西北旱作区马铃薯适宜品种筛选试验
XianDai NongYe KeJi(2022)
Abstract
为筛选出适合西北旱作区种植的马铃薯品种,本文对10个马铃薯新品种的田间性状及产量开展了比较试验.结果表明,10个参试马铃薯品种出苗率、叶面积指数和冠层温度差异不显著.参试品种株高为87.6~123.4 cm,以青薯9号株高最高;主茎数为1.6~3.2个,陇薯13号、定薯3号、定薯4号、天薯12号、青薯9号、丽薯7号、庄薯3号与对照陇薯6号之间主茎个数差异性不显著.经过田间抗病性统计,青薯9号抗晚疫病、抗早疫病最强,定薯3号、陇薯13号、陇薯14号和定薯4号次之,陇薯6号和丽薯7号抗病性最差.青薯9号产量最高,为72107.1 kg/hm2,较对照陇薯6号增产43.9%.结合田间各性状及产量构成情况,青薯9号、陇薯14号、陇薯13号、定薯3号、定薯4号和天薯11号田间表现较好、产量较高、综合性状好,可以在西北旱作区大面积种植.
More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
Related Papers
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