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Rainfall is the primary water source for hydrographic basins. Hence, the quantification and knowledge of its temporal and spatial distribution are indispensable in dimensioning hydraulic projects. This study aimed at assessing the fit of a series of rainfall data to different probability models, as well as estimating parameters of the intensity-duration-frequency (IDF) equation for rain stations of the Paraíba State, Brazil. The rainfall data of each station were obtained from the Brazilian Water Agency databanks. To estimate the maximum daily rainfall of each station and return period (5, 10, 15, 25, 50 and 100 years), the following probability distributions were used: Gumbel, Log-Normal II, Log-Normal III, Pearson III and Log-Pearson III. The estimation of rainfall in durations of 5-1,440 min was carried out by daily rainfall disaggregation. The adjustment of the IDF equation was performed via nonlinear multiple regression, using the nonlinear generalized reduced gradient interaction method. When compared to the data observed, the intense rainfall equations for most stations showed goodness of fit with coefficients of determination above 0.99, which supports the methodology applied in this study.

Soil surveys often contain multi-component map units comprising two or more soil classes, whose spatial distribution within the map unit is not represented. Digital Soil Mapping tools supported by information from soil surveys make it possible to predict where these classes are located. The aim of this study was to develop a methodology to increase the detail of conventional soil maps by means of spatial disaggregation of multi-component map units and to predict the spatial location of the derived soil classes. Three digital maps of terrain variables -slope, landforms, and topographic wetness indexwere correlated with the soil map and 72 georeferenced profiles from the Porto Alegre soil survey. Explicit rules that expressed regional soil-landscape relationships were formulated based on the resulting combinations. These rules were used to select typical areas of occurrence of each soil class and to train a decision tree model to predict the occurrence of individualized soil classes. Validation of the soil map predictions was conducted by comparison with available soil profiles. The soil map produced showed high agreement (80.5 % accuracy) with the soil classes observed in the soil profiles; Ultisols and Lithic Udorthents were predicted with greater accuracy. The soil variables selected in this study were suitable to represent the soil-landscape relationships, suggesting potential use in future studies. This approach developed a more detailed soil map relevant to current demands for soil information and has potential to be replicated in other areas in which data availability is similar.

In Brazil several digital soil class mapping studies were carried out from 2006 onwards to maximize the use of existing maps and information and to provide estimates for wider areas. However, there is no consensus on which methods have produced superior results in the predictive value of soil maps. This study conducts a systematic review of digital soil class mapping in Brazil and aims to analyze the factors which can improve the accuracy of digital soil class maps. Data from 334 digital soil class mapping studies were grouped and analyzed by Student's t-test, Wilcoxon-Mann-Whitney test and Kruskal-Wallis test. When conventional maps were used for validation, the studies showed average values of 63 % and when field samples were used, 56 % for Overall Accuracy. Studies compatible with the Planimetric Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD) averaged between 4 % and 15 % higher accuracy than those of the incompatible group. There seems to be no evidence that increasing the number of variables and samples results in more accurate soil map prediction, but studies using variables related to four soil-forming factors enhanced accuracy. From a density of 0.08 MU km-2 and upwards, it became more difficult for studies to obtain greater accuracy. Artificial neural network classifiers and Decision Tree models seem to be producing more accurate digital soil class maps.

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Resumo: A soja no Cerrado do sudoeste piauiense ocupa posição de destaque na produção agrícola. No entanto, os níveis de produtividades ainda estão abaixo da média nacional, fato associado, entre outros fatores, à escolha inadequada da cultivar. , e apenas a Monsoy 8527 RR ficou abaixo de 3.000 kg ha -1 . As maiores contribuições de incremento na produtividade foram observadas para cultivares P98 Y70 e Nidera 8843, com 51 e 62%, respectivamente. Palavras-chave: Competição de cultivares. Glycine max (L.) Merrill. Produtividade de grãos. Abstract:The soybean in the southwestern region of the Cerrado in Piaui occupies a prominent position in agricultural production. However, levels of productivity are still below the national average, a fact due, among other factors, to the inappropriate choice of cultivar. The aim of this study was to select the most productive soybean cultivars to be used in the region of the Cerrado in Piauí , and only Monsoy 8527 RR had a productivity of less than 3,000 kg ha -1 . The greatest contributions to an increase in productivity were seen in the P98 Y70 and Nidera 8843 cultivars, with 51% and 62% respectively.

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