Impact of grain morphology and the genotype by environment interactions on test weight of spring and winter wheat (Triticum aestivum L.)
Wheat ( Triticum aestivum L.) market grades and prices are determined in part by test weight (TW). Millers value high TW because it is typically associated with higher flour extraction rates and better end-use quality. Test weight is expected to be influenced by other directly quantifiable grain att...
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| Veröffentlicht in: | Euphytica 2018-07, Vol.214 (7), p.1-16, Article 125 |
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| creator | Yabwalo, Dalitso N. Berzonsky, William A. Brabec, Daniel Pearson, Thomas Glover, Karl D. Kleinjan, Jonathan L. |
| description | Wheat (
Triticum aestivum
L.) market grades and prices are determined in part by test weight (TW). Millers value high TW because it is typically associated with higher flour extraction rates and better end-use quality. Test weight is expected to be influenced by other directly quantifiable grain attributes such as grain length (GL), grain width (GW), shape, single-grain-density (SGD), thousand-grain-weight (TGW), and packing efficiency (PE). The objectives of this study were to: (1) determine the primary morphological grain attributes that comprise TW measurements for winter and spring wheat classes; and (2) determine TW stability and genotype and genotype × environment interactions (GEIs) of the attributes that comprise TW. A market class representative group of 32 hard spring and 24 hard winter wheat cultivars was grown at several locations in South Dakota in 2011 and 2012. A regularized multiple regression algorithm was used to develop a TW model and determine what grain attribute reliably predicts TW. A GGE biplot was used for stability and GEI analyses whereas a linear mixed model was used for variance analyses. Data were collected on eight grain traits: TW, SGD, TGW, protein concentration, GW, GL, shape, size, and PE. Observations showed that in both spring and winter wheat, SGD accounted for over 90% of the phenotypic variation of TW. Cultivars with stable and high TW were identified in both wheat classes. Apart from TW; significant (
p |
| doi_str_mv | 10.1007/s10681-018-2202-7 |
| format | Article |
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Triticum aestivum
L.) market grades and prices are determined in part by test weight (TW). Millers value high TW because it is typically associated with higher flour extraction rates and better end-use quality. Test weight is expected to be influenced by other directly quantifiable grain attributes such as grain length (GL), grain width (GW), shape, single-grain-density (SGD), thousand-grain-weight (TGW), and packing efficiency (PE). The objectives of this study were to: (1) determine the primary morphological grain attributes that comprise TW measurements for winter and spring wheat classes; and (2) determine TW stability and genotype and genotype × environment interactions (GEIs) of the attributes that comprise TW. A market class representative group of 32 hard spring and 24 hard winter wheat cultivars was grown at several locations in South Dakota in 2011 and 2012. A regularized multiple regression algorithm was used to develop a TW model and determine what grain attribute reliably predicts TW. A GGE biplot was used for stability and GEI analyses whereas a linear mixed model was used for variance analyses. Data were collected on eight grain traits: TW, SGD, TGW, protein concentration, GW, GL, shape, size, and PE. Observations showed that in both spring and winter wheat, SGD accounted for over 90% of the phenotypic variation of TW. Cultivars with stable and high TW were identified in both wheat classes. Apart from TW; significant (
p
< 0.0001) genotype, environment, and GEI variances were observed for GW and SGD, a more direct measure of which could help improve genetic gain for TW.</description><identifier>ISSN: 0014-2336</identifier><identifier>EISSN: 1573-5060</identifier><identifier>DOI: 10.1007/s10681-018-2202-7</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Biomedical and Life Sciences ; Biotechnology ; Cultivars ; Data processing ; Genotype & phenotype ; Genotype-environment interactions ; Genotypes ; Grain ; Life Sciences ; Markets ; Morphology ; Phenotypic variations ; Plant Genetics and Genomics ; Plant Pathology ; Plant Physiology ; Plant Sciences ; Pricing ; Proteins ; Regression analysis ; Spring ; Spring wheat ; Stability analysis ; Triticum aestivum ; Variance analysis ; Wheat ; Winter wheat</subject><ispartof>Euphytica, 2018-07, Vol.214 (7), p.1-16, Article 125</ispartof><rights>Springer Nature B.V. 2018</rights><rights>COPYRIGHT 2018 Springer</rights><rights>Euphytica is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c355t-c3074f14c7a639572b52da9ee2b43d15c02248bcb175f2a2c137f758ffe47c23</citedby><cites>FETCH-LOGICAL-c355t-c3074f14c7a639572b52da9ee2b43d15c02248bcb175f2a2c137f758ffe47c23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10681-018-2202-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10681-018-2202-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Yabwalo, Dalitso N.