Fungal diversity within organic and conventional farming systems in Central Highlands of Kenya

Open Access Article; Published online: 30 June 2020Fungal diversity in agro-ecosystems is influenced by various factors related to soil and crop management practices. However, due to the complexity in fungal cultivation, only a limited number has been extensively studied. In this study, amplicon sequencing of the Internal Transcribed Spacer (ITS) region was used to explore their diversity and composition within long-term farming system comparison trials at Chuka and Thika in Kenya. Sequences were grouped into operational taxonomic units (OTUs) at 97% similarity and taxonomy assigned via BLASTn against UNITE ITS database and a curated database derived from GreenGenes, RDPII and NCBI. Statistical analyses were done using Vegan package in R. A total of 1,002,188 high quality sequences were obtained and assigned to 1,128 OTUs; they were further classified into eight phyla including Ascomycota, Basidiomycota, Chytridiomycota, Glomeromycota, Calcarisporiellomycota, Kickxellomycota, Mortierellomycota and unassigned fungal phyla. Ascomycota was abundant in conventional systems at Chuka site while Basidiomycota and Chytridiomycota were dominant in conventional systems in both sites. Kickxellomycota and Calcarisporiellomycota phyla were present in all organic systems in both sites. Conventional farming systems showed a higher species abundance and diversity compared to organic farming systems due to integration of organic and inorganic inputs

Biogeographical survey of soil microbiomes across sub-Saharan Africa:structure, drivers, and predicted climate-driven changes

BACKGROUND: Top-soil microbiomes make a vital contribution to the Earth’s ecology and harbor an extraordinarily high biodiversity. They are also key players in many ecosystem services, particularly in arid regions of the globe such as the African continent. While several recent studies have documented patterns in global soil microbial ecology, these are largely biased towards widely studied regions and rely on models to interpolate the microbial diversity of other regions where there is low data coverage. This is the case for sub-Saharan Africa, where the number of regional microbial studies is very low in comparison to other continents. RESULTS: The aim of this study was to conduct an extensive biogeographical survey of sub-Saharan Africa’s top-soil microbiomes, with a specific focus on investigating the environmental drivers of microbial ecology across the region. In this study, we sampled 810 sample sites across 9 sub-Saharan African countries and used taxonomic barcoding to profile the microbial ecology of these regions. Our results showed that the sub-Saharan nations included in the study harbor qualitatively distinguishable soil microbiomes. In addition, using soil chemistry and climatic data extracted from the same sites, we demonstrated that the top-soil microbiome is shaped by a broad range of environmental factors, most notably pH, precipitation, and temperature. Through the use of structural equation modeling, we also developed a model to predict how soil microbial biodiversity in sub-Saharan Africa might be affected by future climate change scenarios. This model predicted that the soil microbial biodiversity of countries such as Kenya will be negatively affected by increased temperatures and decreased precipitation, while the fungal biodiversity of Benin will benefit from the increase in annual precipitation. CONCLUSION: This study represents the most extensive biogeographical survey of sub-Saharan top-soil microbiomes to date. Importantly, this study has allowed us to identify countries in sub-Saharan Africa that might be particularly vulnerable to losses in soil microbial ecology and productivity due to climate change. Considering the reliance of many economies in the region on rain-fed agriculture, this study provides crucial information to support conservation efforts in the countries that will be most heavily impacted by climate change. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-022-01297-w

Special Issue: Selected Papers from the '10(th) Applications of Advanced Technologies in Transportation' Conference

Open Access Journal; Published online: 13 Aug 2020Management practices such as tillage, crop rotation, irrigation, organic and inorganic inputs application are known to influence diversity and function of soil microbial populations. In this study, we investigated the effect of conventional versus organic farming systems at low and high input levels on structure and diversity of prokaryotic microbial communities. Soil samples were collected from the ongoing long-term farming system comparison trials established in 2007 at Chuka and Thika in Kenya. Physicochemical parameters for each sample were analyzed. Total DNA and RNA amplicons of variable region (V4—V7) of the 16S rRNA gene were generated on an Illumina platform using the manufacturer’s instructions. Diversity indices and statistical analysis were done using QIIME2 and R packages, respectively. A total of 29,778,886 high quality reads were obtained and assigned to 16,176 OTUs at 97% genetic distance across both 16S rDNA and 16S rRNA cDNA datasets. The results pointed out a histrionic difference in OTUs based on 16S rDNA and 16S rRNA cDNA. Precisely, while 16S rDNA clustered by site, 16S rRNA cDNA clustered by farming systems. In both sites and systems, dominant phylotypes were affiliated to phylum Actinobacteria, Proteobacteria and Acidobacteria. Conventional farming systems showed a higher species richness and diversity compared to organic farming systems, whilst 16S rRNA cDNA datasets were similar. Physiochemical factors were associated differently depending on rRNA and rDNA. Soil pH, electrical conductivity, organic carbon, nitrogen, potassium, aluminium, zinc, iron, boron and micro-aggregates showed a significant influence on the observed microbial diversity. The observed higher species diversity in the conventional farming systems can be attributed to the integration of synthetic and organic agricultural inputs. These results show that the type of inputs used in a farming system not only affect the soil chemistry but also the microbial population dynamics and eventually the functional roles of these microbes

