02200naa a2200325 a 450000100080000000500110000800800410001902400520006010000290011224500990014126000090024030000100024952012740025965000130153365000230154665000130156965000190158270000190160170000180162070000200163870000220165870000170168070000240169770000210172170000190174270000250176170000230178670000190180977300460182821593532023-12-08       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1007/s11295-023-01596-92DOI1 aEVANGELISTA, J. S. P. C.  aModeling covariance structures and optimizing jatropha curcas breeding.h[electronic resource]  c2023  a11 p.  aJatropha curcas has become a prominent source of biofuel, especially because of the high oil content in its fruit. The aim of this study was to test different statistic models and compare the best-fitted model with the compound symmetry model and study the grain yield persistence of J. curcas progenies. A total of 730 individuals from 73 half-sib families were evaluated for the fruit yield trait over six crop years. Repeated measures models with different covariance structures for the genetic and non-genetic effects were tested. Results show an increase up to in accuracy upon modeling the genetic and non-genetic effects when compared to the compound symmetry model. The selection gain obtained via the best-fit model for 10, 15, 20, and 25 selected best progenies was around 3 to 2% higher than gain obtained via the standard statistical model used by breeders (compound symmetry model). The harvests evaluated exhibited accuracies of high magnitude. The ten progenies that stood out with the best genetic performance are also those with the greatest persistence and greatest accumulated yield. Combining modeling of covariance structures for grain yield and selecting for persistence of production can sustain a successful long-term J. curcas breeding program.  aBiofuels  aGenetic covariance  aJatropha  aPlant breeding1 aPEIXOTO, M. A.1 aCOELHO, I. F.1 aFERREIRA, F. M.1 aMARÇAL, T. de S.1 aALVES, R. S.1 aCHAVES, S. F. da S.1 aRODRIGUES, E. V.1 aLAVIOLA, B. G.1 aRESENDE, M. D. V. de1 aDIAS, K. O. das G.1 aBHERING, L. L.  tTree Genetics & Genomesgv. 19, 21, 2023.