Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (1): 177-184.doi: 10.3724/SP.J.1006.2021.03011

• RESEARCH NOTES • Previous Articles    

Remote sensing monitoring on maize flood stress and yield evaluation at different stages

SUI Xue-Yan1(), LIANG Shou-Zhen1(), ZHANG Jin-Ying2, WANG Meng1, WANG Yong1, HOU Xue-Hui1, ZHANG Xiao-Dong3,*()   

  1. 1Shandong Institute of Agricultural Sustainable Development / Key Laboratory of East China Urban Agriculture, Ministry of Agriculture and Shandong Rural Affairs, Jinan 250100, Shandong, China
    2Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250013, Shandong, China
    3Shandong Center of Crop Germplasm Resources, Jinan 250100, Shandong, China
  • Received:2020-02-28 Accepted:2020-08-20 Online:2021-01-12 Published:2020-09-10
  • Contact: ZHANG Xiao-Dong E-mail:sdnkysxy@163.com;szliang_cas@163.com;zxdong2002@163.com
  • Supported by:
    Agricultural Great Application Technology Innovative Projects of Shandong Province(1-0504);Agricultural and Rural Resources Monitoring and Statistics Projects of Ministry of Agriculture and Rural Affairs(061721301112422018)

RichHTML376

14

PDF

243

Abstract:

In order to set up remote sensing monitoring and evaluation technology of maize flood stress and loss, a simulation experiment with different flood stress degrees was developed at different stages. Chlorophyll content, canopy spectral reflectance and coverage were monitored in vivo, and yield level was tested finally. The results showed that chlorophyll content decreased under flood stress at jointing and filling stages. The decreasing extend was significant at jointing stage and the value of maximal relative change reached -56.30%. There was little influence on chlorophyll at silking stage. Flood stress could reduce the coverage, especially at jointing stage, the most serious treatment was only 46.33%, followed by silking stage and filling stage. Flood stress reduced production at last, and the reduction was more serious in the early stage than in the later stage. There was a negative correlation between reflectance and flood stress degree at jointing stage and silking stage. It was extremely significantly difference at near infrared platform bands and significantly at green peak bands. There was no significant positive correlation between reflectance and flood stress degree at filling stage. Reflectance and spectral indexes with extremely significant correlation can be used to monitor flood. Two models with DSIPI were set up to evaluate flood loss at jointing and silking stage separately.

Key words: maize, flood stress, remote sensing, monitoring, evaluation

Table 1

Experimental design"

生长时期
Growing stage		 处理
Treatment		 胁迫持续天数
Flooding period (d)		 开始时间
Starting time (month/day)		 结束时间
Ending time (month/day)
拔节期
Jointing stage		 E-9 9 7/31 8/9
E-7 7 7/31 8/7
E-5 5 7/31 8/5
E-3 3 7/31 8/3
E-1 1 7/31 8/1
E-0 0 (CK)
吐丝期
Silking stage		 M-9 9 8/8 8/17
M-7 7 8/8 8/15
M-5 5 8/8 8/13
M-3 3 8/8 8/11
M-1 1 8/8 8/9
M-0 0 (CK)
灌浆期
Filling stage		 L-9 9 8/23 9/1
L-7 7 8/23 8/30
L-5 5 8/23 8/28
L-3 3 8/23 8/26
L-1 1 8/23 8/24
L-0 0 (CK)

Fig. 1

Chlorophyll content before and after flood stress in maize Treatments are the same as those given in Table 1. Different letters indicate significant differences among treatments at the 0.05 probability level."

Fig. 2

Effects of water stress on maize coverage degree Treatments are the same as those given in Table 1. Values marked with different letters indicate significant differences among treatments at the 0.05 probability level."

