Research on the optimal combination of plant growth regulators based on quadratic universal rotation combination

doi:10.6048/j.issn.1001-4330.2025.07.016

Xinjiang Agricultural Sciences ›› 2025, Vol. 62 ›› Issue (7): 1709-1719.DOI: 10.6048/j.issn.1001-4330.2025.07.016

• Cotton column • Previous Articles Next Articles

Research on the optimal combination of plant growth regulators based on quadratic universal rotation combination

YANG Liu¹^,²(), TANG Guangmu¹^,²(), LIU Jiao², ZHU Jie¹^,², GUO Keyu¹^,², ZHANG Yunshu¹^,², MA Haigang², XU Wanli¹^,²()

1. College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
2. Key Laboratory of Saline-alkali Soil Improvement and Utilization (Saline-Alkali Land in Arid and Semi-Arid Regions), Ministry of Agriculture and Rural Affairs/Research Institute of Soil, Insitute of Agricultural Resources and Environment, Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences, Urumqi 830091, China

Received:2024-12-08 Online:2025-07-20 Published:2025-09-05
Correspondence author: TANG Guangmu, XU Wanli
Supported by:
Key R&D Program of Xinjiang Uygur Autonomous Region "Research on Green Key Technologies of Agricultural and Forestry Waste Pyrolysis Products"(2022B02013-3);Graduate Research Innovation Project of Xinjiang Agricultural University(XJAUGRI2024038)

基于二次通用旋转组合的植物生长调节剂最优组合

杨柳¹^,²(), 唐光木¹^,²(), 刘娇², 朱杰¹^,², 郭珂妤¹^,², 张云舒¹^,², 马海刚², 徐万里¹^,²()

1.新疆农业大学资源与环境学院, 乌鲁木齐 830052
2.新疆维吾尔自治区农业科学院农业资源与环境研究所/农业农村部盐碱地改良与利用(干旱半干旱区盐碱地)重点实验室, 乌鲁木齐 830091

通讯作者: 唐光木,徐万里
作者简介:杨柳(1998-),女,内蒙古呼伦贝尔人,硕士研究生,研究方向为土壤改良与利用,(E-mail)59959383@qq.com
基金资助:
新疆维吾尔自治区重点研发计划项目“农林废弃物热解产物绿色关键技术研究”(2022B02013-3);新疆农业大学研究生科研创新项目(XJAUGRI2024038)

Abstract

Abstract:

【Objective】 In order to investigate the effects of compound sodium dinitrophenol (CSN), plant polysaccharides (DSK), succinyl (D2), and compound nanooxygen (FN-6) on the stress resistance of cotton plants, the optimal combination ratio is going to be sought. 【Methods】 This study used a quadratic universal rotation combination design to determine the content of plant peroxidase (POD), catalase (CAT), superoxide dismutase (SDO), malondialdehyde (MDA), and indoleacetic acid (IAA). Design Expert 13 was used to establish a functional model for it. F-test was used to analyze the significance of each regression model and regression coefficient. Afterwards, the model would be used to analyze the effect of PGR application on plant enzyme activity and endogenous IAA content and entropy weight method was used to select plant POD activity as a representative indicator to simulate and screen the optimal combination plan for its regression model. Then the optimal application ratio was obtained. At the same time, the optimization results of representative indicators were verified through four comprehensive evaluation methods: TOPSIS method (Ci), entropy method (Si), factor analysis (ε j), and rank sum ratio comprehensive evaluation method (RSR). The consistent results showed that the best application effect was achieved at the zero level, and the application amount could affect plant enzyme activity and endogenous IAA content. 【Results】 With the increase of PGR application, the activities of POD, SOD, CAT and endogenous IAA in plants showed a trend of first increasing and then decreasing, while the MDA content showed a trend of first decreasing and then increasing. The impact of various factors on plant POD activity was as follows: DSK>FN-6>CSN>D2, the impact on plant CAT was as follows: FN-6>CSN>DSK>D2, and the impact on plant SOD, MDA, and endogenous IAA content was as follows: CSN>DSK>FN-6>D2. 【Conclusion】 Through experimental simulation, the optimal application plan is CSN: 446.04-448.73 mg/hm² FN-6:526.28-528.96 mg/hm², DSK:526.28-528.96 mg/hm², D2:446.04-448.73 mg/hm².

