木榄胎生繁殖体产量预测模型

doi:10.11707/j.1001-7488.20130724

Abstract

Abstract:

Construction of a mathematic model for forecasting viviparous propagule yield of Bruguiera gymnorrhiza is important for mangrove forestations and mangrove management. In this study, viviparous propagules of B. gymnorrhiza were used as the research object in Mangrove Nation Natural Reserve at Gaoqiao, Zhanjiang city, Guangdong province. The indexes of different layers were investigated, and then the multivariate analysis in regressive and path analysis were conducted to construct a mathematic model for forecasting viviparous propagule yield of B. gymnorrhiza. The result showed that: B. gymnorrhiza seed yield was very significantly correlated with the diameters of basal stem and crown. It also had significantly positive correlation with the diameter and crotch angle of lateral branches in first class, leaf length, or leaf-shaped index, suggesting the seed yield had close relationships with phenotypic characteristics. Thus, we could regulate the yield by changing the phenotypic characteristics. With multiple stepwise regression analysis we constructed effective regression equation: Y =-148.131+127.471 X₄-11.113 X₅+2.048 X₇, which can be used for forecasting and controlling B. gymnorrhiza viviparous propagule yield. The results of path analysis showed that the factors determining the seed yield were X₄ > X₇ > X₅. Thus, increasing the crown is the best way to increase the seed yield for individual B. gymnorrhiza.

Key words: Bruguiera gymnorrhiza, viviparous propagule, forecast yield, forecasting model

CLC Number:

S718.54

Huang Jianjian, Liu Suqing, Li Jiping, Han Weidong. Construction of a Mathematic Model for Forecasting Viviparous Propagule Yield of Bruguiera gymnorriza[J]. Scientia Silvae Sinicae, 2013, 49(7): 163-169.

References

陈强. 1996. 通径分析及其在林业试验统计分析中的应用.云南林业科技, 74(1): 54-61.

陈幼生,喻方圆. 1993. 利用开花结实信息段预测中、幼龄杉木球果产量的研究. 林业科学, 29(5): 443-448.

蔡由生,刘海利,龚景旭,等. 2011. 中国湛江木榄(Bruguiera gymnorrhiza)的化学成分研究. 中国海洋药物,30(1): 15-18.

杜鹃. 2012. 通径分析在Excel和SPSS中的实现.陕西气象,(1): 45-47.

高惠璇. 2005. 应用多元统计分析.北京: 北京大学出版社,324-341.

郭庆水,徐立新,袁潜华. 2010. 木榄Na⁺/H⁺逆向转运蛋白基因的克隆. 热带生物学报, 1(2): 105-109.

韩维栋,高秀梅. 2009. 雷州半岛红树林生态系统及其保护策略研究.广州: 华南理工大学出版社, 1-10.

贺江舟,龚明福,范君华,等. 2010. 逐步回归及通径分析在主成分分析中的应用.新疆农业科学,47(3): 431-437.

孔凡斌. 2002. 马尾松开花结实特征及种子产量预测. 林业科学研究, 15(2): 150-155.

廖宝文,陈玉军,郑松发,等. 2002. 深圳湾红树林植物木榄种源筛选早期试验. 林业科学研究, 15(6): 133-139.

李昉,李晓明,王斌贵. 2010. 海洋红树林植物木榄化学成分研究. 海洋科学,34(10): 24-27.

宋丛文,周志翔,冯春莲. 1996. 杉木种子产量预测方法的初步研究. 华中农业大学学报, 15(3): 279-283.

沙守峰,伊凯,刘志,等. 2004. 苹果杂种树叶片在预选中的应用研究.北方果树, 26(3): 18-23.

陶毅明,陈燕珍,梁杨琳. 2009. 铅胁迫对木榄幼苗抗氧化酶的影响. 生态学杂志, 28(2): 342-345.

王冬梅,沈颂东. 1999. 逐步回归分析法.工业技术经济, 16(3): 54,57.

喻方圆,陈幼生,吴琼美,等. 1996. 福建省杉木种子园种子产量预测方法的研究. 南京林业大学学报, 20(2): 10-15.

喻方圆,陈幼生,肖石海,等. 1997a. 杉木种子园球果产量预测方法的研究. 林业科学,33(3): 225-233.

喻方圆,陈幼生,肖石海,等. 1997b. 马尾松母树林种子产量预测方法的研究.林业科学研究, 10(6): 634-638.

姚贻强,李桂荣,梁士楚. 2009. 广西防城港红树植物木榄种群结构的研究. 海洋环境科学, 28(3): 301-304.

于凌杰,金国虔,叶波平. 2009. 环境因子对药用植物木榄遗传多态性及其分布的影响. 海峡药学, 21(3): 21-26.

叶波平,管悦,许媛媛,等. 2010. 木榄幼苗叶片cDNA文库的构建及其表达序列标签分析. 热带亚热带植物学报, 18(2): 140-150.

中国科学院四川分院林业研究所园林化研究室. 1960. 杉木开花结实及产量预测的研究. 林业科学,(1): 8-13.

周志翔,宋丛文,冯春莲. 1998. 马尾松种子产量预测方法初步研究. 林业科学,34(5): 129-133.

周志翔,宋丛文,冯春莲. 1999. 杉木种子园种子产量预测预报模型研究. 林业科学,35(1): 124-127.

郑燕影,张少华,刘睿,等. 2008. 影响木榄叶片总黄酮含量的主因素分析. 中国天然药物,6(5): 78-93.

Abreu, A D, Ramalho A P, dos Santos J B. 2002.Prediction of seed-yield potential of common bean populations. Genetics and Molecular Biology, 25(3): 323-327.

Byram T D, Richardson J W, Lowe W J. 1999.Predicting seed yields from loblolly pine seed orchards: A simulation approach using TImpSim. Southern Journal of Applied Forestry, 23(2): 117-123.

Lewis D C, Potte T, Weckert S E. 1991. The effect of nitrogen, phosphorus and potassium fertilizer applications on the seed yield of sunflower (Helianthus annuus L.) grown on sandy soils and the prediction of phosphorus and potassium responses by soil tests. Nutrient Cycling in Agroecosystems, 28(2): 185-190.

Millar K D L, Ebbs S D, Gibson D J, et al. 2011.Evaluation of physiological parameters for the prediction of seed yield and quality for soybean (Glycine max) plants grown in the presence of weed competition. Plant Biosystems, 145(1): 1-11.

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Construction of a Mathematic Model for Forecasting Viviparous Propagule Yield of Bruguiera gymnorriza

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