种子密度和齿轮比对播种量的影响

D. Kehayov, A. Atanasov, Ilian Bozhkov, I. Zahariev
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摘要

这项研究旨在建立种子播种密度之间的关系,齿轮传动比的传动系统Saxonia A200条播机和种子播种。在普罗夫迪夫农业大学农业机械化系进行了实验室试验。主要变量是播种材料的密度和播种机的齿轮比(播种机的转速)。这两个因素中的每一个都在三个层面上发生变化。试验选用密度为250 kg·m的混合草种、密度为537 kg·m的燕麦和密度为825 kg·m的小麦。传动比的变化如下:最低齿轮的传动比为70.5,最高齿轮的传动比为1.25,平均齿轮的传动比为35,0。结果表明,传动比对播种量变化的影响要大于播种量的影响。大约61.1%的变化是由于齿轮比,33.3%的变化是由于种子密度。结果发现,播种量变化的5.6%是由于本研究未考虑的其他因素造成的。为了确定各因素与观测指标之间的函数关系,进行了回归分析。修正后的多重相关系数R = 0.937,证实所选因素与观察变量之间存在较强的相关性。所得模型的显著性水平为p < 0.001。得到了一个适当的回归模型,可用于预测和求解优化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Influence of seed density and gear ratio on quantity of sowed seeds
The study aims to establish the relationship between the density of sown seeds, the gear ratio in the transmission system of the Saxonia A200 seed drill and the amount of seeds sown. A laboratory experiment was performed at the Department of Mechanization of Agriculture at the Agricultural University of Plovdiv. The main variables were the density of the material to be sown and the gear ratio (rotation speed of the seed drill). Each of the two factors changes on three levels. The experiment was performed with grass mixture seeds with a density of 250 kg·m, oats with a density of 537 kg·mand wheat with a density of 825 kg·m. Gear ratio changes as follows: at the lowest gear the gear ratio is 70.5, at the highest gear the gear ratio is 1.25 and at the average gear the gear ratio is 35, 0. It was found that the gear ratio has a stronger effect on the change in the amount of seed sown compared to the density of the seed. About 61.1% of this change is due to the gear ratio and 33.3% to seed density. It was found that 5.6% of the change in the amount of seeds sown was due to other factors not considered in the present study. To determine the functional relationship between the factors and the observed indicator the regression analysis was performed. The corrected multiple correlation coefficient was R = 0.937, confirming the strong relationship between the selected factors and the observed variable. The level of significance in the obtained model was p < 0.001. An adequate regression model is obtained, which can be used for forecasting and solving optimization problems.
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