Key words: secondary universal rotation combination design; plant growth regulators; plant enzyme activity; seeking optimization

摘要：

【目的】探究复合双硝基酚钠(CSN)、植多苷(DSK)、丁双酰(D2)及复合奈氧(FN-6)对棉花植株抗逆能力的影响,寻求最佳配施比例。【方法】采用二次通用旋转组合设计,通过测定植株过氧化物酶(POD)、过氧化氢酶(CAT)、超氧化物歧化酶(SDO)、丙二醛(MDA)及吲哚乙酸(IAA)含量,使用Design-Expert 13为其建立函数模型,F检验,分析各回归模型及各项回归系数的显著性,利用模型分析PGR施量对植株酶活性及内源IAA含量的影响,使用熵权法选取植株POD活性为代表性指示指标对其回归模型进行模拟与筛选优化组合方案,得到最优施用比例。同时,通过TOPSIS法(Ci)、熵值法(Si)、因子分析(εj)、秩和比综合评价法(RSR)四种综合评价方法对代表性指标寻优结果进行验证,结果一致均为处在零水平施用效果最佳,施量可影响植株酶活性及内源IAA含量。【结果】随着PGR施量的增加植株POD、SOD、CAT活性与内源IAA含量呈先增加后降低趋势,MDA含量呈先下降后增加趋势。各因素对植株POD活性的影响大小为DSK>FN-6>CSN>D2,对植株CAT的影响大小为FN-6>CSN>DSK>D2,对植株SOD、MDA及内源IAA含量的影响大小为均CSN>DSK>FN-6>D2。【结论】最佳配施方案为CSN:446.04~448.73 mg/hm²、FN-6:526.28~528.96 mg/hm²、DSK:526.28~528.96 mg/hm²、D2:446.04~448.73 mg/hm²。

关键词: 二次通用旋转组合设计, 植物生长调节剂, 植株酶活, 寻优

CLC Number:

S143.8

YANG Liu, TANG Guangmu, LIU Jiao, ZHU Jie, GUO Keyu, ZHANG Yunshu, MA Haigang, XU Wanli. Research on the optimal combination of plant growth regulators based on quadratic universal rotation combination[J]. Xinjiang Agricultural Sciences, 2025, 62(7): 1709-1719.

杨柳, 唐光木, 刘娇, 朱杰, 郭珂妤, 张云舒, 马海刚, 徐万里. 基于二次通用旋转组合的植物生长调节剂最优组合[J]. 新疆农业科学, 2025, 62(7): 1709-1719.

