Crops ›› 2023, Vol. 39 ›› Issue (3): 205-214.doi: 10.16035/j.issn.1001-7283.2023.03.029

Previous Articles     Next Articles

Analysis of Influencing Factors of Maize Yield under Different Ecological Conditions

Guo Shulei1(), Wang Ying2(), Wei Liangming1, Zhang Xin1, Liu Yan3, Wu Weihua4, Lu Daowen5, Lei Xiaobing6, Wang Zhenhua1(), Lu Xiaomin1()   

  1. 1Institute of Cereal Crops, Henan Academy of Agricultural Sciences, Zhengzhou 450002, Henan, China
    2Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, Henan, China
    3Nanyang Academy of Agricultural Sciences, Nanyang 473000, Henan, China
    4Luohe Academy of Agricultural Sciences, Luohe 462000, Henan, China
    5Anyang Academy of Agricultural Sciences, Anyang 455000, Henan, China
    6Luoyang Academy of Agriculture and Forestry Sciences, Luoyang 471023, Henan, China
  • Received:2021-11-08 Revised:2022-02-18 Online:2023-06-15 Published:2023-06-16

RichHTML

4

PDF (PC)

147

Abstract:

The difference of environmental factors under different ecological conditions are important reasons for the difference of maize yield. In order to clarify the main yield components in different regions, 15 inbred lines and 18 hybrids were evaluated and screened by using the climate environment pressure and adversity stress in different ecological areas. Combined with drought and shading stress treatment, and the contribution effects of yield components to yield in different regions were analyzed, the evaluation method of selecting suitable high- yield varieties in different regions was clarified. The results showed that the contribution effects of ear diameter, grain number per row, ear length, 100-grain weight and ear position coefficient directly related to yield were greater than those of disease and lodging, and these yield factors had different contribution and effects on yield under the influence of different climatic environments. The optimal linear regression model of yield were established in different areas with more yield factors, the higher the yield was. There are five, four, four and two factors directly related to yield were introduced into the optimal linear equations in Nanyang, Luoyang, Anyang and Luohe under different ecological environments, respectively. The multi-site joint identification showed that the yields of Zhengdan 1868 and LX201 did not decrease after drought stress, the yields decreased relatively less after shading stress, the average yields were relatively higher, and the comprehensive resistance were better in this study.

Key words: Maize, Yield factor, Environment factor, Path analysis

Table 1

Environmental factor parameters of different test locations"

地点
Location		 海拔
Altitude
(m)		 有效积温
Effective accumulated
temperature (℃)		 降雨量
Rainfall
(mm)		 日照时数
Sunshine
hour (h)		 日均最高气温
Average daily maximum
temperature (℃)		 日均最低气温
Average daily minimum
temperature (℃)		 最高气温
Maximum
temperature (℃)
南阳Nanyang 130 3247 493 574 28.20 20.36 37
漯河Luohe 57 3131 453 531 29.25 20.61 39
洛阳Luoyang 136 3003 370 554 27.63 20.30 39
安阳Anyang 67 3073 447 669 28.66 20.17 40

Table 2

Multi-site analysis of variance for different traits"

项目
Item		 产量
Yield		 百粒重
100-seed weight		 穗位系数
Ear position coefficient		 穗长
Ear length		 穗粗
Ear thickness		 穗行数
Ear rows		 行粒数
Kernels per row		 突尖
Barren ear tip
材料Material 95.25** 40.42** 10.56** 73.92** 39.40** 22.64** 38.76** 29.15**
地点Location 86.67** 186.23** 45.97** 68.77** 181.39** 8.25** 156.89** 64.63**
重复Repeat 0.37 2.51 0.19 0.24 2.36 0.67 0.13 2.73
材料×地点Material×location 56.99** 70.46** 14.03** 114.56** 21.16** 10.39** 105.19** 36.35**

Table 3

Correlation coefficients of yield traits of different materials"

