Farming options for The Netherlands explored by multi-objective modelling
Identifieur interne : 001098 ( Istex/Corpus ); précédent : 001097; suivant : 001099Farming options for The Netherlands explored by multi-objective modelling
Auteurs : H. F. M. Ten Berge ; M. K. Van Ittersum ; W. A. H. Rossing ; G. W. J. Van De Ven ; J. SchansSource :
- European Journal of Agronomy [ 1161-0301 ] ; 2000.
English descriptors
Abstract
Intensive agriculture in The Netherlands has a price in the form of environmental degradation and the diminution of nature and landscape values. A reorientation of farming is needed to find a new balance between economic goals and rural employment, and care for clean water and air, animal well-being, safe food, and the preservation of soil, landscape and biodiversity. The search for farm systems that meet such multiple goals requires a systematic combination of (a) agrotechnical, agroecological and agroeconomic knowledge, with (b) the stakeholders’ joint agreement on normative objectives, to arrive at conceptual new designs followed by (c) empirical work to test, adapt and refine these under real commercial farming conditions. In this paper explorative modelling at the whole farm level is presented as a method that effectively integrates component knowledge at crop or animal level, and outlines the consequences of particular choices on scientific grounds. This enables quantitative consideration of a broad spectrum of alternative farming systems, including very innovative and risky ones, before empirical work starts. It thus contributes to a transparent learning and development process needed to arrive at farm concepts acceptable to both entrepreneurs and society. Three case studies are presented to illustrate the method: dairy farming on sandy soils; highly intensified flower bulb industry in sensitive areas in the western Netherlands; and integrated arable farming. Trade-offs between economic and environmental objectives were assessed in all three cases, as well as virtual farm configurations that best satisfy specified priority settings of objectives. In two of the three cases the mutual reinforcement and true integration of modelling and on-farm empirical research appeared difficult, but for obvious reasons. Only in the flower bulb case was the explorative approach utilized to its full potential by involving a broad platform of stakeholders. The other two case studies lacked such formalised platforms and their impact remained limited. Three critical success factors for explorative modelling are identified: to cover a well-differentiated spectrum of possible production technologies; early timing of modelling work relative to empirical farm prototyping; and involvement of stakeholders throughout.
Url:
DOI: 10.1016/S1161-0301(00)00078-2
Links to Exploration step
ISTEX:1881BB2E5C7CABE26587E7C8E09E04695EC34CDFLe document en format XML
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Box 16, 6700 AA Wageningen, The Netherlands</mods:affiliation></affiliation><affiliation><mods:affiliation>Corresponding author. Tel.: +31-31-7475951</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: h.f.m.tenberge@ab.wag-ur.nl</mods:affiliation></affiliation></author><author><name sortKey="Van Ittersum, M K" sort="Van Ittersum, M K" uniqKey="Van Ittersum M" first="M. K." last="Van Ittersum">M. K. Van Ittersum</name><affiliation><mods:affiliation>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</mods:affiliation></affiliation><affiliation><mods:affiliation>Group Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</mods:affiliation></affiliation></author><author><name sortKey="Rossing, W A H" sort="Rossing, W A H" uniqKey="Rossing W" first="W. A. H." last="Rossing">W. A. H. Rossing</name><affiliation><mods:affiliation>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</mods:affiliation></affiliation><affiliation><mods:affiliation>Group Biological Farming Systems, Wageningen University, Marijkeweg 22, 6709 PG Wageningen, The Netherlands</mods:affiliation></affiliation></author><author><name sortKey="Van De Ven, G W J" sort="Van De Ven, G W J" uniqKey="Van De Ven G" first="G. W. J." last="Van De Ven">G. W. J. Van De Ven</name><affiliation><mods:affiliation>Centre for Environmental Science (CML), P.O. Box 9518, 2300 RA Leiden, The Netherlands</mods:affiliation></affiliation></author><author><name sortKey="Schans, J" sort="Schans, J" uniqKey="Schans J" first="J." last="Schans">J. Schans</name><affiliation><mods:affiliation>Plant Protection Service, P.O. Box 9102, 6700 HC Wageningen, The Netherlands</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">European Journal of Agronomy</title><title level="j" type="abbrev">EURAGR</title><idno type="ISSN">1161-0301</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2000">2000</date><biblScope unit="volume">13</biblScope><biblScope