欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2021, Vol. 47 ›› Issue (1): 177-184.doi: 10.3724/SP.J.1006.2021.03011

• 研究简报 • 上一篇    

不同生育时期玉米洪涝胁迫遥感监测与评估

隋学艳1(), 梁守真1(), 张金盈2, 王猛1, 王勇1, 侯学会1, 张晓冬3,*()   

  1. 1山东省农业可持续发展研究所 / 农业农村部华东都市农业重点实验室, 山东济南 250100
    2山东省国土测绘院, 山东济南 250013
    3山东省农作物种质资源中心, 山东济南 250100
  • 收稿日期:2020-02-28 接受日期:2020-08-20 出版日期:2021-01-12 网络出版日期:2020-09-10
  • 通讯作者: 张晓冬
  • 作者简介:隋学艳, E-mail: sdnkysxy@163.com|梁守真, E-mail: szliang_cas@163.com
  • 基金资助:
    山东省农业重大应用技术创新项目(1-0504);农业农村部农业农村资源监测统计项目(061721301112422018)

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 Published:2021-01-12 Published online:2020-09-10
  • Contact: ZHANG Xiao-Dong
  • 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)

RichHTML

22

PDF

617

摘要：

为建立玉米洪涝灾害遥感监测和评估技术, 本研究开展不同生育期不同程度的模拟试验, 持续活体监测叶片叶绿素、冠层光谱和覆盖度并测产。结果表明, 拔节期洪涝胁迫可显著降低叶绿素含量, 最大程度相对变化值达-56.30%, 吐丝期胁迫对叶绿素无影响, 灌浆期胁迫促使叶绿素下降但并不明显。洪涝胁迫能降低覆盖度, 拔节期影响最大, 胁迫严重的覆盖度仅46.33%, 吐丝期次之, 灌浆期较小。洪涝胁迫造成的减产, 生育前期影响比后期严重。拔节期和吐丝期各波段反射率与洪涝胁迫程度呈负相关关系, 近红外平台波段达到极显著水平, 绿峰波段次之, 达到显著水平。灌浆期各波段反射率与洪涝胁迫程度呈正相关关系, 相关性未达到显著水平。与胁迫程度呈极显著相关的光谱反射率和植被指数可以用于监测玉米洪涝灾害。最终基于胁迫之后与胁迫前的结构不敏感色素指数SIPI的差值DSIPI, 建立拔节期和吐丝期洪涝胁迫产量损失率评估模型。

关键词: 玉米, 涝灾, 遥感, 监测, 评估

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

表1

试验设计"

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)

图1

洪涝胁迫前后玉米叶绿素含量 处理同表1。不同字母表示同期处理间在0.05水平差异显著。"

图2

洪涝胁迫对玉米覆盖度的影响 处理同表1。不同字母表示同期处理间在0.05水平差异显著。"

图3

拔节期洪涝胁迫各处理8月14日光谱反射率曲线"

图4

吐丝期洪涝胁迫各处理8月23日光谱反射率曲线"

图5

灌浆期洪涝胁迫各处理9月3日光谱反射率曲线"

图6

各时期洪涝胁迫后光谱反射率与胁迫天数的相关性"

表2

洪涝胁迫天数与光谱形状参数和植被指数的相关性"

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

表3

拔节期和吐丝期洪涝胁迫玉米产量损失率模型"

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

图7

拔节期玉米洪涝胁迫产量损失评估DSIPI模型拟合图"

图8

吐丝期玉米洪涝胁迫产量损失评估DSIPI模型拟合图"

