30 research outputs found

    The Infection and Impact of Azorhizobium Caulinodans ORS571 on Wheat (Triticum Aestivum L.)

    Get PDF
    Based on our previous study, cereal crop wheat (Triticum aestivum L.) could be infected by rhizobia Azorhizobium caulinodans ORS571, and form para-nodules with the induction of 2.4-dichlorophenoxyacetic acid, a common plant growth regulator. To enhance this infection and the potential agricultural application, we compared six different infection methods (Direct seed dip; Seed germination dip; Pruned-root dip; Foliar spray; Circum-soil dip; Seed dip and circum-soil dip) for achieving the high efficient infection of A. caulinodans into wheat plants by employing a green fluorescent protein (gfp)-labeled Azorhizobium caulinodans strain ORS571. With proper methods, copious rhizobia could enter the interior and promote the growth of wheat to the hilt. Circum-soil dip was proved to be the most efficient method, seed germination dip and pruned-root dip is the last recommended to infect wheat, seed germination dip and seed dip and circum-soil dip showed better effects on plant growth, pruned-root dip did not show too much effect on plant growth. This study laid the foundation for understanding the interaction between rhizobia and cereal crops and the growth-promoting function of rhizobia

    Rapid, high-resolution measurement of leaf area and leaf orientation using terrestrial LiDAR scanning data

    No full text
    The rapid evolution of high performance computing technology has allowed for the development of extremely detailed models of the urban and natural environment. Although models can now represent sub-meter-scale variability in environmental geometry, model users are often unable to specify the geometry of real domains at this scale given available measurements. An emerging technology in this field has been the use of terrestrial LiDAR scanning data to rapidly measure the three-dimensional geometry of trees, such as the distribution of leaf area. However, current LiDAR methods suffer from the limitation that they require detailed knowledge of leaf orientation in order to translate projected leaf area into actual leaf area. Common methods for measuring leaf orientation are often tedious or inaccurate, which places constraints on the LiDAR measurement technique. This work presents a new method to simultaneously measure leaf orientation and leaf area within an arbitrarily defined volume using terrestrial LiDAR data. The novelty of the method lies in the direct measurement of the fraction of projected leaf area G from the LiDAR data which is required to relate projected leaf area to total leaf area, and in the new way in which radiation transfer theory is used to calculate leaf area from the LiDAR data. The method was validated by comparing LiDAR-measured leaf area to (1) 'synthetic' or computer-generated LiDAR data where the exact area was known, and (2) direct measurements of leaf area in the field using destructive sampling. Overall, agreement between the LiDAR and reference measurements was very good, showing a normalized root-mean-squared-error of about 15% for the synthetic tests, and 13% in the field

    Rice sprout endophytic Enterobacter

    No full text
    corecore