Fig. 3

Spectral reflectance curves of flooding stress treatments at jointing stage on August 14"

Fig. 4

Spectral reflectance curves of flooding stress treatments at silking stage on August 23"

Fig. 5

Spectral reflectance curves of flooding stress treatments at filling stage on September 3"

Fig. 6

Correlation between spectral reflectance and flooding stress period of treatments at different stages"

Table 2

Correlation of flooding stress period with spectral shape parameters and vegetation indexes"

名称
Parameters		 缩写
Abbreviation		 作者及年代
Author and year		 拔节期
Jointing stage		 吐丝期
Silking stage		 灌浆期
Filling stage
差值植被指数
Difference vegetation index		 DVI [867, 671] Richardso et al. (1977) [22] -0.9491** -0.8220* -0.3405
DVI [550, 464] -0.8906* -0.2548 -0.7216
DVI [550, 671] -0.9515** -0.6641 -0.6018
比值植被指数
Ratio vegetation index		 RVI [867, 671] -0.9274** -0.9373** 0.7446
归一化差值植被指数
Normalization difference
vegetation index		 NDVI [550, 671] Rouse et al. (1974) [23] 0.9635** 0.7552 -0.5113
NDVI [671, 867] -0.9432** -0.9481** 0.6828
红谷位置
Location of red valley		 λ0 Liu (2002) [24] -0.9660** -0.8396* 0.6613
红边峰值
Maximum value of the first
derivative at the red edge		 RFDMax -0.9614** -0.7990 -0.2701
吸收谷红谷深度
Depth of red absorption valley		 Depth [670] -0.9384** -0.7593 0.6003
反射峰绿峰深度
Depth of green peak		 P_Depth [540] -0.9378** -0.5346 0.5522
吸收谷红谷面积
Area of red absorption valley		 Area [670] -0.9378** -0.6942 0.6217
反射峰绿峰面积
Area of green reflectance peak		 P_Area [540] -0.9220** -0.5719 0.5028
红边位置
Red edge position		 λp -0.9194** -0.8586* 0.7817
红边宽度
Red edge width		 σ -0.7454 -0.7925 0.8302*
归一化反射峰绿峰深度
Normalized depth of green
reflectance peak		 P_ND [540] 0.5344 0.6719 0.7217
归一化吸收谷红谷深度
Normalized depth of red
absorption valley		 ND [670] 0.9020* 0.0476 -0.6463
抗大气植被指数
Visible atmospherically resistant index		 Vari700 Singh et al. (2002) [25] -0.9442** -0.8037 0.6074
VariGreen -0.9695** -0.8293* 0.5179
光化学反射指数
Photochemical reflectance index		 PRI [570, 531] Gamom et al. (1992) [26] -0.9546** -0.8661* 0.5112
最优土壤调节植被指数
Optimization of soil-adjusted
regulatory vegetation index		 OSAVI Rondeaux et al. (1996) [27] -0.9527** -0.8567* 0.2736
土壤调整植被指数
Soil-adjusted vegetation index		 SAVI Huete et al. (1988) [28] -0.9493** -0.8384* -0.1070
转化型叶绿素吸收反射指数
Tidal constituent and residual interpolation		 TCARI Haboudane et al. (2002) [29] -0.5457 -0.3166 -0.7241
叶绿素含量指数
Canopy chlorophyll inversion index		 CCII Haboudane et al. (2002) [29] 0.5441 0.6918 -0.7130
绿度指数
Green normalization difference vegetation index		 GreenNDVI Baret et al. (1991) [30] 0.8977* 0.8795* 0.8039
结构不敏感色素指数
Structure insensitive pigment index		 SIPI Penuelas et al. (1995) [31] 0.9514** 0.9564** -0.5422

Table 3

Model of maize yield loss rate under flooding stress at jointing stage and silking stage"

参数
Parameter		 参数计算公式
Parameter calculation
formula		 拔节期洪涝胁迫玉米产量损失率模型
Model of maize yield loss rate under flooding stress at jointing stage		 吐丝期洪涝胁迫玉米产量损失率模型
Model of maize yield loss rate under flooding stress at silking stage
DNDVI NDVIafter - NDVIbefore y = -86.051 x2 + 470.53 x + 9.5564
(-0.04 < x < 0.14, 0 < y < 75%)
R2 = 0.8969		 y = -2803.8 x2 - 586.71 x + 13.8
(-0.07 < x < 0.03, 0 < y < 45%)
R2 = 0.9337
DRVI RVIafter - RVIbefore y = -1163.9 x2 - 855.71 x + 9.8187
(-0.09 < x < 0.02, 0 < y < 75%)
R2 = 0.8951		 y = -9118.2 x2 + 1024.7 x + 13.888
(-0.02 < x < 0.05, 0 < y < 45%)
R2 = 0.9320
DSIPI SIPIafter - SIPIbefore y = -19600 x2 - 1985.3 x +15.814
(-0.06 < x < 0.01, 0 < y < 75%)
R2 = 0.9433		 y = -13599 x2 -814.11 x + 26.53
(-0.03 < x < 0.03, 0 < y < 45%)
R2 = 0.9697

