Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-nitrogen status.
Identifieur interne : 000706 ( Main/Exploration ); précédent : 000705; suivant : 000707Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-nitrogen status.
Auteurs : Kim S. Ely [États-Unis] ; Angela C. Burnett [États-Unis] ; Wil Lieberman-Cribbin [États-Unis] ; Shawn P. Serbin [États-Unis] ; Alistair Rogers [États-Unis]Source :
- Journal of experimental botany [ 1460-2431 ] ; 2019.
Descripteurs français
- KwdFr :
- Analyse spectrale (MeSH), Cucumis sativus (physiologie), Cucurbita (physiologie), Cycle de l'azote (MeSH), Cycle du carbone (MeSH), Feuilles de plante (physiologie), Helianthus (physiologie), Lycopersicon esculentum (physiologie), Ocimum basilicum (physiologie), Phaseolus (physiologie), Populus (physiologie), Produits agricoles (physiologie), Soja (physiologie).
- MESH :
English descriptors
- KwdEn :
- Carbon Cycle (MeSH), Crops, Agricultural (physiology), Cucumis sativus (physiology), Cucurbita (physiology), Helianthus (physiology), Lycopersicon esculentum (physiology), Nitrogen Cycle (MeSH), Ocimum basilicum (physiology), Phaseolus (physiology), Plant Leaves (physiology), Populus (physiology), Soybeans (physiology), Spectrum Analysis (MeSH).
- MESH :
Abstract
Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.
DOI: 10.1093/jxb/erz061
PubMed: 30799496
Affiliations:
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Burnett</name><affiliation wicri:level="2"><nlm:affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Lieberman Cribbin, Wil" sort="Lieberman Cribbin, Wil" uniqKey="Lieberman Cribbin W" first="Wil" last="Lieberman-Cribbin">Wil Lieberman-Cribbin</name><affiliation wicri:level="2"><nlm:affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Serbin, Shawn P" sort="Serbin, Shawn P" uniqKey="Serbin S" first="Shawn P" last="Serbin">Shawn P. Serbin</name><affiliation wicri:level="2"><nlm:affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Rogers, Alistair" sort="Rogers, Alistair" uniqKey="Rogers A" first="Alistair" last="Rogers">Alistair Rogers</name><affiliation wicri:level="2"><nlm:affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author></analytic><series><title level="j">Journal of experimental botany</title><idno type="eISSN">1460-2431</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Carbon Cycle (MeSH)</term><term>Crops, Agricultural (physiology)</term><term>Cucumis sativus (physiology)</term><term>Cucurbita (physiology)</term><term>Helianthus (physiology)</term><term>Lycopersicon esculentum (physiology)</term><term>Nitrogen Cycle (MeSH)</term><term>Ocimum basilicum (physiology)</term><term>Phaseolus (physiology)</term><term>Plant Leaves (physiology)</term><term>Populus (physiology)</term><term>Soybeans (physiology)</term><term>Spectrum Analysis (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse spectrale (MeSH)</term><term>Cucumis sativus (physiologie)</term><term>Cucurbita (physiologie)</term><term>Cycle de l'azote (MeSH)</term><term>Cycle du carbone (MeSH)</term><term>Feuilles de plante (physiologie)</term><term>Helianthus (physiologie)</term><term>Lycopersicon esculentum (physiologie)</term><term>Ocimum basilicum (physiologie)</term><term>Phaseolus (physiologie)</term><term>Populus (physiologie)</term><term>Produits agricoles (physiologie)</term><term>Soja (physiologie)</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Cucumis sativus</term><term>Cucurbita</term><term>Feuilles de plante</term><term>Helianthus</term><term>Lycopersicon esculentum</term><term>Ocimum basilicum</term><term>Phaseolus</term><term>Populus</term><term>Produits agricoles</term><term>Soja</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Crops, Agricultural</term><term>Cucumis sativus</term><term>Cucurbita</term><term>Helianthus</term><term>Lycopersicon esculentum</term><term>Ocimum basilicum</term><term>Phaseolus</term><term>Plant Leaves</term><term>Populus</term><term>Soybeans</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Carbon Cycle</term><term>Nitrogen Cycle</term><term>Spectrum Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse spectrale</term><term>Cycle de l'azote</term><term>Cycle du carbone</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30799496</PMID><DateCompleted><Year>2020</Year><Month>05</Month><Day>26</Day></DateCompleted><DateRevised><Year>2020</Year><Month>05</Month><Day>26</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1460-2431</ISSN><JournalIssue CitedMedium="Internet"><Volume>70</Volume><Issue>6</Issue><PubDate><Year>2019</Year><Month>03</Month><Day>27</Day></PubDate></JournalIssue><Title>Journal of experimental botany</Title><ISOAbbreviation>J Exp Bot</ISOAbbreviation></Journal><ArticleTitle>Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-nitrogen status.