unit="issue">2–3</biblScope><biblScope unit="page" from="263">263</biblScope><biblScope unit="page" to="277">277</biblScope></imprint><idno type="ISSN">1161-0301</idno></series><idno type="istex">1881BB2E5C7CABE26587E7C8E09E04695EC34CDF</idno><idno type="DOI">10.1016/S1161-0301(00)00078-2</idno><idno type="PII">S1161-0301(00)00078-2</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1161-0301</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Exploration</term><term>Farming systems</term><term>Intensive farming</term><term>Modelling</term><term>Multiple goal</term><term>Optimisation</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Intensive agriculture in The Netherlands has a price in the form of environmental degradation and the diminution of nature and landscape values. A reorientation of farming is needed to find a new balance between economic goals and rural employment, and care for clean water and air, animal well-being, safe food, and the preservation of soil, landscape and biodiversity. The search for farm systems that meet such multiple goals requires a systematic combination of (a) agrotechnical, agroecological and agroeconomic knowledge, with (b) the stakeholders’ joint agreement on normative objectives, to arrive at conceptual new designs followed by (c) empirical work to test, adapt and refine these under real commercial farming conditions. In this paper explorative modelling at the whole farm level is presented as a method that effectively integrates component knowledge at crop or animal level, and outlines the consequences of particular choices on scientific grounds. This enables quantitative consideration of a broad spectrum of alternative farming systems, including very innovative and risky ones, before empirical work starts. It thus contributes to a transparent learning and development process needed to arrive at farm concepts acceptable to both entrepreneurs and society. Three case studies are presented to illustrate the method: dairy farming on sandy soils; highly intensified flower bulb industry in sensitive areas in the western Netherlands; and integrated arable farming. Trade-offs between economic and environmental objectives were assessed in all three cases, as well as virtual farm configurations that best satisfy specified priority settings of objectives. In two of the three cases the mutual reinforcement and true integration of modelling and on-farm empirical research appeared difficult, but for obvious reasons. Only in the flower bulb case was the explorative approach utilized to its full potential by involving a broad platform of stakeholders. The other two case studies lacked such formalised platforms and their impact remained limited. Three critical success factors for explorative modelling are identified: to cover a well-differentiated spectrum of possible production technologies; early timing of modelling work relative to empirical farm prototyping; and involvement of stakeholders throughout.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>H.F.M. ten Berge</name><affiliations><json:string>Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands</json:string><json:string>Corresponding author. Tel.: +31-31-7475951</json:string><json:string>E-mail: h.f.m.tenberge@ab.wag-ur.nl</json:string></affiliations></json:item><json:item><name>M.K. van Ittersum</name><affiliations><json:string>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</json:string><json:string>Group Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</json:string></affiliations></json:item><json:item><name>W.A.H. Rossing</name><affiliations><json:string>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</json:string><json:string>Group Biological Farming Systems, Wageningen University, Marijkeweg 22, 6709 PG Wageningen, The Netherlands</json:string></affiliations></json:item><json:item><name>G.W.J. van de Ven</name><affiliations><json:string>Centre for Environmental Science (CML), P.O. Box 9518, 2300 RA Leiden, The Netherlands</json:string></affiliations></json:item><json:item><name>J. Schans</name><affiliations><json:string>Plant Protection Service, P.O. Box 9102, 6700 HC Wageningen, The Netherlands</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Farming systems</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Exploration</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Modelling</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Intensive farming</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Multiple goal</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Optimisation</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>Full-length article</json:string></originalGenre><abstract>Intensive agriculture in The Netherlands has a price in the form of environmental degradation and the diminution of nature and landscape values. A reorientation of farming is needed to find a new