WeChat Mini Program
Old Version Features

    • 优化施肥量控制马铃薯早疫病的发生

    Chunmei CAO,Wengang LI,Xiaojiao WANG,Bing HU,Tuanrong LIN,Jingshan GUO,Fuqiang LIU,Qin YANG

    Chinese Potato Journal（2014）

    Cited 5|Views9
    Abstract
    马铃薯早疫病是一种常见的马铃薯病害，采用农艺措施控制病害的发生是一举多得的好办法。本试验通过施肥处理控制早疫病危害试验，结果表明，马铃薯早疫病发生早晚与施肥多少直接相关，施用60 kg/667m2和40 kg/667m2马铃薯复合肥，对早疫病防治效果为93.39%和88.56%，增产26.61%和22.18%，纯增效益405元/667m2和365元/667m2。因此，施40~60 kg/667m2马铃薯复合肥可以作为防治马铃薯早疫病发生的一项农业措施进行应用。
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    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