</ArticleTitle><Pagination><MedlinePgn>1789-1799</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/jxb/erz061</ELocationID><Abstract><AbstractText>Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.</AbstractText><CopyrightInformation>Published by Oxford University Press on behalf of the Society for Experimental Biology 2019.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ely</LastName><ForeName>Kim S</ForeName><Initials>KS</Initials><AffiliationInfo><Affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Burnett</LastName><ForeName>Angela C</ForeName><Initials>AC</Initials><AffiliationInfo><Affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lieberman-Cribbin</LastName><ForeName>Wil</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Serbin</LastName><ForeName>Shawn P</ForeName><Initials>SP</Initials><AffiliationInfo><Affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rogers</LastName><ForeName>Alistair</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Exp Bot</MedlineTA><NlmUniqueID>9882906</NlmUniqueID><ISSNLinking>0022-0957</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D057486" MajorTopicYN="Y">Carbon Cycle</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018556" MajorTopicYN="N">Crops, Agricultural</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018553" MajorTopicYN="N">Cucumis sativus</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D028464" MajorTopicYN="N">Cucurbita</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006368" MajorTopicYN="N">Helianthus</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018551" MajorTopicYN="N">Lycopersicon esculentum</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058458" MajorTopicYN="Y">Nitrogen Cycle</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018649" MajorTopicYN="N">Ocimum basilicum</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D027805" MajorTopicYN="N">Phaseolus</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018515" MajorTopicYN="N">Plant Leaves</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013025" MajorTopicYN="N">Soybeans</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013057" MajorTopicYN="Y">Spectrum Analysis</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Amino acids</Keyword><Keyword MajorTopicYN="Y">PLSR</Keyword><Keyword MajorTopicYN="Y">carbohydrates</Keyword><Keyword MajorTopicYN="Y">carbon</Keyword><Keyword MajorTopicYN="Y">leaf traits</Keyword><Keyword MajorTopicYN="Y">metabolites</Keyword><Keyword MajorTopicYN="Y">nitrogen</Keyword><Keyword MajorTopicYN="Y">remote sensing</Keyword><Keyword MajorTopicYN="Y">source–sink</Keyword><Keyword MajorTopicYN="Y">spectroscopy</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>10</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>02</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>2</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>5</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>2</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">30799496</ArticleId><ArticleId IdType="pii">5336640</ArticleId><ArticleId IdType="doi">10.1093/jxb/erz061</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>État de New York</li></region></list><tree><country name="États-Unis"><region name="État de New York"><name sortKey="Ely, Kim S" sort="Ely, Kim S" uniqKey="Ely K" first="Kim S" last="Ely">Kim S. Ely</name></region><name sortKey="Burnett, Angela C" sort="Burnett, Angela C" uniqKey="Burnett A" first="Angela C" last="Burnett">Angela C. Burnett</name><name sortKey="Lieberman Cribbin, Wil" sort="Lieberman Cribbin, Wil" uniqKey="Lieberman Cribbin W" first="Wil" last="Lieberman-Cribbin">Wil Lieberman-Cribbin</name><name sortKey="Rogers, Alistair" sort="Rogers, Alistair" uniqKey="Rogers A" first="Alistair" last="Rogers">Alistair Rogers</name><name sortKey="Serbin, Shawn P" sort="Serbin, Shawn P" uniqKey="Serbin S" first="Shawn P" last="Serbin">Shawn P. Serbin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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