Surface runoff and soil erosion in a natural regeneration area of the Brazilian Cerrado

Falcão, Karina dos Santos; Panachuki, Elói; Monteiro, Felipe das Neves; Menezes, Roniedison da Silva; Rodrigues, Dulce Buchala Bicca; Sone, Jullian Souza; Oliveira, Paulo Tarso Sanches de

doi:10.1016/j.iswcr.2020.04.004

Cited by 36 publications

(23 citation statements)

References 27 publications

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“…On the other hand, overgrazing leads to soil deterioration due to animal trampling, compacting the soil surface and causing soil sealing and crusting (e.g., higher BD in the topsoil layer and lower BD in subsequent layers for the treatments with livestock production, CL-L and PP). This leads to a decrease in water infiltration, as in other studies (Panachuki et al, 2006;Falcão et al, 2020) that, in turn, also causes an increase in soil erosion. In contrast, we noted good levels of stable soil aggregates in PP (MWD and GMD), contributing to soil erosion control.…”

Section: Long-term Effects On Soil Physical Properties and Soil Erosionsupporting

confidence: 63%

Soil physical properties and interrill erosion in agricultural production systems after 20 years of cultivation

Pavei¹,

Panachuki²,

Salton³

et al. 2021

Revista Brasileira De Ciência Do Solo

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Section: Long-term Effects On Soil Physical Properties and Soil Erosionsupporting

confidence: 63%

Soil physical properties and interrill erosion in agricultural production systems after 20 years of cultivation

Pavei¹,

Panachuki²,

Salton³

et al. 2021

Revista Brasileira De Ciência Do Solo

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“…This demonstrated that the proposed spatial-based model (SSLM) is suitable and accurate for predicting, quantifying, and mapping wind erosion under this study. Overall, many studies have concluded that different land use/land cover have varied impacts on soil erosion [68][69][70]. There are four land use/land cover types in the study area, and in the following order of total area: barren land (82.7%) > agricultural land (13.4%) > sand dunes (3.1%) > built-up land (0.7%), considering the Dakhla Oasis in 2018 [56].…”

Section: Based On the Sslmmentioning

confidence: 99%

“…There are four land use/land cover types in the study area, and in the following order of total area: barren land (82.7%) > agricultural land (13.4%) > sand dunes (3.1%) > built-up land (0.7%), considering the Dakhla Oasis in 2018 [56]. The soil types in the study area with high clay concentrations in the top layers may increase the water runoff and, consequently, the erodibility [70,71]. Due to the very low annual precipitation in the study area, the effective soil erosion factors are the K-Factor, C-Factor, and P-Factor, which are associated with soil characteristics and management practices [70].…”

Section: Based On the Sslmmentioning

confidence: 99%

“…The soil types in the study area with high clay concentrations in the top layers may increase the water runoff and, consequently, the erodibility [70,71]. Due to the very low annual precipitation in the study area, the effective soil erosion factors are the K-Factor, C-Factor, and P-Factor, which are associated with soil characteristics and management practices [70]. Furthermore, very low soil erosion risk intensity was most prominent, indicating that there is currently no erosion hazard due to water erosion, while wind erosion ranged from very slight to high across the study area.…”

Section: Based On the Sslmmentioning

confidence: 99%

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Modeling and Assessing Potential Soil Erosion Hazards Using USLE and Wind Erosion Models in Integration with GIS Techniques: Dakhla Oasis, Egypt

