| 000 | 02573nab a22004217a 4500 | ||
|---|---|---|---|
| 001 | G80112 | ||
| 003 | MX-TxCIM | ||
| 005 | 20241216115547.0 | ||
| 008 | 210727s2004 ne |||p|op||| 00| 0 eng d | ||
| 022 | _a1573-5060 (Online) | ||
| 022 | _a0014-2336 | ||
| 024 | 8 | _ahttps://doi.org/10.1023/B:EUPH.0000040500.86428.e8 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 072 | 0 | _aF01 | |
| 072 | 0 | _aU10 | |
| 090 | _aCIS-4178 | ||
| 100 | 1 |
_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959 |
|
| 245 | 1 | 0 | _aStatistical methods for classifying genotypes |
| 260 |
_aDordrecht (Netherlands) : _bSpringer, _c2004. |
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| 340 | _aComputer File | ||
| 500 | _aPeer review | ||
| 500 | _aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0014-2336 | ||
| 520 | _aIn genetic resource conservation and plant breeding, multivariate data on continuous and categorical traits are collected with the objective of selecting genotypes and accessions that best represent the entire population or gene collection with the minimum loss of genetic diversity. Therefore, the best numerical classification strategy is the one that produces the most compact and well-separated groups, that is, minimum variability within each group and maximum variability among groups. In this study, we review geometric classification techniques as well as statistical models based on mixed distribution models. The two-stage sequential clustering strategy uses all variables, continuous and categorical, and it tends to form more homogeneous groups of individuals than other clustering strategies. The sequential clustering strategy can be applied to three-way data comprising genotypes × environments × attributes. This approach groups genotypes with consistent responses for most of the continuous and categorical traits across environments. | ||
| 536 | _aGenetic Resources Program | ||
| 546 | _aText in English | ||
| 591 | _a0409|Springer|AL-Biometrics Program | ||
| 594 | _aCCJL01 | ||
| 650 | 7 |
_aEnvironmental factors _2AGROVOC _94558 |
|
| 650 | 7 |
_aGenetic Correlation _2AGROVOC _99128 |
|
| 650 | 7 |
_aStatistical methods _92624 _2AGROVOC |
|
| 650 | 7 |
_aGenotypes _2AGROVOC _91134 |
|
| 650 | 7 |
_aPlant breeding _2AGROVOC _91203 |
|
| 700 | 1 |
_aFranco, J. _8CFRN01 _gFormerly Genetic Resources Program _9494 |
|
| 773 | 0 |
_dDordrecht (Netherlands) : Springer, 2004. _tEuphytica _n630219 _gv. 137, no. 1, p. 19-37 _wG444298 _x0014-2336 |
|
| 856 | 4 |
_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/323 |
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| 942 |
_cJA _2ddc _n0 |
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| 999 |
_c24874 _d24874 |
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