Fig. 7

Fitting chart of maize yield loss rate with DSIPI under flooding stress at jointing stage"

Fig. 8

Fitting chart of maize yield loss rate with DSIPI under flooding stress at silking stage"

[1] 滕腾. 中国玉米进出口贸易形势及影响因素分析. 南京农业大学硕士学位论文, 江苏南京, 2015.
Teng T. Analysis on the Situation and Influence Factors of Chinese Maize Export and Import Trade. MS Thesis of Nanjing Agricultural University, Nanjing, Jiangsu, China, 2015 (in Chinese with English abstract).
[2] 中华人民共和国国家统计局. 中国统计年鉴. 北京: 中国统计出版社, 2019 [2020-03-01]. http://www.stats.gov.cn/tjsj/ndsj/2019/indexch.htm.
National Bureau of Statistics of the People’s Republic of China. Citations format of China Statistical Yearbook. Beijing: China Statistics Publishing House, 2019 [2020-03-01]. http://www.stats.gov.cn/tjsj/ndsj/2019/indexch.htm (in Chinese).
[3] 潘瑞炽. 植物生理学. 北京: 高等教育出版社, 2012. pp 104-105.
Pan R Z. Plant Physiology. Beijing: Higher Education Press, 2012. pp 104-105(in Chinese).
[4] 余卫东. 黄淮海地区涝渍胁迫对夏玉米的影响及时空变化研究. 中国农业大学博士学位论文, 北京, 2014.
Yu W D. Research on the Effect of Waterlogging on the Growth and Yield of Summer Maize and Its Temporal-spatial Distribution in Huang-Huai-Hai Region. PhD Dissertation of China Agricultural University, Beijing, China, 2014 (in Chinese with English abstract).
[5] 周新国, 韩会玲, 李彩霞, 郭树龙, 郭冬冬, 陈金平. 拔节期淹水玉米的生理性状和产量形成. 农业工程学报, 2014,30(9):119-125.
Zhou X G, Han H L, Li C X, Guo S L, Guo D D, Chen J P. Physiological characters and yield formation of corn (Zea mays L.) under waterlogging stress in jointing stage. Trans CSAE, 2014,30(9):119-125 (in Chinese with English abstract).
[6] 梁哲军, 陶洪斌, 王璞. 淹水解除后玉米幼苗形态及光合生理特征恢复. 生态学报, 2009,29:3977-3986.
Liang Z J, Tao H B, Wang P. Recovery effects of morphology and photosynthetic characteristics of maize (Zea mays L.) seedlings after waterlogging. Acta Ecol Sin, 2009,29:3977-3986 (in Chinese with English abstract).
[7] 任佰朝, 朱玉玲, 董树亭, 刘鹏, 赵斌, 张吉旺. 大田淹水对夏玉米光合特性的影响. 作物学报, 2015,41:329-338.
Ren B C, Zhu Y L, Dong S T, Liu P, Zhao B, Zhang J W. Effects of waterlogging on photosynthetic characteristics of summer maize under field conditions. Acta Agron Sin, 2015,41:329-338 (in Chinese with English abstract).
[8] 杨京平, 陈杰. 不同生长时期土壤渍水对春玉米生长发育的影响. 浙江农业学报, 1998,10(4):20-24.
Yang J P, Chen J. The effects of soil waterlogging at different growth stages on the growth and development of spring corn. Acta Agric Zhejiangensis, 1998,10(4):20-24 (in Chinese with English abstract).
[9] 刘祖贵, 刘战东, 肖俊夫, 南纪琴, 巩文军. 苗期与拔节期淹涝抑制夏玉米生长发育、降低产量. 农业工程学报, 2013,29(5):44-52.
Liu Z G, Liu Z D, Xiao J F, Nan J Q, Gong W J. Waterlogging at seedling and jointing stages inhibits growth and development, reduces yield in summer maize. Trans CSAE, 2013,29(5):44-52 (in Chinese with English abstract).
[10] 郭庆法, 王庆成, 汪黎明. 中国玉米栽培学. 上海: 上海科学技术出版社, 2004. p 47.
Guo Q C, Wang Q C, Wang L M. China Maize Cultivation. Shanghai: Shanghai Scientific and Technical Publishers, 2004. p 47 (in Chinese).
