Improved genetic distance-based spatial deployment can effectively minimize inbreeding in seed orchard

Inbreeding in seed orchards is expected to increase with the advancement of breeding cycles, which results in the delivery of crops with suboptimal genetic gain, reduced genetic diversity, and lower seed set. Here, a genetic distance-dependent method for clonal spatial deployment in seed orchards was developed and demonstrated, which reduced the inbreeding levels. The method's main evaluation parameter of inbreeding is the genetic distance among individuals and the deployment method used an improved adaptive parallel genetic algorithm (IAPGA) based on Python language. Using inbreeding-prone Chinese Mongolian pine breeding population material originating from a single natural population, the proposed method was compared to a traditional orchard design and a distance-based design; namely, complete randomized block (RCB) and optimum neighborhood (ONA) designs, respectively.

With the advancement of selective breeding cycles, group separation among orchard related individuals is expected to increase. Based on the genetic distance among individuals, the IAPGA design was superior in significantly reducing the inbreeding level as compared to the two existing designs, confirming its suitability to advanced-generation orchards where relatedness among parents is common. In the 1st, 2nd, and mixed generations clonal deployment schemes, the IAPGA design produced lower inbreeding with 87.22%, 81.49%, and 87.23% of RCB, and 92.78%, 91.30%, and 91.67% of ONA designs, respectively.

The IAPGA clonal deployment proposed in this study has the obvious advantage of controlling inbreeding, and it is expected to be used in clonal deployment in seed orchards on a large-scale. Further studies are needed to focus on the actual states of pollen dispersal and mating in seed orchards, and more assumptions should be taken into account for the optimized deployment method.