ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    Application of mixed‐effects modelling and rule‐based models to explain copper variation in soil profiles of southern Germany (2022)
    Blackwell Publishing Ltd | Oxford, UK
    Details
    Publication Date: 2022-10-01
    Description: Copper (Cu) is an essential element for plants and microorganisms and at larger concentrations a toxic pollutant. A number of factors controlling Cu dynamics have been reported, but information on quantitative relationships is scarce. We aimed to (i) quantitatively describe and predict soil Cu concentrations (CuAR) in aqua regia considering site‐specific effects and effects of pH, soil organic carbon (SOC) and cation exchange capacity (CEC), and (ii) study the suitability of mixed‐effects modelling and rule‐based models for the analysis of long‐term soil monitoring data. Thirteen uncontaminated long‐term monitoring soil profiles in southern Germany were analysed. Since there was no measurable trend of increasing CuAR concentrations with time in the respective depth ranges of the sites, data from different sampling dates were combined and horizon‐specific regression analyses including model simplifications were carried out for 10 horizons. Fixed‐ and mixed‐effects models with the site as a random effect were useful for the different horizons and significant contributions (either of main effects or interactions) of SOC, CEC and pH were present for 9, 8 and 7 horizons, respectively. Horizon‐specific rule‐based cubist models described the CuAR data similarly well. Validations of cubist models and mixed‐effects models for the CuAR concentrations in A horizons were successful for the given population after random splitting into calibration and validation samples, but not after independent validations with random splitting according to sites. Overall, site, CEC, SOC and pH provide important information for a description of CuAR concentrations using the different regression approaches. Highlights: Information on quantitative relationships for factors controlling Cu dynamics is scarce. Site, CEC, SOC and pH provide important information for a description of Cu concentrations. Validations of cubist models and mixed‐effects models for A horizons were successful for a closed population of sites.
    Description: Bavarian State Ministry of the Environment and Consumer Protection http://dx.doi.org/10.13039/501100010219
    Description: Ministry of Agriculture and Environment Mecklenburg‐Western Pomerania
    Keywords: ddc:631.4
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 2
    facet.materialart.
    Unknown
    Optimised use of data fusion and memory‐based learning with an Austrian soil library for predictions with infrared data (2023)
    Blackwell Publishing Ltd | Oxford, UK
    Details
    Publication Date: 2023-12-12
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Infrared spectroscopy in the visible to near‐infrared (vis–NIR) and mid‐infrared (MIR) regions is a well‐established approach for the prediction of soil properties. Different data fusion and training approaches exist, and the optimal procedures are yet undefined and may depend on the heterogeneity present in the set and on the considered scale. The objectives were to test the usefulness of partial least squares regressions (PLSRs) for soil organic carbon (SOC), total carbon (C〈sub〉t〈/sub〉), total nitrogen (N〈sub〉t〈/sub〉) and pH using vis–NIR and MIR spectroscopy for an independent validation after standard calibration (use of a general PLSR model) or using memory‐based learning (MBL) with and without spiking for a national spectral database. Data fusion approaches were simple concatenation of spectra, outer product analysis (OPA) and model averaging. In total, 481 soils from an Austrian forest soil archive were measured in the vis–NIR and MIR regions, and regressions were calculated. Fivefold calibration‐validation approaches were carried out with a region‐related split of spectra to implement independent validations with n ranging from 47 to 99 soils in different folds. MIR predictions were generally superior over vis–NIR predictions. For all properties, optimal predictions were obtained with data fusion, with OPA and spectra concatenation outperforming model averaging. The greatest robustness of performance was found for OPA and MBL with spiking with 〈italic toggle="no"〉R〈/italic〉〈sup〉2〈/sup〉 ≥ 0.77 (N), 0.85 (SOC), 0.86 (pH) and 0.88 (C〈sub〉t〈/sub〉) in the validations of all folds. Overall, the results indicate that the combination of OPA for vis–NIR and MIR spectra with MBL and spiking has a high potential to accurately estimate properties when using large‐scale soil spectral libraries as reference data. However, the reduction of cost‐effectiveness using two spectrometers needs to be weighed against the potential increase in accuracy compared to a single MIR spectroscopy approach.〈/p〉
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:631.4 ; data fusion ; independent validation ; infrared spectroscopy ; MBL ; nitrogen ; outer product analysis ; pH ; soil organic carbon ; spiking ; total carbon
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 3
    facet.materialart.
    Unknown
    Importance of sources of variability, scales and experimental design: A case study about the effects of biochar and slurry application on soil properties in agricultural silty loam soils (2021)
    Blackwell Publishing Ltd | Oxford, UK
    Details
    Publication Date: 2022-04-01
    Description: In designed experiments, different sources of variability and an adequate scale of measurement need to be considered, but not all approaches in common usage are equally valid. In order to elucidate the importance of sources of variability and choice of scale, we conducted an experiment where the effects of biochar and slurry applications on soil properties related to soil fertility were studied for different designs: (a) for a field‐scale sampling design with either a model soil (without natural variability) as an internal control or with composited soils, (b) for a design with a focus on amendment variabilities, and (c) for three individual field‐scale designs with true field replication and a combined analysis representative of the population of loess‐derived soils. Three silty loam sites in Germany were sampled and the soil macroaggregates were crushed. For each design, six treatments (0, 0.15 and 0.30 g slurry‐N kg−1 with and without 30 g biochar kg−1) were applied before incubating the units under constant soil moisture conditions for 78 days. CO2 fluxes were monitored and soils were analysed for macroaggregate yields and associated organic carbon (C). Mixed‐effects models were used to describe the effects. For all soil properties, results for the loess sites differed with respect to significant contributions of fixed effects for at least one site, suggesting the need for a general inclusion of different sites. Analysis using a multilevel model allowed generalizations for loess soils to be made and showed that site:slurry:biochar and site:slurry interactions were not negligible for macroaggregate yields. The use of a model soil as an internal control enabled observation of variabilities other than those related to soils or amendments. Experiments incorporating natural variability in soils or amendments resulted in partially different outcomes, indicating the need to include all important sources of variability. Highlights Effects of biochar and slurry applications were studied for different designs and mixed‐effects models were used to describe the effects. Including an internal control allowed observation of, e.g., methodological and analytical variabilities. The results suggested the need for a general inclusion of different sites. Analysis using a multilevel model allowed generalizations for loess soils. The results indicated the need to include all important sources of variability.
    Keywords: ddc:631.4
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...