Figures/Tables 10

References 35

[1]	Ma Z Y, Zhang Y, Wu L Q, et al. High-quality genome assembly and resequencing of modern cotton cultivars provide resources for crop improvement[J]. Nature Genetics, 2021, 53(9): 1385-1391. DOI PMID
[2]	Hou J X, Wan H, Liang K H, et al. Biochar amendment combined with partial root-zone drying irrigation alleviates salinity stress and improves root morphology and water use efficiency in cotton plant[J]. Science of The Total Environment, 2023, 904: 166978.
[3]	Zhang J B, Wang Y, Zhang S P, et al. The BEL1-like transcription factor GhBLH5-A05 participates in cotton response to drought stress[J]. The Crop Journal, 2024, 12(1): 177-187.
[4]	Wang J Z, Ding J L, Yu D L, et al. Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China[J]. Geoderma, 2019, 353: 172-187.
[5]	侯汶君, 麻冬梅, 张玲, 等. 表油菜素内酯对盐胁迫下湖南稷子幼苗的缓解作用[J]. 西北植物学报, 2024, 44(4): 517.
	HOU Wenjun, MA Dongmei, ZHANG Ling, et al. The alleviating effect of brassinolide on the seedlings of Hunan millet under salt stress[J]. Acta Botanica Boreali-Occidentalia Sinica, 2024, 44(4): 517.
[6]	Zhang H M, Zhu J H, Gong Z Z, et al. Abiotic stress responses in plants[J]. Nature Reviews Genetics, 2022, 23(2): 104-119.
[7]	Zhao C Z, Zhang H, Song C P, et al. Mechanisms of plant responses and adaptation to soil salinity[J]. The Innovation, 2020, 1(1): 100017.
[8]	孙雨桐, 刘德帅, 齐迅, 等. 茉莉酸调控植物生长发育和胁迫的研究进展[J]. 生物技术通报, 2023, 39(11): 99-109. DOI
	SUN Yutong, LIU Deshuai, QI Xun, et al. Advances in jasmonic acid regulating plant growth and development as well as stress[J]. Biotechnology Bulletin, 2023, 39(11): 99-109. DOI
[9]	韦飞扬, 杜伟锋, 吴杭莎, 等. 植物生长调节剂在中药材种植方面的应用现状及其对中药材质量和产量影响的研究进展[J]. 中华中医药杂志, 2022, 37(3): 1587-1590.
	WEI Feiyang, DU Weifeng, WU Hangsha, et al. Application of plant growth regulators in Chinese medicinal materials cultivation and reasearch progress of its impact on quality and yield of Chinese medicinal materials[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2022, 37(3): 1587-1590.
[10]	Shah F A, Wei X, Wang Q J, et al. Karrikin improves osmotic and salt stress tolerance via the regulation of the redox homeostasis in the oil plant Sapium sebiferum[J]. Frontiers in Plant Science, 2020, 11: 216.
[11]	Ogawa-Ohnishi M, Yamashita T, Kakita M, et al. Peptide ligand-mediated trade-off between plant growth and stress response[J]. Science, 2022, 378(6616): 175-180. DOI PMID
[12]	Liu H W, Brettell L E, Qiu Z G, et al. Microbiome-mediated stress resistance in plants[J]. Trends in Plant Science, 2020, 25(8): 733-743. DOI PMID
[13]	马春梅, 田阳青, 赵强, 等. 植物生长调节剂复配对棉花产量的影响[J]. 作物杂志, 2022(6): 181-185.
	MA Chunmei, TIAN Yangqing, ZHAO Qiang, et al. Effects of plant growth regulator compound on cotton yield[J]. Crops, 2022(6): 181-185.
[14]	林燕翠, 曹涛, 唐中令, 等. 固相萃取-高效液相色谱法测定畜禽肉中复合硝基酚钠残留[J]. 食品工业科技, 2015, 36(16): 82-85.
