Spatial analysis increases efficiency of progeny testing of Chinese fir

We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir （Cunninghamia lanceolata （Lamb.） Hook） on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height （DBH） and tree...

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Bibliographic Details
Published in:Journal of forestry research 2017-05, Vol.28 (3), p.445-452
Main Authors: Bian, Liming, Zheng, Renhua, Su, Shunde, Lin, Huazhong, Xiao, Hui, Wu, Harry Xiaming, Shi, Jisen
Format: Article
Language:English
Subjects:
Autoregressive models
Biomedical and Life Sciences
Blocking
Cunninghamia lanceolata
Design factors
Design of experiments
Environmental gradient
Evergreen trees
Forest Science
Forestry
Forestry research
Hills
Life Sciences
Original Paper
Parameter estimation
Plant breeding
Progeny
Skogsvetenskap
Spatial analysis
Spatial data
Trees
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Summary:We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir （Cunninghamia lanceolata （Lamb.） Hook） on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height （DBH） and tree height data had significant spatial auto-correlations among rows and columns. Adding a firstorder separable autoregressive term more effectively modelled the spatial variation than did the incomplete block （IB） model used for the experimental design. The spatial model also accounted for effects of experimental design factors and greatly reduced residual variances. The spatial analysis rel- ative to the IB analysis improved estimation of genetic parameters with the residual variance reduced 13 and 19% for DBH and tree height, respectively; heritability increased 35 and 51% for DBH and tree height, respectively; and genetic gain improved 3-5%. Fitting global trend and postblocking did not improve the analyses under IB model. The use of a spatial model or combined with a design model is recommended for forest genetic trials, particularly with global trend and local spatial variation of hilly sites.
ISSN:1007-662X
1993-0607
1993-0607
DOI:10.1007/s11676-016-0341-z