Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (3): 869-876.doi: 10.3724/SP.J.1006.2023.24030

• RESEARCH NOTES • Previous Articles    

High-throughput phenotyping models for quality traits in peanut kernels

JI Hong-Chang1(), HU Chang-Li1, QIU Xiao-Chen1, WU Lan-Rong2, LI Jing-Jing1, LI Xin1, LI Xiao-Ting1, LIU Yu-Han1, TANG Yan-Yan1, ZHANG Xiao-Jun1, WANG Jing-Shan1, QIAO Li-Xian1,*()   

  1. 1College of Agriculture, Qingdao Agricultural University / Shandong Peanut Industry Collaborative Innovation Center / Shandong Key Laboratory of Dryland Agricultural Technology, Qingdao 266109, Shandong, China
    2Qingdao Agricultural Technology Extension Center, Qingdao 266100, Shandong, China
  • Received:2022-01-24 Accepted:2022-07-22 Online:2023-03-12 Published:2022-08-01
  • Contact: QIAO Li-Xian E-mail:17806285316@163.com;lxqiao73@163.com
  • Supported by:
    Special Key Project of Qingdao Science and Technology Benefiting the People Demonstration and Guidance(20-3-4-25-nsh);Shandong Agricultural Improved Seed Project(2020LZGC001);Natural Science Foundation of Shandong(ZR2020MC102)

RichHTML408

14

PDF

368

Abstract:

Peanut is one of the important oil crops. Its kernel quality directly affects its processing characteristics and is an important index for peanut quality evaluation. Establishing a high-throughput phenotyping model for peanut kernel quality and evaluating peanut kernel quality quickly and efficiently might significantly improve the efficiency of peanut breeding. In this study, the spectra of 175 peanut kernel samples (140 RIL populations derived from Yuhua 14 × LOP 215 and 35 other breeding lines) were collected by Antaris II Fourier Transform Near Infrared Spectroscopy Analyzer (Thermo company), and the oil content, protein content, sugar content, and fatty acid content of seed kernel were determined by Soxhlet extraction method, Dumas nitrogen method, anthrone colorimetry, and gas chromatography, respectively. Partial least squares (PLS) was used to construct the near-infrared calibration models of oil content, protein content, sugar content and some fatty acid content of peanut kernel. 30 other peanut materials which were not involved in the modelling were selected to verify the model externally. The determination coefficients (R2) of the models were greater than 0.90, indicating that the models could be applied to the high-throughput prediction of peanut kernel quality traits. This study provides a detection platform for high-throughput phenotypic analysis of peanut kernel quality traits.

Key words: peanut, kernel, oil content, near infrared model, RIL population

Table 1

Soluble sugar standard solution for standard curve drawing"

试剂Reagent 标准品配比Standard sample ratio
100 µg mL-1标样体积 Volume of standard sample (mL) 0 0.2 0.4 0.6 0.8 1.0
蒸馏水 H2O (mL) 2.0 1.8 1.6 1.4 1.2 1.0
标准样品总量 Standard sample quality (µg) 0 20.0 40.0 60.0 80.0 100.0

Fig. 1

Standard curve of soluble sugar contents"

Fig. 2

Sample combustion curve of Dumas nitrogen determinator"

Fig. 3

Chemical values of contents of fat (A), protein (B), and soluble sugar (C) in 175 peanut samples"

Fig. 4

Chemical values of contents of oleic acid (A), linoleic acid (B), and palmitic acid (C) in 175 peanut samples"

Fig. 5

Determination coefficients of the contents of fat, protein, soluble sugar, oleic acid, linoleic acid, and palmitic acid (A-F) in peanut"

Fig. 6

Correlation between predicted values and chemical measured values of fat, protein, soluble sugar, oleic acid, linoleic acid, and palmitic acid contents (A-F) in peanut"

