ARGILA DIGITAL PDF

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Argila Digital Pdf

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Pages Liked by This Page. E-mail: lenask gmail. The study was carried out in a Cerrado area with lithological diversity, in the state of Mato Grosso do Sul, Brazil, using morphometric attributes, TM Landsat 5 sensor data, and lithology as predictive covariates.

The surface layer data 0.

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The performed analysis by Random Forest models showed that channel base level, elevation, and lithology were the most important ones to explain the variability. The spatial resolution of the predictive covariates has little effect on attribute predictions, and the Random Forest approach has potential use for estimating soil properties. No entanto, Smith et al.

Vaze et al.

Neste sentido, Bartholomeus et al. Isto corrobora a afirmativa de Smith et al. Akpa et al.

No entanto, os resultados do presente estudo foram inferiores aos de Wiesmeier et al. Conforme destacado por Grimm et al.

Wiesmeier et al. Guo et al. O teor de areia variou de ,96 a ,69 g kg-1, no conjunto 1, e de ,57 a ,19 g kg-1, no conjunto 2.

Digital mapping of soil particle-size fractions for Nigeria. Soil Science Society of America Journal, v.

Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging spectroscopy. DOI: Hyper-scale digital soil mapping and soil formation analysis. Geoderma, v. Digital mapping of soil carbon in a viticultural region of Southern Brazil.

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Random forests. Machine Learning, v. Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions. A regional-scale assessment of digital mapping of soil attributes in a tropical hillslope environment. Journal of Irrigation and Drainage Engineering, v.

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Influence of topography on soil properties.Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach. Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem.

Professor Mezkyta Education. Classification and regression by randomForest. Vaze et al.

View abstract. R News, v.

The study was carried out in a Cerrado area with lithological diversity, in the state of Mato Grosso do Sul, Brazil, using morphometric attributes, TM Landsat 5 sensor data, and lithology as predictive covariates. Page Transparency See More.

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