欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2009, Vol. 35 ›› Issue (7): 1320-1327.doi: 10.3724/SP.J.1006.2009.01320

• 耕作栽培·生理生化 • 上一篇    下一篇

利用冠层光谱监测小麦籽粒蛋白质积累动态

冯伟1,2,朱艳1,曹卫星1,*,朱云集2,郭天财2   

  1. 1南京农业大学/江苏省相信农业高技术研究重点实验室，江苏南京210095；2河南农业大学/国家小麦工程技术研究中心，河南郑州450002
  • 收稿日期:2008-11-24 修回日期:2009-03-16 出版日期:2009-07-12 网络出版日期:2009-05-19
  • 通讯作者: 曹卫星, E-mail: E-mail: caow@njau.edu.cn
  • 基金资助:

    本研究由国家自然科学基金项目（30671215，30871448）和国家“十一五”科技支撑计划重大项目（2008BADA4B02，2006BAD02A07）资助。

Monitoring Grain Protein Accumulation Dynamics with Canopy Reflectance Spectra in Wheat

FENG Wei1,2,ZHU Yan1,CAO Wei-Xing1,*,ZHU Yun-Ji2,GUO Tian-Cai2   

  1. 1Nanjing Agricultural University/Hi-Tech Key Laboratory of Information Agriculture of Jiangsu Province，Nanjing210095，China；2Henan Agricultural University/National Engineering Research Centre for Wheat,Zhengzhou 450002,China
  • Received:2008-11-24 Revised:2009-03-16 Published:2009-07-12 Published online:2009-05-19
  • Contact: CAO Wei-Xing, E-mail: E-mail: caow@njau.edu.cn

PDF

1737

被引次数

9

摘要：

于2003—2006年连续3个生长季, 利用不同小麦品种在不同施氮水平下进行大田试验，在小麦不同生育期采集田间冠层高光谱数据并测定植株氮素含量、生物量和籽粒蛋白质积累量(GPA)。通过定量分析小麦籽粒蛋白质积累量、冠层氮素营养指标及高光谱参数的相互关系，确立了能够准确预测小麦籽粒蛋白质积累动态的敏感光谱参数及定量模型。结果表明，在籽粒灌浆期间冠层氮素营养指标(CNNI)自开花期随时间进程的积分累积值与对应时期籽粒蛋白质积累状况存在显著的定量关系，其中植株氮积累量(PNA)表现最好。对冠层氮素营养指标的光谱估算，在不同品种、氮素水平、生育时期和年度间可以使用统一的光谱模型。根据特征光谱参数-冠层氮素营养指标-籽粒蛋白质积累量这一技术路径，以冠层氮素营养指标为交接点将两部分模型链接，建立高光谱参数与籽粒蛋白质积累量间定量方程。经不同年际独立数据的检验，基于SDr/SDb–PNA–GPA技术路径建立模型可以估算小麦籽粒生长过程中蛋白质积累动态，预测精度和相对误差分别为0.954和13.1%。因此，利用关键特征光谱参数可以实时监测小麦籽粒生长进程中蛋白质积累状况。

关键词: 小麦, 光谱信息, 籽粒蛋白质, 积累动态, 监测模型

Abstract:

Grain protein is an important index indicating wheat quality status, and nondestructive and quick assessments of grain protein accumulationdynamics is necessary for cultural regulation and quality classification in wheat (Triticum aestivum L.) production. The objectives of this study were to determine the relationships between plant nitrogen nutrition status, grain protein accumulation, and canopy reflectance spectra in winter wheat, therefore, to derive regression equations for monitoring grain protein accumulation with canopy hyper-spectral remote sensing. Three types of cultivars, i.e., high protein content (Xuzhou 26 and Yumai 34), medium protein content (Yangmai 10, Yangmai 12, and Huaimai 20), and low protein content (Ningmai 9) were used in three field experiments under different nitrogen levels in the growing seasons of 2003–2006. Time-course measurements were taken on canopy hyperspectral reflectance, plant weight, nitrogen content and grain protein accumulation (GPA) during the experimental periods. The results showed that the cumulative value of canopy nitrogen nutrition index (CNNI) from anthesis to specific day were highly correlated with grain protein accumulation at corresponding day across grain filling with the best predictor of plant N accumulation (PNA). According to the regression analyses between vegetation indices and canopy nitrogen nutrition index,several key spectral parameters could accurately estimate the changes in plant N status across different growth stages, nitrogen levels, and growing seasons with the same spectral parameters for each wheat cultivar. According to the technical route of key spectral parameters-canopy N nutrition index-grain protein accumulation, estimating models on grain protein accumulation were constructed on the basis of canopy hyper-spectral parameters by linking the above two models with canopy N nutrition index as intersection in wheat. Tests with other independent dataset showed that the key spectral index SDr/SDb on the basis of the technical route of SDr/SDb-PNA-GNA could be used to predict grain protein accumulation from 7d after anthesis to maturity, with the predictive precision (R2) of 0.954 and the relative error (RE) of 13.1%, respectively. It can be concluded that dynamic change of grain protein accumulation in wheat could be monitored directly with key vegetation spectral index.