</creatorcontrib><creatorcontrib>Berzonsky, William A.</creatorcontrib><creatorcontrib>Brabec, Daniel</creatorcontrib><creatorcontrib>Pearson, Thomas</creatorcontrib><creatorcontrib>Glover, Karl D.</creatorcontrib><creatorcontrib>Kleinjan, Jonathan L.</creatorcontrib><title>Impact of grain morphology and the genotype by environment interactions on test weight of spring and winter wheat (Triticum aestivum L.)</title><title>Euphytica</title><addtitle>Euphytica</addtitle><description>Wheat (
Triticum aestivum
L.) market grades and prices are determined in part by test weight (TW). Millers value high TW because it is typically associated with higher flour extraction rates and better end-use quality. Test weight is expected to be influenced by other directly quantifiable grain attributes such as grain length (GL), grain width (GW), shape, single-grain-density (SGD), thousand-grain-weight (TGW), and packing efficiency (PE). The objectives of this study were to: (1) determine the primary morphological grain attributes that comprise TW measurements for winter and spring wheat classes; and (2) determine TW stability and genotype and genotype × environment interactions (GEIs) of the attributes that comprise TW. A market class representative group of 32 hard spring and 24 hard winter wheat cultivars was grown at several locations in South Dakota in 2011 and 2012. A regularized multiple regression algorithm was used to develop a TW model and determine what grain attribute reliably predicts TW. A GGE biplot was used for stability and GEI analyses whereas a linear mixed model was used for variance analyses. Data were collected on eight grain traits: TW, SGD, TGW, protein concentration, GW, GL, shape, size, and PE. Observations showed that in both spring and winter wheat, SGD accounted for over 90% of the phenotypic variation of TW. Cultivars with stable and high TW were identified in both wheat classes. Apart from TW; significant (
p
< 0.0001) genotype, environment, and GEI variances were observed for GW and SGD, a more direct measure of which could help improve genetic gain for TW.</description><subject>Biomedical and Life Sciences</subject><subject>Biotechnology</subject><subject>Cultivars</subject><subject>Data processing</subject><subject>Genotype & phenotype</subject><subject>Genotype-environment interactions</subject><subject>Genotypes</subject><subject>Grain</subject><subject>Life Sciences</subject><subject>Markets</subject><subject>Morphology</subject><subject>Phenotypic variations</subject><subject>Plant Genetics and Genomics</subject><subject>Plant Pathology</subject><subject>Plant Physiology</subject><subject>Plant Sciences</subject><subject>Pricing</subject><subject>Proteins</subject><subject>Regression analysis</subject><subject>Spring</subject><subject>Spring wheat</subject><subject>Stability analysis</subject><subject>Triticum aestivum</subject><subject>Variance analysis</subject><subject>Wheat</subject><subject>Winter wheat</subject><issn>0014-2336</issn><issn>1573-5060</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kc9uGyEQxlHVSHHdPEBuSL20h3UHWBb7GEVNG8lSL74jFg9rIi9sAcfyG_SxS7yVeqqQYITm982fj5B7BisGoL5mBt2aNcDWDefAG_WOLJhUopHQwXuyAGBtw4XobsmHnF8AYKMkLMjv53EyttDo6JCMD3SMaTrEYxwu1IQ9LQekA4ZYLhPS_kIxvPoUw4ihUB8Kpgr7GDKNgRbMhZ7RD4erXp6SD8NV5XxNpecDmkI_75Iv3p5GairgX2uwXX35SG6cOWa8-_suye7p2-7xR7P9-f358WHbWCFlqTeo1rHWKtOJjVS8l3xvNoi8b8WeSQuct-ve9kxJxw23TCin5No5bJXlYkk-zbJTir9Otb5-iacUakXNoRNMbqBuaUlWc9Zgjqh9cLHUQevZ4-htDOh8_X9QrK3dMAkVYDNgU8w5odN1-NGki2ag3wzSs0G6GqTfDNKqMnxm5kVh-tfK_6E_WTCUUA</recordid><startdate>20180701</startdate><enddate>20180701</enddate><creator>Yabwalo, Dalitso N.</creator><creator>Berzonsky, William A.</creator><creator>Brabec, Daniel</creator><creator>Pearson, Thomas</creator><creator>Glover, Karl D.</creator><creator>Kleinjan, Jonathan L.</creator><general>Springer Netherlands</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SN</scope><scope>7SS</scope><scope>7T7</scope><scope>7TM</scope><scope>7X2</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>M0K</scope><scope>M2P</scope><scope>M7N</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>RC3</scope></search><sort><creationdate>20180701</creationdate><title>Impact of grain morphology and the genotype by environment interactions on test weight of spring and winter wheat (Triticum aestivum L.)