	LIN Yancui, CAO Tao, TANG Zhongling, et al. Solid phase extraction-determination of nitrophenols sodium residue in meat of livestock and poultry by HPLC[J]. Science and Technology of Food Industry, 2015, 36(16): 82-85.
[15]	李文科, 黄斌, 李梦露, 等. α-萘乙酸钠和复硝酚钠对亚适宜温光胁迫下辣椒抗逆性的影响[J]. 中国蔬菜, 2022(2): 40-46.
	LI Wenke, HUANG Bin, LI Menglu, et al. Studies on sodium naphthalene-1-acetate and compound sodium nitrophenolate enhancing pepper resistance to stress under intensity of sub-optimal temperature and light[J]. China Vegetables, 2022(2): 40-46.
[16]	彭田伟, 谢会雅, 李思军, 等. 复硝酚钠和枯草芽孢杆菌复配对烟苗生长和生理指标的影响[J]. 中国农业科技导报, 2022, 24(4): 154-161. DOI
	PENG Tianwei, XIE Huiya, LI Sijun, et al. Effects of sodium dinitrate with Bacillus sbutilis complex on growth and physiological indexes of tobacco seedlings[J]. Journal of Agricultural Science and Technology, 2022, 24(4): 154-161. DOI
[17]	王学萍, 程世敏, 黄丽娜, 等. 几种植物生长调节剂对黄灯笼辣椒产量及养分吸收的影响[J]. 热带农业科学, 2022, 42(10): 18-23.
	WANG Xueping, CHENG Shimin, HUANG Lina, et al. Effects of several plant growth regulators on the production and nutrient uptake of yellow lantern chili[J]. Chinese Journal of Tropical Agriculture, 2022, 42(10): 18-23.
[18]	何倩倩, 张婕, 穆霄鹏, 等. 叶面喷施植物生长调节剂对欧李嫩枝扦插生根与生理变化的影响[J]. 植物生理学报, 2024, 60(1): 108-116.
	HE Qianqian, ZHANG Jie, MU Xiaopeng, et al. Effects of foliar spraying plant growth regulator on rooting and physiological changes of Cerasus humilis softwood cuttings[J]. Plant Physiology Journal, 2024, 60(1): 108-116.
[19]	高惠璇. 应用多元统计分析[M]. 北京: 北京大学出版社, 2005.
	GAO Huixuan. Applied multivariate statistical analysis[M]. Beijing: Peking University Press, 2005.
[20]	文旭, 王浩, 黄刚, 等. 基于因子分析的母线负荷异常数据辨识方法[J]. 重庆大学学报, 2021, 44(8): 91-102.
	WEN Xu, WANG Hao, HUANG Gang, et al. Identification method of abnormal data in bus load based on factor analysis[J]. Journal of Chongqing University, 2021, 44(8): 91-102.
[21]	Shih H S, Shyur H J, Lee E S. An extension of TOPSIS for group decision making[J]. Mathematical and Computer Modelling, 2007, 45(7/8): 801-813.
[22]	刘浩然, 汤少梁. 基于TOPSIS法与秩和比法的江苏省基本医疗服务均等化水平研究[J]. 中国全科医学, 2016, 19(7): 819-823.
	LIU Haoran, TANG Shaoliang. The equalization of basic medical services in Jiangsu Province based on TOPSIS method and RSR method[J]. Chinese General Practice, 2016, 19(7): 819-823.
[23]	甘浪雄, 张怀志, 卢天赋, 等. 基于熵权法的水上交通安全因素[J]. 中国航海, 2021, 44(2): 53-58.
	GAN Langxiong, ZHANG Huaizhi, LU Tianfu, et al. A study on influencing factors of maritime traffic safety with entropy weight method[J]. Navigation of China, 2021, 44(2): 53-58.
[24]	田凤调. 秩和比法及其应用[M]. 北京: 中国统计出版社, 1993.
	TIAN (Feng)(Diao\|Tiao). The methodology of RSR and its applications[M]. Beijing: China Statistics Press, 1993.
[25]	Altmann M, Altmann S, Rodriguez P A, et al. Extensive signal integration by the phytohormone protein network[J]. Nature, 2020, 583(7815): 271-276.