balance between economic goals and rural employment, and care for clean water and air, animal well-being, safe food, and the preservation of soil, landscape and biodiversity. The search for farm systems that meet such multiple goals requires a systematic combination of (a) agrotechnical, agroecological and agroeconomic knowledge, with (b) the stakeholders’ joint agreement on normative objectives, to arrive at conceptual new designs followed by (c) empirical work to test, adapt and refine these under real commercial farming conditions. In this paper explorative modelling at the whole farm level is presented as a method that effectively integrates component knowledge at crop or animal level, and outlines the consequences of particular choices on scientific grounds. This enables quantitative consideration of a broad spectrum of alternative farming systems, including very innovative and risky ones, before empirical work starts. It thus contributes to a transparent learning and development process needed to arrive at farm concepts acceptable to both entrepreneurs and society. Three case studies are presented to illustrate the method: dairy farming on sandy soils; highly intensified flower bulb industry in sensitive areas in the western Netherlands; and integrated arable farming. Trade-offs between economic and environmental objectives were assessed in all three cases, as well as virtual farm configurations that best satisfy specified priority settings of objectives. In two of the three cases the mutual reinforcement and true integration of modelling and on-farm empirical research appeared difficult, but for obvious reasons. Only in the flower bulb case was the explorative approach utilized to its full potential by involving a broad platform of stakeholders. The other two case studies lacked such formalised platforms and their impact remained limited. Three critical success factors for explorative modelling are identified: to cover a well-differentiated spectrum of possible production technologies; early timing of modelling work relative to empirical farm prototyping; and involvement of stakeholders throughout.</abstract><qualityIndicators><score>8</score><pdfVersion>1.2</pdfVersion><pdfPageSize>536 x 674 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>6</keywordCount><abstractCharCount>2338</abstractCharCount><pdfWordCount>7749</pdfWordCount><pdfCharCount>48222</pdfCharCount><pdfPageCount>15</pdfPageCount><abstractWordCount>338</abstractWordCount></qualityIndicators><title>Farming options for The Netherlands explored by multi-objective modelling</title><pii><json:string>S1161-0301(00)00078-2</json:string></pii><genre><json:string>research-article</json:string></genre><host><volume>13</volume><pii><json:string>S1161-0301(00)X0020-2</json:string></pii><pages><last>277</last><first>263</first></pages><issn><json:string>1161-0301</json:string></issn><issue>2–3</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><title>European Journal of Agronomy</title><publicationDate>2000</publicationDate></host><categories><wos><json:string>science</json:string><json:string>agronomy</json:string></wos><scienceMetrix><json:string>applied sciences</json:string><json:string>agriculture, fisheries & forestry</json:string><json:string>agronomy & agriculture</json:string></scienceMetrix></categories><publicationDate>2000</publicationDate><copyrightDate>2000</copyrightDate><doi><json:string>10.1016/S1161-0301(00)00078-2</json:string></doi><id>1881BB2E5C7CABE26587E7C8E09E04695EC34CDF</id><score>0.038804144</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/1881BB2E5C7CABE26587E7C8E09E04695EC34CDF/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/1881BB2E5C7CABE26587E7C8E09E04695EC34CDF/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/1881BB2E5C7CABE26587E7C8E09E04695EC34CDF/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Farming options for The Netherlands explored by multi-objective modelling</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>©2000 Elsevier Science B.V.</p></availability><date>2000</date></publicationStmt><notesStmt><note type="content">Fig. 1: Schematic representation of the methodology followed in explorative modelling of farming systems as linear combinations of so-called ‘activities’. Activities represent crops (or animals) with specified production technologies.</note><note type="content">Fig. 2: Optimal allocation patterns of farm land to crop activities as calculated by the Dairy Farming Model, under different priorities: minimum N-surplus, minimum P-surplus, minimum ammonia volatilisation, minimum nitrate leaching, maximum labour income, and maximum milk production. The top bar represents the actual land use pattern at the ‘De Marke’ experiment farm.