Maize Endophytic Plant Growth-Promoting Bacteria Can Alleviate Plant Saline and Alkaline Stress.

Soil salinization is currently one of the main abiotic stresses that restrict plant growth. Plant endophytic bacteria can alleviate abiotic stress. The aim of the current study was to isolate, characterize, and assess the plant growth-promoting and saline and alkaline stress-alleviating traits of () isolates from maize.

GDSL in (LpGDSL) Confers Saline-Alkali Resistance to the Plant by Enhancing the Lignin Content and Balancing the ROS.

In order to explore the response mechanism of () to saline-alkali stress, we successfully cloned (GDSL lipase, Gly-Asp-Ser-Leu) from . The qRT-PCR results indicated that the expression was higher in the leaves of , and the expression of the reached the highest level at 12 h in leaves under 11 mM HO, 200 mM NaCl, 25 mM NaCO, and 20 mM NaHCO. The bacteriophage overexpressing was more tolerant than the control under different NaHCO contents.

MYB4 in affects plant saline-alkaline tolerance.

DC ( DC) plays an important role in the rational utilization of salinized soil. To explore the molecular mechanism of salt-tolerant , the was cloned. LpMYB4 close relationship with and throughout the phylogenetic tree construction.

Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data.

To meet the challenge of food security, it is necessary to obtain information about rice fields accurately, quickly and conveniently. In this study, based on the analysis of existing rice fields extraction methods and the characteristics of intra-annual variation of normalized difference vegetation index (NDVI) in the different types of ground features, the NDVI difference method is used to extract rice fields using Sentinel data based on the unique feature of rice fields having large differences in vegetation between the pre-harvest and post-harvest periods. Firstly, partial correlation analysis is used to study the influencing factors of the rice harvesting period, and a simulation model of the rice harvesting period is constructed by multiple regression analysis with data from 32 sample points.