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Spatial analysis increases efficiency of progeny testing of Chinese fir

Bian, Liming, Zheng, Renhua, Su, Shunde, Lin, Huazhong, Xiao, Hui, Wu, Harry Xiaming, Shi, Jisen
Journal of forestry research 2017 v.28 no.3 pp. 445-452
Cunninghamia lanceolata, autocorrelation, breeding programs, experimental design, forests, genetic improvement, heritability, hills, models, progeny, progeny testing, tree and stand measurements, tree breeding, trees, variance
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 first-order 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 relative 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 post-blocking 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.