Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-07-19

AUTHORS

Ixchel M. Hernández-Ochoa, Thomas Gaiser, Kurt-Christian Kersebaum, Heidi Webber, Sabine Julia Seidel, Kathrin Grahmann, Frank Ewert

ABSTRACT

Intensive agriculture in Germany is not only highly productive but has also led to detrimental effects in the environment. Crop diversification together with new field arrangements considering soil heterogeneities can be an alternative to improve resource use efficiency (RUE), ecosystem services (ESS), and biodiversity. Agroecosystem models are tools that help us to understand and design diversified new field arrangements. The main goal of this study was to review the extent to which agroecosystem models have been used for crop diversification design at field and landscape scale by considering soil heterogeneities and to understand the model requirements for this purpose. We found several agroecosystem models available for simulating spatiotemporal crop diversification at the field scale. For spatial crop diversification, simplified modelling approaches consider crop interactions for light, water, and nutrients, but they offer restricted crop combinations. For temporal crop diversification, agroecosystem models include the major crops (e.g., cereals, legumes, and tuber crops). However, crop parameterization is limited for marginal crops and soil carbon and nitrogen (N). At the landscape scale, decision-making frameworks are commonly used to design diversified cropping systems. Within-field soil heterogeneities are rarely considered in field or landscape design studies. Combining static frameworks with dynamic agroecosystems models can be useful for the design and evaluation of trade-offs for ESS delivery and biodiversity. To enhance modeling capabilities to simulate diversified cropping systems in new field arrangements, it will be necessary to improve the representation of crop interactions, the inclusion of more crop species options, soil legacy effects, and biodiversity estimations. Newly diversified field arrangement design also requires higher data resolution, which can be generated via remote sensing and field sensors. We propose the implementation of a framework that combines static approaches and process-based models for new optimized field arrangement design and propose respective experiments for testing the combined framework. More... »

PAGES

74

References to SciGraph publications

  • 2019-07-03. Evaluation of hydroclimatic variables for maize yield estimation using crop model and remotely sensed data assimilation in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2004-01. Carbon sequestration potential of organic agriculture in northern Europe – a modelling approach in NUTRIENT CYCLING IN AGROECOSYSTEMS
  • 2009-10-02. Adapting the CROPGRO model for saline soils: the case for a common bean crop in IRRIGATION SCIENCE
  • 2009-03. Mixing plant species in cropping systems: concepts, tools and models. A review in AGRONOMY FOR SUSTAINABLE DEVELOPMENT
  • 2013-04. Spatio-temporal analysis of crop rotations and crop sequence patterns in Northern Germany: potential implications on plant health and crop protection in JOURNAL OF PLANT DISEASES AND PROTECTION
  • 2012-08-19. LandscapeDNDC: a process model for simulation of biosphere–atmosphere–hydrosphere exchange processes at site and regional scale in LANDSCAPE ECOLOGY
  • 2015-04-10. Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review in AGRONOMY FOR SUSTAINABLE DEVELOPMENT
  • 2015-06-10. How to implement biodiversity-based agriculture to enhance ecosystem services: a review in AGRONOMY FOR SUSTAINABLE DEVELOPMENT
  • 2019-03-08. Current knowledge and future research opportunities for modeling annual crop mixtures. A review in AGRONOMY FOR SUSTAINABLE DEVELOPMENT
  • 2014-10-25. Reduced nitrogen leaching by intercropping maize with red fescue on sandy soils in North Europe: a combined field and modeling study in PLANT AND SOIL
  • 2018-10-19. Soil state variables in space and time: first steps towards linking proximal soil sensing and process modelling in PRECISION AGRICULTURE
  • 2006-08-18. Modelling nitrogen dynamics in soil–crop systems with HERMES in NUTRIENT CYCLING IN AGROECOSYSTEMS
  • 1995-06. Some general principles of landscape and regional ecology in LANDSCAPE ECOLOGY
  • 2021-03-29. Risk of pesticide pollution at the global scale in NATURE GEOSCIENCE
  • 2014-01-21. Simulating the yields of bioenergy and food crops with the crop modeling software BioSTAR: the carbon-based growth engine and the BioSTAR ET0 method in ENVIRONMENTAL SCIENCES EUROPE
  • 2021-01-08. Accuracy and uncertainty analysis of staple food crop modelling by the process-based Agro-C model in INTERNATIONAL JOURNAL OF BIOMETEOROLOGY
  • 2018. Satellite Farming, An Information and Technology Based Agriculture in NONE
  • 2020-04-20. Diverse approaches to crop diversification in agricultural research. A review in AGRONOMY FOR SUSTAINABLE DEVELOPMENT
  • 2017-02-17. Development of heat and drought related extreme weather events and their effect on winter wheat yields in Germany in THEORETICAL AND APPLIED CLIMATOLOGY
  • 1998-10. WaNuLCAS, a model of water, nutrient and light capture in agroforestry systems in AGROFORESTRY SYSTEMS
  • 2020-04-16. Digital agriculture to design sustainable agricultural systems in NATURE SUSTAINABILITY
