02508naa a2200325 a 450000100080000000500110000800800410001902200140006002400350007410000200010924501330012926000090026252015680027165000100183965000160184965000100186565000120187565000350188765000230192265000220194565000240196765300180199165300220200965300170203170000190204870000260206770000190209370000160211277300540212820944622018-08-28       2018    bl uuuu     u00u1 u    #d  a1573-50607 a10.1007/s10681-018-2246-82DOI1 aFARIA, L. C. de  aEfficiency of methods for genetic progress estimation in common bean breeding using database information.h[electronic resource]  c2018  aThe 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.  aBeans  aGrain yield  aArroz  aFeijão  aMelhoramento Genético Vegetal  aPhaseolus Vulgaris  aRegressão Linear  aSeleção Genética  aGenetic grain  aLinear regression  aMixed models1 aMELO, P. G. S.1 aSOUZA, T. L. P. O. de1 aPEREIRA, H. S.1 aMELO, L. C.  tEuphyticagv. 214, n. 9, article 164, Sept. 2018.