Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds
Keywords: ANOVA test, spatial variability, fuzzy logic, typical values
AbstractThe estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme; 2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property.
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How to Cite
Menezes, M., Silva, S., Mello, C., Owens, P., & Curi, N. (2018). Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds. Scientia Agricola, 75(2), 144-153. https://doi.org/10.1590/1678-992x-2016-0097
Soils and Plant Nutrition
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