Soil microbial communities in cider apple tree orchards in Asturias, Spain
Description
This dataset provides a comprehensive characterization of the soil microbial communities (prokaryotes and fungi) in apple orchards across Asturias, Spain. The data includes high-throughput sequencing results of the 16S rRNA gene (V3-V4 region) and the Internal Transcribed Spacer 2 (ITS2) region from 12 soil samples, representing six commercial orchards under different agronomic management systems (organic and integrated), plantation ages, and locations. These data are intended to serve as a robust baseline for studying the ecological drivers of the apple tree rhizosphere microbiome and the impact of agricultural practices on soil health in temperate fruit crops.
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Steps to reproduce
1. Field Sampling and Experimental Design Soil samples were collected from the rhizosphere of apple trees at a depth of 30 cm in six commercial orchards in Asturias, Spain. A composite sampling strategy was used: five random sub-samples (one per tree) were pooled into a single representative sample per orchard. Each orchard was sampled twice to provide biological replicates. Sampling was conducted during the flowering stage under dry weather conditions. 2. Soil Physicochemical Characterization Soil moisture was determined gravimetrically using an automated moisture analyzer (HB43, Mettler-Toledo, LLC). Additional physicochemical parameters, including pH, organic matter, and nutrient content (N, P, K), were analyzed by KUDAM (Alicante, Spain) following standard soil science protocols. 3. DNA Extraction and Library Preparation Total DNA was extracted from 1 g of soil using the NucleoSpin® Soil DNA Extraction Kit (Macherey-Nagel, Germany). Microbial communities were characterized by amplifying the V3-V4 region of the 16S rRNA gene (primers 341F/785R) and the ITS2 region for fungi (primers ITS3/ITS4). PCR products were sequenced on an Illumina MiSeq platform (2x300 bp) by Microomics Systems S.L.. Negative controls and a Mock Community (Zymo Research, USA) were included to ensure quality. 4. Bioinformatic Workflow Raw demultiplexed reads were processed using QIIME2. The DADA2 pipeline was used for quality filtering (phred score > 20), denoising, and chimera removal to generate Amplicon Sequence Variants (ASVs). Taxonomic assignment was performed using a Bayesian Classifier against the SILVA database (v138) for prokaryotes and the UNITE database (v8) for fungi. 5. Statistical Analysis and Data Normalization ASV tables were normalized using the rarefaction method (vegan package in R). Alpha diversity (Richness, Shannon, Pielou’s evenness) and Beta diversity (Jaccard distance and PCoA) were calculated using the R software v. 4.5.0.
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Funders
- Agencia de Ciencia, Competitividad Empresarial e Innovación Asturiana (SEKUENS) and FEDER fundsGrant ID: IDI/2024/000777 (SUSTCROP-II)