  • 2019-07-26. Modeling crop yield and nitrogen use efficiency in wheat and maize production systems under future climate change in NUTRIENT CYCLING IN AGROECOSYSTEMS
  • 2017-10-23. Comparing crop rotations between organic and conventional farming in SCIENTIFIC REPORTS
  • 2020-11-20. Changes in agriculture-biodiversity trade-offs in relation to landscape context in the Argentine Chaco in LANDSCAPE ECOLOGY
  • 2003-01. Using competitive and facilitative interactions in intercropping systems enhances crop productivity and nutrient-use efficiency in PLANT AND SOIL
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13593-022-00805-4

    DOI

    http://dx.doi.org/10.1007/s13593-022-00805-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1149599708


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/05", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Environmental Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/07", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Agricultural and Veterinary Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0502", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Environmental Science and Management", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0503", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Soil Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0703", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Crop and Pasture Production", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany", 
              "id": "http://www.grid.ac/institutes/grid.10388.32", 
              "name": [
                "Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hern\u00e1ndez-Ochoa", 
            "givenName": "Ixchel M.", 
            "id": "sg:person.013603464737.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013603464737.30"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany", 
              "id": "http://www.grid.ac/institutes/grid.10388.32", 
              "name": [
                "Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gaiser", 
            "givenName": "Thomas", 
            "id": "sg:person.013767154271.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013767154271.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Global Change Research Institute CAS, Brno, Czech Republic", 
              "id": "http://www.grid.ac/institutes/grid.426587.a", 
              "name": [
                "Data Analysis & Simulation: Ecosystem Modelling, Leibniz Center for Agricultural Landscape Research, ZALF, M\u00fcncheberg, Germany", 
                "Global Change Research Institute CAS, Brno, Czech Republic"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kersebaum", 
            "givenName": "Kurt-Christian", 
            "id": "sg:person.014657474307.35", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014657474307.35"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Agricultural Landscape Systems: Integrated Crop System Analysis and Modelling, Leibniz Center for Agricultural Landscape Research, ZALF, M\u00fcncheberg, Germany", 
              "id": "http://www.grid.ac/institutes/grid.433014.1", 
              "name": [
                "Agricultural Landscape Systems: Integrated Crop System Analysis and Modelling, Leibniz Center for Agricultural Landscape Research, ZALF, M\u00fcncheberg, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Webber", 
            "givenName": "Heidi", 
            "id": "sg:person.010760664607.06", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010760664607.06"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany", 
              "id": "http://www.grid.ac/institutes/grid.10388.32", 
              "name": [
                "Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Seidel", 
            "givenName": "Sabine Julia", 
            "id": "sg:person.016252160663.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016252160663.00"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Data analysis & Simulation: Dimensionality Assessment and Reduction; Land Use Governance: Resource-Efficient Cropping Systems, Leibniz Center for Agricultural Landscape Research, ZALF, M\u00fcncheberg, Germany", 
              "id": "http://www.grid.ac/institutes/grid.433014.1", 
              "name": [
                "Data analysis & Simulation: Dimensionality Assessment and Reduction; Land Use Governance: Resource-Efficient Cropping Systems, Leibniz Center for Agricultural Landscape Research, ZALF, M\u00fcncheberg, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Grahmann", 
            "givenName": "Kathrin", 
            "id": "sg:person.011116520652.09", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011116520652.09"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Leibniz Center for Agricultural Landscape Research, ZALF, M\u00fcncheberg, Germany", 
              "id": "http://www.grid.ac/institutes/grid.433014.1", 
              "name": [
                "Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany", 
                "Leibniz Center for Agricultural Landscape Research, ZALF, M\u00fcncheberg, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ewert", 
            "givenName": "Frank", 
            "id": "sg:person.012101273060.01", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012101273060.01"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s10705-019-10013-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1119901656", 
