%0 Journal Article
%4 sid.inpe.br/mtc-m21b/2017/11.30.12.20
%2 sid.inpe.br/mtc-m21b/2017/11.30.12.20.41
%@doi 10.4236/ojss.2017.77011
%@issn 2162-5360
%T Spatial modeling of soil lime requirements with uncertainty assessment using geostatistical sequential indicator simulation
%D 2017
%9 journal article
%A Ortiz, Jussara de Oliveira,
%A Felgueiras, Carlos Alberto,
%A Camargo, Eduardo Celso Gerbi,
%A Rennó, Camilo Daleles,
%A Ortiz, Manoel Jimenez,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Geopixel Soluções em Geotecnologias e TI
%@electronicmailaddress jussara.ortiz@inpe.br
%@electronicmailaddress carlos.felgueiras@inpe.br
%@electronicmailaddress eduardo.camargo@inpe.br
%@electronicmailaddress camilo.renno@inpe.br
%B Open Journal of Soil Science
%V 7
%P 133-148
%K Spatial Modeling of Soil Attributes, Indicator Geostatistics, Joint Simulation, Principal Component Analyses, Spatial Uncertainty Analyses.
%X This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Paraná state, Brazil.
%@language en
%3 ortiz_spatial.pdf