@Article{OrtizFelCamRenOrt:2017:SpMoSo,
               author = "Ortiz, Jussara de Oliveira and Felgueiras, Carlos Alberto and 
                         Camargo, Eduardo Celso Gerbi and Renn{\'o}, Camilo Daleles and 
                         Ortiz, Manoel Jimenez",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Geopixel Solu{\c{c}}{\~o}es em 
                         Geotecnologias e TI}",
                title = "Spatial modeling of soil lime requirements with uncertainty 
                         assessment using geostatistical sequential indicator simulation",
              journal = "Open Journal of Soil Science",
                 year = "2017",
               volume = "7",
                pages = "133--148",
             keywords = "Spatial Modeling of Soil Attributes, Indicator Geostatistics, 
                         Joint Simulation, Principal Component Analyses, Spatial 
                         Uncertainty Analyses.",
             abstract = "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, Paran{\'a} state, Brazil.",
                  doi = "10.4236/ojss.2017.77011",
                  url = "http://dx.doi.org/10.4236/ojss.2017.77011",
                 issn = "2162-5360",
             language = "en",
           targetfile = "ortiz_spatial.pdf",
        urlaccessdate = "27 abr. 2024"
}