Mapping soil erosion hotspots and assessing the potential impacts of land management practices in the highlands of Ethiopia

Land degradation is increasing in severity and extent in many parts of the world. Success in arresting land degradation entails an improved understanding of its causes, process, indicators and impacts. Various scientific methodologies have been employed to assess land degradation globally. However, the use of local community knowledge in elucidating the causes, process, indicators and effects of land degradation has seen little application by scientists and policy makers. Land degradation may be a physical process, but its underlying causes are firmly rooted in the socio-economic, political and cultural environment in which land users operate. Analyzing the root causes and effects of land degradation from local community knowledge, perception and adapting strategies perspective will provide information that is essential for designing and promoting sustainable land management practices. The main objective of this study was to analyze the perceptions of farmers’ on the impact of land degradation hazard on agricultural land productivity decline associated with soil erosion and fertility loss. The study used a multistage sampling procedure to select sample respondent households. The sample size of the study was 120 household heads and 226 farm plots managed by these farmers. The primary data of the study were collected by using semi-structured Interview, focus group discussions and field observation. Both descriptive statistics and econometric techniques were used for data analysis. Descriptive results show that 57percent of the respondents were perceived the severity and its consequence on agricultural land productivity. The following indicators of soil erosion and fertility loss were generally perceived and observed by farmers’ in the study area: gullies formations, soil accumulation around clumps of vegetation, soil deposits on gentle slopes, exposed roots, muddy water, sedimentation in streams and rivers, change in vegetation species, increased runoff, and reduced rooting depth. The direct human activities which were perceived to be causing land degradation in the study area include: deforestation and clearing of vegetation, overgrazing, steep slope cultivation and continuous cropping. The farmers’ possibility of perceiving the impact of land degradation hazard on agricultural land productivity was primarily determined by institutional, psychological, demographic and by bio-physical factors. Farmers who perceive their land as deteriorating and producing less than desired, tend to adopt improved land management practices. On the other hand, farmers who perceive their land to be fertile tend to have low adoption of conservation practices. In order to overcome this land degradation and its consequent effects, the study recommended a need for the government to enforce effective policies to control and prevent land degradation and these policies should be community inclusive /participatory founded up on indigenous and age-honored knowledge and tradition of farmers' natural resource management as well as introduced scientific practices.

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Soil erosion in Austria – National calculations using regional data delivering local results for the ÖPUL programme

10.5194/egusphere-egu2020-21695 ◽

2020 ◽

Author(s):

Elmar Schmaltz ◽

Georg Dersch ◽

Christine Weinberger ◽

Carmen Krammer ◽

Peter Strauss

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Management Practices ◽

Error Sources ◽

Rainfall Events ◽

Erosion Rates ◽

Environmentally Responsible ◽

Spatially Distributed ◽

Local Results ◽

Erosion Hotspots

Empirical models, such as the Revised Universal Soil Loss Equation (RUSLE) are in use since the 1950s to estimate the mean annual soil loss for single agricultural fields or spatially-distributed for larger areas (municipalities, regions or states). A particular focus on the computation of the RUSLE lies in the calculation of the respective factors on which the equation is built on and represent the erosivity of rainfall events, the erodibility of soils, the topography and land management. However, the RUSLE is highly susceptible to large errors in the prediction of the erosion rates of single agricultural parcels, due to the high variability of these factors in large areas (e.g. on national scale).In this study, we present a parcel-sharp erosion map for the entire territory of Austria. We discuss frequent error sources of the factor computations and their consequences for the representativeness of erosion maps at nation-scale. Based on our results we discuss furthermore regional erosion hotspots and evaluate nationally funded management practices for soil erosion reduction as they are defined in the Austrian programme for an environmentally responsible agriculture (&#214;PUL).Since our approach depicts a novelty for Austria, we further describe opportunities for analysis of our results and highlight potential sources of errors, as well as regional and legal discrepancies of the distribution of national funds for soil conservation.

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Development of a Coupled Water Quality Model

Transactions of the ASABE ◽

10.13031/trans.12002 ◽

2017 ◽

Vol 60 (4) ◽

pp. 1153-1170 ◽

Cited By ~ 1

Author(s):

Lili Wang ◽

Dennis C. Flanagan ◽

Keith A. Cherkauer

Keyword(s):

Water Quality ◽

Time Series ◽

Soil Erosion ◽

Total Phosphorus ◽

Land Management ◽

Management Practices ◽

Nitrate Nitrogen ◽

Storm Events ◽

Nutrient Losses ◽

Soluble Phosphorus

Abstract. . Nonpoint-source (NPS) pollutants, especially from agriculture, continue to be a primary source of waterquality degradation problems. Effective land management decisions at the field scale must be made to minimize nutrient losses that could pollute streams. Existing NPS models often cannot directly estimate the impacts of different land management practices or determine the effectiveness of combined best management practices (BMPs) in a distributed way at the farm scale. In many cases, they rely on application of the Universal Soil Loss Equation (USLE) or its improved versions, which represent fields in a lumped fashion and use empirical rather than process-based modeling methodologies. In this study, a coupled Water Erosion Prediction Project and Water Quality (WEPP-WQ) model was completed, updated, improved, and evaluated for simulation of hydrology, soil erosion, and water quality. The WEPP model is a well-established process-based model that simulates runoff and erosion processes from a hillslope. The water quality components are based on those of the Soil and Water Assessment Tool (SWAT). A single overland flow element (OFE) on a hillslope is used to represent a single soil and land use management. The WEPP-WQ model was tested by comparing simulated values from the coupled model with observed nutrient and sediment concentrations in surface runoff following storm events at experimental sites near Waterloo in northeastern Indiana and at the Throckmorton Purdue Agricultural Center in west central Indiana. Time series evaluation of the WEPP-WQ model was performed with observed nutrient and sediment losses from an experimental plot near Tifton, Georgia. The model performed quite well in simulating nutrient losses for single storm events, with R2 greater than 0.8, Nash-Sutcliffe efficiency (NSE) greater than 0.65, and percent bias (PBIAS) less than 31% for runoff, sediment, nitrate nitrogen, total nitrogen, soluble phosphorus, and total phosphorus losses. In predicting time series nutrient loss, the WEPP-WQ model simulated daily nitrate nitrogen losses adequately, with the ratio of the root mean square error to the standard deviation of measured data (RSR) less than 0.7, NSE greater than 0.55, and PBIAS within the range of ±40%. Comparisons between simulated soluble phosphorus, total phosphorus, and literature results were performed due to the absence of an available observational dataset. The WEPP-WQ model with a single OFE in this study provides a basic but important step for the development of WEPP-WQ models with multiple OFEs that can evaluate the effectiveness of BMPs Keywords: Modeling, Nitrogen, Phosphorus, Soil erosion, Water quality, WEPP.

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