[26]	Cao Z X, Wang X P, Gao Y. Effect of plant growth regulators on cotton seedling root growth parameters and enzyme activity[J]. Plants, 2022, 11(21): 2964.
[27]	屈旭, 焦禹顺, 王仁汉, 等. 赤霉素和复硝酚钠对辣椒种子萌发及幼苗活力的影响[J]. 中国瓜菜, 2019, 32(11): 59-63.
	QU Xu, JIAO Yushun, WANG Renhan, et al. Effects of gibberellin and sodium nitrophenolate on seed germination and seedling vigor of pepper[J]. China Cucurbits and Vegetables, 2019, 32(11): 59-63.
[28]	徐加利, 尹红增, 周海燕, 等. 复硝酚钠和胺鲜酯·复硝酚钠对大棚番茄生长和果实品质的影响[J]. 植物医生, 2019(1): 23-26.
	XU Jiali, YIN Hongzeng, ZHOU Haiyan, et al. The effects of 1.8% compound sodium nitrophenolate WaterAqua and 3% amine Hexanoate·Compound SodiumNitrophenolate water aqua on the growth andQuality of greenhouse-grown tomato[J]. Plant Doctor, 2019(1): 23-26.
[29]	俞遴, 陈康民, 周斌, 等. 高温胁迫下NO对铁线莲光合特性和活性氧代谢的影响[J]. 分子植物育种, 2023, 21(9): 3051-3056.
	YU Lin, CHEN Kangmin, ZHOU Bin, et al. Effects of NO on photosynthetic characteristics and active oxygen metabolism of Clematis under high temperature stress[J]. Molecular Plant Breeding, 2023, 21(9): 3051-3056.
[30]	史锋厚, 赵瑞, 罗帅, 等. 红缨海棠嫩枝扦插生根解剖学观察[J]. 经济林研究, 2018, 36(3): 94-99.
	SHI Fenghou, ZHAO Rui, LUO Shuai, et al. The anatomical observation on the rooting of the soft-wood cutting of Malus ‘Hongying’[J]. Non-wood Forest Research, 2018, 36(3): 94-99.
[31]	李钊, 雷晓, 肖雨沁, 等. 两种植物生长调节剂及用量对烟草幼苗根系发育的影响[J]. 山东农业大学学报(自然科学版), 2022, 53(1): 123-130.
	LI Zhao, LEI Xiao, XIAO Yuqin, et al. Effects of two plant growth regulators and dosages on root development of tobacco seedlings[J]. Journal of Shandong Agricultural University (Natural Science Edition), 2022, 53(1): 123-130.
[32]	王泳超, 郑博元, 顾万荣, 等. γ-氨基丁酸对盐胁迫下玉米幼苗根系氧化损伤及内源激素的调控[J]. 农药学学报, 2018, 20(5): 607-617.
	WANG Yongchao, ZHENG Boyuan, GU Wanrong, et al. γ-Aminobutyric acid on oxidative damage and endogenous hormones in maize seedling roots under salt stress[J]. Chinese Journal of Pesticide Science, 2018, 20(5): 607-617.
[33]	Jiang Y X, Rong H T, Wang Y F, et al. Single-atom cobalt nanozymes promote spinal cord injury recovery by anti-oxidation and neuroprotection[J]. Nano Research, 2023, 16(7): 9752-9759.
[34]	王庆彬, 彭春娥, 孟慧, 等. 芸苔素内酯·吲哚乙酸·赤霉酸在不同氮浓度下对小白菜产量和品质的影响[J]. 农业资源与环境学报, 2021, 38(4): 626-635.
	WANG Qingbin, PENG Chune, MENG Hui, et al. Effects of brassinolactone · indoleacetic acid ·gibberellic acid on the yield and quality of pakchoi (Brassica chinensis L.) under different nitrogen concentrations[J]. Journal of Agricultural Resources and Environment, 2021, 38(4): 626-635.
[35]	张倩, 张明才, 刘明, 等. 氮肥-生长调节剂对寒地春玉米植株形态及产量的互作效应研究[J]. 中国农业大学学报, 2014, 19(5): 29-37.
	ZHANG Qian, ZHANG Mingcai, LIU Ming, et al. Interaction of nitrogen fertilizer and plant growth regular on plant morphology and yield in spring maize of cold region[J]. Journal of China Agricultural University, 2014, 19(5): 29-37.

