| 000 | 02819nam a22004697a 4500 | ||
|---|---|---|---|
| 001 | G78714 | ||
| 003 | MX-TxCIM | ||
| 005 | 20211006084747.0 | ||
| 008 | 121211s ||||f| 0 p|p||0|| | | ||
| 040 | _aMX-TxCIM | ||
| 072 | 0 | _aA50 | |
| 072 | 0 | _aF03 | |
| 082 | 0 | 4 |
_a631.53 _bBOO |
| 100 | 1 |
_aLoffler, C.M. _uBook of abstracts: Arnel R. Hallauer international symposium on plant breeding |
|
| 110 | 0 | _aCentro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT), Mexico DF (Mexico) | |
| 111 | 2 |
_aArnel R. Hallauer International Symposium on Plant Breeding _cMexico, D.F. (Mexico) _d17-22 Aug 2003 |
|
| 245 | 0 | 0 | _aClassification of maize environments using crop simulation and GIS |
| 260 |
_aMexico, DF (Mexico) _bCIMMYT : _c2003 |
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| 300 | _ap. 242-243 | ||
| 340 | _aPrinted | ||
| 520 | _aThe effectiveness of a product evaluation system largely depends on the genetic correlation between multi-environment trials (MET) and the target population of environments (TPE) (Comstock 1977). Previous classifications of maize environments relied mainly on climatic and soil data (e.g., Pollak and Corbett l993; Runge 1968). While useful to describe environmental variables affecting crop productivity I these efforts did not quantify the impact of these variables on the genetic correlations among testing sites. Consequently I plant breeders have more extensively used classifications of environments based on similarity of product discrimination in product evaluation trials (e.g., Cooper et al. 1993). However, these efforts frequently fail to provide a long-term assessment of the TPE, mainly due to the cost and impracticality of collecting empirical performance data for long-term studies. Using a crop simulation modeL Chapman et al. (2000) integrated soils and long-term weather data to classify highly variable sorghum environments in Australia. For a subset of six testing locations, they found that three drought stress environment types had a consistent relationship with simulated yield. The purpose of this study was to investigate the applicability of this approach to the characterization of the milder US maize environments. | ||
| 546 | _aEnglish | ||
| 591 | _a0309|AGRIS 0301|AL-Maize Program | ||
| 593 | _aJuan Carlos Mendieta | ||
| 595 | _aCPC | ||
| 650 | 1 | 0 |
_aClimatic factors _91048 |
| 650 | 1 | 0 |
_aCrop husbandry _91058 |
| 650 | 1 | 0 |
_91081 _aDrought stress _gAGROVOC |
| 650 | 1 | 0 | _aEnvironmental factors |
| 650 | 1 | 0 | _aExperimentation |
| 650 | 1 | 0 | _aGenetic correlation |
| 650 | 1 | 0 |
_aGrain yield _91339 |
| 650 | 1 | 7 |
_aMaize _gAGROVOC _2 _91173 |
| 650 | 1 | 0 | _aSoils |
| 650 | 1 | 0 |
_aStatistical analysis _91276 |
| 650 | 1 | 0 |
_aTaxonomy _91284 |
| 650 | 1 | 0 |
_91151 _aHybrids _gAGROVOC |
| 700 | 1 |
_aFast, T., _ecoaut. |
|
| 700 | 1 |
_aMerrill, R., _ecoaut. |
|
| 700 | 1 |
_aWei, J., _ecoaut. |
|
| 942 | _cPRO | ||
| 999 |
_c7014 _d7014 |
||