RESUMO O intemperismo do material de origem é o processo inicial que garantirá ao solo maior ou menor fertilidade, tornando o estudo desse processo fundamental para entender a dinâmica do solo. Grande parte dos estudos de intemperismo é baseada em índices que calculam a taxa de dissolução dos feldspatos (CIA, CIW e PIA, por exemplo) muitas vezes não refletindo a complexidade do intemperismo ocorrendo no perfil de solo. Outras metodologias- como balanço geoquímico, diferença elementar (DE) e índice W- têm sido desenhadas para melhorar esses estudos. Avaliar se essas metodologias serão mais eficientes que os índices CIA, CIW e PIA para apontar as variações intempéricas em perfis recentes desenvolvidos de basalto é o objetivo desse trabalho. Foram utilizados dados de óxidos principais para calcular o balanço geoquímico, DE, CIA, CIW, PIA e W e analisar a intensidade do intemperismo em dois perfis originados de basalto no Estado de São Paulo. Foram fatorados também os índices Ki e Kr e a relação Fe o/Fed e analisada a mineralogia dos horizontes dos solos e da rocha de origem. O índice W e a DE mostraram maior variação intempérica que CIA, CIW e PIA. O balanço geoquímico, a mineralogia e as razões Ki e Kr, Feo /Fed foram eficientes em explicar as variações que ocorreram entre os horizontes. A junção da DE com o balanço geoquímico trouxe novas informações para a análise do intemperismo que a observação isolada de CIA, CIW e PIA.

ABSTRACT Weathering of the parent material is the initial process that plays an essential role in soil fertility. The study of this process has become crucial to better understand soil dynamics. Most studies on weathering are based on indexes that calculate the feldspar dissolution rate (for example, CIA, CIW, and PIA) which not always reflect the complexity in the soil profile. Other methodologies have been designed to improve these approaches, such as geochemical balance, difference of elemental contents (DE), and the W index. This work aim to assess the efficiency of these methods to identify variations in recent soil profiles developed from diabase as compared to the CIA, CIW, and PIA indexes. Data on the main oxides were used to calculate the geochemical balance, DE, CIA, CIW, PIA, and W indexes to analyze weathering intensity in two profiles originated from diabase in the São Paulo State, Brazil. The Ki and Kr indexes and Feo /Fed ratio were also factored and we analyzed the mineralogy of soil horizons and the source rock. The W index and DE presented greater weathering variation than the CIA, CIW, and PIA in both profiles. The geochemical balance, mineralogy, and the Ki, Kr and Feo /Fed ratios explained the variations between the horizons. The DE and geochemical balance together generated new information for the analysis of the weathering process than the isolated indexes.

ABSTRACT Applying the upcoming technologies in agriculture has been a major economic, environmental and social challenge for scientists and farmers. In order to overcome such challenge, this study evaluated the advantages and limitations of using geostatistics and machine learning for soil mapping in agricultural practices and soil surveys. The study occurred in Tocantins State, Brazil, and consisted into seven areas with a total extension of 17.24 km2, 222 meters regular gridded resulting in one-point sampling per 0.0493 km2 of five randomly sampled cores within a 1 m circle radius. It was collected 332 georeferenced soil samples at 0-20 cm depth using an auger and then, soil laboratory analyses performed. Afterward, liming rate maps were originated from the predicted soil attributes clay, cation exchange capacity and base saturation comparing four methods: ordinary kriging, random forest, cubist, support vector machine and the best model results of each soil attribute. Evaluating the methods, the Pearson’s index presented strong results for soil attributes predicted by random forest and ordinary kriging. Machine learning methods can be successfully applied for soil mapping in agricultural practices and soil surveys using less soil samples rather than geostatistical framework.