Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.
Identifieur interne : 002473 ( PubMed/Corpus ); précédent : 002472; suivant : 002474Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.
Auteurs : Pierre Casadebaig ; Bangyou Zheng ; Scott Chapman ; Neil Huth ; Robert Faivre ; Karine ChenuSource :
- PloS one [ 1932-6203 ] ; 2016.
English descriptors
- KwdEn :
- Adaptation, Physiological (genetics), Adaptation, Physiological (physiology), Australia, Computational Biology, Computer Simulation, Crops, Agricultural (genetics), Crops, Agricultural (physiology), Droughts, Ecosystem, Models, Biological, Quantitative Trait, Heritable, Rain, Triticum (genetics), Triticum (physiology).
- MESH :
- geographic : Australia.
- genetics : Adaptation, Physiological, Crops, Agricultural, Triticum.
- physiology : Adaptation, Physiological, Crops, Agricultural, Triticum.
- Computational Biology, Computer Simulation, Droughts, Ecosystem, Models, Biological, Quantitative Trait, Heritable, Rain.
Abstract
A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.
DOI: 10.1371/journal.pone.0146385
PubMed: 26799483
Links to Exploration step
pubmed:26799483Le document en format XML
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(QAAFI), The University of Queensland, St Lucia, QLD 4350, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Huth, Neil" sort="Huth, Neil" uniqKey="Huth N" first="Neil" last="Huth">Neil Huth</name><affiliation><nlm:affiliation>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Faivre, Robert" sort="Faivre, Robert" uniqKey="Faivre R" first="Robert" last="Faivre">Robert Faivre</name><affiliation><nlm:affiliation>INRA, UR875 MIAT, 31326 Castanet-Tolosan, France.</nlm:affiliation></affiliation></author><author><name sortKey="Chenu, Karine" sort="Chenu, Karine" uniqKey="Chenu K" first="Karine" last="Chenu">Karine Chenu</name><affiliation><nlm:affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350, Australia.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno 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Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Chapman, Scott" sort="Chapman, Scott" uniqKey="Chapman S" first="Scott" last="Chapman">Scott Chapman</name><affiliation><nlm:affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Huth, Neil" sort="Huth, Neil" uniqKey="Huth N" first="Neil" last="Huth">Neil Huth</name><affiliation><nlm:affiliation>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Faivre, Robert" sort="Faivre, Robert" uniqKey="Faivre R" first="Robert" last="Faivre">Robert Faivre</name><affiliation><nlm:affiliation>INRA, UR875 MIAT, 31326 Castanet-Tolosan, France.</nlm:affiliation></affiliation></author><author><name sortKey="Chenu, Karine" sort="Chenu, Karine" uniqKey="Chenu K" first="Karine" last="Chenu">Karine Chenu</name><affiliation><nlm:affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350, Australia.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adaptation, Physiological (genetics)</term><term>Adaptation, Physiological (physiology)</term><term>Australia</term><term>Computational Biology</term><term>Computer Simulation</term><term>Crops, Agricultural (genetics)</term><term>Crops, Agricultural (physiology)</term><term>Droughts</term><term>Ecosystem</term><term>Models, Biological</term><term>Quantitative Trait, Heritable</term><term>Rain</term><term>Triticum (genetics)</term><term>Triticum (physiology)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Australia</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Adaptation, Physiological</term><term>Crops, Agricultural</term><term>Triticum</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Adaptation, Physiological</term><term>Crops, Agricultural</term><term>Triticum</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Computational Biology</term><term>Computer Simulation</term><term>Droughts</term><term>Ecosystem</term><term>Models, Biological</term><term>Quantitative Trait, Heritable</term><term>Rain</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26799483</PMID><DateCreated><Year>2016</Year><Month>01</Month><Day>23</Day></DateCreated><DateCompleted><Year>2016</Year><Month>07</Month><Day>25</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><Issue>1</Issue><PubDate><Year>2016</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.</ArticleTitle><Pagination><MedlinePgn>e0146385</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0146385</ELocationID><Abstract><AbstractText>A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Casadebaig</LastName><ForeName>Pierre</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>INRA, UMR1248 AGIR, 31326 Castanet-Tolosan, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zheng</LastName><ForeName>Bangyou</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>CSIRO Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chapman</LastName><ForeName>Scott</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huth</LastName><ForeName>Neil</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Faivre</LastName><ForeName>Robert</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>INRA, UR875 MIAT, 31326 Castanet-Tolosan, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chenu</LastName><ForeName>Karine</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350, Australia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. 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Appl Genet. 2008 Dec;118(1):123-37</RefSource><PMID Version="1">18818897</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000222" MajorTopicYN="N">Adaptation, Physiological</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001315" MajorTopicYN="N" Type="Geographic">Australia</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="Y">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018556" MajorTopicYN="N">Crops, Agricultural</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000502" 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