[1] 廖伯寿, 殷艳, 马霓. 中国油料作物产业发展回顾与展望. 农学学报, 2018, 8(1): 107-112.
Liao B S, Yin Y, Ma N. Review and prospect of China’s oil crop industry. J Agric, 2018, 8(1): 107-112. (in Chinese with English abstract)
[2] 金华丽, 李琳琳. 近红外光谱技术测定花生蛋白质含量研究. 河南工业大学学报(自然科学版), 2014, 35(1): 26-29.
Jin H L, Li L L. Determination of peanut protein content by near infrared spectroscopy. J Henan Univ Technol (Nat Sci Edn), 2014, 35(1): 26-29. (in Chinese with English abstract)
[3] 宋丽华, 刘立峰, 陈焕英, 穆国俊, 卢萍萍. 花生籽仁蛋白质含量近红外光谱模型的建立. 中国农学通报, 2011, 27(15): 85-89.
Song L H, Liu L F, Chen H Y, Mu G J, Lu P P. Establishment of near infrared spectral model of protein content in peanut kernel. Chin Agric Sci Bull, 2011, 27(15): 85-89. (in Chinese with English abstract)
[4] 马寅斐, 何东平, 王文亮, 刘丽娜, 徐同成, 陶海腾, 杜方岭. 我国花生产业的现状分析. 农产品加工学刊, 2011, (7): 122-124.
Ma Y F, He D P, Wang W L, Liu L N, Xu T C, Tao H T, Du F L. Analysis on the current situation of China’s peanut industry. Acad Period Farm Prod Proc, 2011, (7): 122-124. (in Chinese with English abstract)
[5] Bishi S K, Lokesh K, Mahatma M K. Misra quality traits of Indian peanut cultivars and their utility as nutritional and functional food. Food Chem, 2015, 167: 107-114.
doi: 10.1016/j.foodchem.2014.06.076 pmid: 25148966
[6] 刘娟, 汤丰收, 张俊, 臧秀旺, 董文召, 易明林, 郝西. 国内花生生产技术现状及发展趋势研究. 中国农学通报, 2017, 33(22): 13-18.
Liu J, Tang F S, Zhang J, Zang X W, Dong W Z, Yi M L, Hao X. Study on the current situation and development trend of peanut production technology in China. Chin Agric Sci Bull, 2017, 33(22): 13-18 (in Chinese with English abstract)
[7] 房元瑾, 孙子淇, 苗利娟, 齐飞艳, 黄冰艳, 郑峥, 董文召, 汤丰收, 张新友. 花生籽仁外观和营养品质特征及食用型花生育种利用分析. 植物遗传资源学报, 2018, 19: 875-886.
Fang Y J, Sun Z Q, Miao L J, Qi F Y, Huang B Y, Zheng Z, Dong W Z, Tang F S, Zhang X Y. Characteristics of appearance and nutritional quality of peanut kernel and analysis of breeding and utilization of edible peanut. J Plant Genet Resour, 2018, 19: 875-886. (in Chinese with English abstract)
[8] 齐伟杰. 现代近红外光谱分析在食品检测中的应用. 中国食品, 2021, (16): 124-125.
Qi W J. Application of modern near infrared spectroscopy in food detection. Chin Food, 2021, (16): 124-125. (in Chinese with English abstract)
[9] Islelb T G, Pattee H E, Giesbrecht F G. Oil, sugar, and starch characteristics in peanut breeding lines selected for low and high oil content and their combining ability. J Agric Food Chem, 2004, 52: 3165-3168.
doi: 10.1021/jf035465y
[10] Jitndra B M, Ram S M, Dilip M B. Near-infrared transmittance spectroscopy: a potential tool for non-destructive determination of oil content in groundnuts. J Sci Food Agric, 2000, 80: 237-240.
doi: 10.1002/(SICI)1097-0010(20000115)80:2<237::AID-JSFA523>3.0.CO;2-9
[11] 雷永, 王志慧, 淮东欣, 高华援, 晏立英, 李建国, 李威涛, 陈玉宁, 康彦平, 刘海龙, 王欣, 薛晓梦, 姜慧芳, 廖伯寿. 花生籽仁蔗糖含量近红外模型构建及在高糖品种培育中的应用. 作物学报, 2021, 47: 332-341.
doi: 10.3724/SP.J.1006.2021.04106
Lei Y, Wang Z H, Huai D X, Gao H Y, Yan L Y, Li J G, Li W T, Chen Y N, Kang Y P, Liu H L, Wang X, Xue X M, Jiang H F, Liao B S. Construction of near infrared model of sucrose content in peanut kernel and its application in cultivation of high sugar varieties. Acta Agron Sin, 2021, 47: 332-341. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2021.04106
