Assessment of Soil Erosion Vulnerability in Western Europe and Potential Impact on Crop Productivity Due to Loss of Soil Depth Using the ImpelERO Model. de la Rosa, D., Moreno, J. A., Mayol, F., & Bonsón, T. 81(3):179–190. Paper doi abstract bibtex Soil erosion continues to be a major concern for the development of sustainable agricultural management systems. Sustainability modelling analysis for soil erosion must include not only vulnerability prediction but also address impact and response assessment, in an integrated way. This paper focuses on the impact of soil erosion on crop productivity and the accommodation of agricultural use and management practices to soil protection. From the Andalucia region in Spain, soil/slope, climate and crop/management information was used to further develop an expert-system/neural-network soil erosion predicting model (named ImpelERO). Based on soil tolerance to water erosion, three regression equations were formulated to examine the effects of soil depth loss on crop productivity reduction. Also, a computerised procedure was developed to find a combination of management practices which would minimise soil loss in each field-unit. The overall approach of ImpelERO was applied in 20 selected benchmark sites from western Europe to quantify the soil erosion vulnerability with several crops, the impact of soil erosion on crop production, and the optimum management strategies. In the Mediterranean sites, soil losses reach an average of almost 50 Mg ha-1 per year, the potential impact of soil erosion on the crop productivity was very important (up to 48\,% reduction in the 2050 time horizon), and the farming practices can be widely modified to protect environmental qualities. The results of this benchmark site analysis of soil erosion, however, must not be extrapolated to large geographical areas without additional spatialisation studies.
@article{delarosaAssessmentSoilErosion2000,
title = {Assessment of Soil Erosion Vulnerability in Western {{Europe}} and Potential Impact on Crop Productivity Due to Loss of Soil Depth Using the {{ImpelERO}} Model},
author = {de la Rosa, D. and Moreno, J. A. and Mayol, F. and Bonsón, T.},
date = {2000-11},
journaltitle = {Agriculture, Ecosystems \& Environment},
volume = {81},
pages = {179--190},
issn = {0167-8809},
doi = {10.1016/s0167-8809(00)00161-4},
url = {https://doi.org/10.1016/s0167-8809(00)00161-4},
abstract = {Soil erosion continues to be a major concern for the development of sustainable agricultural management systems. Sustainability modelling analysis for soil erosion must include not only vulnerability prediction but also address impact and response assessment, in an integrated way. This paper focuses on the impact of soil erosion on crop productivity and the accommodation of agricultural use and management practices to soil protection. From the Andalucia region in Spain, soil/slope, climate and crop/management information was used to further develop an expert-system/neural-network soil erosion predicting model (named ImpelERO). Based on soil tolerance to water erosion, three regression equations were formulated to examine the effects of soil depth loss on crop productivity reduction. Also, a computerised procedure was developed to find a combination of management practices which would minimise soil loss in each field-unit. The overall approach of ImpelERO was applied in 20 selected benchmark sites from western Europe to quantify the soil erosion vulnerability with several crops, the impact of soil erosion on crop production, and the optimum management strategies. In the Mediterranean sites, soil losses reach an average of almost 50 Mg ha-1 per year, the potential impact of soil erosion on the crop productivity was very important (up to 48\,\% reduction in the 2050 time horizon), and the farming practices can be widely modified to protect environmental qualities. The results of this benchmark site analysis of soil erosion, however, must not be extrapolated to large geographical areas without additional spatialisation studies.},
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number = {3},
options = {useprefix=true}
}
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