Key words: Wheat（Triticumaestivum L.), Canopy spectral information, Grain protein, Accumulation dynamics, Monitoring model

[1] Huang Z, Turner B J, Dury S J, Wallis I R, Foley W J. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sens Environ, 2004, 93: 18-29

[2] Thenkabail P S, Smith R B, Pauw E D. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens Environ, 2000, 71: 158-182

[3] Liu Z-Y(刘占宇), Huang J-F(黄敬峰), Wu X-H(吴新宏), Dong Y-P(董永平), Wang F-M(王福民), Liu P-T(刘朋涛). Hyperspectral remote sensing estimation models on vegetation coverage of natural grassland. Chin J Appl Ecol (应用生态学报), 2006, 17(6): 997-102 (in Chinese with English abstract)

[4] Ding S-Y(丁圣彦), Li H-M(李昊民), Qian L-X(钱乐祥). Research advances in remote sensing techniques in estimation of vegetation biochemical material content. Chin J Ecol, 2004, 23(4): 109-117 (in Chinese with English abstract)

[5] Cho M A, Skidmore A K. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sens Environ, 2006, 101: 181-193

[6] Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ, 2002, 81: 337-354

[7] Hansen P M., Jørgensen J R, Thomsen A. Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression. J Agric Sci, 2002, 139: 307-318

[8] Xue L-H(薛利红), Zhu Y(朱艳), Zhang X(张宪), Cao W-X(曹卫星). Predicting wheat grain quality with canopy reflectance spectra. Acta Agron Sin (作物学报), 2004, 30(10): 1036-1041 (in Chinese with English abstract)

[9] Wang Z J, Wang J H, Liu L Y, Huang W J, Zhao C J, Wang C Z. Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR). Field Crops Res, 2004, 90: 311-321

[10] Li Y-X(李映雪), Zhu Y(朱艳), Tian Y-C(田永超), You X-T(尤小涛), Zhou D-Q(周冬琴), Cao W-X(曹卫星). Relationship of grain protein content and relevant quality traits to canopy reflectance spectra in wheat. Sci Agric Sin (中国农业科学), 2005, 38(7): 1332-1338(in Chinese with English abstract)

[11] Basnet B, Apan A, Kelly R, Jensen T, Strong W, Butler D. Relating satellite imagery with grain protein content. In: Proceedings of the Spatial Sciences Conference, Canberra, Australia, 2003. pp 22-27

[12] Tian Y-C(田永超), Zhu Y(朱艳), Cao W-X(曹卫星), Fan X-M(范雪梅), Liu X-J(刘小军). Monitoring protein and starch accumulation in wheat grains with leaf SPAD and canopy spectral reflectance. Sci Agric Sin (中国农业科学), 2004, 37(6): 808-813 (in Chinese with English abstract)

[13] Woodard H J, Bly A. Relationship of nitrogen management to winter wheat yield and grain protein in South Dakota. J Plant Nutr, 1998, 21: 217-233

[14] Gebbing T, Schnyder H. Pre-anthesis reserve utilization for protein and carbohydrate synthesis in grains of wheat. Plant Physiol, 1999, 121: 871-878

