Sabiia Seb
PortuguêsEspañolEnglish
Embrapa
        Busca avançada

Botão Atualizar


Botão Atualizar

Ordenar por: 

RelevânciaAutorTítuloAnoImprime registros no formato resumido
Registros recuperados: 2
Primeira ... 1 ... Última
Imagem não selecionada

Imprime registro no formato completo
Diffuse reflectance spectroscopy for estimating soil organic carbon and make nitrogen recommendations Scientia Agricola
Rosin,Nicolas Augusto; Dalmolin,Ricardo Simão Diniz; Horst-Heinen,Taciara Zborowski; Moura-Bueno,Jean Michel; Silva-Sangoi,Daniely Vaz da; Silva,Leandro Souza da.
ABSTRACT: Diffuse reflectance spectroscopy (DRS) has the potential to predict soil organic carbon (SOC). However, it is still little used as a matter of routine in soil laboratories in Brazil. The objective of this study was to make evaluations as to whether SOC predicted by spectral techniques can replace measurement by routine chemical methods with no loss in quality and be applied in the recommendation of nitrogen fertilizer as well as identifying the best prediction strategies to use. A data set containing 2,471 samples from six soil spectral libraries (SSL) was used to develop spectroscopic models for SOC content prediction, including consideration of sample stratification and preprocessing techniques. The SOC was quantified through the...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Soil attributes prediction; Soil fertility; Proximal soil sensing; Chemometric; Green chemistry.
Ano: 2021 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162021000501402
Imagem não selecionada

Imprime registro no formato completo
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
Registros recuperados: 2
Primeira ... 1 ... Última
 

Empresa Brasileira de Pesquisa Agropecuária - Embrapa
Todos os direitos reservados, conforme Lei n° 9.610
Política de Privacidade
Área restrita

Embrapa
Parque Estação Biológica - PqEB s/n°
Brasília, DF - Brasil - CEP 70770-901
Fone: (61) 3448-4433 - Fax: (61) 3448-4890 / 3448-4891 SAC: https://www.embrapa.br/fale-conosco

Valid HTML 4.01 Transitional