Optimal mating of Pinus taeda L. under different scenarios using differential evolution algorithm

A newly developed software, AgMate, was used to perform optimized mating for monoecious Pinus taeda L. breeding. Using a computational optimization procedure called differential evolution (DE), AgMate was applied under different breeding population sizes scenarios (50, 100, 150, 200, 250) and candidate contribution scenarios (max use of each candidate was set to 1 or 8), to assess its efficiency in maximizing the genetic gain while controlling inbreeding. Real pedigree data set from North Carolina State University Tree Improvement Co-op with 962 Pinus taeda were used to optimize objective functions accounting for coancestry of parents and expected genetic gain and inbreeding of the future progeny. AgMate results were compared with those from another widely used mating software called MateSel (Kinghorn, 1999). For the proposed mating list for 200 progenies, AgMate resulted in an 83.7% increase in genetic gain compared with the candidate population. There was evidence that AgMate performed similarly to MateSel in managing coancestry and expected genetic gain, but MateSel was superior in avoiding inbreeding in proposed mate pairs. The developed algorithm was computationally efficient in maximizing the objective functions and flexible for practical application in monoecious diploid conifer breeding. Study implicationsA dataset from a breeding population of loblolly pine (Pinus taeda L.) was analyzed using an optimal mating software, AgMate (developed by the authors), to optimize the selection, contribution, and mating of candidates simultaneously. The software helps breeders make decisions on which tree to cross with which tree and how many times, such that the trees are not related to each other and would result in the best performing progenies. AgMate is effective in meeting the breeding objectives for monoecious species. The open-source, easy-to-use, and flexible AgMate software, also available as a website, is invaluable in helping breeders to create optimal matings for future generations, which balance the pursuit of maximizing genetic gain while maintaining genetic diversity.