[11] Zaidi P H, Rafique S, Rai P K, Singh N N, Srinivasan G. Tolerance to excess moisture in maize (Zea mays L.): susceptible crop stages and identification of tolerant genotypes. Field Crops Res, 2004,90:189-202.
doi: 10.1016/j.fcr.2004.03.002
[12] Ren B Z, Zhang J W, Li X, Fan X, Dong S T, Liu P, Zhao B. Effects of waterlogging on the yield and growth of summer maize under field conditions. Can J Plant Sci, 2014,94:23-31.
doi: 10.4141/CJPS2013-175
[13] 孙忠翔, 唐培坤, 康尧强. 涝害对玉米生长发育的影响及应对措施. 现代化农业, 2014, (6):7-8.
Sun Z X, Tang P K, Kang R Q. The effect of flood stress on maize’s growth and countermeasures. Modern Agric, 2014, (6):7-8 (in Chinese with English abstract).
[14] 浦瑞良, 宫鹏. 高光谱遥感及其应用. 北京: 高等教育出版社, 2000. pp 1-254.
Pu R L, Gong P. Hyperspectral Remote Sensing and Its Application. Beijing: Higher Education Press, 2000. pp 1-254(in Chinese).
[15] 毕京佳. 基于遥感和GIS的洪水淹没范围估测与模拟研究. 中国科学院研究生院(海洋研究所)硕士学位论文, 北京, 2016.
Bi J J. Estimation and Simulation of Flood Inundation Using Remote Sensing and GIS. MS Thesis of Institute of Oceanology, Chinese Academy of Sciences, Beijing, China, 2016 (in Chinese with English abstract).
[16] 苏亚丽. 耕地暴雨洪水灾害多源卫星遥感监测方法研究. 西安科技大学硕士学位论文, 陕西西安, 2018.
Su Y L. Study on Remote Sensing Monitoring Method of Multi-source Satellite for Rainstorm and Flood in Farmland. MS Thesis of Xi’an University of Science and Technology, Xi’an, Shaanxi, China, 2018 (in Chinese with English abstract).
[17] 姜波, 孟灵, 邢前国. 2018年夏季莱州湾南部寿光台风洪水受害区遥感监测. 环境影响评价, 2019,41(5):83-87.
Jiang B, Meng L, Xing Q G. Monitoring of flood disaster footprints based on remote sensing: Shouguang floods of 2018, southern Laizhou Bay. Environ Impact Assess, 2019,41(5):83-87 (in Chinese with English abstract).
[18] 王一明. 干旱条件下基于WOFOST模型与遥感数据同化的玉米产量模拟改进研究. 中国科学院大学(中国科学院遥感与数字地球研究所)硕士学位论文, 北京, 2018.
Wang Y M. Improvement of Crop Yield Estimation under Drought Condition Based on WOFOST Model and Remote Sensing Data Assimilation. MS Thesis of Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China, 2018 (in Chinese with English abstract).
[19] 靳华安, 王锦地, 柏延臣, 陈桂芬, 薛华柱. 基于作物生长模型和遥感数据同化的区域玉米产量估算. 农业工程学报, 2012,28(6):162-173.
Jin H A, Wang J D, Bai Y C, Chen G F, Xue H Z. Estimation on regional maize yield based on assimilation of remote sensing data and crop growth model. Trans CSAE, 2012,28(6):162-173 (in Chinese with English abstract).
[20] 侯英雨, 王建林, 毛留喜, 宋迎波. 美国玉米和小麦产量动态预测遥感模型. 生态学杂志, 2009,28:2142-2146.
Hou Y Y, Wang J L, Mao L X, Song Y B. Dynamic estimation models of corn and wheat yields in USA based on remote sensing data. Chin J Ecol, 2009,28:2142-2146 (in Chinese with English abstract).
[21] 赵英时. 遥感应用分析原理与方法. 北京: 科学出版社, 2013. pp 362-367.
Zhao Y S. The Principle and Method of Analysis of Remote Sensing Application. Beijing: Science Press, 2014. pp 362-367(in Chinese).
[22] Richardson A J, Wiegand C L. Distinguishing vegetation from soil background information. Photogram Eng Remote Sens, 1977,43:1541-1552.