Additional file 8 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 8. Figure S8-A. Predicted prokaryotic Shannon biodiversity index values (expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries used in this study, for 2040-2060 and 2080-2100 under two distinct GH emission scenarios (SSP126 and SSP585), and comparison with current predicted Shannon biodiversity as estimated by SEM. Pairwise significance values of differences in biodiversity means between the different years and scenarios are represented by the brackets with the following nomenclature: * - p-value \u3c 0.05; ** - p-value \u3c 0.01; *** - p-value \u3c 0.001

Additional file 18 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 18. Table S7. Number of samples allocated for each country, and number of samples collected

Additional file 1 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 1. Figure S1. Distribution of samples across the 9 African countries according to their land cover (LC) classification. Land cover codes used were the following: LC_1 - Rainfed croplands; LC_2 - Mosaic Cropland (50-70%) / Vegetation (grassland, shrubland, forest) (20-50%); LC_3 - Mosaic Vegetation (grassland, shrubland, forest) (50-70%) / Cropland (20-50%); LC_4 - Closed to open (\u3e15%) broadleaved evergreen and/or semi-deciduous forest (\u3e5m); LC_5 - Closed (\u3e40%) broadleaved deciduous forest (\u3e5m); LC_6 - Open (15-40%) broadleaved deciduous forest (\u3e5m); LC_10 - Mosaic Forest/Shrubland (50-70%) / Grassland (20-50%); LC_11 - Mosaic Grassland (50-70%) / Forest/Shrubland (20-50%); LC_12 - Closed to open (\u3e15%) shrubland (\u3c5m); LC_13 - Closed to open (\u3e15%) grassland; LC_14 - Sparse (\u3e15%) vegetation (woody vegetation, shrubs, grassland); LC_17 - Closed to open (\u3e15%) vegetation (grassland, shrubland, woody vegetation) on regularly flooded or waterlogged soil; LC_18 - Artificial surfaces and associated areas (urban areas \u3e50%); LC_19 - Bare areas

Additional file 10 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 10. Figure S8-C. Predicted fungal Shannon biodiversity values (expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries used in this study, for 2040-2060 and 2080-2100 under two distinct GH emission scenarios (SSP126 and SSP585), and comparison with current predicted Shannon biodiversity as estimated by SEM. Pairwise significance values of differences in biodiversity means between the different years and scenarios are represented by the brackets with the following nomenclature: * - p-value \u3c 0.05; ** - p-value \u3c 0.01; *** - p-value \u3c 0.001

Additional file 7 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 7. Figure S7. MIROC6 model predictions for mean annual temperature (oC) (A) and mean annual precipitation (mm) (B) under too different GH emission scenarios (SSP126 and SSP585), predicted for 2040-2060 and 2080-2100 temporal windows. The predicted datasets are grouped according to country, as indicated by the vertical dashed lines

Additional file 14 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 14. Table S3. Metadata of the dominant phylotypes, including taxonomy, functional predictions (based on FAPROTAX and manual curation), and ecological groups based on the main environmental predictor

Additional file 11 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 11. Figure S8-D. Predicted abundance values of PGPF (expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries used in this study, for 2040-2060 and 2080-2100 under two distinct GH emission scenarios (SSP126 and SSP585), and comparison with current predicted Shannon biodiversity as estimated by SEM. Pairwise significance values of differences in biodiversity means between the different years and scenarios are represented by the brackets with the following nomenclature: * - p-value \u3c 0.05; ** - p-value \u3c 0.01; *** - p-value \u3c 0.001