WeChat Mini Program
Old Version Features

氮肥运筹对钵苗机插水稻性状及产量的影响

Journal of Anhui Agricultural Sciences(2017)

Cited 3|Views3
Abstract
[目的]研究不同氮肥运筹对钵苗机插水稻性状及产量的影响.[方法]以杂交籼稻 Ⅱ 优118和常规粳稻武运粳27为材料,研究不同基蘖肥和穗肥比例对水稻茎蘖动态、成穗率、产量及其构成因素的影响.[结果]基蘖肥:穗肥为7∶3的氮肥运筹方式有利于提高穗数,增加产量;Ⅱ 优118以基蘖肥178.5 kg/hm2、总用氮量255 kg/hm2的处理的产量最高;武运粳27以基蘖肥252.0 kg/hm2、总用氮量360 kg/hm2的处理的群体产量最高.[结论] 该研究为钵苗机插水稻的最佳氮肥运筹方式的推广提供理论基础.
More
Translated text
求助PDF
上传PDF
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