Nugget Effect Influence on Spatial Variability of Agricultural Data

Guedes, Luciana Pagliosa Carvalho; Bach, Raquel T.; Uribe-Opazo, Miguel Ángel

doi:10.1590/1809-4430-eng.agric.v40n1p96-104/2020

Cited by 15 publications

(7 citation statements)

References 27 publications

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“…Some researchers use metrics together to better assess spatial variability and optimize decision-making: Taylor et al (2007) using the NE and the MCD; Souza et al (2008) using the NE and the integral scale J2; Oldoni and Bassoi (2016) using SPD and SDI; Amaral and Della Justina (2019) and Guedes et al (2020) using the NE and SDI. These findings show the need for further studies to propose and evaluate the performance of the indexes, mainly for the SDM that is being proposed.…”

Section: Resultsmentioning

confidence: 99%

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Geostatistical-based index for spatial variability in soil properties

Neto¹,

Seidel²,

Oliveira³

2020

Revista Brasileira De Ciência Do Solo

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The assessment of spatial variability of environmental variables such as soil properties is important for site-specific management. A geostatistical index that allows quantifying and characterizing the structure of spatial variability is fundamental in this context. Thus, this study aimed to develop a new spatial dependency index, called the Spatial Dependence Measure (SDM) for the spherical, exponential, Gaussian, cubic, pentaspherical, and wave semivariogram models; and comparing it with some of the indexes available in the literature. The SDM is also dimensionless, in the same way as the Spatial Dependence Index (SDI), also considering more parameters of the semivariogram, when compared to the Spatial Dependence Degree (SPD) and Relative Nugget Effect (NE) indexes. In a simulation data study, it is observed that the SDI and SDM indexes showed an advantage over the SPD (or NE). To exemplify the application of the SDM in the proposal for the classification of soil properties, we used estimates of geostatistical parameters presented in the two studies. The results indicate that the SDM can be a measure that, analyzed together with the SDI, can help to improve the description of the spatial variability structure. Thus, this study expands the number of geostatisitcal-based measures and increases the power of decision on the description of the degree of spatial variability of agricultural and soil attributes.

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Section: Resultsmentioning

confidence: 99%

“…To exemplify the application of the SDM in the proposal for classification of soil properties, we use estimates of geostatistical parameters presented in the studies by Oldoni and Bassoi (2016) and Guedes et al (2020). The authors used the SPD (or NE) and SDI indexes.…”

Section: Methodsmentioning

confidence: 99%

Geostatistical-based index for spatial variability in soil properties

Neto¹,

Seidel²,

Oliveira³

2020

Revista Brasileira De Ciência Do Solo

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“…Por ejemplo, si con base en experimentos anteriores o estudios piloto se tiene información de la variabilidad espacial de un sitio, se puede comparar las estimaciones de 'sill' y 'range' con los valores estimados Cuadro 2 y en función de la comparación, adecuar el número de repeticiones para ese sitio en particular. La estimación del parámetro 'sill' es una estimación de la varianza residual de la variable regionalizada (Diggle y Ribeiro, 2010;Guedes et al, 2020); por tanto, si es menor se podría disminuir el número de repeticiones recomendadas en este trabajo.…”

Section: Discussionunclassified

Determinación de la potencia estadística de experimentos de rendimiento en maíz

Vargas-Rojas

García²

2022

Remexca

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El análisis prospectivo de la potencia estadística de una prueba de hipótesis debería ser una de las etapas más importantes de cualquier experimento; sin embargo, se omite con frecuencia. En particular, para Costa Rica, no se encontraron investigaciones relacionadas con este tema para experimentos de rendimiento en el cultivo de maíz. El objetivo de este trabajo fue determinar la potencia estadística de un diseño completamente aleatorizado para experimentos de rendimiento en el cultivo de maíz (Zea mays) mediante la simulación de ensayos de uniformidad. Para realizar los cálculos de potencia se estimaron los parámetros del proceso de correlación espacial de un ensayo de uniformidad establecido en Santa Cruz, Costa Rica en el año 2018. Dichas estimaciones fueron utilizadas para realizar 10 000 simulaciones de campos aleatorios de mayor tamaño, lo que permitió superponer diferente número de repeticiones y estimar la potencia estadística para detectar una diferencia de 10% con respecto a la media en un experimento con un diseño completamente aleatorizado a un nivel de significación de 5%. Se obtuvo la potencia 80% con ocho repeticiones y se concluye que, bajo las condiciones experimentales de este trabajo, en ensayos de rendimiento en el cultivo de maíz, para detectar una diferencia de medias 10% a un nivel de significación 5%, se deben usar ocho o más repeticiones.

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“…This may be caused by random factors such as industrial pollution. Guedes et al (2020) simulated the impact of different levels of Nugget Effect on Kriging valuation and noted that high Nugget Effect would negatively affect the efficiency of Kriging-based prediction. High Nugget Effect would negatively affect Kriging predictions and reduce the efficiency of Kriging-based spatial predictions.…”

Section: Variogram Fitting and Model Cross-validationmentioning

confidence: 99%

Spatial Distribution and Evaluation of Heavy Metal Pollution in Soil Based on Three-Dimensional Model and Kriging Interpolation

Liu¹,

Zhang²,

Ye³

et al. 2022

American Journal of Biochemistry and Biotechnology

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Soil heavy metal pollution is an important content for soil environmental governance. Based on the three-Dimensional (3D) geological modeling technology and ordinary Kriging method, this study described and analyzed the soil pollution status and spatial distribution characteristics of a site in Zhongshan, Guangxi. The results showed that combining 3D geological model and the Kriging interpolation method can well evaluate soil heavy metal pollution. The 3D geological model showed that the pollutant concentration in soil ranked As>Pb>Mo and the pollution degree decreased gradually from top to bottom. As such, the miscellaneous fill soil layer was the layer that needs to be repaired. In addition, the single-factor pollution level and Nemerow comprehensive index method were used to evaluate the pollution risk of the site. The evaluation results showed that the soil quality was seriously threatened by As and the heavy pollution (Pi>3) accounted for 61.10%. Furthermore, attention should be paid to the potential risk of Pb in the miscellaneous fill layer. This study can provide a 3D visualization method for comprehensive analysis of soil heavy metal pollution and a scientific reference for the formulation and implementation of subsequent site remediation strategies.

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Nugget Effect Influence on Spatial Variability of Agricultural Data

Cited by 15 publications

References 27 publications

Geostatistical-based index for spatial variability in soil properties

Geostatistical-based index for spatial variability in soil properties

Determinación de la potencia estadística de experimentos de rendimiento en maíz

Spatial Distribution and Evaluation of Heavy Metal Pollution in Soil Based on Three-Dimensional Model and Kriging Interpolation

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