Study on Denitrification Intensity in Rice Rhizosphere Soil under Water Management Model

2014 ◽  
Vol 1010-1012 ◽  
pp. 584-587 ◽  
Author(s):  
Han Xiang Chen ◽  
Ge Lan Ma ◽  
Zhi Gang Chen ◽  
Lei Chen ◽  
Ke Zhang
Keyword(s):  
Rhizosphere Soil ◽  
Control Mode ◽  
Growth Stages ◽  
Water Control ◽  
Intensity Effect ◽  
Continuous Irrigation ◽  
Rice Rhizosphere ◽  
Field Ecosystem ◽  
Irrigation Patterns ◽  
Different Growth Stages

A typical rice field ecosystem as the research object, using the indoor pot experiment to study the three kinds of water driven (shallow layer of continuous irrigation (C), alternating wet and dry (J), and water control mode (G)) on the effect of rice rhizosphere soil denitrification intensity. Effect of changes in soil moisture caused by the three different irrigation patterns on different growth stages of rice on rhizosphere denitrification was comparatively analyzed,referring to the rhizosphere denitrification capacity during different rice growth stage.The results showed that: soil denitrification intensity with different water managements changed significantly, showing C>J>G. And with the growth of plants, under three different water managements, denitrification intensity showed a downward trend. Mature reached to a minimum. All have to be higher than control groups.

Microbial Diversity in Tobacco Rhizosphere Soil at Different Growth Stages

2021 ◽  
Vol 15 (5) ◽  
pp. 606-614
Author(s):  
Yanan Ruan ◽  
Shengguang Xu ◽  
Zuoxin Tang ◽  
Xiaolin Liu ◽  
Qirui Zhang ◽  
...  
Keyword(s):  
Bacterial Community ◽  
Microbial Diversity ◽  
Rhizosphere Soil ◽  
Principal Component ◽  
Alpha Diversity ◽  
Illumina Miseq ◽  
Shannon Index ◽  
Growth Stages ◽  
Soil Microbial Diversity ◽  
Different Growth Stages

Rhizosphere microorganisms are the main participants of material transformation and energy cycle in soil. To further explore its composition and variation, the tobacco rhizosphere soil were sequenced by Illumina MiSeq, the microbial community at different growth stages were analyzed and compared. The analysis of Alpha diversity showed that, the Chao1 index, Shannon index of bacteria and Chao1 index of fungi in rhizosphere soil were the highest in tobacco budding stage, while the peak of Shannon index of fungi appeared in tobacco material stage. Principal component analysis (PCA) further showed that at different growth stages, Proteobacteria was the dominant, followed by Actinobacteria, Acidobacteria and Gemmatimonadetes for bacterials; Ascomycota was the dominant, followed by Zygomycota and Basidiomycota for fungi. Under field conditions, the microbial abundance changed with the growth of tobacco, and the microbial diversity reached the peak at budding stage. The bacterial community and abundance between budding and mature stages was highly similar, while the bacterial community in vigorous growth stage is quite different. The similarity of fungal community in budding stage was very low, compared with the other stages; while in other stages was high. This study provides a theoretical basis for further understanding the relationship between tobacco rhizosphere soil microbial diversity and variation, tobacco growth and soil diseases.

Cytokinins from terminal buds of Euphoria longana during different growth stages

1997 ◽  
Vol 99 (1) ◽  
pp. 185-189
Author(s):  
Wen-Shaw Chen ◽  
Kuang-Liang Huang ◽  
Hsiao-Ching Yu
Keyword(s):  
Growth Stages ◽  
Terminal Buds ◽  
Different Growth Stages

Effects of Meteorological Factors at Different Growth Stages on Yield Traits of Maize (Zea maysL.) in Heilonggang Basin

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Growth Stages ◽  
Yield Traits ◽  
Different Growth Stages

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

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