Correlation between Soil Bacterial Community Structure and Soil Properties in Cultivation Sites of 13-Year-Old Wild-Simulated Ginseng (Panax ginseng C.A. Meyer)

Soil properties are one of the major factors determining the growth of vegetation. These properties drive the selection of the dominant bacterial community profiles, which eventually determines the soil quality and fertility. The abundance of preferential bacterial community assists in better productivity of a particular type of vegetation. The increasing focus on the health and well-being of the human population has resulted in a shift in paradigm to concentrate on the cultivation of medicinal plants such as Wild-simulated ginseng (WSG). These plant species take a long time for their growth and are generally cultivated in the mountainous forest trenches of Far East countries like South Korea. This study was conducted to decipher the bacterial community profiles and their correlation with soil chemical properties, which would give a broader idea about the optimum growing conditions of such an important medicinal plant. The important edaphic factor determined in this study was the soil pH, which was recorded to be acidic in all the studied cultivation sites. In agreement with the edaphic factor, the relative abundance of Acidobacteria was found to be highest as this phylum prefers to grow in acidic soils. Moreover, the total organic matter, total nitrogen and cation exchange capacity were found to be significantly correlated with the bacterial community. Hence, these results will help to identify the suitable cultivation sites for WSG and increase the productivity of these medicinal plants.

A Review : Improving the Village wise Soil Parameter and Predict the Crop Suggestion

India economy majorly depend on agriculture that play important role in the survival of the people. It's remain the major provider for farmers and source of revenue of our country. The main focus of this survey is on how to improve the soil quality and predict the Crop selection. We are going to study the Edaphic factor, Classification problem and prediction of village wise soil parameters. That is done by collecting number of soil testing samples for finding soil fertility indices and pH values which represent a detail overview on application of machine learning in agriculture base . Mostly above problem are solved using two advance classifier Xgboost and Logistical regression which also achieve better accuracy in these area. By applying machine learning in real time data which enabled program to present high testimonial and deep perceptivity for experts and farmers to make correct decision and take proper action