</note><note type="content">Fig. 3: Labour income as calculated by the dairy farming model, under different priorities: minimum N-surplus, minimum P-surplus, minimum ammonia volatilisation, minimum nitrate leaching, maximum labour income, and maximum milk production. The top bar represents labour income at the De Marke experiment farm.</note><note type="content">Fig. 4: Iso-lines of calculated maximum farm gross margin (index) attainable at different levels of pesticide input (kg a.i./ha) and nutrient surplus (kg N/ha) for a 15 ha flower bulb farm with three full-time workers, on a sandy soil, with a possibility to rent additional ‘fresh’ (=healthy) land. All solutions meet the environmental requirements for the year 2000. An index value of 100 represents the most profitable systems (gross margin Dfl 205 000/ha). Letters refer to ‘stations’ on the paths to reduce pesticide input (A–B–C–D) and nitrogen surplus (A–E–F–G). The inset shows the crop rotations and the acreage of healthy clay land rented corresponding to letters on the map.</note><note type="content">Fig. 5: Maximum gross margin per ha for arable farming on clay soil under constraints on pesticide input (kg a.i./ha) and nitrogen loss (kg N/ha) for 1992–1993 price levels including EU support regulations. Letters A, B and C refer to rotation schemes shown in circles (right), where shading refers to intensive crop protection regimes.</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Farming options for The Netherlands explored by multi-objective modelling</title><author xml:id="author-1"><persName><forename type="first">H.F.M.</forename><surname>ten Berge</surname></persName><email>h.f.m.tenberge@ab.wag-ur.nl</email><affiliation>Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands</affiliation><affiliation>Corresponding author. Tel.: +31-31-7475951</affiliation></author><author xml:id="author-2"><persName><forename type="first">M.K.</forename><surname>van Ittersum</surname></persName><affiliation>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</affiliation><affiliation>Group Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</affiliation></author><author xml:id="author-3"><persName><forename type="first">W.A.H.</forename><surname>Rossing</surname></persName><affiliation>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</affiliation><affiliation>Group Biological Farming Systems, Wageningen University, Marijkeweg 22, 6709 PG Wageningen, The Netherlands</affiliation></author><author xml:id="author-4"><persName><forename type="first">G.W.J.</forename><surname>van de Ven</surname></persName><affiliation>Centre for Environmental Science (CML), P.O. Box 9518, 2300 RA Leiden, The Netherlands</affiliation></author><author xml:id="author-5"><persName><forename type="first">J.</forename><surname>Schans</surname></persName><affiliation>Plant Protection Service, P.O. Box 9102, 6700 HC Wageningen, The Netherlands</affiliation></author></analytic><monogr><title level="j">European Journal of Agronomy</title><title level="j" type="abbrev">EURAGR</title><idno type="pISSN">1161-0301</idno><idno type="PII">S1161-0301(00)X0020-2</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2000"></date><biblScope unit="volume">13</biblScope><biblScope unit="issue">2–3</biblScope><biblScope unit="page" from="263">263</biblScope><biblScope unit="page" to="277">277</biblScope></imprint></monogr><idno type="istex">1881BB2E5C7CABE26587E7C8E09E04695EC34CDF</idno><idno type="DOI">10.1016/S1161-0301(00)00078-2</idno><idno type="PII">S1161-0301(00)00078-2</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2000</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Intensive agriculture in The Netherlands has a price in the form of environmental degradation and the diminution of nature and landscape values. A reorientation of farming is needed to find a new balance between economic goals and rural employment, and care for clean water and air, animal well-being, safe food, and the preservation of soil, landscape and biodiversity. The search for farm systems that meet such multiple goals requires a systematic combination of (a) agrotechnical, agroecological and agroeconomic knowledge, with (b) the stakeholders’ joint agreement on normative objectives, to arrive at conceptual new designs followed by (c) empirical work to test, adapt and refine these under real commercial farming conditions. In this paper explorative modelling at the whole farm level is presented as a method that effectively integrates component knowledge at crop or animal level, and outlines the consequences of particular choices on scientific grounds. This enables quantitative consideration of a broad spectrum of alternative farming systems, including very innovative and risky ones, before empirical work starts. It thus contributes to a transparent learning and development process needed to arrive at farm concepts acceptable to both entrepreneurs and society. Three case studies are presented to illustrate the method: