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The Different Pollination Combinations in Castean henryi Determined by Auto Discrete Analyzers and Atomic Absorption Spectrometry |
ZOU Feng, ZHANG Xu-hui, YUAN De-yi*, ZHU Zhou-jun, TAN Lu-man, LIU Dong-ming |
Key Laboratory of Cultivation and Protection for Non-Wood Forest Trees of Ministry of Education, Key Laboratory of Non-Wood Forest Product of State Forestry Administration, Central South University of Forestry and Technology, Changsha 410004,China |
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Abstract To elucidate the xenia effects of mineral elements on different pollination combinations in Castanea henryi, using the chinquapin cultivars “Huali No.1”, “Huali No.2”, “Huali No.3” and “Huangzhen” as materials, we investigated the xenia effects of mineral elements in C. henryi by auto dicsrete analyzers and atomic absorption spectrometry. Twenty combinations of self-, cross-, and natural pollination were undertaken. The results revealed that eight mineral elements of Castanea henryi seeds were significant differences. Xenia obviously has its mineral elements, especially iron and znic. The fruit of “Huali No.2”דHuangzhen” showed the highest iron and zinc content, which were 162.13 and 41.79 μg·g-1, respectively. The fruit of “Huangzhen”דHuali No.1” showed an increased manganese content of 165.67 μg·g-1, which provides a reference for the utilization of this variety as a manganese fertilizer.Through principal component analysis, the best combination was “Huali No.2”דHuangzhen” in the 19 combinations. The results can give a basis for planting design of varieties and improving fruit quality in C. henryi.
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Received: 2017-08-15
Accepted: 2018-01-30
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Corresponding Authors:
YUAN De-yi
E-mail: csuftyuanyi@126.com
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[1] FAN Xiao-ming, YUAN De-yi, TANG Jing, et al(范晓明,袁德义,唐 静,等). Scientia Silvae Sinicae(林业科学),2014,50(10): 42.
[2] Fan Xiaoming,Yuan Deyi,Tang Jing, et al. Trees, 2015,29: 1713.
[3] MA Hai-quan, JIANG Xi-bing, GONG Bang-chu, et al(马海泉,江锡兵,龚榜初,等). Journal of Zhejiang Forestry Science & Technology(浙江林业科技), 2013,33(1): 62.
[4] ZHANG Xu-hui, YUAN De-yi, ZOU Feng, et al(张旭辉,袁德义,邹 锋,等). Acta Horticulturae Sinica(园艺学报),2016,43(1): 61.
[5] CAO Yong-qing, YAO Xiao-hua, TENG Jian-hua, et al(曹永庆,姚小华,腾建华,等). Journal of Nanjing Forestry University(南京林业大学学报),2016, 40(5): 55.
[6] QI Xing-jiang, ZHENG Xi-liang, REN Hai-ying,et al(戚行江,郑锡良,任海英,等). Journal of Fruit Science(果树学报),2017, 34(7): 861.
[7] Sabir A. Plant Biology, 2015, 17: 567.
[8] MA Jing, YANG Xiao-hong, MA Xiao-jun,et al(马 静,杨晓红,马小军,等). Guihaia(广西植物),2009, 29(6): 905.
[9] WANG Zheng-jia, ZHANG Bin, XIA Guo-hua,et al(王正加,张 斌,夏国华,等). Journal of Fruit Science(果树学报),2010, 27(6): 908.
[10] Wang Qian, Su Shuchai, Zhao Di,et al. Natural Resources, 2012, 3: 66.
[11] YANG Liu, ZHAO Zhi-heng, SHI Zhuo-gong(杨 柳,赵志珩,石卓功). Journal of Northwest Forestry University(西北林学院学报),2012, 27(6): 75.
[12] CHEN Jia-jia, SHI Zhuo-gong(陈佳佳,石卓功). Journal of Central South University of Forestry and Technology(中南林业科技大学学报),2009, 29(6): 152.
[13] PAN Hai-fa, XU Yi-liu, ZHANG Yi,et al(潘海发, 徐义流, 张 怡,等). Plant Nutrition and Fertilizer Science(植物营养与肥料学报),2011, 17(4): 1024.
[14] GONG Xin-ming, GUAN Jun-feng, ZHANG Ji-shu, et al(龚新明, 关军锋, 张继澍, 等). Plant Nutrition and Fertilizer Science(植物营养与肥料学报), 2009, 15(4): 942.
[15] GUAN Jun-feng(关军锋). Fruit Quality Research(果品品质研究). Shijiazhuang:Hebei Science & Technology Press(石家庄:河北科学技术出版社), 2001. 153.
[16] LI Juan, CHEN Jie-zhong, HUANG Yong-jing,et al(李 娟,陈杰忠,黄永敬,等). Journal of Fruit Science(果树学报),2011, 28(4): 668. |
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