In breeding forest trees, as for livestock, the goal is to capture as much genetic gain as possible for the breeding objective, while limiting longand short-term inbreeding. The Southern Tree Breeding Association STBA) is responsible for breeding Australia's two main commercial forest tree species and has adopted algorithms and methods commonly used in animal breeding to achieve this balance. Discrete generation breeding is the norm for most tree breeding programmes. However, the STBA uses an overlapping generation strategy, with a new stream of breeding initiated each year. A feature of the species bred by the STBA ('Pinus radiata' and 'Eucalyptus globulus') is the long interval (up to 7 years) between when an individual is mated and when its progeny is first assessed in field trials and performance data included in the national performance database. Mate selection methods must therefore recognize the large pool of unmeasured progeny generated over recent years of crossing. In addition, the substantial delay between when an individual is selected in a field trial and when it is clonally copied into a mating facility (breeding arboretum) means that selection and mating must occur as a two-step process. In this article, we describe modifications to preselection and mate selection algorithms that allow unmeasured progeny (juveniles) to be recognized. We also demonstrate that the addition of hypothetical new progeny to the juvenile pool is important for computing the increase in average co-ancestry in the population. Methods outlined in this article may have relevance to animal breeding programmes where between mating and progeny measurement, new rounds of mating are initiated.

TREEPLAN is an advanced analytical tool providing accurate and precise predictions of genetic values to operational tree breeders. This project aimed to exploit TREEPLAN's analytical power for the benefit of all sections of the forest growing industry; in particular, the deployment sector and programs not traditionally associated with the Southern Tree Breeding Association. The outputs include: the routine inclusion of reproductive and fitness traits into the assessment framework; better prediction of stand performance through competition models; the undertaking of pilot studies using data from large industry programs; and the better modelling of the genetic structures of hybrid populations.

The POTATOPLAN project aims to adapt comprehensive genetic evaluation systems used in forest tree and livestock breeding to potatoes. These systems use Best Linear Unbiased Prediction (BLUP) to incorporate all measurements from all relatives and correlated traits to best predict the additive and total genetic values of all genotypes for all traits. This enables optimal selection of genotypes for breeding and deployment as any genotype at any stage of testing can be compared with any other, including commercial varieties. The method can remove biases due to natural and artificial environmental variation while accounting for GxE, genetic trends over time due to selection, differences in data amount and quality, and varying sampling procedures. The models have been adapted to potatoes by estimating additive genetic effects using an additive relationship matrix that recognises the polyploid nature of most potato varieties. Family merit score counts (number of selected progeny) are converted into a binary selection trait for the genotypes in the family, with a value of 1 for selected named progeny and 0 for unselected progeny. This trait is used to predict the value of the selected progeny for all traits that contribute to the merit score through the genetic correlations determined on an experimental basis. Measurements of genotype performance using different plot or sample sizes is accounted for by using weighted analysis with the individual plant or standard sample size error variance as the reference point. These approaches should allow the more widespread adoption of BLUP methods in potato and other crop breeding. POTATOPLAN also includes a comprehensive database of pedigree, measurements, trial designs, genetic and other model parameters, and resultant genetic values to make them useable for breeders and growers.

A procedure has been developed for the prediction of genetic merit and the simultaneous assessment of multiple genotypes for subsequent use in gene detection. The system utilises a large volume of genotype information but ignores pedigree. With a simple additive model of inheritance, high correlations between estimates of molecular breeding value and highly reliable progeny test estimated breeding values were observed (0.70–0.77).