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Diverse projects are being carried out worldwide focusing on development of more accurate soil maps and one of the most valuable sources of data are the existing soil maps. This work aimed to (i) compare two data mining tools, KnowledgeMiner and decision trees, to retrieve legacy soil data from a detailed soil map, (ii) to create and validate the predicted soil maps in the field with the objective to identify the best method for modeling and refining soil maps, (iii) extrapolating soils information to the surrounding similar areas and (iv) to assess the accuracy of this soil map. The study was carried out in Minas Gerais state, Southeastern Brazil. From a detailed soil map, information of 12 terrain attributes was retrieved from the entire polygon of each mapping unit of the map (MUP) and from a circular buffer around the sampled points (CBP). KnowledgeMiner and decision trees were employed to retrieve information per soil class and soil maps were created per method. A field validation of 20 samples was chosen by a cost-constrained conditioned Latin hypercube sampling scheme and the accuracy of all maps was assessed using a global index, Kappa index, and errors of omission and commission. The KnowledgeMiner MUP map had a greater accuracy than the other methods, being even more accurate than the original map, accounting for 80% of global index and a Kappa index of 0.6524. The information extracted by KnowledgeMiner provided rules for mapping the watershed surroundings with 70.97% of global index and a kappa index of 0.5586. Legacy soil data extracted by KnowledgeMiner from a detailed soil map and used to model soil class distribution outperformed decision trees, promoted improvements on the existing soil map, and allows for the creation of a low cost soil map for the surroundings of the study area. [ABSTRACT FROM AUTHOR]

In soil surveys, the number of collected samples is commonly reduced by factors that hamper field activities, such as rugged terrain and lack of roads. Conditioned Latin hypercube (CLH) sampling has been used to properly capture soil variability across the landscape, whereas cost-constrained conditioned Latin hypercube (CCLH) sampling limits the sampling to areas of easy access. The objectives of this work were to: (i) compare the efficiency of CLH and CCLH sampling systems to create soil maps, considering the number of soil classes covered per system, (ii) compare both systems to map soil A horizon thickness, and (iii) generate a detailed soil map of the study area to assist in decision makings. The study was performed in Minas Gerais, Brazil. A digital elevation model (DEM) and its terrain derivatives were the basis for CLH and CCLH to determine the sampling points. The CCLH system also required a cost map that represented the difficulty of reaching every place in the area. At the sampling locations, soil information was observed, allowing for the creation of those maps that were further validated in the field. Kappa index, global index (Gl), RMSE, 1:1 ratio graphic, and R² were the comparison parameters. Conditioned Latin hypercube presented higher accuracy than CCLH to represent both soil classes and soil attributes, although the samples were spread out in the area. Cost-constrained conditioned Latin hypercube was less representative than CLH, but it may contribute to soil sampling in areas of difficult access, common in developing countries, such as Brazil. [ABSTRACT FROM AUTHOR]

The variability of mineralogical and chemical characteristics of soils reflects their parent material, the different weathering degrees and soil formation processes. Through the association of portable X-ray fluorescence (pXRF) spectrometry and X-ray diffraction (XRD), this work aimed to evaluate the concentration of major and minor elements and their relationship with the mineralogy of the sand, silt and clay fractions in a catena of soils derived from quartzite with different weathering degrees in Southern Minas Gerais State, Brazil. Five soil profiles were described and sampled: Lithic Ustorthent, Typic Ustorthent, Typic Dystrustept, Xanthic Haplustox, and Typic Ustifolist. Sand, silt and clay fractions were separated and analyzed with pXRF and XRD for characterization of elemental contents and mineralogy, respectively. For all soils, Si content was higher in the sand fraction, corresponding to quartz, confirmed by XRD, a predominant mineral in quartzite and in the sand fraction of tropical soils. Al content was always higher in the clay fraction, reflecting the presence of kaolinite, gibbsite and mica. Quartz, mica and kaolinite were the dominant minerals in the silt fraction. Si in the sand fraction showed a strong and negative correlation with K and Ca, while Al in the sand fraction showed high and positive correlations with Ca and Fe. Fe, K, Ca and Al were released from the most weathered soils, while Si remained almost constant due to its large presence in quartz. The pXRF in tandem with XRD provided in-depth clarifications on the chemical and mineralogical differentiation of the sand, silt and clay fractions of soils along weathering. • A sequence of quartzite-derived soils was investigated via pXRF and DRX. • Elemental contents and mineralogy of the textural fractions were correlated. • Detailed chemical and mineralogical differences along weathering were detected. • Investigations of processes driving soil variation were improved with pXRF and DRX. [ABSTRACT FROM AUTHOR]

