.461 -475, Jul -Sept, 2017. potência. A partir de uma premissa que considera o patamar-equivalente como o valor de semivariância que coincide com o ponto em que as curvas ajustadas do modelo potência se interceptam, pode-se construir dois índices para avaliação de tal dependência. O primeiro, DE * , é obtido a partir da relação entre a contribuição-equivalente (α) e o patamar-equivalente (C * = C0 + α), e varia de 0 a 100% (baseado no cálculo de áreas de dependência espacial). O segundo, IDE * , além da relação anterior, considera um fator de modelo equivalente (FM * ), que depende do expoente β, o qual descreve a força da dependência espacial no modelo potência (baseado em áreas de correlação espacial). O IDE * , para β próximo de 2, assume sua maior escala, variando de 0 a 66.67%. Ambos os índices possuem distribuição simétrica, e permitem a classificação da dependência espacial em fraca, moderada e forte.

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Distribuição espacial das frações granulométricas argila e areia total em um Latossolo Vermelho-Amarelo

Cited by 5 publications

References 15 publications

Probability of Particle-Size Fractions Occurrence in Different Landforms

Probability of Particle-Size Fractions Occurrence in Different Landforms

SPATIAL DISTRIBUTION OF PHYSIOLOGICAL QUALITY OF Joannesia princeps Vell. SEEDS1

Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

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