[15] Xie Z-J(谢祝捷), Jiang D(姜东), Cao W-X(曹卫星), Dai T-B(戴廷波), Qi J(荊奇). Effects of post-anthesis soil water status on the activities of key regulatory enzymes of starch and protein accumulation in wheat grains. J Plant Physiol Mol Biol (植物生理与分子生物学学报), 2003, 29(4): 309-316(in Chinese with English abstract)
Wang J-H(王纪华), Huang W-J(黄文江), Zhao C-J(赵春江), Yang M-H(杨敏华), Wang Z-J(王之杰). The inversion of leaf biochemical components and grain quality indicators of winter wheat with spectral reflectance. J Remote Sens (遥感学报), 2003, 7: 277-284(in Chinese with English abstract)
[1] 毛嘉琦, 黄朋雨, 赵佳佳, 郑兴卫, 武棒棒, 郝宇琼, 屈非, 刘成, 马朋涛, 郑军. 山西小麦品种白粉病抗性评价及抗病基因分子检测[J]. 作物学报, 2026, 52(6): 1669-1681.
[2] 胡川, 赵凯男, 黄修利, 吴金芝, 任开明, 王贺正, 付国占, 黄明, 李友军. 一次灌溉下耕作方式和氮肥用量对旱地小麦产量和品质的影响[J]. 作物学报, 2026, 52(6): 1830-1846.
[3] 陈雪燕, 何华川, 李政嘉, 董新盼, 李藕琪, 刘小云, 李丹萍, 陈志伟, 刘国霞, 吕胜源, 吴印莹, 赵振东, 曹新有, 万何平. 水培盐碱复合胁迫下‘济麦60’苗期根系有机酸分泌动态变化及其转录调控机制[J]. 作物学报, 2026, 52(6): 1859-1875.
[4] 高沛阳, 李瑾璇, 董宇奎, 石玉, 张振, 张永丽. 测墒补灌下小麦分蘖发生和成穗对施氮量的响应[J]. 作物学报, 2026, 52(6): 1847-1858.
[5] 张献丰, 郭利建, 李康春, 孔斌雪, 刘玉芳, 车卓, 杨德龙. 小麦ABHD6基因家族鉴定与粒重功能标记开发[J]. 作物学报, 2026, 52(6): 1711-1727.
[6] 翟胜男, 曹新有, 李豪圣, 李吉虎, 李法计, 刘金栋, 夏先春, 吕莹莹, 马瑞峰, 王颖, 耿洪伟, 刘建军. 小麦Pod-A1、Pod-D1和Pod-2D位点等位变异对籽粒过氧化物酶活性的遗传效应分析[J]. 作物学报, 2026, 52(6): 1593-1603.
[7] 习千辉, 徐梓瑗, 刘梦梦, 王宏艺, 郎凯琳, 井震海, 陈锋, 赵磊. 小麦籽粒铜含量的全基因组关联分析及候选基因预测[J]. 作物学报, 2026, 52(6): 1604-1617.
[8] 王壮壮, 武紫君, 张永新, 张芯源, 袁丽雪, 陈如雪, 刘世举, 段剑钊, 冯伟, 王同朝, 王永华. 豫东南黏壤潮土区水氮优化协同提高冬小麦产量和氮素利用效率[J]. 作物学报, 2026, 52(5): 1501-1521.
[9] 何万龙, 耿洪伟, 张飞飞, 米克热阿依·阿巴白克热, 罗紫洋, 李鹏程, 周钊宇, 程宇坤. 基于深度学习的小麦重要病害图像识别系统的研究[J]. 作物学报, 2026, 52(5): 1401-1417.
[10] 张振, 冯连杰, 石玉, 于振文, 张永丽. 节水补灌下不同穗型小麦产量形成差异研究[J]. 作物学报, 2026, 52(5): 1522-1535.
[11] 侯思宇, 王国璀, 韦金贵, 谢玮欣, 殷文, 樊志龙, 柴强, 胡发龙. 绿肥配施化学氮肥对西北干旱灌区小麦干物质积累及产量形成的影响[J]. 作物学报, 2026, 52(4): 1208-1219.
[12] 尚云秋, 赵竹, 陈欢, 丁永刚, 乔玉强, 李玮, 张向前, 曹承富, 杜世州. 长期定位耕作方式对雨养小麦籽粒灌浆和产量形成的影响[J]. 作物学报, 2026, 52(4): 1236-1250.
[13] 乔宇馨, 李程越, 康晓玉, 张鑫琪, 贾绍辉, 刘倩, 曹亚丽, 史鑫蕊, 郝兴宇, 李萍. 基于APSIM模型的长期免耕秸秆覆盖对旱地小麦增产效应研究[J]. 作物学报, 2026, 52(4): 1181-1192.
[14] 李灿, 张喜伟, 朱博涛, 张沛沛. 小麦GSK激酶TaSK41的功能分析及互作蛋白的筛选[J]. 作物学报, 2026, 52(3): 677-687.
[15] 侯洁, 付朵朵, 武海峰, 郝宇琼, 郑兴卫, 武棒棒, 周凯, 李晓华, 郑军, 赵佳佳. 山西省小麦地方品种的染色体多样性及遗传效应分析[J]. 作物学报, 2026, 52(3): 746-763.
Viewed
Full text


Abstract

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