指标
Index		 产量
Yield		 百粒重
100-seed
weight		 穗位系数
Ear position
coefficient		 穗长
Ear
length		 穗粗
Ear
thickness		 穗行数
Ear
rows		 行粒数
Kernels
per row		 秃尖长
Barren ear
tip length
产量Yield 0.306** 0.137 0.400** 0.491** 0.106 0.255** -0.117
百粒重100-seed weight 0.180** -0.104 0.291** 0.141 -0.449** -0.014 0.022
穗位系数Ear position coefficient 0.211** 0.411** 0.213** 0.402** 0.135 0.427** 0.320**
穗长Ear length 0.481** -0.097 0.009 0.478** 0.216** 0.445** 0.169*
穗粗Ear thickness 0.556** 0.640** 0.399** 0.203** 0.419** 0.395** 0.085
穗行数Ear rows -0.063 -0.111 -0.304** -0.070 0.124 0.334** 0.031
行粒数Kernels per row 0.352** -0.232** -0.053 0.701** -0.042 -0.070 0.444**
秃尖长Barren ear tip length 0.159* 0.041 -0.166* 0.191** 0.232** 0.265** -0.135*

Table 4

Changes in yield of different materials in different locations"

材料类型
Material type		 地点
Location		 均值
Mean (kg/hm2)		 极小值
Min. (kg/hm2)		 极大值
Max. (kg/hm2)		 标准偏差
Standard deviation		 变异系数
Variation coefficient		 偏度
Skewness		 峰度
Kurtosis
杂交种Hybrid 南阳 11 690.25 8524.95 14 635.95 94.69 0.12 0.05 0.11
漯河 10 447.95 8673.75 13 675.65 76.65 0.11 0.73 0.35
洛阳 10 818.75 8762.55 12 802.80 72.60 0.10 -0.11 -0.91
安阳 12 248.55 9972.00 14 500.50 74.00 0.09 -0.04 -0.59
自交系Inbred line 南阳 3542.70 1081.95 6920.25 99.51 0.42 0.34 -0.65
漯河 3184.65 1685.25 5278.35 60.16 0.28 0.78 -0.04
洛阳 5351.40 3216.15 7629.75 94.19 0.26 0.32 -1.35
安阳 4371.90 2331.60 7427.85 76.74 0.26 0.58 0.47

Table 5

Regression models and regression coefficients established under four ecological environments"

模型
Model		 非标准系数Unstandardized coefficient 标准系数
Standardized coefficient		 t P R R2
B 标准误差Standard error
1 常量 -628.775 46.718 -13.459 0.000 0.782 0.611
x3 73.525 2.955 0.782 24.885 0.000
2 常量 -1098.498 55.364 -19.842 0.000 0.847 0.718
x3 43.709 3.508 0.465 12.461 0.000
x4 212.769 17.421 0.456 12.214 0.000
3 常量 -1072.308 53.662 -19.983 0.000 0.859 0.738
x3 29.194 4.308 0.310 6.777 0.000
x4 199.827 16.984 0.428 11.766 0.000
x6 8.661 1.589 0.224 5.450 0.000
4 常量 -1125.673 52.515 -21.435 0.000 0.871 0.758
x3 24.641 4.225 0.262 5.833 0.000
x4 156.826 18.033 0.336 8.697 0.000
x6 10.108 1.551 0.262 6.517 0.000
x1 8.964 1.585 0.177 5.654 0.000
5 常量 -1009.523 59.975 -16.832 0.000 0.875 0.766
x3 22.072 4.208 0.235 5.245 0.000
x4 179.249 18.683 0.384 9.594 0.000
x6 10.895 1.539 0.282 7.079 0.000
x1 8.751 1.560 0.173 5.610 0.000
x2 -462.171 121.42 -0.102 -3.806 0.000

Table 6

Partial correlation coefficients between yield-related traits"

