Matches in SemOpenAlex for { <https://semopenalex.org/work/W2754435056> ?p ?o ?g. }
- W2754435056 endingPage "88" @default.
- W2754435056 startingPage "77" @default.
- W2754435056 abstract "Abstract Quantitative soil mineralogy has been identified as a key factor influencing PROFILE weathering estimates, and is often calculated with normative methods, such as the “Analysis to Mineralogy” (‘A2M’) model. In Sweden and other countries, there is a large request for accurate base cation weathering estimates in order to establish how sustainable harvest of biomass should be performed in the future. However, there is a lack of knowledge around the accuracy of the arithmetic mean output of A2M estimates, the most common A2M solution used in weathering studies. To our knowledge, a thorough investigation of how A2M input data affect the arithmetic mean output (center of gravity of the A2M solution space) is missing. In this study, the indirect geochemical normative method (A2M) was compared with a direct x-ray powder diffraction method (XRPD) to quantify soil mineralogy at two sites and 8 soil profiles, at a 10 cm depth interval. We explored the hypothesis that normative calculations performed with A2M produce an output in closer agreement with the mineralogy obtained from XRPD, if site specific mineralogical input data are used rather than regional data. Site-specific mineralogical input data consisted of mineral stoichiometry data measured by electron microprobe analysis (EMPA) and mineral identity determined by XRPD, whereas regional mineral input data were based on previously reported data on mineral stoichiometry and mineral identity, derived from three geological regions in Sweden. The results from this comparison showed that the site-specific approach yielded relatively low average biases and root mean square errors (RMSE) for most minerals, with the exception of quartz (Average bias of − 4.8 wt%, RMSE of 5.3 wt%) at the Asa site. The regional approach yielded deviating results for K-feldspar and dioctahedral mica, with high average biases and RMSE for dioctahedral mica (Asa: 7.8 wt%, 9.0 wt%; Flakaliden: 12.8 wt%, 15.5 wt%) and for K-feldspar (Asa: − 5.2 wt%, 6.1 wt%; Flakaliden: − 5.6 wt%, 6.7 wt%). The results from this study were supported by a close agreement between measured geochemistry and normalized geochemistry derived from a back calculation of the XRPD mineralogy (i.e. mineral budgeting). In conclusion, our findings suggest that A2M results in combination with site-specific mineralogical input data are improved independent of study site and soil profile. However, for future weathering studies it might be beneficial to find constraints of how to select a solution from the entire A2M solution space which is in better agreement with the XRPD mineralogy." @default.
- W2754435056 created "2017-09-25" @default.
- W2754435056 creator A5032554858 @default.
- W2754435056 creator A5054830046 @default.
- W2754435056 creator A5070171597 @default.
- W2754435056 creator A5080338031 @default.
- W2754435056 creator A5085809480 @default.
- W2754435056 creator A5088595936 @default.
- W2754435056 date "2018-01-01" @default.
- W2754435056 modified "2023-09-26" @default.
- W2754435056 title "Comparison of measured (XRPD) and modeled (A2M) soil mineralogies: A study of some Swedish forest soils in the context of weathering rate predictions" @default.
- W2754435056 cites W1796417131 @default.
- W2754435056 cites W1882691180 @default.
- W2754435056 cites W1957523232 @default.
- W2754435056 cites W1964575642 @default.
- W2754435056 cites W1966069245 @default.
- W2754435056 cites W1967757629 @default.
- W2754435056 cites W1969205198 @default.
- W2754435056 cites W1969593434 @default.
- W2754435056 cites W1972699738 @default.
- W2754435056 cites W1980814401 @default.
- W2754435056 cites W1981703461 @default.
- W2754435056 cites W1984649440 @default.
- W2754435056 cites W1989958265 @default.
- W2754435056 cites W1999577832 @default.
- W2754435056 cites W2002676968 @default.
- W2754435056 cites W2006415014 @default.
- W2754435056 cites W2009266068 @default.
- W2754435056 cites W2009329941 @default.
