Unequal clonal deployment improves genetic gains at constant diversity levels for clonal forestry

Maximizing genetic gain at an acceptable diversity level is an ideal outcome of selection and deployment. Based on this criterion, this study investigated the efficiency of unequal clonal deployment strategies for clonal forestry and compared them with truncation selection and equal deployment (truncation deployment). Two unequal deployment strategies were considered: (1) deploying the clones in linear relationship to their genetic values (linear deployment) and (2) optimizing genetic gain at a given diversity level using an algorithm (optimal deployment). All strategies were applied to candidate clone sets constructed from two clonal tests of spruces with one having a complex relationship of clone, half-sib, and full-sib and the other having a simpler relationship of clone and half-sib. At a constant diversity level, substantially more expected gains were obtained by the unequal deployment strategies than by truncation deployment. Optimal deployment was at least equal to linear deployment. Optimal deployment's superiority was more evident when the candidate clones in the set were more closely related, having less available diversity for selection, and/or when higher diversity levels were demanded, but diminished when the candidate clones were unrelated or equally related. We recommend using optimal deployment for clonal forestry, although in some cases, linear deployment might be a near-optimal alternative. As current clonal tests are based on advanced breeding cycles, candidate clones for selection are inevitably related to some degree, so optimal deployment is likely to become preferred.