Modelling the impacts of pests and diseases on agricultural systems
Identifieur interne : 002073 ( Pmc/Curation ); précédent : 002072; suivant : 002074Modelling the impacts of pests and diseases on agricultural systems
Auteurs : M. Donatelli [Italie] ; R. D. Magarey [États-Unis] ; S. Bregaglio [Italie] ; L. Willocquet [France] ; J. P. M. Whish [Australie] ; S. Savary [France]Source :
- Agricultural Systems [ 0308-521X ] ; 2017.
Abstract
The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.
Url:
DOI: 10.1016/j.agsy.2017.01.019
PubMed: 28701814
PubMed Central: 5485649
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<front><div type="abstract" xml:lang="en"><p>The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Agric Syst</journal-id>
<journal-id journal-id-type="iso-abbrev">Agric Syst</journal-id>
<journal-title-group><journal-title>Agricultural Systems</journal-title>
</journal-title-group>
<issn pub-type="ppub">0308-521X</issn>
<publisher><publisher-name>Elsevier Applied Science [etc.]</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">28701814</article-id>
<article-id pub-id-type="pmc">5485649</article-id>
<article-id pub-id-type="publisher-id">S0308-521X(17)30104-X</article-id>
<article-id pub-id-type="doi">10.1016/j.agsy.2017.01.019</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Modelling the impacts of pests and diseases on agricultural systems</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Donatelli</surname>
<given-names>M.</given-names>
</name>
<email>marcello.donatelli@crea.gov.it</email>
<xref rid="af0005" ref-type="aff">a</xref>
<xref rid="cr0005" ref-type="corresp">⁎</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Magarey</surname>
<given-names>R.D.</given-names>
</name>
<xref rid="af0010" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Bregaglio</surname>
<given-names>S.</given-names>
</name>
<xref rid="af0005" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Willocquet</surname>
<given-names>L.</given-names>
</name>
<xref rid="af0015" ref-type="aff">c</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Whish</surname>
<given-names>J.P.M.</given-names>
</name>
<xref rid="af0020" ref-type="aff">d</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Savary</surname>
<given-names>S.</given-names>
</name>
<xref rid="af0015" ref-type="aff">c</xref>
</contrib>
</contrib-group>
<aff id="af0005"><label>a</label>
CREA - Council for Agricultural Research and Economics, Research Center for Agriculture and Environment, via di Corticella 133, I-40128, Bologna, Italy</aff>
<aff id="af0010"><label>b</label>
Center for Integrated Pest Management, North Carolina State University, Raleigh, NC 27606, USA</aff>
<aff id="af0015"><label>c</label>
AGIR, Université de Toulouse, INRA, INPT, INP- EI PURPAN, Castanet-Tolosan, France</aff>
<aff id="af0020"><label>d</label>
CSIRO Agriculture and Food, 203 Tor St Toowoomba, Qld 4350, Australia</aff>
<author-notes><corresp id="cr0005"><label>⁎</label>
Corresponding author. <email>marcello.donatelli@crea.gov.it</email>
</corresp>
</author-notes>
<pub-date pub-type="pmc-release"><day>1</day>
<month>7</month>
<year>2017</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub"><month>7</month>
<year>2017</year>
</pub-date>
<volume>155</volume>
<fpage>213</fpage>
<lpage>224</lpage>
<history><date date-type="received"><day>29</day>
<month>2</month>
<year>2016</year>
</date>
<date date-type="rev-recd"><day>26</day>
<month>1</month>
<year>2017</year>
</date>
<date date-type="accepted"><day>30</day>
<month>1</month>
<year>2017</year>
</date>
</history>
<permissions><copyright-statement>© 2017 The Author(s)</copyright-statement>
<copyright-year>2017</copyright-year>
<license license-type="CC BY" xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</license-p>
</license>
</permissions>
<abstract id="ab0005"><p>The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.</p>
</abstract>
<abstract abstract-type="author-highlights" id="ab0010"><title>Highlights</title>
<p><list list-type="simple"><list-item id="li0005"><label>•</label>
<p>Overview of the current state of development in coupling pest and disease models to crop models</p>
</list-item>
<list-item id="li0010"><label>•</label>
<p>Technical and scientific challenges in coupling pest and disease model to crop models</p>
</list-item>
<list-item id="li0015"><label>•</label>
<p>Roadmap to improve the simulation of the impacts caused by plant diseases and pests</p>
</list-item>
</list>
</p>
</abstract>
<kwd-group id="ks0005"><title>Keywords</title>
<kwd>Model coupling</kwd>
<kwd>Model integration</kwd>
<kwd>Process-based models</kwd>
<kwd>Yield loss</kwd>
<kwd>Modelling frameworks</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>
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