Exploring the Physiological Basis of Yield Enhancement in New Generation Rice (NGR): a Comparative Assessment with Non-Ngr Rice Genotypes
PLANT PHYSIOLOGY REPORTS(2023)
ICAR-National Rice Research Institute
Abstract
As the research is progressing toward sustainable yield increment with minimal environmental impact, the breeding of New Generation Rice (NGR) has emerged as a promising strategy that is based on the ideotype concept. We performed a study to find out the influence of a few key morpho-physiological and yield-related traits on grain yield in NGR and non-NGR genotypes to see whether a few important physiological traits viz. flag leaf photosynthetic rate can be used as a selection criterion for enhanced grain yield in rice. For this, a panel of 211 genotypes including 47 NGR lines and 164 non-NGR lines was studied for their physiological and yield attributing traits such as flag leaf area, SPAD chlorophyll meter reading, total chlorophyll content, photosynthetic rate, stomatal conductance, transpiration rate, straw weight, total biomass, harvest index, pushing resistance, culm strength, and grain yield. Trait-specific analysis indicated a positive association of grain yield with straw weight, total biomass, harvest index, and pushing resistance for both groups. However, a significantly positive correlation and regression coefficient of grain yield with the photosynthetic rate was observed only in the case of the NGR lines, while the association was non-significant in non-NGR lines. Additionally, the photosynthetic rate was found to be significantly correlated with straw weight, total biomass, and pushing resistance in NGR lines. Thus, grain yield in NGR lines can be predicted by photosynthetic rate. This makes NGR lines an efficient candidate for breeding programmes with a target of breaking the yield ceiling, using photosynthetic rate as selection criteria.
MoreTranslated text
Key words
Biomass,Correlation,Grain yield,Harvest index,Photosynthetic rate,Pushing resistance
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