Spatial analysis increases efficiency of progeny testing of Chinese fir
We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir （Cunninghamia lanceolata （Lamb.） Hook） on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height （DBH） and tree...
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| Veröffentlicht in: | Journal of forestry research 2017-05, Vol.28 (3), p.445-452 |
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| creator | Bian, Liming Zheng, Renhua Su, Shunde Lin, Huazhong Xiao, Hui Wu, Harry Xiaming Shi, Jisen |
| description | We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir （Cunninghamia lanceolata （Lamb.） Hook） on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height （DBH） and tree height data had significant spatial auto-correlations among rows and columns. Adding a firstorder separable autoregressive term more effectively modelled the spatial variation than did the incomplete block （IB） model used for the experimental design. The spatial model also accounted for effects of experimental design factors and greatly reduced residual variances. The spatial analysis rel- ative to the IB analysis improved estimation of genetic parameters with the residual variance reduced 13 and 19% for DBH and tree height, respectively; heritability increased 35 and 51% for DBH and tree height, respectively; and genetic gain improved 3-5%. Fitting global trend and postblocking did not improve the analyses under IB model. The use of a spatial model or combined with a design model is recommended for forest genetic trials, particularly with global trend and local spatial variation of hilly sites. |
| doi_str_mv | 10.1007/s11676-016-0341-z |
| format | Article |
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Diameter at breast height （DBH） and tree height data had significant spatial auto-correlations among rows and columns. Adding a firstorder separable autoregressive term more effectively modelled the spatial variation than did the incomplete block （IB） model used for the experimental design. The spatial model also accounted for effects of experimental design factors and greatly reduced residual variances. The spatial analysis rel- ative to the IB analysis improved estimation of genetic parameters with the residual variance reduced 13 and 19% for DBH and tree height, respectively; heritability increased 35 and 51% for DBH and tree height, respectively; and genetic gain improved 3-5%. Fitting global trend and postblocking did not improve the analyses under IB model. The use of a spatial model or combined with a design model is recommended for forest genetic trials, particularly with global trend and local spatial variation of hilly sites.</description><identifier>ISSN: 1007-662X</identifier><identifier>ISSN: 1993-0607</identifier><identifier>EISSN: 1993-0607</identifier><identifier>DOI: 10.1007/s11676-016-0341-z</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Autoregressive models ; Biomedical and Life Sciences ; Blocking ; Cunninghamia lanceolata ; Design factors ; Design of experiments ; Environmental gradient ; Evergreen trees ; Forest Science ; Forestry ; Forestry research ; Hills ; Life Sciences ; Original Paper ; Parameter estimation ; Plant breeding ; Progeny ; Skogsvetenskap ; Spatial analysis ; Spatial data ; Trees</subject><ispartof>Journal of forestry research, 2017-05, Vol.28 (3), p.445-452</ispartof><rights>The Author(s) 2016</rights><rights>COPYRIGHT 2017 Springer</rights><rights>Copyright Springer Science & Business Media 2017</rights><rights>Copyright © Wanfang Data Co. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c521t-1d3e8be58d3a7ce6b05d93efd55fcc59be1feaa12c00946e11a27e4f6a8a56eb3</citedby><cites>FETCH-LOGICAL-c521t-1d3e8be58d3a7ce6b05d93efd55fcc59be1feaa12c00946e11a27e4f6a8a56eb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/85224X/85224X.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11676-016-0341-z$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11676-016-0341-z$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://res.slu.se/id/publ/88759$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Bian, Liming</creatorcontrib><creatorcontrib>Zheng, Renhua</creatorcontrib><creatorcontrib>Su, Shunde</creatorcontrib><creatorcontrib>Lin, Huazhong</creatorcontrib><creatorcontrib>Xiao, Hui</creatorcontrib><creatorcontrib>Wu, Harry Xiaming</creatorcontrib><creatorcontrib>Shi, Jisen</creatorcontrib><creatorcontrib>Sveriges lantbruksuniversitet</creatorcontrib><title>Spatial analysis increases efficiency of progeny testing of Chinese fir</title><title>Journal