[23] Rouse J W, Haas R H, Schell J A, Deering D W, Harlan J C. Monitoring the Vernal Advancement and Retrogradation of Natural Vegetation, NASAGSFC, Type I, Final Report, Greenbelt, MD, USA. 1974. p 371. http://www.doc88.com/p-6035220137582.html.
[24] 刘良云. 高光谱遥感在精准农业中的应用研究. 中国科学院遥感应用研究所博士学位论文, 北京, 2002.
Liu L Y. Applications of Hyperspectral Remote Sensing in Precision Agriculture. PhD Dissertation of Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China, 2002 (in Chinese with English abstract).
[25] Singh R, Semwal D P, Rai A. Small area estimation of crop yield using remote sensing satellite data. Int J Remote Sens, 2002,23:49-56.
[26] Penuelas J, Filella I, Gamon J A. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol, 1995,131:291-296.
[27] Lukina E V, Freeman K W, Wynn K J, Thomason W E, Mullen R W, Stone M L. Nitrogen fertilization optimization algorithm based on in-season estimates of yields and plant nitrogen uptake. Plant Nutr, 2001,24:885-898.
[28] Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sens Environ, 1988,25:295-309.
[29] Haboudane D, Miller J R, Tremblay N, Zarco-Tejada P J, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ, 2002,81:416-426.
doi: 10.1016/S0034-4257(02)00018-4
[30] Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Environ, 1991,35:161-173.
[31] Penuelas J, Baret F, Filella I. Semiempirical indexes to assess carotenoids chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 1995,31:221-230.
[32] 王利民, 刘佳, 邵杰, 杨福刚, 高建孟. 基于高光谱的春玉米大斑病害遥感监测指数选择. 农业工程学报, 2017,33(5):170-177.
Wang L M, Liu J, Shao J, Yang F G, Gao J M. Remote sensing index selection of leaf blight disease in spring maize based on hyper spectral data. Trans CSAE, 2017,33(5):170-177 (in Chinese with English abstract).
[33] 王成业. 洪涝灾害对夏玉米生长发育及产量的影响. 河南农业科学, 2010, (8):20-21.
Wang C Y. The effect on growth and development and yield of summer maize. J Henan Agric Sci, 2010, (8):20-21 (in Chinese with English abstract).
[34] 陈振, 梁守真, 王猛, 颜丙囤, 姚慧敏, 隋学艳, 王勇. 人工模拟洪涝胁迫对夏玉米叶部性状及产量的影响. 山东农业科学, 2017,49(4):26-29.
Chen Z, Liang S Z, Wang M, Yan B T, Yao H M, Sui X Y, Wang Y. Effects of simulated flooding stress on yield and leaf characteristics of summer corn. Shandong Agric Sci, 2017,49(4):26-29 (in Chinese with English abstract).
[1] WANG Dan, ZHOU Bao-Yuan, MA Wei, GE Jun-Zhu, DING Zai-Song, LI Cong-Feng, ZHAO Ming. Characteristics of the annual distribution and utilization of climate resource for double maize cropping system in the middle reaches of Yangtze River [J]. Acta Agronomica Sinica, 2022, 48(6): 1437-1450.
[2] 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.
[3] CHEN Jing, REN Bai-Zhao, ZHAO Bin, LIU Peng, ZHANG Ji-Wang. Regulation of leaf-spraying glycine betaine on yield formation and antioxidation of summer maize sowed in different dates [J]. Acta Agronomica Sinica, 2022, 48(6): 1502-1515.
