Predicting soil erosion in conservation tillage cotton production systems using the revised universal soil loss equation (RUSLE)

Nyakatawa, Ermson Z.; Reddy, K. C.; Lemunyon, J. L.

doi:10.1016/s0167-1987(00)00178-1

Cited by 86 publications

(41 citation statements)

References 12 publications

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“…Therefore, C map of the watershed is mainly comprised of values of 0.03 and 0.63, respectively, for forest and agriculture. The higher C factor values indicate higher soil erosion potential as the C factor is a ratio of soil loss in a cover management sequence to soil loss from the bare soil unit plot (Nyakatawa et al, 2001).…”

Section: Results and Discussion Factors In Uslementioning

confidence: 99%

Assessment of soil erosion and sediment yield in Liao watershed, Jiangxi Province, China, Using USLE, GIS, and RS

Chen

Lim

et al. 2010

J. Earth Sci.

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Soil erosion by water is a serious problem all over the world. In China, about 1 790 000 km 2 of land suffers from water erosion, which accounts for 18.3% of China's total area. This study was conducted in the Liao (潦) watershed in Jiangxi (江西) Province to assess annual soil erosion and sediment yield using the Universal Soil Loss Equation (USLE). A geographic information system (GIS) was used to generate maps of the USLE factors, which include rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover (C), and conservation practice (P) factors. By integrating these factors in a GIS, a spatial distribution of soil erosion over the Liao watershed was obtained. The soil erosion was found to vary from nil for flat and well-covered areas to more than 500 t/ha/a in mountainous places with sparse vegetation. The average soil erosion is 18.2 t/ha/a with a standard deviation of 109.3 t/ha/a. The spatial distribution of erosion classes was estimated. About 39.5% of the watershed is under the tolerant erosion rate, and 60.5% of the study area experienced erosion to different extents. A spatially distributed sediment delivery ratio(SDR) module was developed to account for soil erosion and deposition. It was found that the SDR value at the outlet of the Liao watershed was 0.206, and the sediment yield was 1.32 million t/a, which was 20% higher than the measured sediment. The results can be used to identify the soil erosion hot spots and develop the best soil erosion management practices and help estimate the quantity of soil that was transported into the downstream Poyang (鄱阳) Lake.

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Section: Results and Discussion Factors In Uslementioning

confidence: 99%

Assessment of soil erosion and sediment yield in Liao watershed, Jiangxi Province, China, Using USLE, GIS, and RS

Chen

Lim

et al. 2010

J. Earth Sci.

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“…The extent to which stresses or forces is absorbed by solid-tosolid contact among the particles, cohesive forces related to chemical bonding of the clay minerals, and surface tension forces within the moisture films (Morgan, 1986). In recent years, many models such as RUSLE (Nyakatawa et al, 2001), EUROSEM (Morgan et al, 1993) and WEPP (Nearing et al, 1989) have been developed to simulate soil erosion. These models have been used to increase the understanding of hydrologic processes and also to act as tools for managing watershed and environmental problems.…”

Section: Introductionmentioning

confidence: 99%

Estimation of surface shear strength in Zagros region of Iran — A comparison of artificial neural networks and multiple-linear regression models

et al. 2009

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“…When productivity of soil is reduced, the outputs derived from renewable natural resource systems of the biosphere are affected [10]. Soil carried by erosion also moves pesticides, soil nutrients, and other harmful chemicals into water bodies as well as ground water resources [11,12]. Soil erosion is also a channel through which carbon is lost from the ecosystem [13], hence the implication for greenhouse gas emissions.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Simulating Soil Loss from an Agro-Ecosystem Using Multi-Agent Simulation: A Case Study for Semi-Arid Ghana

Badmos

Agodzo

Villamor

et al. 2015

Land

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Soil loss is not limited to change from forest or woodland to other land uses/covers. It may occur when there is agricultural land-use/cover modification or conversion. Soil loss may influence loss of carbon from the soil, hence implication on greenhouse gas emission. Changing land use could be considered actually or potentially successful in adapting to climate change, or may be considered maladaptation if it creates environmental degradation. In semi-arid northern Ghana, changing agricultural practices have been identified amongst other climate variability and climate change adaptation measures. Similarly, some of the policies aimed at improving farm household resilience toward climate change impact might necessitate land use change. The heterogeneity of farm household (agents) cannot be ignored when addressing land use/cover change issues, especially when livelihood is dependent on land. This paper therefore presents an approach for simulating soil loss from an agro-ecosystem using multi-agent simulation (MAS). We adapted a universal soil loss equation as a soil loss sub-model in the Vea-LUDAS model (a MAS model). Furthermore, for a 20-year simulation period, we presented the impact of agricultural land-use adaptation OPEN ACCESSLand 2015, 4 608 strategy (maize cultivation credit i.e., maize credit scenario) on soil loss and compared it with the baseline scenario i.e., business-as-usual. Adoption of maize as influenced by maize cultivation credit significantly influenced agricultural land-use change in the study area. Although there was no significant difference in the soil loss under the tested scenarios, the incorporation of human decision-making in a temporal manner allowed us to view patterns that cannot be seen in single step modeling. The study shows that opening up cropland on soil with a high erosion risk has implications for soil loss. Hence, effective measures should be put in place to prevent the opening up of lands that have high erosion risk.

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Predicting soil erosion in conservation tillage cotton production systems using the revised universal soil loss equation (RUSLE)

Cited by 86 publications

References 12 publications

Assessment of soil erosion and sediment yield in Liao watershed, Jiangxi Province, China, Using USLE, GIS, and RS

Assessment of soil erosion and sediment yield in Liao watershed, Jiangxi Province, China, Using USLE, GIS, and RS

Estimation of surface shear strength in Zagros region of Iran — A comparison of artificial neural networks and multiple-linear regression models

An Approach for Simulating Soil Loss from an Agro-Ecosystem Using Multi-Agent Simulation: A Case Study for Semi-Arid Ghana

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