性状
Trait		 偏相关系数
Partial correlation coefficient		 显著水平
Significant
r(y, x3) 0.782** 0.000
r(y, x4) 0.779** 0.000
r(y, x6) 0.722** 0.000
r(y, x1) 0.578** 0.000
r(y, x2) 0.192** 0.000

Table 7

Regression model and regression coefficient established at Nanyang test site"

模型
Model		 非标准系数Unstandardized coefficient 标准系数
Standardized coefficient		 t P R R2
B 标准误差Standard error
6 常量 -1234.016 183.585 -6.722 0.000 0.929 0.864
x3 25.937 7.626 0.269 3.401 0.001
x6 25.485 3.628 0.458 7.025 0.000
x1 15.398 4.356 0.220 3.535 0.001
x2 -597.283 195.612 -0.133 -3.053 0.003
x8 -23.957 7.991 -0.123 -2.998 0.003
x4 16.852 6.800 0.108 2.478 0.015

Table 8

Regression model and regression coefficients established at Luohe test site"

模型
Model		 非标准系数Unstandardized coefficient 标准系数
Standardized coefficient		 t P R R2
B 标准误差Standard error
4 常量 76.600 152.706 -1.278 0.212 0.962 0.925
x6 2.568 4.182 0.991 6.023 0.000
x13 2.356 0.304 0.197 2.193 0.037
x12 -83.437 25.156 -0.296 -3.451 0.002
x11 -16.202 6.126 -0.154 -2.412 0.023

Table 9

Regression model and regression coefficients established at Luoyang test site"

模型
Model		 非标准系数Unstandardized coefficient 标准系数
Standardized coefficient		 t P R R2
B 标准误差Standard error
6 常量 -1211.546 143.002 -8.472 0.000 0.986 0.973
x6 22.264 2.151 0.541 10.350 0.000
x1 15.997 2.150 0.458 7.441 0.000
x5 43.133 7.579 0.332 5.691 0.000
x11 21.713 5.507 0.157 3.943 0.001
x4 -104.706 41.767 -0.230 -2.507 0.019
x12 45.885 18.831 0.101 2.437 0.022

Table 10

Regression model and regression coefficients established at Anyang test site"

模型
Model		 非标准系数Unstandardized coefficient 标准系数
Standardized coefficient		 t P R R2
B 标准误差Standard error
3 常量 -1633.504 162.686 -10.041 0.000 0.929 0.921
x4 279.961 52.047 0.454 5.379 0.000
x6 18.645 3.324 0.445 5.610 0.000
x1 14.410 4.253 0.197 3.388 0.000

Fig.1

Yield heat map of hybrids and inbred lines at different locations"

Table 11

HSC, WSI and CDRI of different materials"