              "https://doi.org/10.1007/s10705-019-10013-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00477-019-01700-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1117736504", 
              "https://doi.org/10.1007/s00477-019-01700-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/2190-4715-26-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043803333", 
              "https://doi.org/10.1186/2190-4715-26-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00484-020-02053-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1134450990", 
              "https://doi.org/10.1007/s00484-020-02053-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-017-14271-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092262089", 
              "https://doi.org/10.1038/s41598-017-14271-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13593-019-0562-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1112634545", 
              "https://doi.org/10.1007/s13593-019-0562-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11119-018-9617-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107723598", 
              "https://doi.org/10.1007/s11119-018-9617-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13593-015-0306-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008932373", 
              "https://doi.org/10.1007/s13593-015-0306-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00133027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049664412", 
              "https://doi.org/10.1007/bf00133027"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41893-020-0510-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1126700196", 
              "https://doi.org/10.1038/s41893-020-0510-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf03356458", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039821260", 
              "https://doi.org/10.1007/bf03356458"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41561-021-00712-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1136738919", 
              "https://doi.org/10.1038/s41561-021-00712-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-03448-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1112157325", 
              "https://doi.org/10.1007/978-3-030-03448-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00704-017-2076-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083864061", 
              "https://doi.org/10.1007/s00704-017-2076-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00271-009-0189-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000964139", 
              "https://doi.org/10.1007/s00271-009-0189-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1022352229863", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026732423", 
              "https://doi.org/10.1023/a:1022352229863"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13593-014-0277-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045140280", 
              "https://doi.org/10.1007/s13593-014-0277-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10980-012-9772-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032368749", 
              "https://doi.org/10.1007/s10980-012-9772-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13593-020-00617-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1126825991", 
              "https://doi.org/10.1007/s13593-020-00617-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10980-020-01155-w", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1132792268", 
              "https://doi.org/10.1007/s10980-020-01155-w"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1051/agro:2007057", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032996860", 
              "https://doi.org/10.1051/agro:2007057"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1026417120254", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032976167", 
              "https://doi.org/10.1023/a:1026417120254"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11104-014-2311-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015142673", 
              "https://doi.org/10.1007/s11104-014-2311-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10705-006-9044-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050652938", 
              "https://doi.org/10.1007/s10705-006-9044-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/b:fres.0000012231.89516.80", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052991634", 
              "https://doi.org/10.1023/b:fres.0000012231.89516.80"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-07-19", 
        "datePublishedReg": "2022-07-19", 
