For self-organizing complex systems, feature extraction methods such as principal component analysis and clustering analysis are often used. Conversely, it means that there is no other good analysis methodology close at hand. In self-organizing complex systems, the properties of the whole can often be neither understood nor predicted by knowledge of its constituents alone, and the interactions between the constituents of the system give rise to new structures and functions, and those will appear on a large scale.
\nIn the case of the rhizobacterial microbiomes, it is possible that when good bacteria is exceeded a certain threshold, opportunistic bacteria become allies and a synergistic effect appears remarkably. The reverse is also true, and when the number of bad bacteria exceeds a certain threshold, the plant dies at once.<\/p>\n
The problem is how to perform feature extraction (in other words, pattern recognition) of such a complex system of rhizobacterial microbiome in the field (farm). At the research level, 16S rRNA read analysis of the rhizobacterial microbiomes and computer analysis on the full use of the obtained big data might be able to visualize the hidden patterns which could not be identified with\u00a0 conventional methods. But, can such a method be used in the field? No, this kind of work will remain in the world of papers. If the technology is not easy for anyone to use, it will not succeed in reality as a business. To be used on site, it has to be cheap, and it has to give immediate results.<\/p>\n
Bacillus, Pseudomonas, Streptomyces, Arthrobacter are said to be representative good bacteria as rhizobacteria, and Enterobacter and Barkholderia are also to be good bacteria.
\nThe bacterial classification of such representative good bacteria and bad bacteria is written below.
\nFirst of all, good bacteria,
\nActinobacteria phylum\u2192 Actinobacteria class \u2192 Streptomyces order \u2192 Streptomycetae \u2192 Streptomyces genus (good guys)
\nProteobacteria phylum \u2192 \u03b3-Proteobacterial class \u2192 Pseudomonad order \u2192 Pseudomonadidae \u2192 Pseudomonas genus (typical good guys)
\nProteobacteria phylum \u2192 \u03b3-Proteobacterial class \u2192 Enterobacter order \u2192 Enterobacteraceae \u2192 Enterobacter genus (good guys)
\nProteobacteria phylum \u2192 \u03b2-Proteobacterial class \u2192 Barkholderia order \u2192 Barkholderidae \u2192 Balkholderia genus (good)
\nFirmicutes phylum\u2192 Bacillus class \u2192 Bacillus order \u2192 Bacillus family \u2192 Bacillus genus (typical good guy)
\nActinobacteria phylum\u2192 Actinobacteria class \u2192 Actinobacteria family\u2192 Micrococcaceae \u2192 Arthrobacter genus (good guys)
\nand bad bacteria,
\nAscomycota phylum \u2192 Hydrangea class \u2192 Pistanthus \u2192 Asteraceae \u2192 Fusarium genus (typical bad guys)<\/p>\n
If we can sense the phylum Actinobacteria, Proteobacteria, and Firmicutes, it will be possible that we can grasp major patterns in the rhizosphere, I think.
\nFor your information, Proteobacteria are Gram-negative bacteria, while Actinobacteria and Firmicutes are Gram-positive bacteria.<\/p>\n","protected":false},"excerpt":{"rendered":"
For self-organizing complex systems, feature extraction methods such as principal component analysis and clust<\/p><\/div>\n