Bioorganic fertilizers can alleviate (a) biotic stresses and sustainably increase crop yields. The effect of bioorganic fertilizers on the rhizosphere bacterial community of Panax notoginseng and soil metabolism remains unknown. Here, we tracked the changes in the soil physicochemical properties, bacterial microbiota responses, and soil metabolic functions after the addition of a bioorganic fertilizer in a P. notoginseng field. The application of a bioorganic fertilizer reduced the soil acidification, improved the organic matter, and increased the contents of the total/available soil nutrients. Soil amendment with a bioorganic fertilizer significantly affected the structure of the rhizosphere bacterial community, leading to the enrichment of specific bacterial consortia such as Rhodanobacter, Arthrobacter, Sphingomonas, Devosia, Pseudolabrys, Luteimonas, Lysobacter, Nitrosospira, and Nakamurella. Previously, many of these genera have been associated with nutrient cycling, plant productivity, and disease suppression. Metabolome analysis further highlighted that the bioorganic fertilizer treatment significantly reduced phenolic acids and flavonoids and enhanced organic acids, saccharides and alcohols, and amino acids. This result indicates a high survival of bacterial microbiota in the rhizosphere and an availability of nutrients for P. notoginseng growth. This work showed that the application of bioorganic fertilizers significantly improves soil health status, alters soil metabolic functions, and stimulates a specific subset of rhizosphere microbiota for nutrient cycling and disease protection in P. notoginseng.

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The primary objective of vectorial road network matching is to identify homonymous roads from two different data sources. Previous methods usually focus on matching road networks with the same coordinate system but rarely with different or unknown coordinate systems, which may lead to nontrivial and nonsystematic deviations (e.g., rotation angle) between homonymous objects. To fill this gap, this study proposes a novel hierarchical road network matching method based on Delaunay triangulation (DTRM). First, the entire urban road network is divided into three levels (L1, L2, L3) by using the principle of stroke. Then, the triangular meshes are constructed from L2, and the minimum matching unit (MMU) in the triangular mesh is used instead of the traditional “node-arc” unit to measure the similarity for the matching of L2. Lastly, a hierarchical matching solution integrating the probabilistic relaxation method and MMU similarity is yielded to identify the matching relationships of the three-level road network. Experiments conducted in Wuhan, China, and Auckland, New Zealand, show that the MMU similarity metrics can effectively calculate the similarity value with different rotation angles, and DTRM has higher precision than the benchmark probability-relaxation-matching method (PRM) and can correctly identify the most matching-relationships with an average accuracy of 89.63%. This study provides a matching framework for road networks with different or even unknown coordinate systems and contributes to the integration and updating of urban road networks.