        "description": "Intensive agriculture in Germany is not only highly productive but has also led to detrimental effects in the environment. Crop diversification together with new field arrangements considering soil heterogeneities can be an alternative to improve resource use efficiency (RUE), ecosystem services (ESS), and biodiversity. Agroecosystem models are tools that help us to understand and design diversified new field arrangements. The main goal of this study was to review the extent to which agroecosystem models have been used for crop diversification design at field and landscape scale by considering soil heterogeneities and to understand the model requirements for this purpose. We found several agroecosystem models available for simulating spatiotemporal crop diversification at the field scale. For spatial crop diversification, simplified modelling approaches consider crop interactions for light, water, and nutrients, but they offer restricted crop combinations. For temporal crop diversification, agroecosystem models include the major crops (e.g., cereals, legumes, and tuber crops). However, crop parameterization is limited for marginal crops and soil carbon and nitrogen (N). At the landscape scale, decision-making frameworks are commonly used to design diversified cropping systems. Within-field soil heterogeneities are rarely considered in field or landscape design studies. Combining static frameworks with dynamic agroecosystems models can be useful for the design and evaluation of trade-offs for ESS delivery and biodiversity. To enhance modeling capabilities to simulate diversified cropping systems in new field arrangements, it will be necessary to improve the representation of crop interactions, the inclusion of more crop species options, soil legacy effects, and biodiversity estimations. Newly diversified field arrangement design also requires higher data resolution, which can be generated via remote sensing and field sensors. We propose the implementation of a framework that combines static approaches and process-based models for new optimized field arrangement design and propose respective experiments for testing the combined framework.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s13593-022-00805-4", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.8454848", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1430196", 
            "issn": [
              "1774-0746", 
              "1773-0155"
            ], 
            "name": "Agronomy for Sustainable Development", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "4", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "42"
          }
        ], 
        "keywords": [
          "crop diversification", 
          "resource use efficiency", 
          "agroecosystem model", 
          "soil heterogeneity", 
          "ecosystem services", 
          "landscape scale", 
          "crop interactions", 
          "cropping systems", 
          "soil legacy effects", 
          "dynamic agroecosystem model", 
          "diversified cropping systems", 
          "field soil heterogeneity", 
          "process-based model", 
          "soil carbon", 
          "crop combinations", 
          "major crops", 
          "marginal crops", 
          "use efficiency", 
          "legacy effects", 
          "heterogeneous landscapes", 
          "species options", 
          "biodiversity estimation", 
          "intensive agriculture", 
          "field scale", 
          "remote sensing", 
          "decision-making framework", 
          "biodiversity", 
          "data resolution", 
          "modelling approach", 
          "crops", 
          "higher data resolution", 
          "diversification", 
          "model requirements", 
          "respective experiments", 
          "detrimental effects", 
          "agriculture", 
          "scale", 
          "landscape", 
          "nutrients", 
          "heterogeneity", 
          "carbon", 
          "nitrogen", 
          "arrangement design", 
          "water", 
          "field arrangement", 
          "sensing", 
          "environment", 
          "static framework", 
          "framework", 
          "static approach", 
          "main goal", 
          "parameterization", 
          "field", 
          "extent", 
          "Germany", 
          "effect", 
          "interaction", 
          "efficiency", 
          "experiments", 
          "alternative", 
          "options", 
          "arrangement", 
          "services", 
          "model", 
          "study", 
          "approach", 
          "combination", 
          "requirements", 
          "tool", 
          "goal", 
          "system", 
          "design", 
          "estimation", 
          "resolution", 
          "evaluation", 
          "review", 
          "implementation", 
          "inclusion", 
          "light", 
          "purpose", 
          "capability", 
          "representation", 
          "sensors", 
          "field sensor", 
          "delivery", 
          "model-based design", 
          "design study"
        ], 
        "name": "Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review", 
        "pagination": "74", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1149599708"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s13593-022-00805-4"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s13593-022-00805-4", 
          "https://app.dimensions.ai/details/publication/pub.1149599708"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:44", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_952.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s13593-022-00805-4"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s13593-022-00805-4'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s13593-022-00805-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13593-022-00805-4'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13593-022-00805-4'


     

    This table displays all metadata directly associated to this object as RDF triples.