材料类型
Material type		 名称
Name		 HSC 排名
Ranking		 遮阴水分胁迫指数
WSI of shading		 排名
Ranking		 干旱水分胁迫指数
WSI of drought		 排名
Ranking		 CDRI 排名
Ranking
杂交种Hybrid 宛玉231 0.986 1 0.703 18 0.025 8 1.0730 2
宛玉471 0.946 8 0.386 3 -0.081 3 1.0913 5
安玉109 0.935 14 0.455 10 0.079 12 2.2475 18
安玉909 0.950 6 0.528 15 0.062 11 1.7839 15
洛玉197 0.945 9 0.395 6 0.086 13 1.6656 13
洛玉199 0.944 10 0.493 14 0.147 15 1.6127 12
漯玉16 0.921 18 0.489 12 0.208 18 1.8845 16
漯玉18 0.933 15 0.443 9 0.181 17 1.4138 9
漯玉197 0.927 17 0.390 5 0.087 14 1.2212 7
郑单1868 0.977 2 0.232 2 -0.007 5 1.1762 6
郑单6095 0.952 5 0.493 13 -0.054 4 2.2416 17
郑单6122 0.938 12 0.429 8 -0.104 2 1.5238 11
郑单7137 0.947 7 0.487 11 0.044 10 1.2877 8
郑单7153 0.932 16 0.552 16 0.158 16 1.6720 14
郑单7167 0.939 11 0.414 7 -0.115 1 1.4286 10
郑单7168 0.955 4 0.389 4 0.017 7 0.9989 1
郑单7603 0.959 3 0.581 17 0.000 6 1.0730 3
郑单819 0.936 13 0.121 1 0.030 9 1.0730 4
平均值Mean 0.946 0.443 0.042 1.4700
自交系Inbred line A5855 0.933 9 0.116 3 -0.015 4 1.4434 3
A7648 0.943 6 0.792 15 0.113 11 2.2356 9
A7682 0.943 4 0.235 4 0.090 10 2.7067 13
L2258 0.858 15 0.491 8 0.165 14 2.4804 11
L2564 0.928 11 0.016 1 0.131 12 2.9141 14
L4653 0.955 1 0.698 13 0.062 8 2.2328 8
L5878 0.917 13 0.535 9 -0.119 1 1.2952 2
L753 0.916 14 0.749 14 -0.057 2 3.6169 15
自交系Inbred line LX201 0.947 3 0.111 2 0.006 5 1.8730 5
LX202 0.948 2 0.299 5 -0.041 3 1.4434 4
LX203 0.943 5 0.387 6 0.050 7 2.2434 10
郑71 0.917 12 0.673 11 0.194 15 2.5233 12
郑72 0.934 8 0.697 12 0.024 6 2.1876 7
郑493 0.930 10 0.405 7 0.164 13 1.9383 6
郑79 0.941 7 0.543 10 0.084 9 1.1915 1
平均值Mean 0.930 0.450 0.057 2.1550

Fig.2

Yield changes of different hybrids (a) and inbred lines (b) after drought and shading treatment"

Table 12

Traits related to shade tolerance and drought tolerance"