dairy farming on sandy soils; highly intensified flower bulb industry in sensitive areas in the western Netherlands; and integrated arable farming. Trade-offs between economic and environmental objectives were assessed in all three cases, as well as virtual farm configurations that best satisfy specified priority settings of objectives. In two of the three cases the mutual reinforcement and true integration of modelling and on-farm empirical research appeared difficult, but for obvious reasons. Only in the flower bulb case was the explorative approach utilized to its full potential by involving a broad platform of stakeholders. The other two case studies lacked such formalised platforms and their impact remained limited. Three critical success factors for explorative modelling are identified: to cover a well-differentiated spectrum of possible production technologies; early timing of modelling work relative to empirical farm prototyping; and involvement of stakeholders throughout.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>Farming systems</term></item><item><term>Exploration</term></item><item><term>Modelling</term></item><item><term>Intensive farming</term></item><item><term>Multiple goal</term></item><item><term>Optimisation</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1999-10-11">Modified</change><change when="2000">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/1881BB2E5C7CABE26587E7C8E09E04695EC34CDF/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity><istex:entity SYSTEM="gr3" NDATA="IMAGE" name="gr3"></istex:entity><istex:entity SYSTEM="gr4" NDATA="IMAGE" name="gr4"></istex:entity><istex:entity SYSTEM="gr5" NDATA="IMAGE" name="gr5"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla" xml:lang="en"><item-info><jid>EURAGR</jid><aid>2243</aid><ce:pii>S1161-0301(00)00078-2</ce:pii><ce:doi>10.1016/S1161-0301(00)00078-2</ce:doi><ce:copyright type="full-transfer" year="2000">Elsevier Science B.V.</ce:copyright></item-info><head><ce:title>Farming options for The Netherlands explored by multi-objective modelling</ce:title><ce:author-group><ce:author><ce:given-name>H.F.M.</ce:given-name><ce:surname>ten Berge</ce:surname><ce:cross-ref refid="AFF1"><ce:sup>a</ce:sup></ce:cross-ref><ce:cross-ref refid="CORR1">*</ce:cross-ref><ce:e-address>h.f.m.tenberge@ab.wag-ur.nl</ce:e-address></ce:author><ce:author><ce:given-name>M.K.</ce:given-name><ce:surname>van Ittersum</ce:surname><ce:cross-ref refid="AFF2"><ce:sup>b</ce:sup></ce:cross-ref><ce:cross-ref refid="AFF5"><ce:sup>e</ce:sup></ce:cross-ref></ce:author><ce:author><ce:given-name>W.A.H.</ce:given-name><ce:surname>Rossing</ce:surname><ce:cross-ref refid="AFF2"><ce:sup>b</ce:sup></ce:cross-ref><ce:cross-ref refid="AFF6"><ce:sup>f</ce:sup></ce:cross-ref></ce:author><ce:author><ce:given-name>G.W.J.</ce:given-name><ce:surname>van de Ven</ce:surname><ce:cross-ref refid="AFF3"><ce:sup>c</ce:sup></ce:cross-ref></ce:author><ce:author><ce:given-name>J.</ce:given-name><ce:surname>Schans</ce:surname><ce:cross-ref refid="AFF4"><ce:sup>d</ce:sup></ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>a</ce:label><ce:textfn>Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>b</ce:label><ce:textfn>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</ce:textfn></ce:affiliation><ce:affiliation id="AFF3"><ce:label>c</ce:label><ce:textfn>Centre for Environmental Science (CML), P.O. Box 9518, 2300 RA Leiden, The Netherlands</ce:textfn></ce:affiliation><ce:affiliation id="AFF4"><ce:label>d</ce:label><ce:textfn>Plant Protection Service, P.O. Box 9102, 6700 HC Wageningen, The Netherlands</ce:textfn></ce:affiliation><ce:affiliation id="AFF5"><ce:label>e</ce:label><ce:textfn>Group Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</ce:textfn></ce:affiliation><ce:affiliation id="AFF6"><ce:label>f</ce:label><ce:textfn>Group Biological Farming Systems, Wageningen University, Marijkeweg 22, 6709 PG Wageningen, The Netherlands</ce:textfn></ce:affiliation><ce:correspondence id="CORR1"><ce:label>*</ce:label><ce:text>Corresponding author. Tel.: +31-31-7475951</ce:text></ce:correspondence></ce:author-group><ce:date-received day="19" month="2" year="1999"></ce:date-received><ce:date-revised day="11" month="10" year="1999"></ce:date-revised><ce:date-accepted day="28" month="11" year="1999"></ce:date-accepted><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Intensive agriculture in The Netherlands has a price in the form of environmental degradation and the diminution of nature and landscape values. A reorientation of farming is needed to find a new balance between economic goals and rural employment, and care for clean water and air, animal well-being, safe food, and the preservation of soil, landscape and biodiversity. The search for farm systems that meet such multiple goals requires a systematic combination of (a) agrotechnical, agroecological and