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  <doc>
    <id>436</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>15</pageLast>
    <pageNumber/>
    <edition/>
    <issue>197</issue>
    <volume/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-04-12</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Patch cropping- a new methodological approach to determine new field arrangements that increase the multifunctionality of agricultural landscapes</title>
    <abstract language="eng">Agricultural intensification decreased land cover complexity by converting small complex arable field geometries into large and simple structures which then were managed uniformly. These changes have led to a variety of negative environmental effects and influence ecosystem services. We present a novel small-scale and site-specific cropping system which splits a large field into small homogeneous sub-fields called ‘patches’ grouped in different yield potentials. A detailed workflow is presented to generate new spatially arranged patches with special focus on preprocessing and filtering of multi-year yield data, the variation in patch sizes and the adaptation of maximum working width to use available conventional farm equipment and permanent traffic lanes. The reduction of variance by the used cluster algorithm depends on the within-field heterogeneity. The patch size, the number of growing seasons (GS) used for clustering and the parallel shift of the patch structure along the permanent traffic lane resulted in a change in relative variance. Independent cross validation showed an increased performance of the classification algorithm with increasing number of GS used for clustering. The applied cluster analysis resulted in robust field segregation according to different yield potential zones and provides an innovative method for a novel cropping system.</abstract>
    <parentTitle language="eng">Computers and Electronics in Agriculture</parentTitle>
    <identifier type="doi">10.1016/j.compag.2022.106894</identifier>
    <identifier type="urn">urn:nbn:de:kobv:eb1-opus-4365</identifier>
    <identifier type="issn">0168-1699</identifier>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Marco Donat</author>
    <author>Jonas Geistert</author>
    <author>Kathrin Grahmann</author>
    <author>Ralf Bloch</author>
    <author>Sonoko D. Bellingrath-Kimura</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Soil Management Zone Delineation; Yield Productivity Zones; Python; Clustering</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Fachbereich Landschaftsnutzung und Naturschutz</collection>
    <collection role="Hochschulbibliographie" number=""/>
    <collection role="Hochschulbibliographie" number="">Zweitveröffentlichung</collection>
    <collection role="Hochschulbibliographie" number="">Referiert</collection>
    <thesisPublisher>Hochschule für nachhaltige Entwicklung Eberswalde</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-hnee/files/436/1-s2.0-S0168169922002113-main.pdf</file>
  </doc>
  <doc>
    <id>801</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue/>
    <volume>16</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-05-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Digital Agricultural Knowledge and Information System (DAKIS): Employing digitalisation to encourage diversified and multifunctional agricultural systems</title>
    <abstract language="eng">Multifunctional and diversified agriculture can address diverging pressures and demands by simultaneously enhancing productivity, biodiversity, and the provision of ecosystem services. The use of digital technologies can support this by designing and managing resource-efficient and context-specific agricultural systems. We present the Digital Agricultural Knowledge and Information System (DAKIS) to demonstrate an approach that employs digital technologies to enable decision-making towards diversified and sustainable agriculture. To develop the DAKIS, we specified, together with stakeholders, requirements for a knowledge-based decision-support tool and reviewed the literature to identify limitations in the current generation of tools. The results of the review point towards recurring challenges regarding the consideration of ecosystem services and biodiversity, the capacity to foster communication and cooperation between farmers and other actors, and the ability to link multiple spatiotemporal scales and sustainability levels. To overcome these challenges, the DAKIS provides a digital platform to support farmers' decision-making on land use and management via an integrative spatiotemporally explicit approach that analyses a wide range of data from various sources. The approach integrates remote and in situ sensors, artificial intelligence, modelling, stakeholder-stated demand for biodiversity and ecosystem services, and participatory sustainability impact assessment to address the diverse drivers affecting agricultural land use and management design, including natural and agronomic factors, economic and policy considerations, and socio-cultural preferences and settings. Ultimately, the DAKIS embeds the consideration of ecosystem services, biodiversity, and sustainability into farmers' decision-making and enables learning and progress towards site-adapted small-scale multifunctional and diversified agriculture while simultaneously supporting farmers' objectives and societal demands.</abstract>
