Dataset Information

Diversity and structure of prokaryotic communities within organic and conventional farming systems in central highlands of Kenya.

ABSTRACT: Management 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.

SUBMITTER: Karanja EN

PROVIDER: S-EPMC7425915 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Diversity and structure of prokaryotic communities within organic and conventional farming systems in central highlands of Kenya.

Karanja Edward Nderitu EN Fliessbach Andreas A Adamtey Noah N Kambura Anne Kelly AK Musyoka Martha M Fiaboe Komi K Mwirichia Romano R

PloS one 20200813 8

Management 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 Keny ...[more]

PMID: 32790770

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Project description:One of the most important challenges in agriculture is to determine the effectiveness and environmental impact of certain farming practices. The aim of present study was to determine and compare the taxonomic composition of the microbiomes established in soil following long-term exposure (14 years) to a conventional and organic farming systems (CFS and OFS accordingly). Soil from unclared forest next to the fields was used as a control. The analysis was based on RT-PCR and pyrosequencing of 16S rRNA genes of bacteria and archaea. The number of bacteria was significantly lower in CFS than in OFS and woodland. The highest amount of archaea was detected in woodland, whereas the amounts in CFS and OFS were lower and similar. The most common phyla in the soil microbial communities analyzed were Proteobacteria (57.9%), Acidobacteria (16.1%), Actinobacteria (7.9%), Verrucomicrobia (2.0%), Bacteroidetes (2.7%) and Firmicutes (4.8%). Woodland soil differed from croplands in the taxonomic composition of microbial phyla. Croplands were enriched with Proteobacteria (mainly the genus Pseudomonas), while Acidobacteria were detected almost exclusively in woodland soil. The most pronounced differences between the CFS and OFS microbiomes were found within the genus Pseudomonas, which significantly (p<0,05) increased its number in CFS soil compared to OFS. Other differences in microbiomes of cropping systems concerned minor taxa. A higher relative abundance of bacteria belonging to the families Oxalobacteriaceae, Koribacteriaceae, Nakamurellaceae and genera Ralstonia, Paenibacillus and Pedobacter was found in CFS as compared with OFS. On the other hand, microbiomes of OFS were enriched with proteobacteria of the family Comamonadaceae (genera Hylemonella) and Hyphomicrobiaceae, actinobacteria from the family Micrococcaceae, and bacteria of the genera Geobacter, Methylotenera, Rhizobium (mainly Rhizobium leguminosarum) and Clostridium. Thus, the fields under OFS and CFS did not differ greatly for the composition of the microbiome. These results, which were also confirmed by cluster analysis, indicated that microbial communities in the field soil do not necessarily differ largely between conventional and organic farming systems.

Project description:BackgroundFollowing global pledges to reduce greenhouse gas (GHG) emissions by 30% by 2030 compared to the baseline level of 2020, improved quantification of GHG emissions from developing countries has become crucial. However, national GHG inventories in most Sub-Saharan African countries use default (Tier I) emission factors (EFS) generated by the Intergovernmental Panel on Climate Change (IPCC) to estimate enteric CH4 emissions from animal agriculture. The present study provides an improved enteric CH4 emission estimate (Tier II) based on animal energy requirements derived from animal characteristics and performance data collected from about 2500 cattle in 480 households from three smallholder farming systems to represent the common dairy farming in the central highlands of Ethiopia. Using average seasonal feed digestibility data, we estimated daily methane production by class of animal and farming system and subsequently generated improved EF.ResultsOur findings revealed that the estimated average EF and emission intensities (EI) vary significantly across farming systems. The estimated value of EF for adult dairy cows was 73, 69, and 34 kg CH4/cow/year for urban, peri-urban, and rural farming systems, respectively. Rural dairy farming had significantly higher emission intensity (EI) estimated at 1.78 CO2-eq per kg of fat protein-corrected milk (FPCM) than peri-urban and urban 0.71 and 0.64 CO2-eq kg-1 FPCM dairy farming systems, respectively. The EF estimates in this study are lower than the IPCC's (2019) default value for both stall-fed high-productive and dual-purpose low-productive cows.ConclusionsThe current findings can be used as a baseline for the national emission inventory, which can be used to quantify the effects of future interventions, potentially improving the country's commitment to reducing GHG emissions. Similarly, this study suggests that increased animal productivity through improved feed has a considerable mitigation potential for reducing enteric methane emissions in smallholder dairy farming systems in the study area.

Dataset Information

Diversity and structure of prokaryotic communities within organic and conventional farming systems in central highlands of Kenya.

Publications

Diversity and structure of prokaryotic communities within organic and conventional farming systems in central highlands of Kenya.

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