    312 TRIPLES      21 PREDICATES      139 URIs      103 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s13593-022-00805-4 schema:about anzsrc-for:05
    2 anzsrc-for:0502
    3 anzsrc-for:0503
    4 anzsrc-for:07
    5 anzsrc-for:0703
    6 schema:author Ne2e8497bc85f4012b47399fbb2794e47
    7 schema:citation sg:pub.10.1007/978-3-030-03448-1
    8 sg:pub.10.1007/bf00133027
    9 sg:pub.10.1007/bf03356458
    10 sg:pub.10.1007/s00271-009-0189-5
    11 sg:pub.10.1007/s00477-019-01700-3
    12 sg:pub.10.1007/s00484-020-02053-1
    13 sg:pub.10.1007/s00704-017-2076-y
    14 sg:pub.10.1007/s10705-006-9044-8
    15 sg:pub.10.1007/s10705-019-10013-4
    16 sg:pub.10.1007/s10980-012-9772-x
    17 sg:pub.10.1007/s10980-020-01155-w
    18 sg:pub.10.1007/s11104-014-2311-6
    19 sg:pub.10.1007/s11119-018-9617-y
    20 sg:pub.10.1007/s13593-014-0277-7
    21 sg:pub.10.1007/s13593-015-0306-1
    22 sg:pub.10.1007/s13593-019-0562-6
    23 sg:pub.10.1007/s13593-020-00617-4
    24 sg:pub.10.1023/a:1022352229863
    25 sg:pub.10.1023/a:1026417120254
    26 sg:pub.10.1023/b:fres.0000012231.89516.80
    27 sg:pub.10.1038/s41561-021-00712-5
    28 sg:pub.10.1038/s41598-017-14271-6
    29 sg:pub.10.1038/s41893-020-0510-0
    30 sg:pub.10.1051/agro:2007057
    31 sg:pub.10.1186/2190-4715-26-1
    32 schema:datePublished 2022-07-19
    33 schema:datePublishedReg 2022-07-19
    34 schema:description Intensive agriculture in Germany is not only highly productive but has also led to detrimental effects in the environment. Crop diversification together with new field arrangements considering soil heterogeneities can be an alternative to improve resource use efficiency (RUE), ecosystem services (ESS), and biodiversity. Agroecosystem models are tools that help us to understand and design diversified new field arrangements. The main goal of this study was to review the extent to which agroecosystem models have been used for crop diversification design at field and landscape scale by considering soil heterogeneities and to understand the model requirements for this purpose. We found several agroecosystem models available for simulating spatiotemporal crop diversification at the field scale. For spatial crop diversification, simplified modelling approaches consider crop interactions for light, water, and nutrients, but they offer restricted crop combinations. For temporal crop diversification, agroecosystem models include the major crops (e.g., cereals, legumes, and tuber crops). However, crop parameterization is limited for marginal crops and soil carbon and nitrogen (N). At the landscape scale, decision-making frameworks are commonly used to design diversified cropping systems. Within-field soil heterogeneities are rarely considered in field or landscape design studies. Combining static frameworks with dynamic agroecosystems models can be useful for the design and evaluation of trade-offs for ESS delivery and biodiversity. To enhance modeling capabilities to simulate diversified cropping systems in new field arrangements, it will be necessary to improve the representation of crop interactions, the inclusion of more crop species options, soil legacy effects, and biodiversity estimations. Newly diversified field arrangement design also requires higher data resolution, which can be generated via remote sensing and field sensors. We propose the implementation of a framework that combines static approaches and process-based models for new optimized field arrangement design and propose respective experiments for testing the combined framework.
    35 schema:genre article
    36 schema:isAccessibleForFree true
    37 schema:isPartOf N41bd46b136ff4c209db531c56c5a10e9
    38 Na25330477693415b8768a051c2489770
    39 sg:journal.1430196
    40 schema:keywords Germany
    41 agriculture
    42 agroecosystem model
    43 alternative
    44 approach
    45 arrangement
    46 arrangement design
    47 biodiversity
    48 biodiversity estimation
    49 capability
    50 carbon
    51 combination
    52 crop combinations
    53 crop diversification
    54 crop interactions
    55 cropping systems
    56 crops
    57 data resolution
    58 decision-making framework
    59 delivery
    60 design
    61 design study
    62 detrimental effects
