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Edaphic and Topographic Factors and their Relationship with Dendrometric Variation of Pinus Taeda L. in a High Altitude Subtropical Climate Rev. Bras. Ciênc. Solo
Horst,Taciara Zborowski; Dalmolin,Ricardo Simão Diniz; Caten,Alexandre ten; Moura-Bueno,Jean Michel; Cancian,Luciano Campos; Pedron,Fabrício de Araújo; Schenato,Ricardo Bergamo.
ABSTRACT The study of the relationships between the yield potential of forest stands and the conditions offered for plant development is fundamental for the adequate management of the forest when aiming at sustainable high yields. However, these relations are not clear, especially in commercial forests, on rugged terrain where relationships between the landscape, soil, and plants are more complex. Considering this, we tested the hypothesis that the morphological aspects of the soil conditioned by topography are the main limiting factors for tree development. Our objective was to evaluate the edaphic and topographic influence on the dendrometric variation of Pinus taeda L. of a forest stand in a subtropical climate at high altitude. For that, Spearman’s...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Forest soil; Soil-landscape relationship; Forest site; Soil use; Forest zoning.
Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100318
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Prediction of soil classes in a complex landscape in Southern Brazil PAB
Moura-Bueno,Jean Michel; Dalmolin,Ricardo Simão Diniz; Horst-Heinen,Taciara Zborowski; Cancian,Luciano Campos; Schenato,Ricardo Bergamo; Dotto,André Carnieletto; Flores,Carlos Alberto.
Abstract: The objective of this work was to evaluate the use of covariate selection by expert knowledge on the performance of soil class predictive models in a complex landscape, in order to identify the best predictive model for digital soil mapping in the Southern region of Brazil. A total of 164 points were sampled in the field using the conditioned Latin hypercube, considering the covariates elevation, slope, and aspect. From the digital elevation model, environmental covariates were extracted, composing three sets, made up of: 21 covariates, covariates after the exclusion of the multicollinear ones, and covariates chosen by expert knowledge. Prediction was performed with the following models: decision tree, random forest, multiple logistic regression,...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Digital soil mapping; Pedometry; Predictive covariates; Predictive models; Soil-landscape relationship.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2019000103808
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