[12] 杨军军, 庞洪利, 韩宏伟, 王秀贞, 孙妍, 王志伟, 王传堂. 花生油油酸亚油酸含量近红外模型构建. 农业与技术, 2021, 41(15): 4-7.
Yang J J, Pang H L, Han H W, Wang X Z, Sun Y, Wang Z W, Wang C T. Construction of near infrared model of oleic acid and linoleic acid content in peanut oil. Agric Technol, 2021, 41(15): 4-7. (in Chinese with English abstract)
[13] 秦利, 刘华, 杜培, 董文召, 黄冰艳, 韩锁义, 张忠信, 齐飞艳, 张新友. 基于近红外光谱法的花生籽仁中蔗糖含量的测定. 中国油料作物学报, 2016, 38: 666-671.
Qin L, Liu H, Du P, Dong W Z, Huang B Y, Han S Y, Zhang Z X, Qi F Y, Zhang X Y. Determination of sucrose content in peanut kernel based on near infrared spectroscopy. Chin J Oil Crop Sci, 2016, 38: 666-671. (in Chinese with English abstract)
[14] 李培武, 陈小媚, 张文. 油料种籽含油量的测定残余法. 中华人民共和国行业标准, NY/T1285-2007.
Li P W, Chen X M, Zhang W. Determination of oil content in oilseeds residual method. Industrial Standard of the People’s Republic of China, NY/T 1285-2007. (in Chinese with English abstract)
[15] 汪红, 魏亮亮, 李通, 王铁良, 许超, 刘冰杰, 郭洁. 杜马斯燃烧法快速测定粮油中粗蛋白质含量研究. 粮油食品科技, 2018, 26(2): 49-53.
Wang H, Wei L L, Li T, Wang T L, Xu C, Liu B J, Guo J. Rapid determination of crude protein content in grains and oils by Dumas combustion method. Cereals Oil Food Sci Technol, 2018, 26(2): 49-53. (in Chinese with English abstract)
[16] 李安妮, 刘敏敏, 庾翠梅, 徐妙颜. 用蒽酮法测定花生荚果和植株可溶性糖和淀粉. 中国油料, 1983, (3): 50-51.
Li A N, Liu M M, Yu C M, Xu M Y. Determination of soluble sugar and starch in peanut pods and plants by anthrone method. Chin Oil, 1983, (3): 50-51. (in Chinese with English abstract)
[17] 金诚诚, 杨振中, 曹莹, 于慧佳. 气相色谱归一化法测定高油酸花生中油酸、亚油酸含量. 农业科技与装备, 2021, (1): 52-55.
Jin C C, Yang Z Z, Cao Y, Yu H J. Determination of oleic acid and linoleic acid in high oleic peanut by gas chromatography normalization. Agric Sci Technol Equip, 2021, (1): 52-55. (in Chinese with English abstract)
[18] 张建成, 王传堂, 王秀贞, 唐月异, 张树伟, 李贵杰. 花生自然风干种子油酸、亚油酸和棕榈酸含量的近红外分析模型构建. 中国农学通报, 2011, 27(3): 90-93.
Zhang J C, Wang C T, Wang X Z, Tang Y Y, Zhang S W, Li G J. Construction of near infrared analysis model for oleic acid, linoleic acid and palmitic acid content of naturally air dried peanut seeds. Chin Agric Sci Bull, 2011, 27(3): 90-93. (in Chinese with English abstract)
[19] 杨传得, 唐月异, 王秀贞, 吴琪, 孙全喜, 王传堂, 关淑艳. 傅立叶近红外漫反射光谱技术在花生脂肪酸分析中的应用. 花生学报, 2015, 44(1): 11-17.
Yang C D, Tang Y Y, Wang X Z, Wu Q, Sun Q X, Wang C T, Guan S Y. Application of fourier near infrared diffuse reflectance spectroscopy in the analysis of peanut fatty acids. J Peanut Sci, 2015, 44(1): 11-17. (in Chinese with English abstract)
[20] 唐月异, 王秀贞, 刘婷, 吴琪, 孙全喜, 王志伟, 张欣, 王传堂, 邵俊飞. 花生自然风干种子蔗糖含量近红外定量分析模型构建. 山东农业科学, 2018, 50(6): 159-162.
Tang Y Y, Wang X Z, Liu T, Wu Q, Sun Q X, Wang Z W, Zhang X, Wang C T, Shao J F. Construction of near infrared quantitative analysis model of sucrose content in naturally air dried peanut seeds. Shandong Agric Sci, 2018, 50(6): 159-162. (in Chinese with English abstract)
[21] 李威涛, 郭建斌, 喻博伦, 徐思亮, 陈海文, 吴贝, 龚廷锋, 黄莉, 罗怀勇, 陈玉宁, 周小静, 刘念, 陈伟刚, 姜慧芳. 基于HPLC-RID的花生籽仁可溶性糖含量检测方法的建立. 作物学报, 2021, 47: 368-375.