of forestry research</title><addtitle>J. For. Res</addtitle><addtitle>Journal of Forestry Research</addtitle><description>We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir （Cunninghamia lanceolata （Lamb.） Hook） on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height （DBH） and tree height data had significant spatial auto-correlations among rows and columns. Adding a firstorder separable autoregressive term more effectively modelled the spatial variation than did the incomplete block （IB） model used for the experimental design. The spatial model also accounted for effects of experimental design factors and greatly reduced residual variances. The spatial analysis rel- ative to the IB analysis improved estimation of genetic parameters with the residual variance reduced 13 and 19% for DBH and tree height, respectively; heritability increased 35 and 51% for DBH and tree height, respectively; and genetic gain improved 3-5%. Fitting global trend and postblocking did not improve the analyses under IB model. The use of a spatial model or combined with a design model is recommended for forest genetic trials, particularly with global trend and local spatial variation of hilly sites.</description><subject>Autoregressive models</subject><subject>Biomedical and Life Sciences</subject><subject>Blocking</subject><subject>Cunninghamia lanceolata</subject><subject>Design factors</subject><subject>Design of experiments</subject><subject>Environmental gradient</subject><subject>Evergreen trees</subject><subject>Forest Science</subject><subject>Forestry</subject><subject>Forestry research</subject><subject>Hills</subject><subject>Life Sciences</subject><subject>Original Paper</subject><subject>Parameter estimation</subject><subject>Plant breeding</subject><subject>Progeny</subject><subject>Skogsvetenskap</subject><subject>Spatial analysis</subject><subject>Spatial data</subject><subject>Trees</subject><issn>1007-662X</issn><issn>1993-0607</issn><issn>1993-0607</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9kUFr3DAQhU1poWnaH9CbaenR6UiyJPsYljYtBHJoC72JsTxytHjljeQlOL--WrwkKZQiBonhexo9vaJ4z-CCAejPiTGlVQUsl6hZ9fCiOGNtKypQoF_mc4Yqpfjv18WblLYAshaiPiuufuxx9jiWGHBckk-lDzYSJkolOeetp2CXcnLlPk4DhaWcKc0-DMfW5tYHSlQ6H98WrxyOid6d9vPi19cvPzffquubq--by-vKSs7mivWCmo5k0wvUllQHsm8FuV5KZ61sO2KOEBm3AG2tiDHkmmqnsEGpqBPnRbXem-5pf-jMPvodxsVM6E0aDx3G42YSmabRss38p5W_x-AwDGY7HWK2msy4LFsOTIMA4Jn7uHLZ5t0hW3wCWdPIOhdXT9SAIxkf3DRHtDufrLnUTDcAtRSZuvgHlVdPO2-nQM7n_l8CtgpsnFKK5B5tMTDH5Mwar8nxmmO85iFr-OkjMhsGis8e_B_Rh9Og2ykMd1n3OElpDg2veSv-ABDFs0Q</recordid><startdate>20170501</startdate><enddate>20170501</enddate><creator>Bian, Liming</creator><creator>Zheng, Renhua</creator><creator>Su, Shunde</creator><creator>Lin, Huazhong</creator><creator>Xiao, Hui</creator><creator>Wu, Harry Xiaming</creator><creator>Shi, Jisen</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><general>Springer Nature B.V</general><general>South China Forestry Multidisciplinary Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, People's Republic of China%Fujian Academy of Forestry, 35 Shangchiqiao, Fuzhou 350012, People's Republic of China%Jiangle Forest Farm, Jiangle 353300, People's Republic of China%Department of Forest Genetics and Plant Physiology, Ume(a) Plant Science Centre, Swedish University of Agricultural Sciences, 90183 Ume(a), Sweden%College of Forestry, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, people's Republic of China</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W95</scope><scope>~WA</scope><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope><scope>ADTPV</scope><scope>AOWAS</scope></search><sort><creationdate>20170501</creationdate><title>Spatial analysis increases efficiency of progeny testing of Chinese fir</title><author>Bian, Liming ; 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| ispartof | Journal of forestry research, 2017-05, Vol.28 (3), p.445-452 |
| issn | 1007-662X 1993-0607 1993-0607 |
| language | eng |
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| source | Springer Nature - Complete Springer Journals; Alma/SFX Local Collection |
| subjects | Autoregressive models Biomedical and Life Sciences Blocking Cunninghamia lanceolata Design factors Design of experiments Environmental gradient Evergreen trees Forest Science Forestry Forestry research Hills Life Sciences Original Paper Parameter estimation Plant breeding Progeny Skogsvetenskap Spatial analysis Spatial data Trees |
| title | Spatial analysis increases efficiency of progeny testing of Chinese fir |
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