[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] 刘恩波, 陈静, 李红星, 于宁宁, 任佰朝, 赵斌, 刘鹏, 张吉旺. 遮阴改变源-库平衡和调节碳水化合物代谢进而抑制夏玉米幼穗发育[J]. 作物学报, 2026, 52(6): 1891-1901.
[2] 梁进宇, 尹嘉德, 王红丽, 张国平, 侯慧芝, 董博, 马明生. 基于无人机高光谱和机器学习的旱地饲用玉米叶片氮含量估测[J]. 作物学报, 2026, 52(6): 1788-1801.
[3] 孙淑凤, 许振南, 黄嘉鑫, 翁建峰, 李新海. 玉米MAPK家族全基因组鉴定及其对拟轮枝镰孢菌感染的响应[J]. 作物学报, 2026, 52(5): 1291-1308.
[4] 张宁宁, 滕雨菲, 任娜娜, 魏兴卓, 闫书豪, 樊可心, 王永宏, 陈文康, 张兴华, 朱万超, 徐淑兔, 薛吉全. 201份玉米自交系抗旱表型评价及可塑性分析[J]. 作物学报, 2026, 52(5): 1309-1325.
[5] 张鸿蓉, 王菲儿, 李盼, 仇海龙, 朱静, 赵连豪, 南运有, 何蔚, 樊志龙, 胡发龙, 柴强, 殷文. 减量20%灌水与25%有机肥替代化肥提高青贮玉米产量的光合特性[J]. 作物学报, 2026, 52(5): 1487-1500.
[6] 杨欣雨, 崔文涛, 迪力尼格尔·阿力木, 汪凯翔, 吴鹏昊, 任姣姣. 玉米穗上叶片数全基因组关联分析和全基因组选择[J]. 作物学报, 2026, 52(5): 1573-1590.
[7] 韩亚鑫, 何冠华, 张小琼, 张登峰, 李永祥, 刘旭洋, 王天宇, 黎裕, 邹华文, 李春辉. 基于RNA-Seq和BSA-Seq联合分析挖掘玉米侧根密度基因资源[J]. 作物学报, 2026, 52(5): 1341-1352.
[8] 杨扬, 常诗惠, 田红丽, 易红梅, 王璐, 任洁, 范亚明, 刘亚维, 王凤格, 赵久然. 不同生态区国审玉米品种的遗传多样性分析[J]. 作物学报, 2026, 52(5): 1352-1364.
[9] 蔡宏玮, 于爱忠, 姜科强, 王鹏飞, 王玉珑, 霍建喆, 庞小能, 尹波, 尚永盼. 干旱灌区有机肥替代部分化肥促进甜玉米产量提升的关键机制[J]. 作物学报, 2026, 52(4): 1166-1180.
[10] 田红丽, 杨扬, 范亚明, 易红梅, 郭丹丹, 王凤格, 赵久然. 适于玉米品种鉴定的一套三等位变异SNP新型标记组合[J]. 作物学报, 2026, 52(4): 993-1005.
[11] 杨亚莉, 徐明睿, 马越飞, 海艺蕊, 刘凯栋, 刘万茂, 孙颖. 玉米根尖及整根响应缺铁的转录组比较研究[J]. 作物学报, 2026, 52(4): 1006-1021.
[12] 马亮, 马璐, 张舒钰, 章慧敏, 王仁明, 宋旭东, 张振良, 冒宇翔, 陆虎华, 陈国清, 郝德荣, 周广飞. 玉米苞叶数目转录组分析及候选基因鉴定[J]. 作物学报, 2026, 52(3): 790-801.
[13] 孟成, 王哲. 玉米ZmPFK基因家族全基因组鉴定及响应胁迫表达分析[J]. 作物学报, 2026, 52(3): 764-779.
[14] 李新浩, 邢梦柯, 周梓惠, 李思烨, 任昊, 王洪章, 赖华江. 外源褪黑素通过协调光反应与暗反应增强玉米苗期的耐热性[J]. 作物学报, 2026, 52(3): 839-856.
[15] 张超, 郭欢, 李忠玲, 岳淑宁, 赵娜. 基于BSA-seq技术定位玉米籽粒花青素关联基因[J]. 作物学报, 2026, 52(3): 780-789.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2