doi: 10.3724/SP.J.1006.2021.04110
Li W T, Guo J B, Yu B L, Xu S L, Chen H W, Wu B, Gong T F, Huang L, Luo H Y, Chen Y N, Zhou X J, Liu N, Chen W G, Jiang H F. Establishment of a method for the determination of soluble sugar content in peanut kernel based on HPLC-RID. Acta Agron Sin, 2021, 47: 368-375. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2021.04110
[22] 曲艺伟, 张鹤, 韩笑, 李雪莹, 王传堂, 王丕武, 姚丹, 张君. 花生脂肪酸近红外模型的建立. 分子植物育种, 2019, 17: 568-578.
Qu Y W, Zhang H, Han X, Li X Y, Wang C T, Wang P W, Yao D, Zhang J. Establishment of near infrared model of peanut fatty acids. Mol Plant Breed, 2019, 17: 568-578. (in Chinese with English abstract)
[23] 纪红昌, 邱晓臣, 柳文浩, 胡畅丽, 孔铭, 胡晓辉, 黄建斌, 杨雪, 唐艳艳, 张晓军, 王晶珊, 乔利仙. 花生籽仁含油量近红外模型的构建及其应用. 中国油料作物学报, 2022, 44: 1089-1097.
Ji H C, Qiu X C, Liu W H, Hu C L, Kong M, Hu X H, Huang J B, Yang X, Tang Y Y, Zhang X J, Wang J S, Qiao L X. Construction and application of near infrared model of oil content in peanut kernel. Chin J Oil Crop Sci, 2022, 44: 1089-1097.
[24] 昝光敏, 张玲, 张延瑞, 盛英华, 周凯, 王贤智. 大豆籽粒脂肪酸组分气相色谱检测方法的建立. 中国农学通报, 2021, 37(9): 118-124. (in Chinese with English abstract)
Jiu G M, Zhang L, Zhang Y R, Sheng Y H, Zhou K, Wang X Z. Establishment of a gas chromatographic method for the determination of fatty acid components in soybean seeds. Chin Agric Sci Bull, 2021, 37(9): 118-124. (in Chinese with English abstract)
[25] 李建国, 薛晓梦, 张照华, 王志慧, 晏立英, 陈玉宁, 万丽云, 康彦平, 淮东欣, 姜慧芳, 雷永, 廖伯寿. 单粒花生主要脂肪酸含量近红外预测模型的建立及其应用. 作物学报, 2019, 45: 1891-1898.
doi: 10.3724/SP.J.1006.2019.94016
Li J G, Xue X M, Zhang Z H, Wang Z H, Yan L Y, Chen Y N, Wan L Y, Kang Y P, Huai D X, Jiang H F, Lei Y, Liao B S. Establishment and application of near infrared prediction model for main fatty acid content of single peanut. Acta Agron Sin, 2019, 45: 1891-1898. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2019.94016
[26] 金华丽, 崔彬彬. 花生种子含油量近红外测定模型建立. 粮食与油脂, 2014, 27(9): 49-51.
Jin H L, Cui B B. Establishment of near infrared measurement model of oil content in peanut seeds. Grain Oil, 2014, 27(9): 49-51. (in Chinese with English abstract)
[27] 禹山林, 朱雨杰, 闵平, 杨庆利, 曹玉良, 王传堂, 刘旭, 周学秋. 傅立叶近红外漫反射非破坏性测定花生种子主要脂肪酸含量. 花生学报, 2010, 39(1): 11-14.
Yu S L, Zhu Y J, Min P, Yang Q L, Cao Y L, Wang C T, Liu X, Zhou X Q. Nondestructive determination of main fatty acids in peanut seeds by fourier near infrared diffuse reflectance. J Peanut Sci, 2010, 39(1): 11-14. (in Chinese with English abstract)
[28] Pattee H E, Islelb T G, Giesbrecht F G. Relationships of sweet, bitter, and roaste peanut sensory attributes with carbohydrate components in peanuts. J Agric Food Chem, 2000, 48: 757-776.
doi: 10.1021/jf9910741
[29] 陈海文, 徐思亮, 郭建斌, 陈伟刚, 罗怀勇, 刘念, 黄莉, 周小静, 吴贝, 姜成红, 任小平, 姜慧芳. 不同花生品种白藜芦醇含量鉴定评价. 中国油料作物学报, 2021, 43: 942-946.
Chen H W, Xu S L, Guo J B, Chen W G, Luo H Y, Liu N, Huang L, Zhou X J, Wu B, Jiang C H, Ren X P, Jiang H F. Evaluation of resveratrol content in different peanut varieties. Chin J Oil Crop Sci, 2021, 43: 942-946. (in Chinese with English abstract)
