<rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
  <rdf:Description rdf:about="https://doi.org/10.1016/j.geodrs.2023.e00610">
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    <dct:license>Open Access</dct:license>
    <dct:created>2023-01-20</dct:created>
    <dc:description>Agricultural soil erosion has negative effects on surface water quality and aquatic ecosystems. A major impediment to agricultural erosion management in Finland has been the lack of high-resolution country-scale data on the spatial distribution of erosion. As a result, erosion mitigation measures have been targeted with limited information. Therefore, we evaluated the performance of the widely used RUSLE model against measurements from experimental fields, used the model to produce a two-metre resolution crop and management independent erosion estimate for all agricultural lands of Finland, and analysed erosion over different spatial scales. RUSLE showed skill (R2 = 0.76, NSE = 0.72) in estimating the observed erosion at experimental fields (55&#8211;2100 kg ha&#8722;1 yr&#8722;1) but with large errors (mean: &#8722;134 kg ha&#8722;1 yr&#8722;1, 90% range: &#8722;711 and 218 kg ha&#8722;1 yr&#8722;1). The evaluation, however, suggests that RUSLE performs similarly in Finland as elsewhere. The analysis of the developed country-scale data, in turn, revealed high erosion regions, and it showed how erosion varies between sub-catchment and between and within field parcels. For example, high-erosion areas concentrated in the proximity of water bodies were identified at the sub-catchment and within-field parcel scales. Altogether, the results demonstrate the predictive skill of RUSLE in high-latitude conditions, fill the earlier data gap in country-scale erosion, provide information for targeting erosion mitigation measures, and considerably improve the understanding of the spatial distribution of erosion in Finland. ; 2023</dc:description>
    <dc:subject>550</dc:subject>
    <dc:subject>500</dc:subject>
    <dc:subject>Agriculture</dc:subject>
    <dc:subject>Water protection</dc:subject>
    <dc:subject>04 agricultural and veterinary sciences</dc:subject>
    <dc:subject>Podzols</dc:subject>
    <dc:subject>Soil erosion</dc:subject>
    <dc:subject>Histosols</dc:subject>
    <dc:subject>0401 agriculture, forestry, and fisheries</dc:subject>
    <dc:subject>RUSLE</dc:subject>
    <dc:subject>Gleysols</dc:subject>
    <dc:subject>Regosols</dc:subject>
    <dc:subject>Stagnosols</dc:subject>
    <dc:subject>ta218</dc:subject>
    <dc:subject>Finland</dc:subject>
    <dc:creator rdf:resource="https://orcid.org/0000-0002-3445-7290"/>
    <dc:creator rdf:resource="https://orcid.org/0000-0002-7764-5160"/>
    <dc:creator>T&#228;htikarhu, Mika, Uusi-K&#228;mpp&#228;, Jaana, Piirainen, Sirpa, Turtola, Eila, R&#228;s&#228;nen, Timo A., </dc:creator>
    <dc:date>2023-03-01</dc:date>
    <dc:type>journalpaper</dc:type>
    <dct:abstract>Agricultural soil erosion has negative effects on surface water quality and aquatic ecosystems. A major impediment to agricultural erosion management in Finland has been the lack of high-resolution country-scale data on the spatial distribution of erosion. As a result, erosion mitigation measures have been targeted with limited information. Therefore, we evaluated the performance of the widely used RUSLE model against measurements from experimental fields, used the model to produce a two-metre resolution crop and management independent erosion estimate for all agricultural lands of Finland, and analysed erosion over different spatial scales. RUSLE showed skill (R2 = 0.76, NSE = 0.72) in estimating the observed erosion at experimental fields (55&#8211;2100 kg ha&#8722;1 yr&#8722;1) but with large errors (mean: &#8722;134 kg ha&#8722;1 yr&#8722;1, 90% range: &#8722;711 and 218 kg ha&#8722;1 yr&#8722;1). The evaluation, however, suggests that RUSLE performs similarly in Finland as elsewhere. The analysis of the developed country-scale data, in turn, revealed high erosion regions, and it showed how erosion varies between sub-catchment and between and within field parcels. For example, high-erosion areas concentrated in the proximity of water bodies were identified at the sub-catchment and within-field parcel scales. Altogether, the results demonstrate the predictive skill of RUSLE in high-latitude conditions, fill the earlier data gap in country-scale erosion, provide information for targeting erosion mitigation measures, and considerably improve the understanding of the spatial distribution of erosion in Finland. ; 2023</dct:abstract>
    <dc:title>Evaluation of RUSLE and spatial assessment of agricultural soil erosion in Finland</dc:title>
    <dc:identifier>10.1016/j.geodrs.2023.e00610</dc:identifier>
    <dct:references>https://doi.org/10.1016/j.geodrs.2023.e00610</dct:references>
    <dct:relation>862695</dct:relation>
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