Future soil loss in highland Ethiopia under changing climate and land use

https://link.springer.com/article/10.1007/s10113-020-01617-

Determinants of farmers’ perception to invest in soil and water conservation technologies in the North-Western Highlands of Ethiopia

Soil erosion by water is a severe and continuous ecological problem in the north-western Highlands of Ethiopia. Limited perception of farmers to practice soil and water conservation (SWC) technologies is one of the major causes that have resulted accelerated soil erosion. Therefore, this paper examines the major determinants of farmers’ perception to use and invest in SWC technologies in Ankasha District, north-western highlands of Ethiopia. A detailed field survey was carried out among 338 households, randomly selected from two rural sample kebeles (called villages here after). Descriptive statistics and logistic regression model were used to analyse the effects of multiple variables on farmers’ perception. The results indicate that educational level of the respondents and their access to trainings were found to have a positive and very significant association (P<0.01) with farmers’ perception. Likewise, land ownership, plot size, slope type, and extension contact positively and significantly influenced farmers’ perception at 5% level of significance. On the other hand, the influence of respondents’ age and plot distance from the homestead was found to be negative and significant (P<0.05). The overall results of this study indicate that the perception of farmers to invest in SWC technologies was highly determined by socioeconomic, institutional, attitudinal and biophysical factors. Thus, a better understanding of constrains that influence farmers' perception is very important while designing and implementing SWC technologies. Frequent contacts between farmers and extension agents and continues agricultural trainings are also needed to increase awareness of the impacts of SWC benefits

The Effect of Spatial Input Data Quality on the Performance of the SWAT Model

Soil and land use information are important inputs for physically-based hydrological modeling such as SWAT. Although fine resolution local or regional data are often preferred for modeling, it is not always reliable that these data can lead to better model performance. In this study, we investigate the effect of input data on the sensitivity and uncertainty of the SWAT model in the Porijõgi catchment in Estonia. We created four model setups using global/regional level data (HWSD soil and CORINE) and local high-resolution spatial data, including the Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). We employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019. The results illustrated that models with high-resolution local soil data performed lower than models with global soil data, but in contrast, in the case of land use datasets, the local high-resolution ETAK dataset improved performance over the CORINE data

The Effect of Spatial Input Data Quality on the Performance of the SWAT Model

Soil and land use information are important inputs for physically-based hydrological modeling such as SWAT. Although fine resolution local or regional data are often preferred for modeling, it is not always reliable that these data can lead to better model performance. In this study, we investigate the effect of input data on the sensitivity and uncertainty of the SWAT model in the Porij&otilde;gi catchment in Estonia. We created four model setups using global/regional level data (HWSD soil and CORINE) and local high-resolution spatial data, including the Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). We employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019. The results illustrated that models with high-resolution local soil data performed lower than models with global soil data, but in contrast, in the case of land use datasets, the local high-resolution ETAK dataset improved performance over the CORINE data