Author(s)

Jussara de Oliveira Ortiz¹, Carlos Alberto Felgueiras¹, Eduardo Celso Gerbi Camargo¹, Camilo Daleles Rennó¹, Manoel Jimenez Ortiz²

¹INPE-Brazilian National Institute for Space Researches DPI-Divisão de Processamento de Imagens, São José dos Campos, Brazil.
²Geopixel Soluções em Geotecnologias e TI, São José dos Campos, Brazil.

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.

Spatial Modeling of Soil Attributes, Indicator Geostatistics, Joint Simulation, Principal Component Analyses, Spatial Uncertainty Analyses

de Oliveira Ortiz, J. , Felgueiras, C. , Camargo, E. , Rennó, C. and Ortiz, M. (2017) Spatial Modeling of Soil Lime Requirements with Uncertainty Assessment Using Geostatistical Sequential Indicator Simulation. Open Journal of Soil Science, 7, 133-148. doi: 10.4236/ojss.2017.77011.