编码值 Enco- ding value	实际值Actual value
编码值 Enco- ding value	X₁(CSN)	X₂(FN-6)	X₃(DSK)	X₄(D2)
2	900	900	900	900
1	693.75	693.75	693.75	693.75
0	487.5	487.5	487.5	487.5
-1	281.25	281.25	281.25	281.25
-2	75	75	75	75

编码值 Enco- ding value	实际值Actual value
编码值 Enco- ding value	X₁(CSN)	X₂(FN-6)	X₃(DSK)	X₄(D2)
2	900	900	900	900
1	693.75	693.75	693.75	693.75
0	487.5	487.5	487.5	487.5
-1	281.25	281.25	281.25	281.25
-2	75	75	75	75

Y	目标函数回归方程 Objective function regression equation	R²
POD	Y₁=45936.29-1589.79 X₁-326.88 X₂+730.63 X₃+210.12 X₄-435.19 X₁ X₂-822.81 X₁ X₃-17.56 X₁ X₄-1351.44 X₂ X₃-770.94 X₂ X₄-829.81 X₃ X₄-3168.87 X₁-3620.12 X₂-2626.25 X₃-2896.00 X₄	0.93
CAT	Y₂=14.54-0.50 X₁+0.92 X₂+0.51 X₃+0.11 X₄+0.58 X₁ X₂-0.29 X₁ X₃-0.03 X₁ X₄+0.42 X₂ X₃+0.28 X₂ X₄-0.40 X₃ X₄-2 X₁-1.39 X₂-2.32 X₃-1.31 X₄	0.84
SOD	Y₃=1771.56-298.19 X₁-27.42 X₂-119.41 X₃-33.32 X₄+71.74 X₁ X₂-156.22 X₁ X₃+101.59 X₁ X₄-58.10 X₂ X₃-0.17 X₂ X₄-38.96 X₃ X₄-223.45 X₁-126.82 X₂-171.45 X₃-308.91 X₄	0.79
MDA	Y₄=1.21+0.72 X₁+0.83 X₂+1.11 X₃+0.96 X₄+0.36 X₁ X₂-0.25 X₁ X₃-0.03 X₁ X₄ +0.002 X₂ X₃+0.06 X₂ X₄+0.06 X₃ X₄+1.07 X₁+1.39 X₂+0.78 X₃+0.80 X₄	0.95
IAA	Y₅=15.63+0.88 X₁+0.50 X₂+0.58 X₃+0.24 X₄-0.15 X₁ X₂+0.005 X₁ X₃+0.04 X₁ X₄ -0.14 X₂ X₃+0.21 X₂ X₄+0.29 X₃ X₄-2.36 X₁-1.54 X₂-1.70 X₃-2 X₄	0.85

Y	目标函数回归方程 Objective function regression equation	R²
POD	Y₁=45936.29-1589.79 X₁-326.88 X₂+730.63 X₃+210.12 X₄-435.19 X₁ X₂-822.81 X₁ X₃-17.56 X₁ X₄-1351.44 X₂ X₃-770.94 X₂ X₄-829.81 X₃ X₄-3168.87 X₁-3620.12 X₂-2626.25 X₃-2896.00 X₄	0.93
CAT	Y₂=14.54-0.50 X₁+0.92 X₂+0.51 X₃+0.11 X₄+0.58 X₁ X₂-0.29 X₁ X₃-0.03 X₁ X₄+0.42 X₂ X₃+0.28 X₂ X₄-0.40 X₃ X₄-2 X₁-1.39 X₂-2.32 X₃-1.31 X₄	0.84
SOD	Y₃=1771.56-298.19 X₁-27.42 X₂-119.41 X₃-33.32 X₄+71.74 X₁ X₂-156.22 X₁ X₃+101.59 X₁ X₄-58.10 X₂ X₃-0.17 X₂ X₄-38.96 X₃ X₄-223.45 X₁-126.82 X₂-171.45 X₃-308.91 X₄	0.79
MDA	Y₄=1.21+0.72 X₁+0.83 X₂+1.11 X₃+0.96 X₄+0.36 X₁ X₂-0.25 X₁ X₃-0.03 X₁ X₄ +0.002 X₂ X₃+0.06 X₂ X₄+0.06 X₃ X₄+1.07 X₁+1.39 X₂+0.78 X₃+0.80 X₄	0.95
IAA	Y₅=15.63+0.88 X₁+0.50 X₂+0.58 X₃+0.24 X₄-0.15 X₁ X₂+0.005 X₁ X₃+0.04 X₁ X₄ -0.14 X₂ X₃+0.21 X₂ X₄+0.29 X₃ X₄-2.36 X₁-1.54 X₂-1.70 X₃-2 X₄	0.85