et al. 2021

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Soil erosion modeling is becoming more significant in the development and implementation of soil management and conservation policies. For a better understanding of the geographical distribution of soil erosion, spatial-based models of soil erosion are required. The current study proposed a spatial-based model that integrated geographic information systems (GIS) techniques with both the universal soil loss equation (USLE) model and the Index of Land Susceptibility to Wind Erosion (ILSWE). The proposed Spatial Soil Loss Model (SSLM) was designed to generate the potential soil erosion maps based on water erosion and wind erosion by integrating factors of the USLE and ILSWE models into the GIS environment. Hence, the main objective of this study is to predict, quantify, and assess the soil erosion hazards using the SSLM in the Dakhla Oasis as a case study. The water soil loss values were computed by overlaying the values of five factors: the rainfall factor (R-Factor), soil erodibility (K-Factor), topography (LS-Factor), crop types (C-Factor), and conservation practice (P-Factor). The severity of wind-driven soil loss was calculated by overlaying the values of five factors: climatic erosivity (CE-Factor), soil erodibility (E-Factor), soil crust (SC-Factor), vegetation cover (VC-Factor), and surface roughness (SR-Factor). The proposed model was statistically validated by comparing its outputs to the results of USLE and ILSWE models. Soil loss values based on USLE and SSLM varied from 0.26 to 3.51 t ha−1 yr−1 with an average of 1.30 t ha−1 yr−1 and from 0.26 to 3.09 t ha−1 yr−1 with a mean of 1.33 t ha−1 yr−1, respectively. As a result, and according to the assessment of both the USLE and the SSLM, one soil erosion class, the very low class (<6.7 t ha−1 yr−1), has been reported to be the prevalent erosion class in the study area. These findings indicate that the Dakhla Oasis is slightly eroded and more tolerable against water erosion factors under current management conditions. Furthermore, the study area was classified into four classes of wind erosion severity: very slight, slight, moderate, and high, representing 1.0%, 25.2%, 41.5%, and 32.3% of the total study area, respectively, based on the ILSWE model and 0.9%, 25.4%, 43.9%, and 29.9%, respectively, according to the SSLM. Consequently, the Dakhla Oasis is qualified as a promising area for sustainable agriculture when appropriate management is applied. The USLE and ILSWE model rates had a strong positive correlation (r = 0.97 and 0.98, respectively), with the SSLM rates, as well as a strong relationship based on the average linear regression (R2 = 0.94 and 0.97, respectively). The present study is an attempt to adopt a spatial-based model to compute and map the potential soil erosion. It also pointed out that designing soil erosion spatial models using available data sources and the integration of USLE and ILSWE with GIS techniques is a viable option for calculating soil loss rates. Therefore, the proposed soil erosion spatial model is fit for calculating and assessing soil loss rates under this study and is valid for use in other studies under arid regions with the same conditions.

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“…The results show the runoff from the largest to the smallest sequentially were grasslands (205.36 mm), cropland (196.12 mm), Gliricidia land (71.89 mm), and teak forest land (62.81 mm). Natural regeneration processes tend to improve soil ecosystem services, improve infiltration, and reduce surface runoff and soil erosion [35]. Then, surface runoff and erosion had led to the observed distribution of some soil properties along the steep slopes [36].…”

Section: Runoff Valuesmentioning

confidence: 99%

The Comparison of Erosion Rates in Grassland, Teak Forest, Crops Land, and Gliricidia Forest Plantation in Wanagama Edu Forest

Kusumandari¹,

Kusmandana²,

Bahari³

2021

International Journal on Advanced Science, Engineering and Information Technology

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Wanagama forest is an integral part of a nearby village. This forest is left unprotected, so local people can easily enter the forest area to take non-timber forest products, including cropland and grass. The increasing demand for grass for feeding the cattle in Wanagama resulted in a larger area of grassland. Therefore, it is highly important to review and measure the erosion rate of the grassland compared to other land uses. This research aims to measure the erosion rates of four land uses and determine the correlation between rainfall and erosion at those four land uses: grassland, teak forest, crops land, and Gliricidia forest plantation. In measuring the erosion rates, this research uses four plots of 22 x 4 meters established at those four land uses. Each plot has two drums at the outlet. We took the erosion samples at every single rainfall event. To investigate the rainfall, there were two ombrometers installed in the field. The results show that the grassland has the lowest erosion rate (0.45 tons/ha) compared to a teak forest (0.5 tons/ha), crops land (0.84 tons/ha), and Gliricidia plantation forest (1.66 tons/ha). The rainfall has a positive coefficient correlation to erosion; the lowest was in grassland (r = 0.723), followed by teak forest (r = 0.828), Gliricidia (r = 0.830), and Crops land (0.873). The higher the rainfall depth will result in the higher erosion rate at the four land uses.

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Surface runoff and soil erosion in a natural regeneration area of the Brazilian Cerrado

Cited by 36 publications

References 27 publications

Soil physical properties and interrill erosion in agricultural production systems after 20 years of cultivation

Soil physical properties and interrill erosion in agricultural production systems after 20 years of cultivation

Modeling and Assessing Potential Soil Erosion Hazards Using USLE and Wind Erosion Models in Integration with GIS Techniques: Dakhla Oasis, Egypt

The Comparison of Erosion Rates in Grassland, Teak Forest, Crops Land, and Gliricidia Forest Plantation in Wanagama Edu Forest

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