[1] SUN Quan-Xi, YUAN Cui-Ling, MOU Yi-Fei, YAN Cai-Xia, ZHAO Xiao-Bo, WANG Juan, WANG Qi, SUN Hui, LI Chun-Juan, SHAN Shi-Hua. Genome-wide identification and expression analysis of SWEET genes from peanut genomes [J]. Acta Agronomica Sinica, 2023, 49(4): 938-954.
[2] LIU Jun-Hua, WU Zheng-Feng, DANG Yan-Xue, YU Tian-Yi, ZHENG Yong-Mei, WAN Shu-Bo, WANG Cai-Bin, LI Lin. Effects of density on population quality and yield of peanut with different plant types under the mode of single-seed precision sowing [J]. Acta Agronomica Sinica, 2023, 49(2): 459-471.
[3] ZOU Xiao-Xia, LIN Yi-Min, ZHAO Ya-Fei, LIU Yan, LIU Juan, WANG Yue-Fu, WANG Wei- Hua. Effects of calcium application on the distribution of photosynthetic carbon in plant-soil system at different peanut pod development stages [J]. Acta Agronomica Sinica, 2023, 49(1): 239-248.
[4] DING Hong, ZHANG Zhi-Meng, XU Yang, ZHANG Guan-Chu, GUO Qing, QIN Fei-Fei, DAI Liang-Xiang. Physiological and transcriptional regulation mechanisms of nitrogen alleviating drought stress in peanut [J]. Acta Agronomica Sinica, 2023, 49(1): 225-238.
[5] WANG Rui-Pu, DONG Zhen-Ying, GAO Yue-Xin, BAO Jian-Xi, YIN Fang-Bing, LI Jin-Ping, LONG Yan, WAN Xiang-Yuan. Genome-wide association study and candidate gene prediction of kernel starch content in maize [J]. Acta Agronomica Sinica, 2023, 49(1): 140-152.
[6] ZHANG Sheng-Zhong, HU Xiao-Hui, CI Dun-Wei, YANG Wei-Qiang, WANG Fei-Fei, QIU Jun-Lan, ZHANG Tian-Yu, ZHONG Wen, YU Hao-Liang, SUN Dong-Ping, SHAO Zhan-Gong, MIAO Hua-Rong, CHEN Jing. QTLs analysis for reticulation thickness based on reconstruction of three dimensional models in peanut pods [J]. Acta Agronomica Sinica, 2022, 48(8): 1894-1904.
[7] WANG Sha-Sha, HUANG Chao, WANG Qing-Chang, CHAO Yue-En, CHEN Feng, SUN Jian-Guo, SONG Xiao. Cloning and functional identification of TaGS2 gene related to kernel size in bread wheat [J]. Acta Agronomica Sinica, 2022, 48(8): 1926-1937.
[8] BAI Dong-Mei, XUE Yun-Yun, HUANG Li, HUAI Dong-Xin, TIAN Yue-Xia, WANG Peng-Dong, ZHANG Xin, ZHANG Hui-Qi, LI Na, JIANG Hui-Fang, LIAO Bo-Shou. Assessment of cold tolerance of different peanut varieties and screening of evaluation indexes at germination stage [J]. Acta Agronomica Sinica, 2022, 48(8): 2066-2079.
[9] XU Yang, ZHANG Zhi-Meng, DING Hong, QIN Fei-Fei, ZHANG Guan-Chu, DAI Liang-Xiang. Regulation of peanut seed germination and spermosphere microbial community structure by calcium fertilizer in acidic red soil [J]. Acta Agronomica Sinica, 2022, 48(8): 2088-2099.
[10] YANG Huan, ZHOU Ying, CHEN Ping, DU Qing, ZHENG Ben-Chuan, PU Tian, WEN Jing, YANG Wen-Yu, YONG Tai-Wen. Effects of nutrient uptake and utilization on yield of maize-legume strip intercropping system [J]. Acta Agronomica Sinica, 2022, 48(6): 1476-1487.
[11] LI Hai-Fen, WEI Hao, WEN Shi-Jie, LU Qing, LIU Hao, LI Shao-Xiong, HONG Yan-Bin, CHEN Xiao-Ping, LIANG Xuan-Qiang. Cloning and expression analysis of voltage dependent anion channel (AhVDAC) gene in the geotropism response of the peanut gynophores [J]. Acta Agronomica Sinica, 2022, 48(6): 1558-1565.