    63 diversification
    64 diversified cropping systems
    65 dynamic agroecosystem model
    66 ecosystem services
    67 effect
    68 efficiency
    69 environment
    70 estimation
    71 evaluation
    72 experiments
    73 extent
    74 field
    75 field arrangement
    76 field scale
    77 field sensor
    78 field soil heterogeneity
    79 framework
    80 goal
    81 heterogeneity
    82 heterogeneous landscapes
    83 higher data resolution
    84 implementation
    85 inclusion
    86 intensive agriculture
    87 interaction
    88 landscape
    89 landscape scale
    90 legacy effects
    91 light
    92 main goal
    93 major crops
    94 marginal crops
    95 model
    96 model requirements
    97 model-based design
    98 modelling approach
    99 nitrogen
    100 nutrients
    101 options
    102 parameterization
    103 process-based model
    104 purpose
    105 remote sensing
    106 representation
    107 requirements
    108 resolution
    109 resource use efficiency
    110 respective experiments
    111 review
    112 scale
    113 sensing
    114 sensors
    115 services
    116 soil carbon
    117 soil heterogeneity
    118 soil legacy effects
    119 species options
    120 static approach
    121 static framework
    122 study
    123 system
    124 tool
    125 use efficiency
    126 water
    127 schema:name Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review
    128 schema:pagination 74
    129 schema:productId N983ce01da5f74e0b9c68e336acde72e4
    130 Ne544209483834abeb3bf56364942bf7f
    131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1149599708
    132 https://doi.org/10.1007/s13593-022-00805-4
    133 schema:sdDatePublished 2022-12-01T06:44
    134 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    135 schema:sdPublisher N662d4d0df311463aa45c9277a9c1cf56
    136 schema:url https://doi.org/10.1007/s13593-022-00805-4
    137 sgo:license sg:explorer/license/
    138 sgo:sdDataset articles
    139 rdf:type schema:ScholarlyArticle
    140 N29d08437b6164b92b72c50e1d2e92ded rdf:first sg:person.012101273060.01
    141 rdf:rest rdf:nil
    142 N41bd46b136ff4c209db531c56c5a10e9 schema:volumeNumber 42
    143 rdf:type schema:PublicationVolume
    144 N662d4d0df311463aa45c9277a9c1cf56 schema:name Springer Nature - SN SciGraph project
    145 rdf:type schema:Organization
    146 N8333245006c34cbd9316300340b160ba rdf:first sg:person.016252160663.00
    147 rdf:rest N89b0a0eff3d84eefa0e3cc858bc16e1f
    148 N8909f86c5903497799634f7ca4ca6640 rdf:first sg:person.013767154271.49
    149 rdf:rest Ne836e2561d4c463b88dc8039314295f9
    150 N89b0a0eff3d84eefa0e3cc858bc16e1f rdf:first sg:person.011116520652.09
    151 rdf:rest N29d08437b6164b92b72c50e1d2e92ded
    152 N983ce01da5f74e0b9c68e336acde72e4 schema:name dimensions_id
    153 schema:value pub.1149599708
    154 rdf:type schema:PropertyValue
    155 Na25330477693415b8768a051c2489770 schema:issueNumber 4
    156 rdf:type schema:PublicationIssue
    157 Nd05f9339606c4dfa9fd918e17908fd0f rdf:first sg:person.010760664607.06
    158 rdf:rest N8333245006c34cbd9316300340b160ba
    159 Ne2e8497bc85f4012b47399fbb2794e47 rdf:first sg:person.013603464737.30
    160 rdf:rest N8909f86c5903497799634f7ca4ca6640
    161 Ne544209483834abeb3bf56364942bf7f schema:name doi
    162 schema:value 10.1007/s13593-022-00805-4
    163 rdf:type schema:PropertyValue
    164 Ne836e2561d4c463b88dc8039314295f9 rdf:first sg:person.014657474307.35
    165 rdf:rest Nd05f9339606c4dfa9fd918e17908fd0f
    166 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
    167 schema:name Environmental Sciences
    168 rdf:type schema:DefinedTerm
    169 anzsrc-for:0502 schema:inDefinedTermSet anzsrc-for:
    170 schema:name Environmental Science and Management
    171 rdf:type schema:DefinedTerm
    172 anzsrc-for:0503 schema:inDefinedTermSet anzsrc-for:
    173 schema:name Soil Sciences
    174 rdf:type schema:DefinedTerm
    175 anzsrc-for:07 schema:inDefinedTermSet anzsrc-for:
    176 schema:name Agricultural and Veterinary Sciences
    177 rdf:type schema:DefinedTerm
    178 anzsrc-for:0703 schema:inDefinedTermSet anzsrc-for:
    179 schema:name Crop and Pasture Production
    180 rdf:type schema:DefinedTerm
