S
Shao Hongbo
Researcher at Chinese Academy of Sciences
Publications - 55
Citations - 2377
Shao Hongbo is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Soil water & Soil salinity. The author has an hindex of 21, co-authored 55 publications receiving 2049 citations. Previous affiliations of Shao Hongbo include Nanjing Agricultural University & Binzhou University.
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Journal ArticleDOI
Antioxidant defense responses: physiological plasticity in higher plants under abiotic constraints
Cheruth Abdul Jaleel,Ksouri Riadh,Ragupathi Gopi,P. Manivannan,Jallali Inès,Hameed Jasim Al-Juburi,Zhao Changxing,Shao Hongbo,Shao Hongbo,Shao Hongbo,Rajaram Panneerselvam +10 more
TL;DR: The related works, which have revealed the changes in the basic antioxidant metabolism of plants under various abiotic constraints, are explored.
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LEA proteins in higher plants: Structure, function, gene expression and regulation
TL;DR: No tissue-specific lea gene expression has been considered as one main regulatory mechanism on the basis of extensive studies with the model plant, Arabidopsisthaliana, and the search for anti-drought inducible genes and their characterization is continued.
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Roles of plant soluble sugars and their responses to plant cold stress
TL;DR: Soluble sugars exert their positive effects to protect plant cells from damage caused by cold stress through many ways including serving as osmoprotectants, nutrients as well as interacting with the lipid bilayer.
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Soil enzymes as indicators of saline soil fertility under various soil amendments
TL;DR: Wang et al. as discussed by the authors investigated the relationship between soil properties and enzyme activities under different amendment types, including Hekang, organic fertilizer, microbial inoculant, and organic fertilizer.
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How does the conversion of land cover to urban use affect net primary productivity? A case study in Shenzhen city, China
TL;DR: Li et al. as discussed by the authors used MODIS-based Normalized Difference Vegetation Index (NDVI) data, Landsat-based land cover map, meteorological data and other field data to drive the CASA productivity model and obtain net primary productivity for the study area.