[12] XU Ning-Kun, LI Bing, CHEN Xiao-Yan, WEI Ya-Kang, LIU Zi-Long, XUE Yong-Kang, CHEN Hong-Yu, WANG Gui-Feng. Genetic analysis and molecular characterization of a novel maize Bt2 gene mutant [J]. Acta Agronomica Sinica, 2022, 48(3): 572-579.
[13] DING Hong, XU Yang, ZHANG Guan-Chu, QIN Fei-Fei, DAI Liang-Xiang, ZHANG Zhi-Meng. Effects of drought at different growth stages and nitrogen application on nitrogen absorption and utilization in peanut [J]. Acta Agronomica Sinica, 2022, 48(3): 695-703.
[14] HUANG Li, CHEN Yu-Ning, LUO Huai-Yong, ZHOU Xiao-Jing, LIU Nian, CHEN Wei-Gang, LEI Yong, LIAO Bo-Shou, JIANG Hui-Fang. Advances of QTL mapping for seed size related traits in peanut [J]. Acta Agronomica Sinica, 2022, 48(2): 280-291.
[15] QU Jian-Zhou, FENG Wen-Hao, ZHANG Xing-Hua, XU Shu-Tu, XUE Ji-Quan. Dissecting the genetic architecture of maize kernel size based on genome-wide association study [J]. Acta Agronomica Sinica, 2022, 48(2): 304-319.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] Li Shaoqing, Li Yangsheng, Wu Fushun, Liao Jianglin, Li Damo. Optimum Fertilization and Its Corresponding Mechanism under Complete Submergence at Booting Stage in Rice[J]. Acta Agronomica Sinica, 2002, 28(01): 115 -120 .
[2] Wang Lanzhen;Mi Guohua;Chen Fanjun;Zhang Fusuo. Response to Phosphorus Deficiency of Two Winter Wheat Cultivars with Different Yield Components[J]. Acta Agron Sin, 2003, 29(06): 867 -870 .
[3] YANG Jian-Chang;ZHANG Jian-Hua;WANG Zhi-Qin;ZH0U Qing-Sen. Changes in Contents of Polyamines in the Flag Leaf and Their Relationship with Drought-resistance of Rice Cultivars under Water Deficiency Stress[J]. Acta Agron Sin, 2004, 30(11): 1069 -1075 .
[4] Yan Mei;Yang Guangsheng;Fu Tingdong;Yan Hongyan. Studies on the Ecotypical Male Sterile-fertile Line of Brassica napus L.Ⅲ. Sensitivity to Temperature of 8-8112AB and Its Inheritance[J]. Acta Agron Sin, 2003, 29(03): 330 -335 .
[5] Wang Yongsheng;Wang Jing;Duan Jingya;Wang Jinfa;Liu Liangshi. Isolation and Genetic Research of a Dwarf Tiilering Mutant Rice[J]. Acta Agron Sin, 2002, 28(02): 235 -239 .
[6] WANG Li-Yan;ZHAO Ke-Fu. Some Physiological Response of Zea mays under Salt-stress[J]. Acta Agron Sin, 2005, 31(02): 264 -268 .
[7] TIAN Meng-Liang;HUNAG Yu-Bi;TAN Gong-Xie;LIU Yong-Jian;RONG Ting-Zhao. Sequence Polymorphism of waxy Genes in Landraces of Waxy Maize from Southwest China[J]. Acta Agron Sin, 2008, 34(05): 729 -736 .
[8] HU Xi-Yuan;LI Jian-Ping;SONG Xi-Fang. Efficiency of Spatial Statistical Analysis in Superior Genotype Selection of Plant Breeding[J]. Acta Agron Sin, 2008, 34(03): 412 -417 .
[9] WANG Yan;QIU Li-Ming;XIE Wen-Juan;HUANG Wei;YE Feng;ZHANG Fu-Chun;MA Ji. Cold Tolerance of Transgenic Tobacco Carrying Gene Encoding Insect Antifreeze Protein[J]. Acta Agron Sin, 2008, 34(03): 397 -402 .
[10] ZHENG Xi;WU Jian-Guo;LOU Xiang-Yang;XU Hai-Ming;SHI Chun-Hai. Mapping and Analysis of QTLs on Maternal and Endosperm Genomes for Histidine and Arginine in Rice (Oryza sativa L.) across Environments[J]. Acta Agron Sin, 2008, 34(03): 369 -375 .
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd