Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis
Identifieur interne : 003037 ( Ncbi/Merge ); précédent : 003036; suivant : 003038Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis
Auteurs : Pierre Casadebaig [France] ; Bangyou Zheng [Australie] ; Scott Chapman [Australie] ; Neil Huth [Australie] ; Robert Faivre [France] ; Karine Chenu [Australie]Source :
- PLoS ONE [ 1932-6203 ] ; 2016.
Descripteurs français
- KwdFr :
- Adaptation physiologique (génétique), Adaptation physiologique (physiologie), Australie, Biologie informatique, Caractère quantitatif héréditaire, Modèles biologiques, Pluie, Produits agricoles (génétique), Produits agricoles (physiologie), Simulation numérique, Sécheresses, Triticum (génétique), Triticum (physiologie), Écosystème.
- MESH :
- Wicri :
- geographic : Australie.
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
Url:
DOI: 10.1371/journal.pone.0146385
PubMed: 26799483
PubMed Central: 4723307
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PMC:4723307Le document en format XML
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<term>Computer Simulation</term>
<term>Crops, Agricultural (genetics)</term>
<term>Crops, Agricultural (physiology)</term>
<term>Droughts</term>
<term>Ecosystem</term>
<term>Models, Biological</term>
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<term>Caractère quantitatif héréditaire</term>
<term>Modèles biologiques</term>
<term>Pluie</term>
<term>Produits agricoles (génétique)</term>
<term>Produits agricoles (physiologie)</term>
<term>Simulation numérique</term>
<term>Sécheresses</term>
<term>Triticum (génétique)</term>
<term>Triticum (physiologie)</term>
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<front><div type="abstract" xml:lang="en"><p>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 <italic>CO</italic>
<sub>2</sub>
(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.</p>
</div>
</front>
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</author>
<author><name sortKey="Cooper, M" uniqKey="Cooper M">M Cooper</name>
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<author><name sortKey="Chapman, Scott" sort="Chapman, Scott" uniqKey="Chapman S" first="Scott" last="Chapman">Scott Chapman</name>
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<author><name sortKey="Huth, Neil" sort="Huth, Neil" uniqKey="Huth N" first="Neil" last="Huth">Neil Huth</name>
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<author><name sortKey="Chenu, Karine" sort="Chenu, Karine" uniqKey="Chenu K" first="Karine" last="Chenu">Karine Chenu</name>
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<author><name sortKey="Casadebaig, Pierre" sort="Casadebaig, Pierre" uniqKey="Casadebaig P" first="Pierre" last="Casadebaig">Pierre Casadebaig</name>
<affiliation wicri:level="3"><nlm:aff id="aff001"><addr-line>INRA, UMR1248 AGIR, 31326 Castanet-Tolosan, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
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<region type="old region" nuts="2">Midi-Pyrénées</region>
<settlement type="city">Castanet-Tolosan</settlement>
</placeName>
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<author><name sortKey="Zheng, Bangyou" sort="Zheng, Bangyou" uniqKey="Zheng B" first="Bangyou" last="Zheng">Bangyou Zheng</name>
<affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>CSIRO Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067, Australia</addr-line>
</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>CSIRO Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067</wicri:regionArea>
<wicri:noRegion>QLD 4067</wicri:noRegion>
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<author><name sortKey="Chapman, Scott" sort="Chapman, Scott" uniqKey="Chapman S" first="Scott" last="Chapman">Scott Chapman</name>
<affiliation wicri:level="1"><nlm:aff id="aff003"><addr-line>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350, Australia</addr-line>
</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350</wicri:regionArea>
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<author><name sortKey="Huth, Neil" sort="Huth, Neil" uniqKey="Huth N" first="Neil" last="Huth">Neil Huth</name>
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</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350</wicri:regionArea>
<wicri:noRegion>QLD 4350</wicri:noRegion>
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<author><name sortKey="Faivre, Robert" sort="Faivre, Robert" uniqKey="Faivre R" first="Robert" last="Faivre">Robert Faivre</name>
<affiliation wicri:level="1"><nlm:aff id="aff005"><addr-line>INRA, UR875 MIAT, 31326 Castanet-Tolosan, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>INRA, UR875 MIAT, 31326 Castanet-Tolosan</wicri:regionArea>
<wicri:noRegion>31326 Castanet-Tolosan</wicri:noRegion>
<wicri:noRegion>31326 Castanet-Tolosan</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Chenu, Karine" sort="Chenu, Karine" uniqKey="Chenu K" first="Karine" last="Chenu">Karine Chenu</name>
<affiliation wicri:level="1"><nlm:aff id="aff006"><addr-line>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350, Australia</addr-line>
</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350</wicri:regionArea>
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<series><title level="j">PLoS ONE</title>
<idno type="eISSN">1932-6203</idno>
<imprint><date when="2016">2016</date>
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<front><div type="abstract" xml:lang="en"><p>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 <italic>CO</italic>
<sub>2</sub>
(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.</p>
</div>
</front>
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<pubmed><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.</title>
<author><name sortKey="Casadebaig, Pierre" sort="Casadebaig, Pierre" uniqKey="Casadebaig P" first="Pierre" last="Casadebaig">Pierre Casadebaig</name>
<affiliation wicri:level="1"><nlm:affiliation>INRA, UMR1248 AGIR, 31326 Castanet-Tolosan, France.</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>INRA, UMR1248 AGIR, 31326 Castanet-Tolosan</wicri:regionArea>