    181 sg:grant.8454848 http://pending.schema.org/fundedItem sg:pub.10.1007/s13593-022-00805-4
    182 rdf:type schema:MonetaryGrant
    183 sg:journal.1430196 schema:issn 1773-0155
    184 1774-0746
    185 schema:name Agronomy for Sustainable Development
    186 schema:publisher Springer Nature
    187 rdf:type schema:Periodical
    188 sg:person.010760664607.06 schema:affiliation grid-institutes:grid.433014.1
    189 schema:familyName Webber
    190 schema:givenName Heidi
    191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010760664607.06
    192 rdf:type schema:Person
    193 sg:person.011116520652.09 schema:affiliation grid-institutes:grid.433014.1
    194 schema:familyName Grahmann
    195 schema:givenName Kathrin
    196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011116520652.09
    197 rdf:type schema:Person
    198 sg:person.012101273060.01 schema:affiliation grid-institutes:grid.433014.1
    199 schema:familyName Ewert
    200 schema:givenName Frank
    201 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012101273060.01
    202 rdf:type schema:Person
    203 sg:person.013603464737.30 schema:affiliation grid-institutes:grid.10388.32
    204 schema:familyName Hernández-Ochoa
    205 schema:givenName Ixchel M.
    206 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013603464737.30
    207 rdf:type schema:Person
    208 sg:person.013767154271.49 schema:affiliation grid-institutes:grid.10388.32
    209 schema:familyName Gaiser
    210 schema:givenName Thomas
    211 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013767154271.49
    212 rdf:type schema:Person
    213 sg:person.014657474307.35 schema:affiliation grid-institutes:grid.426587.a
    214 schema:familyName Kersebaum
    215 schema:givenName Kurt-Christian
    216 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014657474307.35
    217 rdf:type schema:Person
    218 sg:person.016252160663.00 schema:affiliation grid-institutes:grid.10388.32
    219 schema:familyName Seidel
    220 schema:givenName Sabine Julia
    221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016252160663.00
    222 rdf:type schema:Person
    223 sg:pub.10.1007/978-3-030-03448-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112157325
    224 https://doi.org/10.1007/978-3-030-03448-1
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1007/bf00133027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049664412
    227 https://doi.org/10.1007/bf00133027
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1007/bf03356458 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039821260
    230 https://doi.org/10.1007/bf03356458
    231 rdf:type schema:CreativeWork
    232 sg:pub.10.1007/s00271-009-0189-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000964139
    233 https://doi.org/10.1007/s00271-009-0189-5
    234 rdf:type schema:CreativeWork
    235 sg:pub.10.1007/s00477-019-01700-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1117736504
    236 https://doi.org/10.1007/s00477-019-01700-3
    237 rdf:type schema:CreativeWork
    238 sg:pub.10.1007/s00484-020-02053-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1134450990
    239 https://doi.org/10.1007/s00484-020-02053-1
    240 rdf:type schema:CreativeWork
    241 sg:pub.10.1007/s00704-017-2076-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1083864061
    242 https://doi.org/10.1007/s00704-017-2076-y
    243 rdf:type schema:CreativeWork
    244 sg:pub.10.1007/s10705-006-9044-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050652938
    245 https://doi.org/10.1007/s10705-006-9044-8
    246 rdf:type schema:CreativeWork
    247 sg:pub.10.1007/s10705-019-10013-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1119901656
    248 https://doi.org/10.1007/s10705-019-10013-4
    249 rdf:type schema:CreativeWork
    250 sg:pub.10.1007/s10980-012-9772-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1032368749
    251 https://doi.org/10.1007/s10980-012-9772-x
    252 rdf:type schema:CreativeWork
    253 sg:pub.10.1007/s10980-020-01155-w schema:sameAs https://app.dimensions.ai/details/publication/pub.1132792268
    254 https://doi.org/10.1007/s10980-020-01155-w
    255 rdf:type schema:CreativeWork
    256 sg:pub.10.1007/s11104-014-2311-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015142673