    <parentTitle language="eng">Environmental Science and Ecotechnology</parentTitle>
    <identifier type="issn">2666-4984</identifier>
    <identifier type="doi">10.1016/j.ese.2023.100274</identifier>
    <identifier type="urn">urn:nbn:de:kobv:eb1-opus-8012</identifier>
    <enrichment key="opus.import.data">@articleMOURATIADOU2023100274, title = The Digital Agricultural Knowledge and Information System (DAKIS): Employing digitalisation to encourage diversified and multifunctional agricultural systems, journal = Environmental Science and Ecotechnology, volume = 16, pages = 100274, year = 2023, issn = 2666-4984, doi = https://doi.org/10.1016/j.ese.2023.100274, url = https://www.sciencedirect.com/science/article/pii/S266649842300039X, author = Ioanna Mouratiadou and Nahleen Lemke and Cheng Chen and Ariani Wartenberg and Ralf Bloch and Marco Donat and Thomas Gaiser and Deepak Hanike Basavegowda and Katharina Helming and Seyed Ali Hosseini Yekani and Marcos Krull and Kai Lingemann and Joseph Macpherson and Marvin Melzer and Claas Nendel and Annette Piorr and Mostafa Shaaban and Peter Zander and Cornelia Weltzien and Sonoko Dorothea Bellingrath-Kimura, keywords = Digital agriculture, Ecosystem services, Decision support system, Farming practices, Biodiversity, Modelling, Small-scale management, abstract = Multifunctional and diversified agriculture can address diverging pressures and demands by simultaneously enhancing productivity, biodiversity, and the provision of ecosystem services. The use of digital technologies can support this by designing and managing resource-efficient and context-specific agricultural systems. We present the Digital Agricultural Knowledge and Information System (DAKIS) to demonstrate an approach that employs digital technologies to enable decision-making towards diversified and sustainable agriculture. To develop the DAKIS, we specified, together with stakeholders, requirements for a knowledge-based decision-support tool and reviewed the literature to identify limitations in the current generation of tools. The results of the review point towards recurring challenges regarding the consideration of ecosystem services and biodiversity, the capacity to foster communication and cooperation between farmers and other actors, and the ability to link multiple spatiotemporal scales and sustainability levels. To overcome these challenges, the DAKIS provides a digital platform to support farmers’ decision-making on land use and management via an integrative spatiotemporally explicit approach that analyses a wide range of data from various sources. The approach integrates remote and in situ sensors, artificial intelligence, modelling, stakeholder-stated demand for biodiversity and ecosystem services, and participatory sustainability impact assessment to address the diverse drivers affecting agricultural land use and management design, including natural and agronomic factors, economic and policy considerations, and socio-cultural preferences and settings. Ultimately, the DAKIS embeds the consideration of ecosystem services, biodiversity, and sustainability into farmers’ decision-making and enables learning and progress towards site-adapted small-scale multifunctional and diversified agriculture while simultaneously supporting farmers’ objectives and societal demands.</enrichment>
    <enrichment key="opus.import.dataHash">md5:e34316383963302309850bfdec2ab8f3</enrichment>
    <enrichment key="opus.import.date">2023-12-08T13:40:39+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpXZs4Pn</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">65731cd7ee99a0.51602905</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Ioanna Mouratiadou</author>
    <author>Nahleen Lemke</author>
    <author>Cheng Chen</author>
    <author>Ariani Wartenberg</author>
    <author>Ralf Bloch</author>
    <author>Marco Donat</author>
    <author>Thomas Gaiser</author>
    <author>Deepak Hanike Basavegowda</author>
    <author>Katharina Helming</author>
    <author>Seyed Ali Hosseini Yekani</author>
    <author>Marcos Krull</author>
    <author>Kai Lingemann</author>
    <author>Joseph Macpherson</author>
    <author>Marvin Melzer</author>
    <author>Claas Nendel</author>
    <author>Annette Piorr</author>
    <author>Mostafa Shaaban</author>
    <author>Peter Zander</author>
    <author>Cornelia Weltzien</author>
    <author>Sonoko Dorothea Bellingrath-Kimura</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital agriculture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ecosystem services</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Decision support system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Farming practices</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Biodiversity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Modelling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Small-scale management</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Fachbereich Landschaftsnutzung und Naturschutz</collection>
    <collection role="Hochschulbibliographie" number=""/>
    <collection role="Hochschulbibliographie" number="">Zweitveröffentlichung</collection>
    <collection role="Hochschulbibliographie" number="">Referiert</collection>
    <thesisPublisher>Hochschule für nachhaltige Entwicklung Eberswalde</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-hnee/files/801/1-s2.0-S266649842300039X-main.pdf</file>
  </doc>
</export-example>
