Development of improved and comprehensive growth and yield models for genetically improved stands

Abstract

& Key message This synthesis of the literature on incorporation of genetic gain into growth and yield models reveals a fundamental challenge associated with the rapid progress in genetics and breeding and limited empirical data on improved stands. Model improvements depend on a better understanding of both the biological basis for gain and of interactions between genetic and non-genetic factors on gain. & Context Continued development of new genetic varieties of trees requires accurate stand growth and yield models to predict growth trajectories and genetic gain of the new varieties using early-age growth data. & Aims To identify how the effects of genetic variety on growth and yield models could be analyzed and genetic information could be incorporated into these models for accurate growth simulation and improved yield prediction of genetically improved stands. & Results Genetic variety may affect one or several of the asymptotic parameters, shape parameters, and rate parameters of growth and yield models, which can be assessed by testing the parameter differences of the models. After determination of the influence of genetic varieties on model parameters and considering the existing general stand growth equation, the genetic gain can be incorporated into growth and yield models by calculation of genetic gain multipliers, adjustment of the site index, and calibration of the new model parameters. & Conclusion Accurate and effective growth and yield models for genetically improved stands require a better understanding of the effects of genetics, environment, and silviculture measures on tree and stand growth.

Advancing height growth models for the improved forest reproductive material of the main tree species in Latvia

“…However, the determined differences in growth trajectories for the improved FRM might not be related solely to genetic effects, which could have interacted with other factors, such as site quality, climatic conditions, management activities etc. (Hamilton and Rehfeldt 1994, Costa e Silva et al 2001, Kimberley et al 2015, Egbäck 2016, resulting in enhanced growth rate and productivity (Deng et al 2020). For instance, rapid early growth of improved silver birch could also be related to rather fertile former agricultural land, where the improved genotypes could better manifest themselves (Kimberley et al 2015), yet the overall management (including planting density, weeding, etc.)…”

“…Furthermore, among other tree variables, we chose to model the height of improved planning stock due to its relative independence from such attributes of stands as density (Weiskittel et al 2011) and serving as a sufficiently reliable proxy for areal production later in the rotation (Liziniewicz et al 2018, Liziniewicz andBerlin 2019). However, we aimed to improve the accuracy of the practically applicable model rather than distinguish a clear genetic effect on the improved FRM category -specific model parameters, which have been reported to be vastly conflicting in earlier studies with a still vague biological basis (Deng et al 2020). Still, we observed an altered growth rate and a potentially different upper asymptote for the best fitted models (Equation 4) with the category ('qualified' or 'tested') dependent genetic multipliers in front of coefficients b 1 , b 2 and b 3 (Figure 3).…”

“…We chose the genetic multiplier approach to quantify height growth differences between improved and unimproved trees. Multipliers are commonly used to modify coefficients of an existing (reference) model built on empirical data from genetically unimproved trees, when limited data of improved material from progeny trials are available (Rehfeldt et al 1991, Carson et al 1999, Gould et al 2008, Gould and Marshall 2010, Kimberley et al 2015, Deng et al 2020. Still, unlike the common approach to quantify genetic gains of different genetic entries, we introduced forest reproductive material category-specific multipliers for improved categories 'qualified' and 'tested' with the aim to apply them straightforward into practice.…”

Production of genetically improved silver birch plantations in southern and central Sweden

“…Such quantification might be done by an implementation of the genetic gain from breeding programs into growth and yield models. The proposed alternatives that have been tested over the world are calculation of genetic gain multipliers, adjustment of site index (SI), and calibration of the model parameters (Weiskittel et al 2011;Deng et al 2020). These implementation efforts are strongly dependent on the availability of the proper experimental data that has been very rare in most of the breeding programs due to high costs of establishment, especially for species of low commercial interest such as birch (Sun et al 2004;Weiskittel et al 2011).…”

“…These implementation efforts are strongly dependent on the availability of the proper experimental data that has been very rare in most of the breeding programs due to high costs of establishment, especially for species of low commercial interest such as birch (Sun et al 2004;Weiskittel et al 2011). In addition, new generations of improved material are likely to be available before results from the experiments are available (Burkhart and Thome 2012;Egbäck et al 2017;Deng et al 2020). Consequently, by the time the tested genetic material reaches maturity, it might already be vanishing from commercial use (Haapanen et al 2016).…”