02442naa a2200265 a 450000100080000000500110000800800410001902400520006010000190011224501160013126000090024730000100025652017120026665000110197865000140198965000190200365300160202270000230203870000190206170000200208070000130210070000180211370000190213177300260215021572532023-10-16       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1007/s10681-023-03179-02DOI1 aRAGALZI, C. M.  aA spatial-based approach applied to early selection stages in a forage breeding program.h[electronic resource]  c2023  a12 p.  aApomictic forage species using hybridization involves a large number of hybrids in the initial breeding stages, requiring modern evaluation strategies. The objective of this study was to develop a strategy called "Within-microsite Checks" to evaluate hybrids from the early phases of the Guineagrass breeding program. The strategy employs common checks in each experimental microsite to assess the quality of the microenvironment in the experiment using plant-based indices linked to spatial (rows and columns) arrangements. The scheme was tested at Embrapa Beef Cattle in Campo Grande, Brazil, where 2,100 hybrids were evaluated in the initial selection stage. Each microsite had 32 plants, two checks, and thirty hybrids, with evaluations done individually and at various times of the year for Canopy Height, Regrowth Density, and Regrowth Speed traits. The plant-based index for each microsite corresponds to the check average for each trait. The mixed model methodology was used to test various random and fixed effects. The plant-based index used as a fixed effect had the greatest impact on the model fitness. Regardless of the trait considered, the association of the plant-based index with the spatial random effects showed the best performance among the models. Estimating the spatial variance improves the accuracy of the variance components. The narrow and broad sense heritability coefficients were high (&gt0.68) for all traits, indicating high prediction accuracy. The "Within-microsite Checks" strategy uses a plant-based index to characterize microsite environmental quality, potentially improving prediction accuracy and selection efficiency in the early stages of forage breeding programs.  aForage  aPhenomics  aPlant breeding  aGuineagrass1 aOLIVEIRA, R. G. de1 aRIBEIRO, A. G.1 aHELIX, C. H. P.1 aJANK, L.1 aSANTOS, M. F.1 aRESENDE, R. T.  tEuphytica, 219, 2023.