Open Access

Several soil conservation practices are used to reduce water erosion and ensure sustainable agriculture. An effective crop management practice is intercropping, in which two or more crops with different architectures and vegetative cycles are grown simultaneously in the same area. We hypothesized that intercropping of corn and jack-bean increases soil cover and reduce soil erosion by water in comparison to monocropping. The objective of this study was to evaluate the effects of different crop systems on soil cover and on soil erosion by water. Soil and water losses from a Typic Hapludox were measured under the following systems: corn cultivation (CO), jack-bean cultivation (JB), intercropping of corn and jack-bean (IC), and bare soil (BS), as a reference for maximum erosion rates. For each crop system, erosion plots with dimensions of 12 × 4 m were set up in the field on a 0.12 m m−1 slope gradient. The experiment was carried out under natural rainfall, over three crop seasons (November to March) from 2011 to 2014. The soil cover index of the systems was monitored during crop growth, and rainfall erosivity for the crop seasons was calculated according to the EI30 index to interpret soil and water losses. A set of linear mixed models was fitted to relate soil losses to rainfall erosivity, crop systems, and soil cover. The average rainfall erosivity in the study area was 6,132 MJ mm ha−1 h−1 per crop season. The results indicate that water losses are directly related to erosivity and are less influenced by soil cover and cultivation systems than the soil losses. A linear maximum value of the soil cover index was achieved 70 days after sowing. Intercropping exhibited greater soil cover than single crops. Total soil losses from the three seasons display the trend: BS > CO > JB > IC. The best fitted model of the linear mixed models indicates that soil loss responses are strongly correlated with rainfall erosivity and soil cover, which nullified the influence of the crop systems in the model.

In the last decades, soils and their agricultural management have received great scientific and political attention due to their potential to act as a sink of atmospheric carbon dioxide (CO2). Agricultural management has strong potential to accelerate soil redistribution, and, therefore, it is questioned if soil redistribution processes affect this potential CO2 sink function. Most studies analysing the effect of soil redistribution upon soil organic carbon (SOC) dynamics focus on water erosion and analyse only relatively small catchments and relatively short time spans of several years to decades. The aim of this study is to widen this perspective by including tillage erosion as another important driver of soil redistribution and by performing a model-based analysis in a 200 km2 sized arable region of northeastern Germany for the period since the conversion from forest to arable land (approx. 1000 years ago). The spatially explicit soil redistribution and carbon (C) turnover model SPEROS-C was applied to simulate lateral soil and SOC redistribution and SOC turnover. The model parameterisation uncertainty was estimated by simulating different realisations of the development of agricultural management over the past millennium. The results indicate that, in young moraine areas, which are relatively dry but have been intensively used for agriculture for centuries, SOC patterns and dynamics are substantially affected by tillage-induced soil redistribution processes. To understand the landscape-scale effect of these redistribution processes on SOC dynamics, it is essential to account for long-term changes following land conversion as typical soil-erosioninduced processes, e.g. dynamic replacement, only take place after former forest soils reach a new equilibrium following conversion. Overall, it was estimated that, after 1000 years of arable land use, SOC redistribution by tillage and water results in a current-day landscape-scale C sink of up to 0.66 ‰ yr−1 of the current SOC stocks.