    257 https://doi.org/10.1007/s11104-014-2311-6
    258 rdf:type schema:CreativeWork
    259 sg:pub.10.1007/s11119-018-9617-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1107723598
    260 https://doi.org/10.1007/s11119-018-9617-y
    261 rdf:type schema:CreativeWork
    262 sg:pub.10.1007/s13593-014-0277-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045140280
    263 https://doi.org/10.1007/s13593-014-0277-7
    264 rdf:type schema:CreativeWork
    265 sg:pub.10.1007/s13593-015-0306-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008932373
    266 https://doi.org/10.1007/s13593-015-0306-1
    267 rdf:type schema:CreativeWork
    268 sg:pub.10.1007/s13593-019-0562-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112634545
    269 https://doi.org/10.1007/s13593-019-0562-6
    270 rdf:type schema:CreativeWork
    271 sg:pub.10.1007/s13593-020-00617-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1126825991
    272 https://doi.org/10.1007/s13593-020-00617-4
    273 rdf:type schema:CreativeWork
    274 sg:pub.10.1023/a:1022352229863 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026732423
    275 https://doi.org/10.1023/a:1022352229863
    276 rdf:type schema:CreativeWork
    277 sg:pub.10.1023/a:1026417120254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032976167
    278 https://doi.org/10.1023/a:1026417120254
    279 rdf:type schema:CreativeWork
    280 sg:pub.10.1023/b:fres.0000012231.89516.80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052991634
    281 https://doi.org/10.1023/b:fres.0000012231.89516.80
    282 rdf:type schema:CreativeWork
    283 sg:pub.10.1038/s41561-021-00712-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1136738919
    284 https://doi.org/10.1038/s41561-021-00712-5
    285 rdf:type schema:CreativeWork
    286 sg:pub.10.1038/s41598-017-14271-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092262089
    287 https://doi.org/10.1038/s41598-017-14271-6
    288 rdf:type schema:CreativeWork
    289 sg:pub.10.1038/s41893-020-0510-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1126700196
    290 https://doi.org/10.1038/s41893-020-0510-0
    291 rdf:type schema:CreativeWork
    292 sg:pub.10.1051/agro:2007057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032996860
    293 https://doi.org/10.1051/agro:2007057
    294 rdf:type schema:CreativeWork
    295 sg:pub.10.1186/2190-4715-26-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043803333
    296 https://doi.org/10.1186/2190-4715-26-1
    297 rdf:type schema:CreativeWork
    298 grid-institutes:grid.10388.32 schema:alternateName Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany
    299 schema:name Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany
    300 rdf:type schema:Organization
    301 grid-institutes:grid.426587.a schema:alternateName Global Change Research Institute CAS, Brno, Czech Republic
    302 schema:name Data Analysis & Simulation: Ecosystem Modelling, Leibniz Center for Agricultural Landscape Research, ZALF, Müncheberg, Germany
    303 Global Change Research Institute CAS, Brno, Czech Republic
    304 rdf:type schema:Organization
    305 grid-institutes:grid.433014.1 schema:alternateName Agricultural Landscape Systems: Integrated Crop System Analysis and Modelling, Leibniz Center for Agricultural Landscape Research, ZALF, Müncheberg, Germany
    306 Data analysis & Simulation: Dimensionality Assessment and Reduction; Land Use Governance: Resource-Efficient Cropping Systems, Leibniz Center for Agricultural Landscape Research, ZALF, Müncheberg, Germany
    307 Leibniz Center for Agricultural Landscape Research, ZALF, Müncheberg, Germany
    308 schema:name Agricultural Landscape Systems: Integrated Crop System Analysis and Modelling, Leibniz Center for Agricultural Landscape Research, ZALF, Müncheberg, Germany
    309 Data analysis & Simulation: Dimensionality Assessment and Reduction; Land Use Governance: Resource-Efficient Cropping Systems, Leibniz Center for Agricultural Landscape Research, ZALF, Müncheberg, Germany
    310 Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany
    311 Leibniz Center for Agricultural Landscape Research, ZALF, Müncheberg, Germany
    312 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...