变异来源 Source of variation	F值
变异来源 Source of variation	POD(Y₁)	CAT(Y₂)	SOD(Y₃)	MDA(Y₄)	IAA(Y₅)
X₁	13.500^***	1.470	17.130^***	21.540^***	4.510^**
X₂	0.571	4.890^**	0.145	28.430^***	1.450
X₃	2.850	1.540	2.750	51.100^***	1.940
X₄	0.236	0.068	0.214	37.900^***	0.331
X₁X₂	0.674	1.290	0.661	3.660^*	0.092
X₁X₃	2.410	0.315	3.140^*	1.740	0.000
0.001	0.003	1.330	0.023	0.007
X₂X₃	6.500^**	0.678	0.434	0.000	0.078
X₂X₄	2.120	0.305	0.000	0.092	0.179
X₃X₄	2.450	0.631	0.195	0.103	0.318
X₁	63.910^***	27.680^***	11.460^***	55.930^***	38.770^***
X₂	83.410^***	13.370^***	3.690^*	95.190^***	16.500^***
X₃	43.900^***	37.400^***	6.750^**	30.030^***	20.010^***
X₄	53.380^***	11.860^***	21.910^***	31.090^***	27.710^***
总模型Model Overall Model	15.570^***	5.820^***	4.340^***	22.130^***	6.280^***
失拟Lf Misfitting Lf	1.140	0.392	0.179	2.050	0.909

Research on the optimal combination of plant growth regulators based on quadratic universal rotation combination

基于二次通用旋转组合的植物生长调节剂最优组合

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 35

Related Articles 1

Recommended Articles

Metrics

处理 Treat- ments	二次通用旋转组合设计 Secondary universal rotation combination design
处理 Treat- ments	X₁(CSN)	X₂(FN-6)	X₃(DSK)	X₄(D2)
1	1	1	1	1
2	1	1	1	-1
3	1	1	-1	1
4	1	1	-1	-1
5	1	-1	1	1
6	1	-1	1	-1
7	1	-1	-1	1
8	1	-1	-1	-1
9	-1	1	1	1
10	-1	1	1	-1
11	-1	1	-1	1
12	-1	1	-1	-1
13	-1	-1	1	1
14	-1	-1	1	-1
15	-1	-1	-1	1
16	-1	-1	-1	-1
17	2	0	0	0
18	-2	0	0	0
19	0	2	0	0
20	0	-2	0	0
21	0	0	2	0
22	0	0	-2	0
23	0	0	0	2
24	0	0	0	-2
25	0	0	0	0
26	0	0	0	0
27	0	0	0	0
28	0	0	0	0
29	0	0	0	0
30	0	0	0	0
31	0	0	0	0

变异来源 Source of variation	POD (Y₁)	CAT (Y₂)	SOD (Y₃)	MDA (Y₄)	IAA (Y₅)
X₁	2.203	1.396	2.319	2.512	1.752
X₂	1.675	1.833	0.729	2.318	1.250
X₃	2.638	1.324	1.829	2.160	1.435
X₄	1.541	0.916	1.078	1.941	0.964

指示指标 Indicator indicators	熵权法 Entropy weighting method
指示指标 Indicator indicators	信息熵值e Information entropy value e	信息效用值d Information utility value d	权重 Weight (%)
POD	0.908	0.092	28.92
CAT	0.940	0.060	18.64
IAA	0.949	0.051	15.95
SOD	0.938	0.062	19.31
MDA	0.945	0.055	17.18

