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Research On Seed And Cone Quantitative Characteristics Of Pinus Armandii

Posted on:2015-11-27Degree:MasterType:Thesis
Country:ChinaCandidate:S Y MaFull Text:PDF
GTID:2283330482976066Subject:Tree genetics and breeding
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Pinus armandii Franch is one of sempervirent and five needle pine species which belongs to Sonko (Pinaceae), Pinus (Pinus L.), one-dimensional tube subgenus (Subgen. Strobus) and endemic to China. Seeds are riched in linoleic and experience two years to maturity. It has been used widely for nutrition and health care. Therefore, research on quantitative characteristics which related to seed and cone is conducive to armandii germplasm development and resource utilization. This study is based on large samples and long time observation which can be sure for the accurated results. From 2009-2013, fruit number and tree growth were recorded, all cones were picked and put in nylon net bags seperatedly. Analysis methods included correlation analysis, probability classification, path analysis method and multiple comparison method. The main results listed as follows:1) There is strong relationship between the shape of pinus armandii and cone productivity. Variable coefficient of diameter at breast height、tree height、crown、ratio of diameter and height、ratio of diameter and crown、ratio of crown and height are rather small. The maximum variable coefficient was average cone productivity during tested 5 years, which reached at 0.96. The influence of crown/height to cone productivity was very significant (0.93).The correlation between height and cone productivity was non-significant and their correlation coefficient was the minimum (0.42).The model of cone productivity and trees shape is: Y=-71.636-32.532X5+257.952X62) Variable coefficients of 13 cone quantitative characteristics were ranged from the maximum to the minimum:empty and flat grain weight> empty and flat grain number > kernel weight> seed weight> cone shell weight> cone weight> average weight of per seed> seed number> 100-seeds weight> ratio of seed> ratio of kernel> cone length> cone diameter。All quantitative characteristics correspond with normal distribution by using K-S test. According to probability stage division, using 4 point-(X-1.2818S)、(X-0.5246S)、(X+0.5246S)、(X+1.2818S) to classify all characteristics in 5 grades. This is the first time to classify Pinus armandii cone quantitative characteristics.3) Using path analysis to statistic 20 quantitative characteristics and 1 dependent (seed productivity) interaction effects. The correlation number of X to Y ranged from the max. to the min. was X11>X13>X14>X10>X17>X9>X8>X12>X20>X19>X2>X3>X1>X6 >X5>X7>X4>X18>X16>X15; Then-weed out those non-correlation independent:X3、 X7、X10、X12、X14、X15、X18、X20, ranged the remained number. The direct coefficient of X to Y was:X10>X11>X4>X2>X20>X8>X17>X9>X16>X19>X1>X6>X5>X13; the indirect coefficient of X to Y was:X13>X19>X17>X9>X6>X5>X8>X1>X20>X16 >X2>X4>X11>X10. Cone shell weight (X10) and seed weight (X11) accounted the main contribution stage (PX10=579.3954, PX11=221.4364). The final model of X and Y is: Y=21.292-0.549X1+3.094X2+27.545X4-10.471X5-45.049X6+0.030X8-0.096X9+130.078X1 +131.184X11-130.221X13-0.607X16-0.083X17-93.321X19+29.154X20;R2=0.99582.4) Different parts of the same cone (upper, middle and lower) seed quantitative traits are quite different. The performance of empty and deflated seed number and empty deflated seed weight showed unanimous trend. Lower part coefficient of variation of empty and deflated seed number reached the maximum of 34%. Variable coefficients of seed number ratio, seed weight, kernel rate and seed full rate in every layer were less than 10%. After multiple comparisons:the difference in the number of seeds, seed number ratio, seed weight focusing in the three layers were significant in 0.01 or 0.05 level.; the difference of empty and deflated seed number, empty and deflated seed weight are non-significant in 0.01 level; kernel rate and full rate are non-significant in each layer.
Keywords/Search Tags:Pinus armandii, Cone, Quantitative characteristics, Probability grading, Path analysis
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