02460naa a2200253 a 450000100080000000500110000800800410001902400520006010000210011224501320013326000090026552016660027465000230194065000130196365000170197670000290199370000170202270000200203970000190205970000170207870000190209570000190211477300730213321491122022-12-01       2022    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1007/s11295-022-01576-52DOI1 aCHAVES, S. F. S.  aApplication of linear mixed models for multiple harvest/site trial analyses in perennial plant breeding.h[electronic resource]  c2022  aThe optimization of perennial plant breeding necessarily involves the evaluation of multi-harvest and/or multi-site trials. In these situations, modeling covariance structures can elevate accuracy. This study aimed to evaluate different covariance structures for multi-harvest and multi-site trial analyses, using two datasets (D1 and D2). In D1, 25 hybrids of Theobroma grandiflorum were evaluated in a complete randomized block design, during twelve consecutive harvest years. In D2, 215 clones of Eucalyptus spp. were evaluated in a complete randomized block design, in four sites. For both datasets, the covariance structures of the random effects were modeled, and their adequacy was tested by the Akaike and Bayesian information criteria. From the selected model, the variance components and genetic parameters were estimated. We also compared the expected genetic gains and the rankings of genotypes based on the genotypic values provided by the basic and the selected models. For D1, the third-order factor analytic model was the most suitable for genetic effects, while for D2, the unstructured model showed the best fit for such effects. The models provided a better insight into the variances dynamics over the harvest years/sites. The genetic gains were 3.52 percentage points higher in D1 and did not change in D2. Despite similar results, the standard model, modeled with covariance structures that assume homogeneity of co-variances, was not the most statistically appropriate model for D2 according to the information criteria. Therefore, the modeling of covariance structures can and should be used in the genetic evaluation of perennial plants  aÁrvore Frutífera  aFloresta  aMelhoramento1 aEVANGELISTA, J. S. P. C.1 aALVES, R. S.1 aFERREIRA, F. M.1 aDIAS, L. A. S.1 aALVES, R. M.1 aDIAS, K. O. G.1 aBHERING, L. L.  tTree Genetics & Genomesgv. 18, n. 6, Article number: 44, Dec. 2022.