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    <title language="eng">Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors</title>
    <abstract language="eng">Soil sensing enables rapid and cost-effective soil analysis. However, a single sensor often does not generate enough information to reliably predict a wide range of soil properties. Within a case-study, our objective was to identify how many and which combinations of soil sensors prove to be suitable for high-resolution soil mapping.&#13;
On a subplot of an agricultural field showing a high spatial soil variability, six in-situ proximal soil sensors (PSSs) next to remote sensing (RS) data from Sentinel-2 were evaluated based on their capabilities to predict a set of soil properties including: soil organic carbon, pH, moisture as well as plant-available phosphorus, magnesium and potassium. The set of PSSs consisted of ion-selective pH electrodes, a capacitive soil moisture sensor, an apparent soil electrical conductivity measuring system as well as passive gamma-ray-, X-ray fluorescence- and nearinfrared spectroscopy. All possible combinations of sensors were exhaustively evaluated and ranked based on their predict</abstract>
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    <author>J. Schmidinger</author>
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    <author>J. Correa</author>
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      <language>eng</language>
      <type>uncontrolled</type>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Precision agriculture</value>
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    <volume>150</volume>
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    <title language="eng">Qualitative and quantitative soil characterization on an agricultural field using a portable shifted excitation Raman difference spectroscopy instrument</title>
    <abstract language="eng">Site-specific farmland management requires comprehensive information about the soil status to derive informed treatment decisions, e.g. for liming or fertilizer recommendations. Standard laboratory methods relying on sample collection have only limited ability to adequately capture the spatial variability of typical agricultural fields. Here, on-site analytical techniques with the potential to measure the soil properties on a substance-specific level and at the required spatial resolution could be very beneficial. Raman spectroscopy is a very promising technique for this purpose as it provides a molecular fingerprint of soil constituents. However, intrinsic soil fluorescence and daylight interference can be major issues masking characteristic Raman signals. Here, we apply an in-house developed portable shifted excitation Raman difference spectroscopy (SERDS) instrument based on a dual-wavelength diode laser emitting around 785 nm to effectively separate the Raman signals of soil from such interferences. SERDS investigations on a selected agricultural field in Germany demonstrate that the Raman spectroscopic signature of 9 soil minerals and organic carbon could successfully be separated from intense backgrounds. Using partial least squares regression against reference analyses, a successful prediction of the soil carbonate (R2 = 0.86, root mean squared error of cross validation RMSECV = 2.49%) and soil organic carbon content (R2 = 0.89, RMSECV = 0.32%) as important soil parameters is realized. The results obtained on-site with the portable instrument were confirmed by SERDS laboratory experiments of collected soil samples thus highlighting the capability and reliability of portable SERDS as promising and complementary tool for precision agriculture</abstract>
    <parentTitle language="eng">The Analyst</parentTitle>
    <identifier type="issn">0003-2654</identifier>
    <identifier type="doi">10.1039/d5an00178a</identifier>
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Ten soil constituents were identified and the contents of calcium carbonate and organic carbon were predicted.&lt;\/jats:p&gt;","DOI":"10.1039\/d5an00178a","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T15:55:46Z","timestamp":1749225346000},"page":"2934-2944","update-policy":"https:\/\/doi.org\/10.1039\/rsc_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Qualitative and quantitative soil characterization on an agricultural field using a portable shifted excitation Raman difference spectroscopy instrument"],"prefix":"10.1039","volume":"150","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7173-2677","authenticated-orcid":false,"given":"Kay","family":"Sowoidnich","sequence":"first","affiliation":[{"name":"Ferdinand-Braun-Institut (FBH), Gustav-Kirchhoff-Stra\u00dfe 4, 12489 Berlin, Germany"}]},{"given":"Stefan","family":"P\u00e4tzold","sequence":"additional","affiliation":[{"name":"Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University of Bonn, Nussallee 13, 53115 Bonn, Germany"}]},{"given":"Markus","family":"Ostermann","sequence":"additional","affiliation":[{"name":"Federal Institute for Materials Research and Testing (BAM), Process Analytical Technology, Richard-Willst\u00e4tter-Stra\u00dfe 11, 12489 Berlin, Germany"}]},{"given":"Bernd","family":"Sumpf","sequence":"additional","affiliation":[{"name":"Ferdinand-Braun-Institut (FBH), Gustav-Kirchhoff-Stra\u00dfe 4, 12489 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1166-5529","authenticated-orcid":false,"given":"Martin","family":"Maiwald","sequence":"additional","affiliation":[{"name":"Ferdinand-Braun-Institut (FBH), Gustav-Kirchhoff-Stra\u00dfe 4, 12489 Berlin, Germany"}]}],"member":"292","published-online":{"date-parts":[[2025]]},"reference":[{"key":"D5AN00178A\/cit1\/1","doi-asserted-by":"crossref","first-page":"4878","DOI":"10.1002\/jsfa.9693","volume":"99","author":"Bhakta","year":"2019","journal-title":"J. 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Agric."}],"container-title":["The Analyst"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/pubs.rsc.org\/en\/content\/articlepdf\/2025\/AN\/D5AN00178A","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T13:42:14Z","timestamp":1750686134000},"score":1,"resource":{"primary":{"URL":"https:\/\/xlink.rsc.org\/?DOI=D5AN00178A"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":47,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,6,23]]}},"URL":"https:\/\/doi.org\/10.1039\/d5an00178a","relation":{},"ISSN":["0003-2654","1364-5528"],"issn-type":[{"type":"print","value":"0003-2654"},{"type":"electronic","value":"1364-5528"}],"subject":[],"published":{"date-parts":[[2025]]}}}</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Kay Sowoidnich</author>
    <author>Stefan Pätzold</author>
    <author>Markus Ostermann</author>
    <author>Bernd Sumpf</author>
    <author>Martin Maiwald</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>XRF</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Raman spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Soil</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SERDS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Precision agriculture</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
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    <type>article</type>
    <publisherName>MDPI AG</publisherName>
    <publisherPlace>Basel, Schweiz</publisherPlace>
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    <title language="eng">Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture—A Case Study</title>
    <abstract language="eng">In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer.&#13;
The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="doi">10.3390/s23167178</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-580777</identifier>
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    <author>A. Erler</author>
    <author>D. Riebe</author>
    <author>T. Beitz</author>
    <author>H.-G. Löhmannsröben</author>
    <author>M. Leenen</author>
    <author>S. Pätzold</author>
    <author>Markus Ostermann</author>
    <author>M. Wójcik</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LIBS</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Precision agriculture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Soil</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multivariate methods</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Feature selection</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">1 Analytische Chemie; Referenzmaterialien</collection>
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