密度对双低油菜苏油7号产量与品质的影响
Journal of Anhui Agricultural Sciences(2017)
Abstract
[目的]研究不同密度对苏油7号产量和主要品质的影响.[方法]通过田间试验,研究密度对苏油7号产量、产量构成因素、有效角果数、有效分枝数、品质性状的影响.[结果]随着密度的增加,产量有先增后减的趋势,密度间产量差异达极显著水平.低密度水平能显著增加单株有效角果数,随着密度降低单株有效角果数减少,单位面积角果数有先增后减的趋势;不同密度对每角粒数和千粒重影响不大.因此,高产主要取决于单位面积有效角果数.随着密度的增加,主轴和一次分枝有效角果数有所减少,二次分枝有效角果数和单株二次有效分枝数则显著减少.籽粒含油率和蛋白质含量随着密度增加而有所降低.[结论]在该试验条件下,为获得较高产量和保证双低品质,苏油7号适宜密度为13.5万株/hm2.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined