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|Title:||Functional diversity of microbial communities in two contrasting maize rhizosphere soils.||Authors:||Kutu, Funso R.
School of Agricultural Sciences
|Keywords:||Zea mays.;Metabolic potential.;Functional diversity.;Microbial diversity.;Shotgun metagenomics.||Issue Date:||2020||Publisher:||Elsevier||Abstract:||The characterization of microbial functional diversity proffers the opportunity to understand and manipulate them in various agricultural and biotechnological processes. This study analyzed the functional diversity and metabolic potentials of microbial communities in the rhizosphere of a former grassland and an intensively cultivated land using a shotgun metagenomic approach. We assumed that functional diversity will vary between the two maize fields and that each soil has an active metabolic profile that differentiates it from the other. Metagenomic DNA was extracted from the rhizosphere, and bulk soil samples from maize fields and sequencing was performed using the shotgun method. The results showed that 14 functional categories were dominant in the former grassland rhizosphere, while 12 functions predominated the intensively cultivated land. In the subsystems at level 2, potassium metabolism was the most abundant functional category with the highest relative abundance of 21.32% observed in GZ3. No significant difference was observed in the alpha diversity studies between the two fields, the Kruskal-Wallis test also revealed an insignificant difference (p = 0.99) in the diversity levels of samples, while 0.51 and 0.01 were obtained as the R- and p-values, respectively from the analysis of similarity (ANOSIM) performed. Nitrate nitrogen (N-NO3) was the most influential physicochemical parameter, with a p value of 0.01 and a contribution % of 81.7%. Furthermore, the high abundance of unknown functions explains that the maize microbiome is still underexplored. Identifying the microbial communities with these functions will help in modeling them to harvest their unique functional benefits for agricultural productivity enhancement.||Description:||Please note that only UMP researchers are shown in the metadata. To access the co-authors, please view the full text.||URI:||https://openscholar.ump.ac.za/handle/20.500.12714/331||DOI:||10.1016/j.rhisph.2020.100282|
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