High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance
High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions....
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Veröffentlicht in: | Plant physiology (Bethesda) 2017-01, Vol.173 (1), p.614-626 |
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creator | Yendrek, Craig R. Tomaz, Tiago Montes, Christopher M. Cao, Youyuan Morse, Alison M. Brown, Patrick J. McIntyre, Lauren M. Leakey, Andrew D. B. Ainsworth, Elizabeth A. |
description | High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (λ = 500–2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO₂]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress. |
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Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO₂]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. 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All Rights Reserved.</rights><rights>2017 American Society of Plant Biologists. All Rights Reserved. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-a3216fe9b1b1004dd393f2cbdbaaa91eb8e532399ea03d90c670ea918ccf41173</citedby><orcidid>0000-0002-3199-8999</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/24876454$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/24876454$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,776,780,799,881,27901,27902,57992,58225</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28049858$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yendrek, Craig R.</creatorcontrib><creatorcontrib>Tomaz, Tiago</creatorcontrib><creatorcontrib>Montes, Christopher M.</creatorcontrib><creatorcontrib>Cao, Youyuan</creatorcontrib><creatorcontrib>Morse, Alison M.</creatorcontrib><creatorcontrib>Brown, Patrick J.</creatorcontrib><creatorcontrib>McIntyre, Lauren M.</creatorcontrib><creatorcontrib>Leakey, Andrew D. B.</creatorcontrib><creatorcontrib>Ainsworth, Elizabeth A.</creatorcontrib><title>High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance</title><title>Plant physiology (Bethesda)</title><addtitle>Plant Physiol</addtitle><description>High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (λ = 500–2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO₂]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.</description><subject>Chimera</subject><subject>Chlorophyll - metabolism</subject><subject>ECOPHYSIOLOGY AND SUSTAINABILITY</subject><subject>High-Throughput Screening Assays - methods</subject><subject>Least-Squares Analysis</subject><subject>Models, Biological</subject><subject>Nitrogen - metabolism</subject><subject>Phenotype</subject><subject>Photosynthesis - physiology</subject><subject>Plant Leaves - chemistry</subject><subject>Plant Leaves - physiology</subject><subject>Zea mays - genetics</subject><subject>Zea mays - physiology</subject><issn>0032-0889</issn><issn>1532-2548</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkVGL1DAUhYMo7uzok89KH4WlY26TtsmLoIs6wogis88hTW_bLJ0mJq0w_nozO-uqEMgN58vJCYeQF0A3AJS_8X4D1YYC5_UjsoKSFXlRcvGYrChNMxVCXpDLGG8ppcCAPyUXhaBcilKsyLS1_ZDvh-CWfvDLnH0bcHLz0dupz1yXfdH2F2Y71F1SjtG60fXW6DHTU5u9t84MeLg774O2c8xu4uni9ugxRI9mDkn6jt2YRj0ZfEaedHqM-Px-X5Objx_219t89_XT5-t3u9zwSs65ZgVUHcoGGqCUty2TrCtM0zZaawnYCEzfZFKipqyV1FQ1xSQIYzoOULM1eXv29UtzwNbgdEqifLAHHY7Kaav-VyY7qN79VGUBtOYsGby-Nwjux4JxVgcbDY6jntAtUYEoUwJxWmtydUZNcDEG7B6eAapODSnvFVTqrqFEv_o32QP7p5IEvDwDt3F24a_ORV3xkrPfwqCYwQ</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Yendrek, Craig R.</creator><creator>Tomaz, Tiago</creator><creator>Montes, Christopher M.</creator><creator>Cao, Youyuan</creator><creator>Morse, Alison M.</creator><creator>Brown, Patrick J.</creator><creator>McIntyre, Lauren M.</creator><creator>Leakey, Andrew D. B.</creator><creator>Ainsworth, Elizabeth A.</creator><general>American Society of Plant Biologists</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3199-8999</orcidid></search><sort><creationdate>20170101</creationdate><title>High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance</title><author>Yendrek, Craig R. ; Tomaz, Tiago ; Montes, Christopher M. ; Cao, Youyuan ; Morse, Alison M. ; Brown, Patrick J. ; McIntyre, Lauren M. ; Leakey, Andrew D. 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B.</au><au>Ainsworth, Elizabeth A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance</atitle><jtitle>Plant physiology (Bethesda)</jtitle><addtitle>Plant Physiol</addtitle><date>2017-01-01</date><risdate>2017</risdate><volume>173</volume><issue>1</issue><spage>614</spage><epage>626</epage><pages>614-626</pages><issn>0032-0889</issn><eissn>1532-2548</eissn><abstract>High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (λ = 500–2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO₂]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.</abstract><cop>United States</cop><pub>American Society of Plant Biologists</pub><pmid>28049858</pmid><doi>10.1104/pp.16.01447</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-3199-8999</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Chimera Chlorophyll - metabolism ECOPHYSIOLOGY AND SUSTAINABILITY High-Throughput Screening Assays - methods Least-Squares Analysis Models, Biological Nitrogen - metabolism Phenotype Photosynthesis - physiology Plant Leaves - chemistry Plant Leaves - physiology Zea mays - genetics Zea mays - physiology |
title | High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance |
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