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The pressure for food production has expanded agriculture frontiers worldwide, posing a threat to water resources. For instance, placing crop systems over hydromorphic soils (HS), have a direct impact on groundwater and influence the recharge of riverine ecosystems. Environmental regulations improved over the past decades, but it is difficult to detect and protect these soils. To overcome this issue, we applied a temporal remote sensing strategy to generate a synthetic soil image (SYSI) associated with random forest (RF) to map HS in an 735,953.8 km2 area in Brazil. HS presented different spectral patterns from other soils, allowing the detection by satellite sensors. Slope and SYSI contributed the most for the prediction model using RF with cross validation (accuracy of 0.92). The assessments showed that 14.5% of the study area represented HS, mostly located inside agricultural areas. Soybean and pasture areas had up to 14.9% while sugar cane had just 3%. Here we present an advanced remote sensing technique that may improve the identification of HS under agriculture and assist public policies for their conservation.

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Cocoa is a staple crop for small farmers, has been promoted as an alternative crop to Erythroxylum coca. Some soils in the Colombian cocoa zone seem to accumulate high levels of Cd in the beans. To comprehend the relationship between different Cd forms and their phytoavailability in the beans, cocoa-producing farms were sampled located in Yacopi municipality using sequential extraction methods to estimate the forms of Cd present in the soil, its phytoavailability and leaching potential. The exchangeable fractions, carbonate, manganese oxide, organic matter, amorphous iron oxide, crystalline iron oxide and residual fraction were determined. It was observed that the highest Cd concentrations were found in the organic matter, carbonate, Mn oxides and amorphous Fe phases, and the lowest in the exchangeable, crystalline Fe oxide and residual fractions. It was obtained through spatial regression that the Cd fractions that explain the Cd contents in beans correspond to the exchangeable ones, associated with manganese oxides, organic matter, crystalline iron oxides and residual fraction, which were fitted to a spatial error model. Modeling indicated that direct and positive impacts of the Cd content in the bean with the exchangeable and residual fractions, and negative impacts with the organic matter and manganese oxides fractions, which shows that these forms are retained in the colloidal phase and are not translocated to the bean, so that variations in the content of these fractions could contribute to a reduction in the Cd content in the bean.

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