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中国农学通报 ›› 2021, Vol. 37 ›› Issue (32): 15-19.doi: 10.11924/j.issn.1000-6850.casb2021-0397

所属专题: 玉米

• 农学·农业基础科学 • 上一篇    下一篇

玉米产量与播种密度的回归模型及相关分析

张正1,2(), 董春林1,3(), 杨睿1,2, 常建忠1,2, 张彦琴1,2   

  1. 1山西农业大学山西有机旱作农业研究院,太原030031
    2黄土高原东部旱作节水技术国家地方联合工程实验室,太原030031
    3有机旱作山西省重点实验室,太原030031
  • 收稿日期:2021-04-14 修回日期:2021-07-28 出版日期:2021-11-15 发布日期:2022-01-07
  • 通讯作者: 董春林
  • 作者简介:张正,男,1983年出生,山西运城人,助理研究员,硕士,研究方向:玉米种质资源创新及新品种选育。通信地址:030032 山西省太原市小店区龙城大街81号西区4座 山西农业大学山西有机旱作农业研究院,E-mail: 25332305@qq.com
  • 基金资助:
    山西省农业科学院农业科技创新研究项目“玉米抗旱自交系创制、鉴定及评价”(YCX2020YQ64);国家重点研发计划项目“西北耐密高产抗旱玉米新品种选育”(2018YFD0100204)

The Regression Model and Correlation Analysis Between Maize Yield and Planting Density

Zhang Zheng1,2(), Dong Chunlin1,3(), Yang Rui1,2, Chang Jianzhong1,2, Zhang Yanqin1,2   

  1. 1Shanxi Institute of Organic Dry Land Farming, Shanxi Agricultural University, Taiyuan 030031
    2National Local Joint Engineering Laboratory of Water-saving Techniques for Dry Farming in the Eastern Loess Plateau, Taiyuan 030031
    3Organic Dry Farming of Shanxi Province Key Laboratory, Taiyuan 030031
  • Received:2021-04-14 Revised:2021-07-28 Online:2021-11-15 Published:2022-01-07
  • Contact: Dong Chunlin

摘要:

为了探索玉米种植密度与产量间的关系,以及不同种植密度间实际产量与标准产量的相互关系,以玉米品种‘并单16’为研究对象,设计6个不同种植密度,分别为52500、56250、60000、63750、67500、71250株/hm2,运用SPSS对标准产量与主要农艺性状中的穗长、穗粗、秃尖长、穗行数、行粒数、百粒重、出籽率、种植密度、实际产量进行线性回归分析,构建回归分析模型。以实际产量、种植密度与标准产量构建的回归模型为y=403.997-0.15×种植密度+0.558×实际产量;标准产量与各性状间的回归模型为y= -123.292-0.037×密度-34.237×穗粗-55.099×穗长+31.950×穗行数+23.801×行粒数+7.023×秃尖长+10.649×籽粒含水量-3.006×百粒重+9.193×出籽率-0.204×实际产量;其中以实际产量、种植密度与标准产量构建的回归模型显著性较好。

关键词: 标准产量, 回归模型, 种植密度, 农艺性状, 相关性

Abstract:

To explore the relationship between maize planting density and yield, and the relationship between actual yield and standard yield among different planting densities, ‘Bingdan 16’ was used as the material, six planting densities were set, which were 52500、56250、60000、63750、67500、71250 plant/hm2, and the SPSS was applied to analyze the linear regression of the standard yield and main agronomic traits like spike length, spike diameter, bald tip length, rows per spike, grains per row, 100-grain weight, seed rate, planting density and actual yield, and then the regression analysis model was constructed. The regression model of actual yield, planting density and standard yield was y=403.997-0.15×planting density+0.558×actual yield. The regression model between standard yield and traits was y=-123.292-0.037×density-34.237× ear diameter-55.099×ear length+31.950 row number+23.801 row number+7.023×bald tip length+10.649×grain water content-3.006×100 grain weight+9.193 seed yield-0.204×actual yield; the regression model constructed by actual yield, planting density and standard yield was more significant.

Key words: standard yield, regression model, planting density, agronomic traits, correlation

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