agroeconomic knowledge, with (b) the stakeholders’ joint agreement on normative objectives, to arrive at conceptual new designs followed by (c) empirical work to test, adapt and refine these under real commercial farming conditions. In this paper explorative modelling at the whole farm level is presented as a method that effectively integrates component knowledge at crop or animal level, and outlines the consequences of particular choices on scientific grounds. This enables quantitative consideration of a broad spectrum of alternative farming systems, including very innovative and risky ones, before empirical work starts. It thus contributes to a transparent learning and development process needed to arrive at farm concepts acceptable to both entrepreneurs and society. Three case studies are presented to illustrate the method: dairy farming on sandy soils; highly intensified flower bulb industry in sensitive areas in the western Netherlands; and integrated arable farming. Trade-offs between economic and environmental objectives were assessed in all three cases, as well as virtual farm configurations that best satisfy specified priority settings of objectives. In two of the three cases the mutual reinforcement and true integration of modelling and on-farm empirical research appeared difficult, but for obvious reasons. Only in the flower bulb case was the explorative approach utilized to its full potential by involving a broad platform of stakeholders. The other two case studies lacked such formalised platforms and their impact remained limited. Three critical success factors for explorative modelling are identified: to cover a well-differentiated spectrum of possible production technologies; early timing of modelling work relative to empirical farm prototyping; and involvement of stakeholders throughout.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Farming systems</ce:text></ce:keyword><ce:keyword><ce:text>Exploration</ce:text></ce:keyword><ce:keyword><ce:text>Modelling</ce:text></ce:keyword><ce:keyword><ce:text>Intensive farming</ce:text></ce:keyword><ce:keyword><ce:text>Multiple goal</ce:text></ce:keyword><ce:keyword><ce:text>Optimisation</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Farming options for The Netherlands explored by multi-objective modelling</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>Farming options for The Netherlands explored by multi-objective modelling</title></titleInfo><name type="personal"><namePart type="given">H.F.M.</namePart><namePart type="family">ten Berge</namePart><affiliation>Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands</affiliation><affiliation>Corresponding author. Tel.: +31-31-7475951</affiliation><affiliation>E-mail: h.f.m.tenberge@ab.wag-ur.nl</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">M.K.</namePart><namePart type="family">van Ittersum</namePart><affiliation>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</affiliation><affiliation>Group Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">W.A.H.</namePart><namePart type="family">Rossing</namePart><affiliation>Department of Theoretical Production Ecology, Wageningen Agricultural University, P.O. Box 430, 6700 AK Wageningen, The Netherlands</affiliation><affiliation>Group Biological Farming Systems, Wageningen University, Marijkeweg 22, 6709 PG Wageningen, The Netherlands</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">G.W.J.</namePart><namePart type="family">van de Ven</namePart><affiliation>Centre for Environmental Science (CML), P.O. Box 9518, 2300 RA Leiden, The Netherlands</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">J.</namePart><namePart type="family">Schans</namePart><affiliation>Plant Protection Service, P.O. Box 9102, 6700 HC Wageningen, The Netherlands</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">2000</dateIssued><dateModified encoding="w3cdtf">1999-10-11</dateModified><copyrightDate encoding="w3cdtf">2000</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Intensive agriculture in The Netherlands has a price in the form of environmental degradation and the diminution of nature and landscape values. A reorientation of farming is needed to find a new balance between economic goals and rural employment, and care for clean water and air, animal well-being, safe food, and the preservation of soil, landscape and biodiversity. The search for farm systems that meet such multiple goals requires a systematic combination of (a) agrotechnical, agroecological and agroeconomic knowledge, with (b) the stakeholders’ joint agreement on normative objectives, to arrive at conceptual new designs followed by (c) empirical work to test, adapt and refine these under real commercial farming conditions. In this paper explorative modelling at the whole farm level is presented as a method that effectively integrates component knowledge at