指标
Index		 耐阴性
Shade
tolerance		 耐旱性
Drought
tolerance		 脱水速率
Dehydration
rate		 穿刺强度
Penetration
strength		 综合抗病性
Comprehensive
disease resistance
耐阴性Shade tolerance 1.000
耐旱性Drought tolerance 0.169 1.000
脱水速率Dehydration rate 0.209 0.448** 1.000
穿刺强度Penetration strength 0.063 -0.061 0.422* 1.000
综合抗病性Comprehensive disease resistance 0.479** 0.032 -0.094 0.306 1.000
[1] 郑洪建, 董树亭, 王空军, 等. 生态因素对玉米品种产量影响及调控的研究. 作物学报, 2001, 27(6):862-868.
[2] 张兴端, 霍仕平, 李求文, 等. 海拔高度对武陵山区玉米品种生育期和产量的影响. 玉米科学, 2006, 14(3):99-101,106.
[3] 孟林, 刘新建, 邬定荣, 等. 华北平原夏玉米主要生育期对气候变化的响应. 中国农业气象, 2015, 36(4):375-382.
[4] 岳伟, 陈曦, 伍琼, 等. 气候变化对安徽省淮北地区夏玉米气象产量的影响. 长江流域资源与环境, 2021, 30(2):407-418.
[5] 刘立涛, 刘晓洁, 伦飞, 等. 全球气候变化下的中国粮食安全问题研究. 自然资源学报, 2018, 33(6):927-939.
doi: 10.31497/zrzyxb.20180436
[6] 王元东, 赵久然, 付修义, 等. 黄欧系玉米育种应用探索与分析. 植物遗传资源学报, 2020, 21(4):866-874.
[7] 郭学峰. 河南省主要气象灾害特征分析. 热带农业工程, 2019, 43(2):203-206.
[8] 崔丽曼. 河南省雷暴大风气候特征及近年变化趋势分析. 气象研究与应用, 2016, 37(3):33-37.
[9] 赵久然. 优良玉米自交系选育新方法. 玉米科学, 2005, 13(2):31-32.
[10] 王向鹏, 张如养, 宋伟, 等. 不同生态区多环境下优良玉米自交系的筛选研究. 北京农学院学报, 2017, 32(4):1-7.
[11] 韩登旭, 杨杰, 邵红雨, 等. 中国骨干玉米自交系抗旱性分析与评价. 西北植物学报, 2012, 32(8):1648-1653.
[12] 段鹏飞. 河南夏玉米主要病害发生特征及其与气候生态因素关系. 郑州:河南农业大学, 2010.
[13] 张镇涛, 杨晓光, 高继卿, 等. 气候变化背景下华北平原夏玉米适宜播期分析. 中国农业科学, 2018, 51(17):3258-3274.
doi: 10.3864/j.issn.0578-1752.2018.17.003
[14] 刘瑞显, 王友华, 陈兵林, 等. 花铃期干旱胁迫下氮素水平对棉花光合作用与叶绿素荧光特性的影响. 作物学报, 2008, 34(4):675-683.
[15] 宋小园, 朱仲元, 刘艳伟, 等. 通径分析在SPSS逐步线性回归中的实现. 干旱区研究, 2016, 33(1):108-113.
[16] 乔宏伟, 武月莲, 宋凤波, 等. 主要病害及倒伏对夏玉米产量影响的研究. 现代农业科学, 2009(2):100-101.
[17] 李言照, 东先旺, 刘光亮, 等. 光温因子对玉米产量及产量构成因素值的影响. 中国生态农业学报, 2002, 10(2):86-89.
[18] 赵向阳, 丛佳慧, 安志超, 等. 冀南夏玉米氮肥效率变异特征与高产限制因子解析. 中国生态农业学报, 2020, 28(3):365-374.
[19] 谭静, 陈洪梅, 韩学莉, 等. 玉米杂交种产量与产量构成因素的相关和通径分析. 华北农学报, 2009, 24(增2):155-158.
[20] 饶宝蓉, 林昇平, 邹荣春, 等. 玉米主要农艺性状的遗传变异、相关性和通径分析. 福建农业学报, 2013, 28(3):223-226.
[21] 鲁珊, 肖荷霞, 毛彩云, 等. 玉米杂交种主要农艺性状的相关和通径分析. 安徽农业科学, 2017, 45(21):26-27,58.
[22] 郝茹雪, 王健, 武宝悦, 等. 不同春玉米品种产量及其构成因素通径分析. 黑龙江农业科学, 2020(1):9-11.
[23] 陶志强, 陈源泉, 李超, 等. 华北低平原不同播种期春玉米的产量表现及其与气象因子的通径分析. 作物学报, 2013, 39(9):1628-1634.
[24] 王曼莉. 生态因素对玉米品种产量影响及调控的研究. 农业开发与装备, 2017(4):92,81.
[25] 李向岭. 生态因素对玉米产量性能的调控效应及其模型的构建. 沈阳:沈阳农业大学, 2011.
[26] 李言照, 东先旺, 刘光亮, 等. 光温因子对玉米产量及产量构成因素值的影响. 中国生态农业学报, 2002, 10(2):86-89.
[1] Wen Shenghui, Yang Junwei, Wang Yang, Li Gongjian, Weng Jianfeng, Duan Canxing, Jia Xin, Wang Jianjun. Research Progress on Discovery of Resistance Genes and Molecular Breeding Utilization of Fungal Diseases in Maize [J]. Crops, 2023, 39(3): 1-11.
[2] Chang Qing, Li Lijun, Qu Jiahui, Zhang Yanli, Han Dongyu, Zhao Xinyao. Yield Advantage and Nitrogen Use Efficiency of Forage Maize-Rape Intercropping Following Wheat in Tumed Plain [J]. Crops, 2023, 39(3): 167-174.
[3] Gao Mutian, Qiu Guanjie, Zhu Tongtong, Li Ruilian, Deng Min, Luo Hongbing, Huang Cheng. Dissecting the Genetic Basis of Flag Leaf in Maize-Teosinte Introgression Line Population [J]. Crops, 2023, 39(3): 51-57.
