Efficiency of methods for genetic progress estimation in common bean breeding using database information
The final field trials to evaluate elite lines developed by the Embrapa national common bean breeding program generated a phenotypic database composed by agronomic traits of 84 elite lines and nine cultivars over a 16-year period (1993–2008) and 450 environments in all Brazilian growing areas. The m...
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description | The final field trials to evaluate elite lines developed by the Embrapa national common bean breeding program generated a phenotypic database composed by agronomic traits of 84 elite lines and nine cultivars over a 16-year period (1993–2008) and 450 environments in all Brazilian growing areas. The main goal of this study was to use this database as a model to compare the consistency of the results obtained from indirect methods for genetic progress estimation for grain yield in common bean breeding, using the direct method as a reference. Three indirect methods for genetic progress estimation were evaluated: (1) linear regression with unadjusted averages, (2) linear regression with averages adjusted by the mixed models, and (3) linear regression with averages adjusted by a fixed effects model with the error exception. The genetic progress estimated by the direct method was 31.3 kg ha
−1
per year (1.34%**). This value was considered as the reference estimate, since it was calculated using the grain yield data from final field trials with all common bean lines evaluated under the same environmental conditions. The estimate obtained using the regression with unadjusted averages of the three best lines by cycle was 25.66 kg ha
−1
per year (1.26%*), similar to the result obtained by the direct method. Considering both methods using fixed and mixed models, the genetic gain estimates were statistically null (0.42% and 0.45%, respectively). Therefore, the regression method with unadjusted means was more informative than the other indirect methods. |
doi_str_mv | 10.1007/s10681-018-2246-8 |
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−1
per year (1.34%**). This value was considered as the reference estimate, since it was calculated using the grain yield data from final field trials with all common bean lines evaluated under the same environmental conditions. The estimate obtained using the regression with unadjusted averages of the three best lines by cycle was 25.66 kg ha
−1
per year (1.26%*), similar to the result obtained by the direct method. Considering both methods using fixed and mixed models, the genetic gain estimates were statistically null (0.42% and 0.45%, respectively). Therefore, the regression method with unadjusted means was more informative than the other indirect methods.</description><identifier>ISSN: 0014-2336</identifier><identifier>EISSN: 1573-5060</identifier><identifier>DOI: 10.1007/s10681-018-2246-8</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Agronomy ; Analysis ; Biomedical and Life Sciences ; Biotechnology ; Breeding ; Crop yield ; Cultivars ; Environmental conditions ; Genetic improvement ; Grain ; Life Sciences ; Methods ; Plant breeding ; Plant Genetics and Genomics ; Plant Pathology ; Plant Physiology ; Plant Sciences ; Regression ; Regression analysis ; Statistical analysis</subject><ispartof>Euphytica, 2018-09, Vol.214 (9), p.1-10, Article 164</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-790b950a4b176f6f6b432ce4e74d203ed282a31bfbdc64ffeff8e1619b047c293</citedby><cites>FETCH-LOGICAL-c355t-790b950a4b176f6f6b432ce4e74d203ed282a31bfbdc64ffeff8e1619b047c293</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/s10681-018-2246-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10681-018-2246-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>de Faria, Luis Cláudio</creatorcontrib><creatorcontrib>Melo, Patrícia Guimarães Santos</creatorcontrib><creatorcontrib>de Souza, Thiago Lívio Pessoa Oliveira</creatorcontrib><creatorcontrib>Pereira, Helton Santos</creatorcontrib><creatorcontrib>Melo, Leonardo Cunha</creatorcontrib><title>Efficiency of methods for genetic progress estimation in common bean breeding using database information</title><title>Euphytica</title><addtitle>Euphytica</addtitle><description>The final field trials to evaluate elite lines developed by the Embrapa national common bean breeding program generated a phenotypic database composed by agronomic traits of 84 elite lines and nine cultivars over a 16-year period (1993–2008) and 450 environments in all Brazilian growing areas. The main goal of this study was to use this database as a model to compare the consistency of the results obtained from indirect methods for genetic progress estimation for grain yield in common bean breeding, using the direct method as a reference. Three indirect methods for genetic progress estimation were evaluated: (1) linear regression with unadjusted averages, (2) linear regression with averages adjusted by the mixed models, and (3) linear regression with averages adjusted by a fixed effects model with the error exception. The genetic progress estimated by the direct method was 31.3 kg ha
−1
per year (1.34%**). This value was considered as the reference estimate, since it was calculated using the grain yield data from final field trials with all common bean lines evaluated under the same environmental conditions. The estimate obtained using the regression with unadjusted averages of the three best lines by cycle was 25.66 kg ha
−1