crop or animal level, and outlines the consequences of particular choices on scientific grounds. This enables quantitative consideration of a broad spectrum of alternative farming systems, including very innovative and risky ones, before empirical work starts. It thus contributes to a transparent learning and development process needed to arrive at farm concepts acceptable to both entrepreneurs and society. Three case studies are presented to illustrate the method: dairy farming on sandy soils; highly intensified flower bulb industry in sensitive areas in the western Netherlands; and integrated arable farming. Trade-offs between economic and environmental objectives were assessed in all three cases, as well as virtual farm configurations that best satisfy specified priority settings of objectives. In two of the three cases the mutual reinforcement and true integration of modelling and on-farm empirical research appeared difficult, but for obvious reasons. Only in the flower bulb case was the explorative approach utilized to its full potential by involving a broad platform of stakeholders. The other two case studies lacked such formalised platforms and their impact remained limited. Three critical success factors for explorative modelling are identified: to cover a well-differentiated spectrum of possible production technologies; early timing of modelling work relative to empirical farm prototyping; and involvement of stakeholders throughout.</abstract><note type="content">Fig. 1: Schematic representation of the methodology followed in explorative modelling of farming systems as linear combinations of so-called ‘activities’. Activities represent crops (or animals) with specified production technologies.</note><note type="content">Fig. 2: Optimal allocation patterns of farm land to crop activities as calculated by the Dairy Farming Model, under different priorities: minimum N-surplus, minimum P-surplus, minimum ammonia volatilisation, minimum nitrate leaching, maximum labour income, and maximum milk production. The top bar represents the actual land use pattern at the ‘De Marke’ experiment farm.</note><note type="content">Fig. 3: Labour income as calculated by the dairy farming model, under different priorities: minimum N-surplus, minimum P-surplus, minimum ammonia volatilisation, minimum nitrate leaching, maximum labour income, and maximum milk production. The top bar represents labour income at the De Marke experiment farm.</note><note type="content">Fig. 4: Iso-lines of calculated maximum farm gross margin (index) attainable at different levels of pesticide input (kg a.i./ha) and nutrient surplus (kg N/ha) for a 15 ha flower bulb farm with three full-time workers, on a sandy soil, with a possibility to rent additional ‘fresh’ (=healthy) land. All solutions meet the environmental requirements for the year 2000. An index value of 100 represents the most profitable systems (gross margin Dfl 205 000/ha). Letters refer to ‘stations’ on the paths to reduce pesticide input (A–B–C–D) and nitrogen surplus (A–E–F–G). The inset shows the crop rotations and the acreage of healthy clay land rented corresponding to letters on the map.</note><note type="content">Fig. 5: Maximum gross margin per ha for arable farming on clay soil under constraints on pesticide input (kg a.i./ha) and nitrogen loss (kg N/ha) for 1992–1993 price levels including EU support regulations. Letters A, B and C refer to rotation schemes shown in circles (right), where shading refers to intensive crop protection regimes.</note><subject lang="en"><genre>Keywords</genre><topic>Farming systems</topic><topic>Exploration</topic><topic>Modelling</topic><topic>Intensive farming</topic><topic>Multiple goal</topic><topic>Optimisation</topic></subject><relatedItem type="host"><titleInfo><title>European Journal of Agronomy</title></titleInfo><titleInfo type="abbreviated"><title>EURAGR</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">200007</dateIssued></originInfo><identifier type="ISSN">1161-0301</identifier><identifier type="PII">S1161-0301(00)X0020-2</identifier><part><date>200007</date><detail type="volume"><number>13</number><caption>vol.</caption></detail><detail type="issue"><number>2–3</number><caption>no.</caption></detail><extent unit="issue pages"><start>83</start><end>278</end></extent><extent unit="pages"><start>263</start><end>277</end></extent></part></relatedItem><identifier type="istex">1881BB2E5C7CABE26587E7C8E09E04695EC34CDF</identifier><identifier type="DOI">10.1016/S1161-0301(00)00078-2</identifier><identifier type="PII">S1161-0301(00)00078-2</identifier><accessCondition type="use and reproduction" contentType="copyright">©2000 Elsevier Science B.V.</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science B.V., ©2000</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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