Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis
Identifieur interne : 000597 ( Pmc/Curation ); précédent : 000596; suivant : 000598Assessment 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.
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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<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>
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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">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
</journal-title-group>
<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, CA USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26799483</article-id>
<article-id pub-id-type="pmc">4723307</article-id>
<article-id pub-id-type="publisher-id">PONE-D-15-28761</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0146385</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Genetics</subject>
<subj-group><subject>Plant Genetics</subject>
<subj-group><subject>Crop Genetics</subject>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Plant Science</subject>
<subj-group><subject>Plant Genetics</subject>
<subj-group><subject>Crop Genetics</subject>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Agriculture</subject>
<subj-group><subject>Crop Science</subject>
<subj-group><subject>Crops</subject>
<subj-group><subject>Cereal Crops</subject>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Physical Sciences</subject>
<subj-group><subject>Chemistry</subject>
<subj-group><subject>Chemical Compounds</subject>
<subj-group><subject>Carbon Dioxide</subject>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Agriculture</subject>
<subj-group><subject>Crop Science</subject>
<subj-group><subject>Crops</subject>
<subj-group><subject>Cereal Crops</subject>
<subj-group><subject>Wheat</subject>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Organisms</subject>
<subj-group><subject>Plants</subject>
<subj-group><subject>Grasses</subject>
<subj-group><subject>Wheat</subject>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Agriculture</subject>
<subj-group><subject>Agrochemicals</subject>
<subj-group><subject>Fertilizers</subject>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Ecology and Environmental Sciences</subject>
<subj-group><subject>Natural Resources</subject>
<subj-group><subject>Water Resources</subject>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Agriculture</subject>
<subj-group><subject>Agricultural Soil Science</subject>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Ecology and Environmental Sciences</subject>
<subj-group><subject>Soil Science</subject>
<subj-group><subject>Agricultural Soil Science</subject>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Agriculture</subject>
<subj-group><subject>Crop Science</subject>
<subj-group><subject>Crop Management</subject>
</subj-group>
</subj-group>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis</article-title>
<alt-title alt-title-type="running-head">Systems Analysis to Assess Plant Traits Impact on Wheat Yield</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Casadebaig</surname>
<given-names>Pierre</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor001">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zheng</surname>
<given-names>Bangyou</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chapman</surname>
<given-names>Scott</given-names>
</name>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Huth</surname>
<given-names>Neil</given-names>
</name>
<xref ref-type="aff" rid="aff004"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Faivre</surname>
<given-names>Robert</given-names>
</name>
<xref ref-type="aff" rid="aff005"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chenu</surname>
<given-names>Karine</given-names>
</name>
<xref ref-type="aff" rid="aff006"><sup>6</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<addr-line>INRA, UMR1248 AGIR, 31326 Castanet-Tolosan, France</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<addr-line>CSIRO Agriculture, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067, Australia</addr-line>
</aff>
<aff id="aff003"><label>3</label>
<addr-line>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St Lucia, QLD 4350, Australia</addr-line>
</aff>
<aff id="aff004"><label>4</label>
<addr-line>CSIRO Agriculture, 203 Tor Street, Toowoomba, QLD 4350, Australia</addr-line>
</aff>
<aff id="aff005"><label>5</label>
<addr-line>INRA, UR875 MIAT, 31326 Castanet-Tolosan, France</addr-line>
</aff>
<aff id="aff006"><label>6</label>
<addr-line>Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor Street, Toowoomba, QLD 4350, Australia</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Wu</surname>
<given-names>Rongling</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>Pennsylvania State University, UNITED STATES</addr-line>
</aff>
<author-notes><fn fn-type="conflict" id="coi001"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: PC KC SC RF. Performed the experiments: PC BZ SC KC. Analyzed the data: PC KC BZ SC NH. Contributed reagents/materials/analysis tools: BZ RF. Wrote the paper: PC BZ SC NH RF KC.</p>
</fn>
<corresp id="cor001">* E-mail: <email>pierre.casadebaig@toulouse.inra.fr</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><year>2016</year>
</pub-date>
<pub-date pub-type="epub"><day>22</day>
<month>1</month>
<year>2016</year>
</pub-date>
<volume>11</volume>
<issue>1</issue>
<elocation-id>e0146385</elocation-id>
<history><date date-type="received"><day>1</day>
<month>7</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>16</day>
<month>12</month>
<year>2015</year>
</date>
</history>
<permissions><copyright-statement>© 2016 Casadebaig et al</copyright-statement>
<copyright-year>2016</copyright-year>
<copyright-holder>Casadebaig et al</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>
, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="pone.0146385.pdf"></self-uri>
<abstract><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>
</abstract>
<funding-group><funding-statement>This study was partly funded by INRA—Environment and Agronomy Division, the Queensland Alliance for Agriculture and Food Innovation (QAAFI), the Grains Research & Development Corporation (GRDC) and the ARC Centre of Excellence for Translational Photosynthesis. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts><fig-count count="8"></fig-count>
<table-count count="2"></table-count>
<page-count count="27"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>All relevant data are within the paper and its Supporting Information files.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>All relevant data are within the paper and its Supporting Information files.</p>
</notes>
</front>
</pmc>
</record>
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