Abstract High-precision estimation of genetic parameters of growth traits is a fundamental requisite for effective selection of individuals to advance breeding cycles and support continued genetic gain in tree improvement. Due to the lack of efficient large-scale cloning techniques of adult trees, plantation forestry of Pinus taeda relies mostly on genetically improved families deployed by seed or clonally propagated from juvenile tissue. The accuracy of genetic parameter estimation is a direct function of the precision by which the genetic relatedness of individuals is measured. To this end, modern tree breeding has increasingly used high-density DNA markers to capture the realized genetic relatedness among individuals into a genomic relationship matrix (GRM) instead of the standard pedigrees. This study aimed to assess the impact of GRM in improving parameter estimation in an operational P. taeda network of progeny trials in southern Brazil. A total of 35 820 individuals from 332 open-pollinated families were measured for growth traits at age five, and 917 of them were genotyped with the Pita50K SNP array. Pedigree-best linear unbiased prediction (ABLUP) and single-step genomic BLUP (ssGBLUP) models were compared for parameter estimates and predictions of individual tree and family genetic values. Estimates of narrow-sense heritability (${h}_i^2$) were moderate to high, varying between 0.46 for height and 0.68 for diameter at breast height using both ABLUP and ssGBLUP models. Additive genetic correlations among trials indicated low magnitude genotype by environment interaction across trials. Preliminary estimates of genomic predictive ability ranged from 0.34 for height to 0.46 for volume using an RRBLUP model applied exclusively to the genotyped trees. Genotyping a relatively small portion of progeny individuals provides improved accuracies in selecting individuals and families opening the possibility of clonally propagating them from juvenile tissue for large-scale deployment of P. taeda clonal plantations.