[4] Li Zhongnan, Wang Yueren, Ma Yiwen, Xiang Yang, Wu Shenghui, Qu Haitao, Li Fulin, Zhang Shuqin, Li Guangfa. Genetic Analysis of Color Traits in Sheath, Silk, Anther and Cob of Isolated Population Based on Maize DH Lines [J]. Crops, 2023, 39(3): 75-79.
[5] Zhang Panpan, Li Chuan, Zhang Meiwei, Zhao Xia, Huang Lu, Liu Jingbao, Qiao Jiangfang. Effects of Nitrification Inhibitor on the Nitrogen Concentration and Yield in Summer Maize Plants and Soil under Reduced Nitrogen Application [J]. Crops, 2023, 39(2): 145-150.
[6] Cui Shuna, Wang Ye, Lu Yuqing, Pan Jinbao, Zhang Qiuzhi. Correlation and Path Analysis of Three Ear Leaves on Yield in Maize [J]. Crops, 2023, 39(2): 201-206.
[7] Meng Yaxuan, Yao Xuhang, Zhou Baoyuan, Liu Yinghui, Yuan Jincheng, Ma Wei, Zhao Ming. Research Progress on Mixed Silage of Zea mays [J]. Crops, 2023, 39(2): 24-29.
[8] Zhang Dongxia, Qin Anzhen. Relationships among Crop Evapotranspiration, Soil Moisture and Temperature in Winter Wheat-Summer Maize Cropping System [J]. Crops, 2022, 38(6): 145-151.
[9] Qiao Jiangfang, Zhang Panpan, Shao Yunhui, Liu Jingbao, Li Chuan, Zhang Meiwei, Huang Lu. Effects of Different Planting Densities and Varieties on Dry Matter Production and Yield Components of Summer Maize [J]. Crops, 2022, 38(6): 186-192.
[10] Guo Huanle, Tang Bin, Li Han, Cao Zhongyang, Zeng Qiang, Liu Liangwu, Chen Zhihui. Comprehensive Evaluation of Phenotypic Traits and Classification of Maize Landraces in Hunan Province [J]. Crops, 2022, 38(6): 33-41.
[11] Xu Chuangye, Zhang Jianjun, Zhou Gang, Zhang Kaipeng, Zhu Xiaohui, Wang Jiaxi, Dang Yi, Zhao Gang, Wang Lei, Li Shangzhong, Fan Tinglu. Screening and Evaluation of New Maize Varieties with Compact Planting, High Yield and Suitable for Mechanical Grain Harvest in Loess Plateau in Eastern Gansu Province [J]. Crops, 2022, 38(5): 104-110.
[12] Li Long, Xiao Rang, Zhang Yongling. Effects of Combined Application of Nitrogen, Phosphorus and Potassium on Seed Maize Yield and Economic Benefit [J]. Crops, 2022, 38(5): 111-117.
[13] Li Yanlu, Wang Junpeng, Yu Xinzhi, Wei Honglei, Chen Qiyu, Zhao Hongxiang, Xu Chen, Bian Shaofeng, Zhang Zhian. Effects of Mulching Different Plastic Films on Accumulation and Distribution of Dry Matter and Nitrogen in Maize in Cold and Cool Areas [J]. Crops, 2022, 38(5): 124-129.
[14] Jia Xiuping, Mao Xuhui, Liang Gensheng, Liu Runping, Liu Feng, Wang Xingzhen. Analysis of Physiological and Biochemical Mechanism and Growth and Development Characteristics of Saline and Alkali Resistance in Sunflower [J]. Crops, 2022, 38(5): 146-152.
[15] Zhang Jianye, Du Qingzhi, Liu Xiang, Deng Jiahui, Jiao Qin, Gong Luo, Jiang Xingyin. The Effects of S-ABA on Germination and Growth of Maize under Salt-Alkali Stress [J]. Crops, 2022, 38(5): 167-173.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!