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滴灌春小麦植株干物质积累与分配特性及对产量的影响
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引用本文:杨建平,吕钊彦,刁 明,李卫华,姜 东.滴灌春小麦植株干物质积累与分配特性及对产量的影响[J].西北农业学报,2021,(1):50~59
DOI:10.7606/j.issn.1004-1389.2021.01.007
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作者单位
杨建平,吕钊彦,刁 明,李卫华,姜 东 (1.石河子大学 农学院新疆石河子 8320002.安徽农业大学 园艺学院合肥 2300003.南京农业大学 农学院南京 210000) 
基金项目:国家自然科学基金新疆地区联合基金重点项目(U1803235);国家现代小麦产业技术体系(CARS-03)。
中文摘要:为明确“一管6”滴灌条件下不同小麦品种、不同行间干物质积累与转运特征及其对产量的影响,以来自新疆、内蒙、宁夏等不同地区的7个春小麦品种为材料,研究距滴管带远近不同行小麦植株(距滴管带最近行记为R1、中间行记为R2,最远行记为R3)叶面积指数、开花期和成熟期植株各器官干物质积累量。结果表明:(1)不同春小麦品种行间叶面积指数大小与其产量呈正相关(R=0.50),‘克春11号’行间叶面积指数最大,依次为‘新春37号’‘宁春4号’行间叶面积指数变异系数最小;(2)试验春小麦品种行间籽粒产量与花后干物质积累量与成熟期干物质积累量均呈正相关(R分别为0.72与0.91),开花期与成熟期各器官干物质积累量均与产量呈正相关。(3)‘新春37号’‘克春11号’‘高原506号’‘宁春53号’与‘宁春4号’的R2与R3花后干物质积累量对籽粒的贡献率低于R1,但其花前干物质积累量转移率均高于R1;‘农麦2号’与‘津强7号’的R2与R3花后干物质积累量对籽粒的贡献率高于R1,但其花前干物质积累量转移率均低于R1;(4)开花期行间叶片干物质积累量的降幅对籽粒产量的降幅影响最大,穗+穗轴行间干物质的降幅对籽粒干物质积累量的降幅影响最小。有效穗数R2与R3相对于R1的降幅增大,开花期与成熟期各器官干物质积累量远近行间的降幅会减小,同时千粒质量与穗粒数的降幅也会减小。‘新春37号’与‘克春11号’在“1管6”滴灌模式下产量水平高,‘宁春4号’在“1管6”滴灌模式下行间产量变异系数小,叶面积指数与干物质积累量的行间稳定性均可为筛选品种在“1管6”滴灌模式下行间产量稳定性的参考指标。穗数的增大会使干物质积累与产量的行间差异变大,千粒质量或者穗粒数的提高有助于新疆滴灌小麦产量的提高。
中文关键词:滴灌  小麦  干物质  转运  主成分分析  冗余分析
 
Accumulation and Distribution of Dry Matter in Plants and Their Contribution to Grain Yield in Drip-irrigated Spring Wheat
Abstract:To understand the spatial variations of dry matter accumulation and distribution of plants at different rows and their contribution to grain yield of spring wheat under “1 tube 6 lines” drip irrigation system, seven spring wheat varieties were collected from different eco-regions in Xinjiang, Inner Mongolia and Ningxia.The plants at the 1st, 2nd and 3rd row adjacent to the drip tube was designated as R1, R2 and R3 plants, respectively. Leaf area index (LAI) and dry matter accumulation in different organs of plants at different rows were recorded at anthesis and maturity stages. We observed that (1) the LAI of plants at different rows among different spring wheat varieties positively correlated with grain yield (R=0.50).The‘Kechun 11’ possessed the highest LAI of plants at different rows, ‘Xinchun 37’ took the second place. In addition, coefficient of variation of LAI between rows was the lowest in ‘Ningchun 4’. (2) Grain yield positively correlated with dry matter which was accumulated during post-anthesis period, and grain yield also positively correlated with dry matter amount at maturity(R=0.72 and 0.91) respectively. In addition, the dry matter accumulation in each organ at the flowering and maturity stages was positively correlated with grain yield. (3) The contribution rate of dry matter accumulation after anthesis in plants at R2 and R3 were lower than R1 in ‘Xinchun 37’‘Kechun 11’ ‘Gaoyuan 506’ ‘Ningchun 53’ and ‘Ningchun 4’. Reversely, the rate of dry matter which was accumulated in vegetative organs before anthesis and redistributed to grains after anthesis were higher than that at R1.‘Nongmai 2’ and ‘Jinqiang 7’ showed opposite patterns in terms of contribution rate to grain and transport efficiency. (4) The decrease of dry matter accumulation in leaf between rows during flowering stage had the greatest impact on the decline of grain yield, and the decrease of spike between rows had the least effect on the decrease of grain dry matter accumulation. The decrease of effective panicle number R2 and R3 relative to R1 increased, the inter-row coefficient of variation of 1 000-grain mass and kernels per spike decreased, it was same as in dry matter accumulation of each organ in anthesis and maturity. ‘Xinchun 37’ and ‘Kechun 11’ showed the best yield performance while the yield variation between rows was the lowest in Ningchun 4 under the “1 tube 6 lines” drip irrigation system. The plasticity of LAI and dry matter accumulation between rows were then reocommended as the variety screening traits for the “1 tube 6 lines” drip irrigation system. More ears per area contributed to the large variations in dry matter accumulation and grain yield, while improvement of thousand kernel mass and grain number per ear contributed to the grain yield of wheat under drip irrigation system in Xinjiang.
keywords:Drip irrigation  Wheat  Dry matter  Distribution  Principal component analysis  Redundancy analysis
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