D. Kehayov, A. Atanasov, Ilian Bozhkov, I. Zahariev
{"title":"Influence of seed density and gear ratio on quantity of sowed seeds","authors":"D. Kehayov, A. Atanasov, Ilian Bozhkov, I. Zahariev","doi":"10.22616/erdev.2022.21.tf056","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":244107,"journal":{"name":"21st International Scientific Conference Engineering for Rural Development Proceedings","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"21st International Scientific Conference Engineering for Rural Development Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22616/erdev.2022.21.tf056","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.