研究论文

氮磷水平和播期对春小麦籽粒灌浆期降落数值动态变化的影响

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  • 1中国科学院大气物理研究所东亚区域气候_环境重点实验室/全球变化东亚区域中心,北京 100029
    2东北农业大学小麦研究室,哈尔滨 150030

录用日期: 2005-10-26

  网络出版日期: 2006-03-30

基金资助

中国科学院知识创新工程重要方向资助项目(KZCX3-SW-218);国家自然科学基金项目(30571079)

EFFECT OF NITROGEN AND PHOSPHORUS LEVEL AND SOWING DATE ON DYNAMIC CHANGE IN GRAIN FALLING NUMBER IN SPRING WHEAT IN GRAIN FILLING

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  • 1Key Laboratory of Regional Climate_Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences & START Regional Center for Temperate East Asia, Beijing 100029, China
    2Northeast Agricultural University, Harbin 150030, China

Accepted date: 2005-10-26

  Online published: 2006-03-30

摘要

利用不同品质类型春小麦(Triticum aestivum)品种在大田条件下研究了灌浆期籽粒降落数值动态变化规律以及氮磷肥与播期的影响效应。结果表明,开花15 d至成熟期间,籽粒降落数值呈先升后降的单峰曲线变化,并推知α_淀粉酶活性(液化值)呈先降后升的单谷曲线变化。在富钾情况下,氮素增施,高蛋白强筋品种降落数值增加,动态曲线峰值上升且出现时间推迟,而高蛋白中筋和低蛋白弱筋品种降落数值则降低,峰值下降且出现时间提前。磷素增施,各基因型品种降落数值均降低;动态曲线峰值、谷值及出现时间因基因型呈不同变化。氮磷(钾)素平衡配施是形成较高降落数值(较低α_淀粉酶活性)的关键。在无水分胁迫情况下,光温互作是影响各基因型籽粒降落数值动态形成的首要条件,其次为降水;且在光温因子互作中,光合有效辐射为最敏感因子。较高的光温条件互作是形成各品种较高降落数值的基础,在此基础上增加光合有效辐射则使高蛋白强筋品种降落数值增加;而以此为基础,在一定水平光照条件限度内,增加光合有效辐射则使高蛋白中筋和低蛋白弱筋品种降落数值增加,超过限度则使其降低。气象条件的影响作用及基因型差异可通过曲线动态变化反映出来。不同基因型降落数值在不同环境条件(氮磷肥水平和气象条件)下变异不同。

本文引用格式

赵秀兰, 李文雄 . 氮磷水平和播期对春小麦籽粒灌浆期降落数值动态变化的影响[J]. 植物生态学报, 2006 , 30(2) : 344 -351 . DOI: 10.17521/cjpe.2006.0046

Abstract

Wheat grain falling number (GFN) is an important index that indicates the alpha_amylase activity and starch quality properties of wheat. The temporal dynamics of GFN and the effects of environmental conditions on GFN during grain filling have been scarcely reported systematically in the literature. In the present paper, field experiments were carried out in Harbin in 2002 to investigate temporal patterns in GFN, and how GFN was affected by N and P treatments and meteorological conditions during grain filling. Three genotypes of spring wheat with different qualities were used: a high protein_rich gluten genotype (`Wildcat'), a high protein_medium gluten genotype (`DN7742') and a low protein_poor gluten genotype (`NKH9'). The N and P applications included four treatments: 225 kg·hm-2 N, 450 kg·hm-2 P2O5 (F1); 300 kg·hm-2 N, 300 kg·hm-2 P2O5 (F2); 300 kg·hm-2 N, 450 kg·hm-2 P2O5 (F3) and 300 kg·hm-2 N, 600 kg·hm-2 P2O5 (F4). The results showed that temporal changes in GFN after anthesis could be fitted with a third_order convex curve (single peak curve), indicating that GFN rose in the initial stages and then fell during the middle and upper stages of grain filling. The effects of genotype and environmental factors on GFN formation were compared using this fitted curve. The temporal pattern in alpha_amylase activity fell to a greater extent during the initial stage and then rose weakly in the medium and upper stage. The results also indicated that from the 15th day after anthesis to maturity, with increasing nitrogen levels, medium phosphorus and high potassium levels (450 kg·hm-2 P2O5, 600-630 kg·hm-2 K2O), GFN in the high protein_rich gluten genotype increased and the peak value rose but was delayed, and GFN decreased and the curve peak values fell but was ahead in both the high protein_medium gluten and low protein_poor gluten genotypes. GFN in the three genotypes all decreased with increasing phosphorus levels in the medium or low nitrogen treatments and at high potassium levels (300 kg·hm-2 N, 600-610 kg·hm-2 K2O), and the curve differed among the three genotypes. The balance of nitrogen, phosphorus and potassium inputs was the key to forming higher GFN (lower alpha_amylase activity). Under conditions of moderate rainfall, the interaction of temperature and sunlight was the predominant influence on temporal formation patterns of GFN for different genotypes during grain filling, rainfall was the second, and PAR was the most sensitive among all the meteorological elements influencing the dynamic formation of GFN. Higher GFN was formed in all genotypes under conditions of high sunlight and high temperatures. Under conditions of high sunlight and temperature, the GFN in the high protein_rich gluten genotype increased with increasing PAR. The effects of meteorological conditions as well as differences among genotypes on GFN could be modeled by curve changes in the third_order equation. The variation of GFN in different genotypes under different environmental conditions (nitrogen and phosphorus fertilization and meteorological conditions) had significant affects on GFN.

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