Genotype by environment interaction in the southern Swedish breeding population of<i>Picea abies</i>using new climatic indices

Berlin, Mats; Jansson, Gunnar; Högberg, Karl-Anders

doi:10.1080/02827581.2014.978889

Cited by 25 publications

(31 citation statements)

References 15 publications

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“…An example here is the genetic entries with extreme PGE which may have been affected by for instance genotype by environment interactions. However, the genetic correlations for height across locations are strong for both Scots pine (0.77) (Hannrup et al 2007) and Norway spruce (0.72) (Berlin et al 2014) in Sweden, which indicates that genotype by environment interactions in general is of low importance in Swedish tree breeding population. In addition, the pattern of the genotype by environment interaction was largely unpredictable with respect to site and locality variables (Hannrup et al 2007).…”

Section: Discussionmentioning

confidence: 98%

Modeling early height growth in trials of genetically improved Norway spruce and Scots pine in southern Sweden

Egbäck¹,

Nilsson²,

Nyström³

et al. 2017

Silva Fenn.

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Highlights• The developed height growth model based on unimproved material predicted the development relatively well for genetically improved Norway spruce.• For genetically improved Scots pine, however, the model needed to be modified.• By incorporating a genetic component into the Scots pine model, the prediction errors were reduced. AbstractGenetically improved Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) are used extensively in operational Swedish forestry plantations to increase production. Depending on the genetic status of the plant material, the current estimated genetic gain in growth is in the range 10-20% for these species and this is expected to increase further in the near future. However, growth models derived solely from data relating to genetically improved material in Sweden are still lacking. In this study we investigated whether an individual tree growth model based on data from unimproved material could be used to predict the height increment in young trials of genetically improved Norway spruce and Scots pine. Data from 11 genetic experiments with large genetic variation, ranging from offspring of plus-trees selected in the late 1940s to highly improved clonal materials selected from well performing provenances were used. The data set included initial heights at the age of 7-15 years and 5-year increments for almost 2000 genetic entries and more than 20 000 trees. The evaluation indicated that the model based on unimproved trees predicted height development relatively well for genetically improved Norway spruce and there was no need to incorporate a genetic component. However, for Scots pine, the model needed to be modified. A genetic component was developed based on the genetic difference recorded within each trial, using mixed linear models and methods from quantitative genetics. By incorporating the genetic component, the prediction errors were significantly reduced for Scots pine. This study provides the first step to incorporate genetic gains into Swedish growth models and forest management planning systems.

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Section: Discussionmentioning

confidence: 98%

Modeling early height growth in trials of genetically improved Norway spruce and Scots pine in southern Sweden

Egbäck¹,

Nilsson²,

Nyström³

et al. 2017

Silva Fenn.

Self Cite

View full text Add to dashboard Cite

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“…Kroon et al (2011) reported that a high average of 0.80 for additive genetic correlation in a study using a bivariate model, indicating a low G × E interaction in Norway spruce. Berlin et al (2014) used a widely accepted multivariate model to estimate genetic correlations and found a low to moderate G × E interaction with an average of 0.72 trial-by-trial genetic correlation in 65 Norway spruce trials in southern Sweden. Estimation of type B genetic correlation between two trials using conventional approach may be inefficient, and often result in estimates of genetic correlation greater than 1 or less than −1 (Cullis et al 2014).…”

Section: Estimate Of G × E Interaction and Cluster Analysismentioning

confidence: 99%

“…To understand and estimate the amount of G × E interaction, genetic correlations between trials are commonly used to explore G × E interaction (Berlin et al 2014;Burdon 1977;Hannrup et al 2008;Smith et al 2015;Wu and Matheson 2005). In Sweden, there are a few published papers that discuss patterns of G × E interaction and their significance for Norway spruce breeding.…”

Section: Estimate Of G × E Interaction and Cluster Analysismentioning

confidence: 99%

“…Within each breeding zone, a low or moderate G × E interaction, demonstrated with Communicated by J. Beaulieu a high genetic correlation, was usually found using the trials from the same test series for tree height. This was attributed to the relatively small geographic area within each test series (Berlin et al 2014). It is highly desirable to combine different test series within adjacent breeding zones for a joint analysis to explore the validity of current delineation of breeding zones using biological data.…”

Section: Introductionmentioning

confidence: 99%

“…Berlin et al (2014) analyzed 65 trials and estimated genetic correlations across trial within each of 17 test series to explore genotype-by-environment (G × E) interaction patterns. However, no convincing G × E interaction patterns justifying the current division of breeding and seed zones were observed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Patterns of additive genotype-by-environment interaction in tree height of Norway spruce in southern and central Sweden

Chen

Karlsson

2017

Tree Genetics & Genomes

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Genotype-by-environment (G × E) interaction for tree height measured at ages 7 to 13 was investigated in 20 large open-pollinated progeny trials for Norway spruce (Picea abies (L.) H. Karst.) in southern and central Sweden. Factor analytic method using spatially adjusted data and a reduced animal model was used to explore the pattern of G × E interaction. Extended factor analyses captured 93.0% of additive G × E interaction variances using three factors. The mean daily temperature less than 3.2°C in May and June explained 27.8% G × E interaction, and it was moderately correlated with the first factor, indicating that spring or autumn frost weather condition could be a main driver for G × E interaction in Norway spruce. Cluster analysis has divided 20 trials into either 6 clusters or 3 clusters. Both sets of clusters reflected the geography of the trials (climates) and the genetic connectedness among testing series, indicating that more trials with better connectedness are required to examine whether current delineation of breeding or seed zones is optimal. Parental stability using latent regression could be used to locate best parents that have the highest breeding values and are highly stable across trials.

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Growth performance and G × E interactions of Liriodendron tulipifera half-sib families across ages in eastern China

Xia

Yang

et al. 2022

Eur J Forest Res

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Genotype by environment interaction in the southern Swedish breeding population ofPicea abiesusing new climatic indices

Cited by 25 publications

References 15 publications

Modeling early height growth in trials of genetically improved Norway spruce and Scots pine in southern Sweden

Modeling early height growth in trials of genetically improved Norway spruce and Scots pine in southern Sweden

Patterns of additive genotype-by-environment interaction in tree height of Norway spruce in southern and central Sweden

Growth performance and G × E interactions of Liriodendron tulipifera half-sib families across ages in eastern China

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