Soil legacy data are important sources of soil information, especially when dealing with limited resources. In countries with high geographical diversity and few financial resources, such as Brazil, they represent an economical alternative to obtaining soil spatial information in higher resolution. By retrieving the soil scientist's knowledge, it can be used as guidance for knowledge-based digital soil mapping approaches. In this sense, this work aimed to evaluate Rule-Based Reasoning and Case-Based Reasoning knowledge-based approaches to predict soil types up to the third categorical level (U.S Soil Taxonomy) in a non-sampled area, by retrieving and then extrapolating the information of a detailed soil legacy map, from a reference area. The study was carried out in Minas Gerais state, Southeastern Brazil. The methodology includes three main steps: i) knowledge acquisition; ii) soil inference; and iii) accuracy and uncertainty assessment. For the validation, 23 independent samples were chosen by means of the Regional Random method, and the accuracy was assessed by Kappa index, Overall Accuracy, Users', and Producers' Accuracy. The uncertainty was evaluated through entropy and exaggeration. A total of 24 inference models were obtained with the Case-Based Reasoning approach, in which the best model had an overall accuracy of 61% and a Kappa index of 0.52. The Rule-based reasoning approach performed better, with an overall accuracy of 82% and 0.75 for Kappa index. These approaches generated a higher accuracy soil map for an unmapped area that was 15 times larger than the reference area and at lower cost. • The terrain surface texture was useful to discriminate Oxisols from Inceptisols. • Hapludoxes and Acrudoxes are strongly related within similar morphometric features. • The greater uncertainty was located between morphological transitions. • To reduce the polygon area increased the uncertainty of predictions for CBR. • The extrapolation of soil type information was successfully achieved by using the RBR. [ABSTRACT FROM AUTHOR]

Naturally dense soils have been commonly managed for coffee plantations in Vertentes Fields physiographic zone of Minas Gerais state, Brazil. Such dense soils in association with the irregular rainfall in recent years have increased risks for crop productivity. Therefore, management practices aiming at improving subsoil root exploration have been recommended for enhancing absorption of water and nutrient uptake, thus mitigating the edaphic drought process and ensuring the development, productivity, and longevity of the coffee crop. A field experiment was carried out in an area of dense Inceptisols through the planting of the coffee crop to investigate the effect of deep furrowing with additional liming on soil quality indexes and as a function of normalized pore distribution by volume [S*(h)]. The experimental design was in randomized blocks with 5 treatments: 40 (conventional soil preparation with furrows at 0.40 m of depth); 60 (soil preparation with furrow to 0.60 m of depth with conventional (60S) and complementary liming application (60C)); 80 (soil preparation with furrow to 0.80 m of depth with conventional (80S) and complementary liming application (80C)). These treatments were compared to an area under native Cerrado vegetation. Undisturbed soil samples were collected at different depths in each treatment. On these samples, water content was measured at different matrix potentials, and physical quality indicators along with the S*(h) were calculated. The results indicated that soil management practices, compared to the native condition, promoted a better distribution of water and air (P AWC , RFC, P MAC and S index) as well S*(h) up to 0.40 m. Below this depth, none of the management practices were able to promote significant improvements (p < 0.05) of these variables in relation to the soil native condition. The 80C treatment had the best air and water availability, with increased of pore size class having functions of drainage and aeration. Moreover, this treatment stood out in comparison to the conventional management (40) for all measured variables, maintaining the adequate conditions of the native area in depth. For the managed areas, the depth of 0.60 m gave the best improved growth and vegetative vigor of the coffee plants. Unlabelled Image • Soil management increases aeration and water availability in dense and shallow Inceptisols. • Optimum limits for physical quality indicators should be proposed considering soil classes. • Deep furrow in shallow and dense soils can mitigate the effects of water deficit for coffee trees. • Physical quality indicators correlate with Normalized Difference Vegetation Index and plant height of coffee trees. [ABSTRACT FROM AUTHOR]