编码值 Encoding value	X₁		X₂		X₃		X₄
编码值 Encoding value	次数 Frequency	频率 Frequency	次数 Frequency	频率 Frequency	次数 Frequency	频率 Frequency	次数 Frequency	频率 Frequency
-2	0	0.000	0	0.000	0	0.000	0	0.000
-1	18	0.300	15	0.286	16	0.264	19	0.326
0	25	0.405	27	0.476	29	0.477	23	0.441
1	17	0.326	15	0.271	15	0.264	17	0.326
2	0	0.000	0	0.000	0	0.000	0	0.000
标准误 Standard error	0.102 4		0.098 7		0.096 4		0.102 5
95%置信区间 95% confidence interval	-0.201~0.201		-0.188~0.188		-0.188~0.188		-0.201~0.201
农艺措施 Agronomic measures	446.04~448.73		526.28~528.96		526.28~528.96		446.04~448.73

处理 Treatments	TOPSIS法 TOPSIS method		熵值法 Entropy method		因子分析法 Factor analysis method		秩和比综合评价法 Rank sum ratio comprehensive evaluation method
处理 Treatments	Ci	排序 Sort	Si	排序 Sort	εj	排序 Sort	RSR	排序 Sort
1	0.205 7	30	0.144 6	30	-1.063 1	30	0.172 1	30
2	0.248 7	27	0.208 0	29	-0.775 4	28	0.233 5	29
3	0.279 0	26	0.250 2	26	-0.591 5	22	0.274 4	26
4	0.298 1	25	0.288 0	22	-0.550 3	19	0.310 9	22
5	0.244 4	29	0.222 4	28	-0.870 5	29	0.247 5	28
6	0.299 4	24	0.275 1	25	-0.641 3	26	0.298 5	25
7	0.299 5	23	0.283 9	23	-0.608 3	25	0.307 0	23
8	0.324 9	20	0.276 4	24	-0.592 0	23	0.299 7	24
9	0.246 2	28	0.237 5	27	-0.721 3	27	0.262 1	27
10	0.417 2	10	0.416 3	9	0.039 4	8	0.435 1	9
11	0.354 8	15	0.350 6	13	-0.274 8	14	0.371 5	13
12	0.380 6	13	0.347 8	15	-0.365 5	16	0.368 8	15
13	0.377 8	14	0.374 2	12	-0.177 0	10	0.394 3	12
14	0.453 7	8	0.449 3	8	0.037 0	9	0.467 1	8
15	0.335 2	18	0.302 6	20	-0.596 5	24	0.325 1	20
16	0.382 7	12	0.348 9	14	-0.189 6	12	0.369 9	14
17	0.136 3	31	0.113 1	31	-1.340 0	31	0.141 7	31
18	0.431 0	9	0.413 7	10	-0.182 2	11	0.432 6	10
19	0.345 3	17	0.322 9	17	-0.283 9	15	0.344 7	17
20	0.317 8	21	0.288 7	21	-0.563 7	21	0.311 7	21
21	0.314 0	22	0.308 4	19	-0.540 2	18	0.330 7	19
22	0.417 0	11	0.392 6	11	-0.254 1	13	0.412 2	11
23	0.334 2	19	0.329 5	16	-0.399 6	17	0.351 2	16
24	0.345 6	16	0.311 2	18	-0.555 6	20	0.333 4	18
25	0.682 8	7	0.763 5	5	1.470 2	5	0.771 2	5
26	0.752 9	4	0.781 5	4	1.602 6	4	0.788 6	4
27	0.868 1	1	0.901 0	1	2.151 6	1	0.904 2	1
28	0.698 8	6	0.701 6	7	1.270 9	7	0.711 2	7
29	0.848 9	2	0.872 4	2	2.068 9	2	0.876 5	2
30	0.827 5	3	0.856 6	3	2.025 6	3	0.861 2	3
31	0.727 7	5	0.744 7	6	1.470 1	6	0.752 9	6