[4] SHAN Lu-Ying, LI Jun, LI Liang, ZHANG Li, WANG Hao-Qian, GAO Jia-Qi, WU Gang, WU Yu-Hua, ZHANG Xiu-Jie. Development of genetically modified maize (Zea mays L.) NK603 matrix reference materials [J]. Acta Agronomica Sinica, 2022, 48(5): 1059-1070.
[5] XU Jing, GAO Jing-Yang, LI Cheng-Cheng, SONG Yun-Xia, DONG Chao-Pei, WANG Zhao, LI Yun-Meng, LUAN Yi-Fan, CHEN Jia-Fa, ZHOU Zi-Jian, WU Jian-Yu. Overexpression of ZmCIPKHT enhances heat tolerance in plant [J]. Acta Agronomica Sinica, 2022, 48(4): 851-859.
[6] LIU Lei, ZHAN Wei-Min, DING Wu-Si, LIU Tong, CUI Lian-Hua, JIANG Liang-Liang, ZHANG Yan-Pei, YANG Jian-Ping. Genetic analysis and molecular characterization of dwarf mutant gad39 in maize [J]. Acta Agronomica Sinica, 2022, 48(4): 886-895.
[7] YAN Yu-Ting, SONG Qiu-Lai, YAN Chao, LIU Shuang, ZHANG Yu-Hui, TIAN Jing-Fen, DENG Yu-Xuan, MA Chun-Mei. Nitrogen accumulation and nitrogen substitution effect of maize under straw returning with continuous cropping [J]. Acta Agronomica Sinica, 2022, 48(4): 962-974.
[8] 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.
[9] YANG Xin, LIN Wen-Zhong, CHEN Si-Yuan, DU Zhen-Guo, LIN Jie, QI Jian-Min, FANG Ping-Ping, TAO Ai-Fen, ZHANG Li-Wu. Molecular identification of a geminivirus CoYVV and screening of resistant germplasms in jute [J]. Acta Agronomica Sinica, 2022, 48(3): 624-634.
[10] SONG Shi-Qin, YANG Qing-Long, WANG Dan, LYU Yan-Jie, XU Wen-Hua, WEI Wen-Wen, LIU Xiao-Dan, YAO Fan-Yun, CAO Yu-Jun, WANG Yong-Jun, WANG Li-Chun. Relationship between seed morphology, storage substance and chilling tolerance during germination of dominant maize hybrids in Northeast China [J]. Acta Agronomica Sinica, 2022, 48(3): 726-738.
[11] 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.
[12] HU Liang-Liang, WANG Su-Hua, WANG Li-Xia, CHENG Xu-Zhen, CHEN Hong-Lin. Identification of salt tolerance and screening of salt tolerant germplasm of mungbean (Vigna radiate L.) at seedling stage [J]. Acta Agronomica Sinica, 2022, 48(2): 367-379.
[13] YAN Yan, ZHANG Yu-Shi, LIU Chu-Rong, REN Dan-Yang, LIU Hong-Run, LIU Xue-Qing, ZHANG Ming-Cai, LI Zhao-Hu. Variety matching and resource use efficiency of the winter wheat-summer maize “double late” cropping system [J]. Acta Agronomica Sinica, 2022, 48(2): 423-436.
[14] ZHANG Qian, HAN Ben-Gao, ZHANG Bo, SHENG Kai, LI Lan-Tao, WANG Yi-Lun. Reduced application and different combined applications of loss-control urea on summer maize yield and fertilizer efficiency improvement [J]. Acta Agronomica Sinica, 2022, 48(1): 180-192.
[15] YU Rui-Su, TIAN Xiao-Kang, LIU Bin-Bin, DUAN Ying-Xin, LI Ting, ZHANG Xiu-Ying, ZHANG Xing-Hua, HAO Yin-Chuan, LI Qin, XUE Ji-Quan, XU Shu-Tu. Dissecting the genetic architecture of lodging related traits by genome-wide association study and linkage analysis in maize [J]. Acta Agronomica Sinica, 2022, 48(1): 138-150.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No.12 Zhongguancun South Street, Beijing 100081,China Email:xbzw@chinajournal.net.cn
Supported by Beijing Magtech Co.Ltd