密度对晋北区大豆农艺性状、经济性状及产量的影响

Effects of Planting Density on Agronomic Traits, Economic Traits and Yield of Soybean in North of Shanxi Province

为了探讨不同种植密度对大豆性状及产量的影响,确定晋北区大豆的适宜种植密度,以晋豆43号为试验材料,设置了6个不同密度处理进行研究,结果表明:随着密度的加大,株高呈先升后降趋势,茎粗、主茎节数、分枝数、单株粒数、单株荚数、单株粒重均下降,百粒重先降后升,产量呈先增后降的趋势,适宜种植密度为21万~27万株/hm^2。相关性分析和通径分析表明:密度是优化大豆群体结构的重要措施,维持合理的种植密度,同时适当地提高单株粒重和分枝数,有利于提高大豆产量。通过SPSS曲线估计,得出种植密度与大豆产量关系的最佳预测模型是二次曲线模型y=b_0+b_1x+b_2x^2,晋北区大豆种植密度与产量关系最佳数学预测模型是y=-5762.478+791.819x-16.147x2,理论最适密度为24.5191万株/hm^2,产量为4 046.61kg/hm^2。该研究为晋北早熟区大豆高产栽培技术提供科学依据。

In order to probe into the effects of different planting density on traits and yield of soybean, the early- maturing soybean variety of Jindou 43 was planted in northern part of Shanxi province. Six different planting densities were set up to study the suitable planting density. The results showed that with the increase of density, the height of soybean showed a trend of increase first and then decrease. The stem diameter, number of nodes, branch number, grain number per plant, pods per plant and grain weight per plant showed a trend of decrease as the density increased. 100-grain weight showed a trend decrease first and then increase, the yield showed a trend of increase and then decline as the density increased. The suitable planting density was 210 000-270 000plants/hm2. Correlation and path analysis showed that planting density was an important measure to optimize the community structure of soybeans. Keeping reasonable planting density and increasing grain weight per plant and branch number could be beneficial to improve the outputs of soybeans. The second-order polynomial model y=b0＋b1x＋bg2 obtained by SPSS curvilinear regression was assayed to analyze the relationship between the planting density and outputs. The equation of this test was y= -5762.478＋791.819x- 16.147x2. The theoretical calculation of planting density was 245 19 lplants/hm： and the yield was 4 046.61kg/hm2. This study had provided the theoretical basis for high production of soybeans in northern part of Shanxi province.