Nas regiões tropicais, o desgaste provocado no solo por ação das águas da chuva, ou seja, a erosão hídrica é a mais importante forma de degradação do solo. Visto que os plantios florestais de eucalipto estão inseridos em ecossistemas sensíveis às perturbações antrópicas em razão de ocorrência de plantações em solos com baixos teores de argila, com baixa fertilidade natural e grande parte das plantações estabelecidas em antigas áreas agrícolas e de pastagens degradadas, surge a necessidade do entendimento dos processos que regem a erosão hídrica e suas relações com as perdas de solo e água nos sistemas florestais. Objetivaram-se com este trabalho calcular os valores de erosividade da chuva (fator R - EI30), estimar a tolerância de perda de solo (T) para as classes representativas nas áreas de estudo, avaliar as perdas de solo e água por erosão hídrica e verificar a influência, por meio de análise de componentes principais (ACP), de atributos físicos e matéria orgânica do solo sobre a erosão hídrica em florestas de eucalipto no estádio de pós-plantio. Os tratamentos constituíram de diferentes sistemas de manejo dos resíduos e da disposição de plantio (nível e desnível), em dois biomas distintos, Cerrado e Floresta, e solo descoberto. Os solos foram classificados como Latossolo Vermelho distrófico típico textura média-alta fase floresta (LVd1) e Latossolo Vermelho distrófico típico textura média-baixa fase cerrado (LVd2). O estudo foi realizado em áreas experimentais de plantio de eucalipto localizadas no município de Três Lagoas, na bacia do Rio Paraná, no leste do Mato Grosso do Sul. O índice de erosividade anual obtido foi de 6.792,7 MJ mm ha-1 h-1 ano-1. Os valores de T variaram de 9,0 a 11,0 Mg ha-1 ano-1, para o LVd2 e LVd1, respectivamente. As perdas de solo apresentaram valores em torno de 0 a 0,505 Mg ha-1 no LVd1 e de 0 a 0,853 Mg ha-1, no LVd2. A ACP evidenciou-se eficiente na discriminação dos sistemas de manejo em razão da interação entre os atributos físicos e matéria orgânica do solo e suas relações com a erosão hídrica, possibilitando visualizar de forma clara a influência do manejo sobre esses atributos e a relação de ambos com as perdas de solo e água.

Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK). The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data.

The use of Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) techniques can contribute to increase the accessibility, accuracy, and resolution of Digital Elevation Models (DEMs) used for soil erosion monitoring. This study aimed to evaluate the use of four DEMs obtained over a year to monitor erosion processes in an erosion-degraded area, with occurrence of rill and gully erosions, and its correlation with accumulated rainfall during the studied period. The DEMs of Geomorphic Change Detection (GCD) of horizontal and vertical resolutions of 0.10 and 0.06 m were obtained. It was possible to detect events of erosion and deposition volumes of the order of 2 m3, with a volumetric error of ∼50 %, in rills and gullies in the initial stage denominated R and GS-I, respectively. Events of the order of 100 m3, with a volumetric error around 14 % were found for advanced gullies, a segment denominated GS-II. In the three studied erosion situations, the deposition volume increased with the accumulated rainfall. The segments R and GS-I presented an inverse relationship between erosion volume and accumulated rainfall during the studied period. This behaviour can be explained by the dynamics of the deposition and erosion volumes during the erosion process. In the GS-II segment, erosion and deposition volumes were proportional and a direct relation with the cumulative rainfall over the studied period and a low percentage of volumetric error were found.

Hydro-sedimentological models might be useful tools for investigating the effectiveness of soil and water conservation practices. However, evaluating the usefulness of such models requires that predictions are tested against observational data and that uncertainty from model parameterization is addressed. Here we aimed to evaluate the capacity of the SWAT model to simulate monthly streamflow and sediment load in the Posses creek catchment (12 km2), Southeast Brazil. The SUFI-2 algorithm from SWAT-CUP was applied for calibration, testing, uncertainty, and sensitivity analysis. The model was calibrated and initially tested using discharge and sediment load data, which were measured at the catchment outlet. Additionally, we used soil loss measurements from erosion plots within the catchment as independent data for model evaluation. Average monthly streamflow simulations obtained satisfactory results, with Nash-Sutcliffe coefficient (NSE) values of 0.75 and 0.51 for the calibration and testing periods, respectively. Sediment load simulations also displayed satisfactory results for calibration (NSE = 0.65) and testing (NSE = 0.52). However, the comparison with independent plot data revealed that SWAT severely overestimated hillslope erosion rates and compensated it with high sediment channel deposition. Moreover, the model was not sensitive to the parameters used for calculating hillslope sediment yields. Therefore, it should be used with caution for evaluating the interactions between land use, soil erosion, and sediment delivery. We found that the commonly used outlet-based approach for model calibration and testing can lead to internal misrepresentations, and models can reproduce the right answer for the wrong reasons.