Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Abstract

ABSTRACT:The 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.

Mapping soil properties in a poorly-accessible area

“…In recent years, there was a considerable advance in DSM due to new approaches, among them, powerful predictive algorithms (Beguin et al, 2017;Sindayihebura et al, 2017); models combining machine-learning and geostatistical tools (Poggio and Gimona, 2017a,b); expert knowledge-based methods (Menezes et al, 2014(Menezes et al, , 2018; and high-resolution soil maps (Nussbaum et al, 2017). However, the limiting factor is often the number of soil data used for model calibration (Samuel-Rosa et al, 2015;Somarathna et al, 2017).…”

Mapping land use capability in tropical conditions adapting criteria to different levels of agricultural management

“…This depends on detailed soil maps which can support decision-making that would avoid processes that lead to soil degradation, such as soil erosion (Kassai;Sisák, 2018). Pedological knowledge, however, is still limited in many tropical developing countries, such as Brazil (Mancini et al, 2019;Menezes et al, 2018;Silva et al, 2014). In the state of Minas Gerais, Brazil, the soil map (UFV, CETEC, UFLA, FEAM, 2010) was elaborated on a 1:650,000 scale, in which the spatial variability of the phenomenon cannot be represented in a detailed way.…”