- W2754435056 cites W2012475005 @default.
- W2754435056 cites W2012486807 @default.
- W2754435056 cites W2012502684 @default.
- W2754435056 cites W2014916333 @default.
- W2754435056 cites W2020859967 @default.
- W2754435056 cites W2027977808 @default.
- W2754435056 cites W2034884578 @default.
- W2754435056 cites W2035545686 @default.
- W2754435056 cites W2037623000 @default.
- W2754435056 cites W2042560625 @default.
- W2754435056 cites W2046710646 @default.
- W2754435056 cites W2064464303 @default.
- W2754435056 cites W2066967590 @default.
- W2754435056 cites W2068890779 @default.
- W2754435056 cites W2087391337 @default.
- W2754435056 cites W2095157870 @default.
- W2754435056 cites W2101895815 @default.
- W2754435056 cites W2108110493 @default.
- W2754435056 cites W2112979714 @default.
- W2754435056 cites W2295236281 @default.
- W2754435056 cites W2499436658 @default.
- W2754435056 cites W2515117320 @default.
- W2754435056 doi "https://doi.org/10.1016/j.geoderma.2017.09.004" @default.
- W2754435056 hasPublicationYear "2018" @default.
- W2754435056 type Work @default.
- W2754435056 sameAs 2754435056 @default.
- W2754435056 citedByCount "12" @default.
- W2754435056 countsByYear W27544350562018 @default.
- W2754435056 countsByYear W27544350562019 @default.
- W2754435056 countsByYear W27544350562020 @default.
- W2754435056 countsByYear W27544350562021 @default.
- W2754435056 countsByYear W27544350562022 @default.
- W2754435056 countsByYear W27544350562023 @default.
- W2754435056 crossrefType "journal-article" @default.
- W2754435056 hasAuthorship W2754435056A5032554858 @default.
- W2754435056 hasAuthorship W2754435056A5054830046 @default.
- W2754435056 hasAuthorship W2754435056A5070171597 @default.
- W2754435056 hasAuthorship W2754435056A5080338031 @default.
- W2754435056 hasAuthorship W2754435056A5085809480 @default.
- W2754435056 hasAuthorship W2754435056A5088595936 @default.
- W2754435056 hasBestOaLocation W27544350561 @default.
- W2754435056 hasConcept C127313418 @default.
- W2754435056 hasConcept C151730666 @default.
- W2754435056 hasConcept C159390177 @default.
- W2754435056 hasConcept C159750122 @default.
- W2754435056 hasConcept C17409809 @default.
- W2754435056 hasConcept C1965285 @default.
- W2754435056 hasConcept C2779343474 @default.
- W2754435056 hasConcept C39432304 @default.
- W2754435056 hasConcept C40724407 @default.
- W2754435056 hasConceptScore W2754435056C127313418 @default.
- W2754435056 hasConceptScore W2754435056C151730666 @default.
- W2754435056 hasConceptScore W2754435056C159390177 @default.
- W2754435056 hasConceptScore W2754435056C159750122 @default.
- W2754435056 hasConceptScore W2754435056C17409809 @default.
- W2754435056 hasConceptScore W2754435056C1965285 @default.
- W2754435056 hasConceptScore W2754435056C2779343474 @default.
- W2754435056 hasConceptScore W2754435056C39432304 @default.
- W2754435056 hasConceptScore W2754435056C40724407 @default.
- W2754435056 hasFunder F4320321033 @default.
- W2754435056 hasFunder F4320322711 @default.
- W2754435056 hasLocation W27544350561 @default.
- W2754435056 hasOpenAccess W2754435056 @default.
- W2754435056 hasPrimaryLocation W27544350561 @default.
- W2754435056 hasRelatedWork W1982499452 @default.
- W2754435056 hasRelatedWork W2007348640 @default.
- W2754435056 hasRelatedWork W2118462866 @default.
- W2754435056 hasRelatedWork W2150524457 @default.
- W2754435056 hasRelatedWork W2267584387 @default.