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The Romanian Soil Survey System does not imply, up to the present date, the use of digital methods in representing field campaign results or for mapping soil parameters. The presented study tests several geostatistical methods to model some soil parameters (soil pH and topsoil humus content), mainly in order to observe the differences induced by the scale of the approach and to test existing data. In this respect, three differently dimensioned analysis scales were chosen, all parts of the same larger region, located in Iaşi County. On the chosen areas the main three categories of methods used in pedometrics were tested: methods of the kriging family (ordinary kriging, cokriging), regression methods applied both globally and locally (Geographically Weighted Regression) and the combined approach of regression-kriging respectively. In order to test the results were used crossvalidation and independent sample validation. The root mean square error (RMSE) was used as selection criteria for the choice of the optimum method. The study proves that among the various interpolation methods tested, the regression-kriging approach gives better results and that the local approach, using GWR, is superior to the global regression approach. Moreover, the pH proved to be more spatially predictable compared to the topsoil humus content.

Our study compares the performances of two statistical methods, namely multiple linear regression and classification and regression trees, for deriving spatial models of soil reaction in the surface horizon. The applications were carried out within a 186 km2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of 235 soil profiles, scattered in the eastern half of the basin. An independent sample of 237 expeditionary pH measurements was used to validate the results within the interpolation area, whereas an independent sample of 50 soil profiles was used to validate the results within the extrapolation area (the western half of the basin). The predictors included geomorphometrical parameters, derived from a 10x10 m digital elevation model, X and Y coordinates of soil profiles and the main soil types for the regression trees approach. The stepwise selection procedure indicated Y coordinate, digital elevation model, wetness index and surface ratio as the best predictors for soil reaction. The correlation between observed and predicted pH values for the training sample suggests a much higher quality of the regression trees spatial model. However, the validation using the two independent samples points out the instability of this model and recommends the regression model more reliable.

This study compares the performance of several statistical methods (multiple linear regression, analysis of covariance, geographically weighted regression, regression kriging, and ordinary kriging) for deriving spatial models of soil parameters. The applications were carried out within a 186-km 2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of approximately 180 soil profiles, scattered in the eastern half of the basin. Two independent samples, each of 50 soil profiles, were used for validation inside (interpolation) and outside (extrapolation) the main sampling area. The predictors included X and Y coordinates of soil profiles, geomorphometrical parameters (altitude, slope, aspect, wetness index, terrain curvature), climate parameters (mean annual temperatures, precipitation, global radiation), the normalized difference vegetation index, the main soil types, land use, and surface lithology. For only three soil variables the geostatistical approach proved to be useful: occurrence depth of calcium carbonates, pH, and base saturation. The best spatial models were achieved using analysis of covariance, geographically weighted regression, and ordinary kriging. The most relevant continuous predictor is the mean annual precipitation, whereas the most relevant qualitative factor is the soil type.

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Soil erosion is triggered by rainfall through the detachment of soil particles and their transport downslope, playing a key role in soil erosion models. Together with the vegetation cover, rainfall is a temporal dynamic factor, inducing corresponding time variations of erosion rates. Under current climate change, rainfall is also changing its characteristics and our study aimed to reveal whether these changes will significantly affect rainfall erosivity in Romania, and implicitly the soil erosion. To achieve this purpose, we developed a statistical non-parametric model for predicting rainfall erosivity on the basis on the modified Fournier index and applied it to future precipitation evolution scenarios. The precipitation data were extracted from the CHESLA database for the Romanian territory for two climate change contrasting scenarios (RCP 4.5 and 8.5). Average predictions from five selected climate models were used in order to minimize prediction uncertainty. The results show that rainfall erosivity is likely to increase, at least during the 2041–2060 period, especially in the south-western, western and eastern part of the country, which may cause a corresponding increase in soil erosion rates, with an average of 1–2 t ha−1 yr−1. During the 2061–2080 period, rainfall erosivity is likely to decrease in central and eastern Romania.

Agricultural land evaluation plays an important role in the pedologic and economic foundation of a sustainable agricultural practice and management. Usually, the agricultural land evaluation framework use soil, environment and land improvements data for computing an index of suitability for various agricultural uses. Because of the punctual availability of soil data, we investigated the use of pedometric and geostatistical techniques (multiple linear regression, logistic regression, kriging) for their spatialisation, to be used further, for computing the suitability index, according to Romanian Agricultural Land Evaluation Methodology. The pedometric techniques were applied to a soil legacy database (620 described soil profiles with analytical data covering 15 villages from Iasi County, made by Iaşi Office for Pedology and Agrochemistry. The results are promising, but the quality of results, depend on the quality and quantity of soil data.