</title><author>Yabwalo, Dalitso N. ; Berzonsky, William A. ; Brabec, Daniel ; Pearson, Thomas ; Glover, Karl D. ; Kleinjan, Jonathan L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-c3074f14c7a639572b52da9ee2b43d15c02248bcb175f2a2c137f758ffe47c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Biomedical and Life Sciences</topic><topic>Biotechnology</topic><topic>Cultivars</topic><topic>Data processing</topic><topic>Genotype & phenotype</topic><topic>Genotype-environment interactions</topic><topic>Genotypes</topic><topic>Grain</topic><topic>Life Sciences</topic><topic>Markets</topic><topic>Morphology</topic><topic>Phenotypic variations</topic><topic>Plant Genetics and Genomics</topic><topic>Plant Pathology</topic><topic>Plant Physiology</topic><topic>Plant Sciences</topic><topic>Pricing</topic><topic>Proteins</topic><topic>Regression analysis</topic><topic>Spring</topic><topic>Spring wheat</topic><topic>Stability analysis</topic><topic>Triticum aestivum</topic><topic>Variance analysis</topic><topic>Wheat</topic><topic>Winter wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yabwalo, Dalitso N.</creatorcontrib><creatorcontrib>Berzonsky, William A.</creatorcontrib><creatorcontrib>Brabec, Daniel</creatorcontrib><creatorcontrib>Pearson, Thomas</creatorcontrib><creatorcontrib>Glover, Karl D.</creatorcontrib><creatorcontrib>Kleinjan, Jonathan L.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Agricultural Science Database</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><jtitle>Euphytica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yabwalo, Dalitso N.</au><au>Berzonsky, William A.</au><au>Brabec, Daniel</au><au>Pearson, Thomas</au><au>Glover, Karl D.</au><au>Kleinjan, Jonathan L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of grain morphology and the genotype by environment interactions on test weight of spring and winter wheat (Triticum aestivum L.)</atitle><jtitle>Euphytica</jtitle><stitle>Euphytica</stitle><date>2018-07-01</date><risdate>2018</risdate><volume>214</volume><issue>7</issue><spage>1</spage><epage>16</epage><pages>1-16</pages><artnum>125</artnum><issn>0014-2336</issn><eissn>1573-5060</eissn><abstract>Wheat (
Triticum aestivum
L.) market grades and prices are determined in part by test weight (TW). Millers value high TW because it is typically associated with higher flour extraction rates and better end-use quality. Test weight is expected to be influenced by other directly quantifiable grain attributes such as grain length (GL), grain width (GW), shape, single-grain-density (SGD), thousand-grain-weight (TGW), and packing efficiency (PE). The objectives of this study were to: (1) determine the primary morphological grain attributes that comprise TW measurements for winter and spring wheat classes; and (2) determine TW stability and genotype and genotype × environment interactions (GEIs) of the attributes that comprise TW. A market class representative group of 32 hard spring and 24 hard winter wheat cultivars was grown at several locations in South Dakota in 2011 and 2012. A regularized multiple regression algorithm was used to develop a TW model and determine what grain attribute reliably predicts TW. A GGE biplot was used for stability and GEI analyses whereas a linear mixed model was used for variance analyses. Data were collected on eight grain traits: TW, SGD, TGW, protein concentration, GW, GL, shape, size, and PE. Observations showed that in both spring and winter wheat, SGD accounted for over 90% of the phenotypic variation of TW. Cultivars with stable and high TW were identified in both wheat classes. Apart from TW; significant (
p
< 0.0001) genotype, environment, and GEI variances were observed for GW and SGD, a more direct measure of which could help improve genetic gain for TW.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10681-018-2202-7</doi><tpages>16</tpages></addata></record> |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Biomedical and Life Sciences Biotechnology Cultivars Data processing Genotype & phenotype Genotype-environment interactions Genotypes Grain Life Sciences Markets Morphology Phenotypic variations Plant Genetics and Genomics Plant Pathology Plant Physiology Plant Sciences Pricing Proteins Regression analysis Spring Spring wheat Stability analysis Triticum aestivum Variance analysis Wheat Winter wheat |
| title | Impact of grain morphology and the genotype by environment interactions on test weight of spring and winter wheat (Triticum aestivum L.) |
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