<wicri:noRegion>31326 Castanet-Tolosan</wicri:noRegion>
<wicri:noRegion>31326 Castanet-Tolosan</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Zheng, Bangyou" sort="Zheng, Bangyou" uniqKey="Zheng B" first="Bangyou" last="Zheng">Bangyou Zheng</name>
<affiliation wicri:level="1"><nlm:affiliation>CSIRO Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>CSIRO Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067</wicri:regionArea>
<wicri:noRegion>QLD 4067</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Chapman, Scott" sort="Chapman, Scott" uniqKey="Chapman S" first="Scott" last="Chapman">Scott Chapman</name>
<affiliation wicri:level="1"><nlm:affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350</wicri:regionArea>
<wicri:noRegion>QLD 4350</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Huth, Neil" sort="Huth, Neil" uniqKey="Huth N" first="Neil" last="Huth">Neil Huth</name>
<affiliation wicri:level="1"><nlm:affiliation>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350</wicri:regionArea>
<wicri:noRegion>QLD 4350</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Faivre, Robert" sort="Faivre, Robert" uniqKey="Faivre R" first="Robert" last="Faivre">Robert Faivre</name>
<affiliation wicri:level="1"><nlm:affiliation>INRA, UR875 MIAT, 31326 Castanet-Tolosan, France.</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>INRA, UR875 MIAT, 31326 Castanet-Tolosan</wicri:regionArea>
<wicri:noRegion>31326 Castanet-Tolosan</wicri:noRegion>
<wicri:noRegion>31326 Castanet-Tolosan</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Chenu, Karine" sort="Chenu, Karine" uniqKey="Chenu K" first="Karine" last="Chenu">Karine Chenu</name>
<affiliation wicri:level="1"><nlm:affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350</wicri:regionArea>
<wicri:noRegion>QLD 4350</wicri:noRegion>
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<date when="2016">2016</date>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.</title>
<author><name sortKey="Casadebaig, Pierre" sort="Casadebaig, Pierre" uniqKey="Casadebaig P" first="Pierre" last="Casadebaig">Pierre Casadebaig</name>
<affiliation wicri:level="3"><nlm:affiliation>INRA, UMR1248 AGIR, 31326 Castanet-Tolosan, France.</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>INRA, UMR1248 AGIR, 31326 Castanet-Tolosan</wicri:regionArea>
<placeName><region type="region" nuts="2">Occitanie (région administrative)</region>
<region type="old region" nuts="2">Midi-Pyrénées</region>
<settlement type="city">Castanet-Tolosan</settlement>
</placeName>
</affiliation>
</author>
<author><name sortKey="Zheng, Bangyou" sort="Zheng, Bangyou" uniqKey="Zheng B" first="Bangyou" last="Zheng">Bangyou Zheng</name>
<affiliation wicri:level="1"><nlm:affiliation>CSIRO Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>CSIRO Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067</wicri:regionArea>
<wicri:noRegion>QLD 4067</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Chapman, Scott" sort="Chapman, Scott" uniqKey="Chapman S" first="Scott" last="Chapman">Scott Chapman</name>
<affiliation wicri:level="1"><nlm:affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350</wicri:regionArea>
<wicri:noRegion>QLD 4350</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Huth, Neil" sort="Huth, Neil" uniqKey="Huth N" first="Neil" last="Huth">Neil Huth</name>
<affiliation wicri:level="1"><nlm:affiliation>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350</wicri:regionArea>
<wicri:noRegion>QLD 4350</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Faivre, Robert" sort="Faivre, Robert" uniqKey="Faivre R" first="Robert" last="Faivre">Robert Faivre</name>
<affiliation wicri:level="1"><nlm:affiliation>INRA, UR875 MIAT, 31326 Castanet-Tolosan, France.</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>INRA, UR875 MIAT, 31326 Castanet-Tolosan</wicri:regionArea>
<wicri:noRegion>31326 Castanet-Tolosan</wicri:noRegion>
<wicri:noRegion>31326 Castanet-Tolosan</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Chenu, Karine" sort="Chenu, Karine" uniqKey="Chenu K" first="Karine" last="Chenu">Karine Chenu</name>
<affiliation wicri:level="1"><nlm:affiliation>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350</wicri:regionArea>
<wicri:noRegion>QLD 4350</wicri:noRegion>
</affiliation>
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<series><title level="j">PloS one</title>
<idno type="eISSN">1932-6203</idno>
<imprint><date when="2016" type="published">2016</date>
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<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="KwdFr" xml:lang="fr"><term>Adaptation physiologique (génétique)</term>
<term>Adaptation physiologique (physiologie)</term>
<term>Australie</term>
<term>Biologie informatique</term>
<term>Caractère quantitatif héréditaire</term>
<term>Modèles biologiques</term>
<term>Pluie</term>
<term>Produits agricoles (génétique)</term>
<term>Produits agricoles (physiologie)</term>
<term>Simulation numérique</term>
<term>Sécheresses</term>
<term>Triticum (génétique)</term>
<term>Triticum (physiologie)</term>
<term>Écosystème</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="génétique" xml:lang="fr"><term>Adaptation physiologique</term>
<term>Produits agricoles</term>
<term>Triticum</term>
</keywords>
<keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Adaptation physiologique</term>
<term>Produits agricoles</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>
<keywords scheme="MESH" xml:lang="fr"><term>Australie</term>
<term>Biologie informatique</term>
<term>Caractère quantitatif héréditaire</term>
<term>Modèles biologiques</term>
<term>Pluie</term>
<term>Simulation numérique</term>
<term>Sécheresses</term>
<term>Écosystème</term>
</keywords>
<keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Australie</term>
</keywords>
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<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>
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