Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment

Different uses of soil legacy data such as training dataset as well as the selection of soil environmental covariables could drive the accuracy of machine learning techniques. Thus, this study evaluated the ability of the Random Forest algorithm to predict soil classes from different training datasets and extrapolate such information to a similar area. The following training datasets were extracted from legacy data: a) point data composed of 53 soil samples; b) 30 m buffer around the soil samples, and soil map polygons excluding: c) 20 m; and d) 30 m from the boundaries of polygons. These four datasets were submitted to principal component analysis (PCA) to reduce multidimensionality. Each dataset derived a new one. Different combinations of predictor variables were tested. A total of 52 models were evaluated by means of error of models, prediction uncertainty and external validation for overall accuracy and Kappa index. The best result was obtained by reducing the number of predictors with the PCA along with information from the buffer around the points. Although Random Forest has been considered a robust spatial predictor model, it was clear it is sensitive to different strategies of selecting training dataset. Effort was necessary to find the best training dataset for achieving a suitable level of accuracy of spatial prediction. To identify a specific dataset seems to be better than using a great number of variables or a large volume of training data. The efforts made allowed for the accurate acquisition of a mapped area 15.5 times larger than the reference area

Spatial prediction of soil properties in two contrasting physiographic regions in Brazil

This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK. In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape

Use of Swine Manure in Agriculture in Southern Brazil: Fertility or Potential Contamination?

A major challenge in agricultural production systems is the maximization of resources used to promote the development of crops with a minimum of environmental impact. In this sense, the use of fertilizers of animal origin has great potential to promote the improvement of soil properties. In southern Brazil, swine manure (SM) is widely used in agricultural areas, allowing nutrient cycling within pig units and reducing costs for chemical fertilizers. Much of this manure is applied in liquid form (PS), but other strategies are often used, such as PS compost and swine bedding (DL). The use of these SMs improves the chemical, biological, and physical attributes of the soil, contributing to increased fertility and productivity of crops. However, prolonged use or applications with high doses of SM can result in the accumulation of metals and phosphorus in soils, representing a risk of contamination of soils and surface water resources, mainly due to losses by runoff, and subsurface, by leaching. Therefore, the adoption of criteria and the rational use of PMs need to be adopted to avoid dangerous effects on the environment, such as plant toxicity and water contamination. The potentialities and risks of SM applications are discussed in this chapter

Prevalence of oral mucosal lesions in a brazilian military police population

Background: Data obtained from oral health surveys are very important for identifying disease-susceptible groups and for developing dental care and prevention programs. So, the purpose of the current article was to investigate the prevalence of oral mucosa lesions (OMLs) in a population of Brazilian police. Material and Methods: Interviews and oral cavity examinations were performed on a sample of 395 police officers who were randomly selected by the calibrated researcher. The number of individuals was obtained by a sample calculation using the finite population correction. The diagnostic criteria were based on the WHO (1997) criteria and adapted to Brazilian surveys. Results: In total, 8.61% of the population presented some OML. Traumatic injuries and benign migratory glossitis (BMG) were the most prevalent lesions. Conclusions: The prevalence of potentially malignant disorders was lower than among the Brazilian population. The most prevalent lesion among the police officers was related to trauma. Patients dissatisfied with oral health had a higher risk of presenting OMLs

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledge-based digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soil-landscape relationship explicit

Carbon pool ratios as scientific support to field morphology in the differentiation of dark subsurface soil horizons

In soil surveys, it is usual to find profiles with an uncommon disposition of horizons. Dark horizons in depth might be either the consequence of erosion and redeposition of soil materials from upslope or an indication of the podzolization process, which forms a spodic horizon. Few laboratory analyses are known to characterize dark subsurface horizons which could allow for the differentiation of spodic from buried A horizons. Some researchers propose C-humic and C-fulvic acid fraction ratios and forms of carbon to analyze characteristics of these horizons. Therefore, this research aimed to characterize dark subsurface horizons found in soils under a Eucalyptus minimum tillage system in the state of Rio Grande do Sul, Brazil, and to relate soil organic carbon to landscape features in toposequences. The characterization was performed by using the following ratios: humic acid and fulvic acid fractions (Cha/Cfa); pyrophosphate extractable-C and organic carbon (Cp/OC); fulvic acid fraction and pyrophosphate extractable-C (Cfa/Cp), and fulvic acid fraction and organic carbon (Cfa/OC). Soil organic carbon was related to slope gradient and Geomorphons in a Geographic Information System (GIS). None of the horizons analyzed met the criteria required for spodic horizon classification, where Cha/Cfa < 0.50, Cfa/OC < 0.30, and the ratio Cp/OC ≥ 0.50 simultaneously with Cfa/Cp ≥ 0.50. A relationship was found between landscape features and soil organic carbon content. The methodology proved to be satisfactory for providing scientific support to field morphology classification of dark subsurface horizons, specifically in the case where they could be misinterpreted as spodic horizons