per year (1.26%*), similar to the result obtained by the direct method. Considering both methods using fixed and mixed models, the genetic gain estimates were statistically null (0.42% and 0.45%, respectively). Therefore, the regression method with unadjusted means was more informative than the other indirect methods.</description><subject>Agronomy</subject><subject>Analysis</subject><subject>Biomedical and Life Sciences</subject><subject>Biotechnology</subject><subject>Breeding</subject><subject>Crop yield</subject><subject>Cultivars</subject><subject>Environmental conditions</subject><subject>Genetic improvement</subject><subject>Grain</subject><subject>Life Sciences</subject><subject>Methods</subject><subject>Plant breeding</subject><subject>Plant Genetics and Genomics</subject><subject>Plant Pathology</subject><subject>Plant Physiology</subject><subject>Plant Sciences</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Statistical analysis</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>eNp1kUtLxTAQhYMoeL36A9wFXFcnjybtUsQXXHCj65Cmk96IbTTpXfjvTangSgYmIZxvcphDyCWDawagbzID1bAKWFNxLlXVHJENq7WoalBwTDYATFZcCHVKznJ-B4BW17Ah-3vvgws4uW8aPR1x3sc-Ux8THXDCOTj6meKQMGeKeQ6jnUOcaJioi-NYbh3a0hJiH6aBHvLSezvbzmYssjJoRc7JibcfGS9-zy15e7h_vXuqdi-Pz3e3u8qJup4r3ULX1mBlx7TypTopuEOJWvYcBPa84Vawzne9U9J79L5BpljbgdSOt2JLrta5xfbXoVg27_GQpvKl4dByoRpRq6K6XlWD_UCz2JyTdaV6HIOLE_pQ3m81k6AlK3vbErYCLsWcE3rzmcoy0rdhYJYEzJqAKQmYJQHTFIavTC7aacD0Z-V_6Ac4E4oE</recordid><startdate>20180901</startdate><enddate>20180901</enddate><creator>de Faria, Luis Cláudio</creator><creator>Melo, Patrícia Guimarães Santos</creator><creator>de Souza, Thiago Lívio Pessoa Oliveira</creator><creator>Pereira, Helton Santos</creator><creator>Melo, Leonardo Cunha</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>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>20180901</creationdate><title>Efficiency of methods for genetic progress estimation in common bean breeding using database information</title><author>de Faria, Luis Cláudio ; Melo, Patrícia Guimarães Santos ; de Souza, Thiago Lívio Pessoa Oliveira ; Pereira, Helton Santos ; Melo, Leonardo Cunha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-790b950a4b176f6f6b432ce4e74d203ed282a31bfbdc64ffeff8e1619b047c293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Agronomy</topic><topic>Analysis</topic><topic>Biomedical and Life Sciences</topic><topic>Biotechnology</topic><topic>Breeding</topic><topic>Crop yield</topic><topic>Cultivars</topic><topic>Environmental conditions</topic><topic>Genetic improvement</topic><topic>Grain</topic><topic>Life Sciences</topic><topic>Methods</topic><topic>Plant breeding</topic><topic>Plant Genetics and Genomics</topic><topic>Plant Pathology</topic><topic>Plant Physiology</topic><topic>Plant Sciences</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Faria, Luis Cláudio</creatorcontrib><creatorcontrib>Melo, Patrícia Guimarães Santos</creatorcontrib><creatorcontrib>de Souza, Thiago Lívio Pessoa Oliveira</creatorcontrib><creatorcontrib>Pereira, Helton Santos</creatorcontrib><creatorcontrib>Melo, Leonardo Cunha</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 Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection (ProQuest)</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>de Faria, Luis Cláudio</au><au>Melo, Patrícia Guimarães Santos</au><au>de Souza, Thiago Lívio Pessoa Oliveira</au><au>Pereira, Helton Santos</au><au>Melo, Leonardo Cunha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficiency of methods for genetic progress estimation in common bean breeding using database information</atitle><jtitle>Euphytica</jtitle><stitle>Euphytica</stitle><date>2018-09-01</date><risdate>2018</risdate><volume>214</volume><issue>9</issue><spage>1</spage><epage>10</epage><pages>1-10</pages><artnum>164</artnum><issn>0014-2336</issn><eissn>1573-5060</eissn><abstract>The final field trials to evaluate elite lines developed by the Embrapa national common bean breeding program generated a phenotypic database composed by agronomic traits of 84 elite lines and nine cultivars over a 16-year period (1993–2008) and 450 environments in all Brazilian growing areas. The main goal of this study was to use this database as a model to compare the consistency of the results obtained from indirect methods for genetic progress estimation for grain yield in common bean breeding, using the direct method as a reference. Three indirect methods for genetic progress estimation were evaluated: (1) linear regression with unadjusted averages, (2) linear regression with averages adjusted by the mixed models, and (3) linear regression with averages adjusted by a fixed effects model with the error exception. The genetic progress estimated by the direct method was 31.3 kg ha
−1
per year (1.34%**). This value was considered as the reference estimate, since it was calculated using the grain yield data from final field trials with all common bean lines evaluated under the same environmental conditions. The estimate obtained using the regression with unadjusted averages of the three best lines by cycle was 25.66 kg ha
−1
per year (1.26%*), similar to the result obtained by the direct method. Considering both methods using fixed and mixed models, the genetic gain estimates were statistically null (0.42% and 0.45%, respectively). Therefore, the regression method with unadjusted means was more informative than the other indirect methods.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10681-018-2246-8</doi><tpages>10</tpages></addata></record> |
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subjects | Agronomy Analysis Biomedical and Life Sciences Biotechnology Breeding Crop yield Cultivars Environmental conditions Genetic improvement Grain Life Sciences Methods Plant breeding Plant Genetics and Genomics Plant Pathology Plant Physiology Plant Sciences Regression Regression analysis Statistical analysis |
title | Efficiency of methods for genetic progress estimation in common bean breeding using database information |
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