Local environmental variation is well known to bias genetic estimates if not accounted for properly.Several authors have recommended to routinely include spatial effects in models for genetic evaluation of trees [e.g. @Gilmour97; @Dutkowski02].In contrast, the competition among trees is a known issue but much less frequently addressed and studied [@Muir05; @Cappa08; @Costa13].It produces a negative autocorrelation among neighbouring trees, which can compensate in part the spatial effect, making both effects more difficult to detect and separate.Moreover, it can have an important impact in the response to selection if not accounted for.First, because of biased genetic estimates. But most importantly, because direct and competition additive genetic effects are often antagonistic.Hence, selecting the *best* genotypes frequently means also selecting the most competitive individuals, which is not necessarily the optimal strategy.In addition, any phenotypic measure could potentially benefit from spatial and competition adjustments, delivering records that are less prone to bias by uncontrolled or hidden environmental factors, and therefore with clearer genetic signal for further use in association and genomic predictions.In this talk, we illustrate the advantages in the use of spatial and competition evaluation models through a Douglas-fir case study from the french breeding program.We also discuss diagnostic tools and procedures, as well as current implementations of spatial and competition models available in the Free and Open Source Software Rpackage `breedR`.