Soil health and microbial drivers in Mediterranean fruit orchards: A comprehensive dataset on cover crop management (CLIMCOVER Project)

This dataset presents the comprehensive results of a two-year research initiative conducted within the framework of the CLIMCOVER project. The study focuses on sustainable intensification in modern fruit tree cultivation, specifically evaluating the transition from traditional bare soil management to the use of cover crops. The primary objective is to quantify the benefits of this agroecological practice regarding soil health, climate change mitigation, and ecosystem multifunctionality. The data facilitates a comparative evaluation of cultivation systems by integrating agronomic, physicochemical, and high-throughput molecular indicators. The dataset includes detailed Site Metadata and Environmental Characterization, providing geographical coordinates (latitude and longitude), climatic variables such as mean annual temperature, precipitation, and potential evapotranspiration, as well as topographical features including soil type, elevation, and slope. It further documents Agricultural Management Practices in depth, covering tillage frequency and depth, pruning residue management, manure and fertilizer applications (type and quantity), herbicide and pesticide usage, and irrigation management. These factors are contextualized with specific Case Study Codification, linking each sample to its treatment (cover crop vs. bare soil), sampling field area, and precise location within the orchard (alleyway vs. tree row). Regarding Soil Physical and Chemical Properties, the dataset provides a high-resolution profile including texture classification (sand, silt, and clay content), bulk density, and hydraulic conductivity. Chemical analysis encompasses pH, electrical conductivity, and a wide array of nutrient fractions: total, organic, and inorganic carbon; nitrogen content (NO3-, NH4+); water-soluble organic carbon and nitrogen; and exchangeable cations (Na, K, Ca, Mg) alongside cation exchange capacity. Additionally, it includes Soil Structural Dynamics through aggregate size distribution, mean weight diameter, and repellency indices. The Biological and Functional Component is a core strength of this dataset. It features extracellular enzyme activities (Urease, Cellulase, beta-glucosidase, Arylsulfatase, and Alkaline phosphatase) and the quantification of greenhouse gas potential (CO2, N2O, and CH4). These are complemented by Visual Soil Assessment (VSA) scores, which provide semi-quantitative data on soil structure, porosity, compaction, and earthworm counts. The Microbial Ecology section contains bioinformatics data derived from Illumina NovaSeq sequencing of the 16S and ITS regions. This includes DNA yields, bacterial and fungal abundance (qPCR), and a comprehensive suite of alpha diversity metrics such as Observed ASVs, Chao1, Shannon, Simpson, Pielou’s evenness, and Faith’s Phylogenetic Diversity. Finally, the dataset records Plant and Productivity Metrics, including crop yield, cover crop biomass (shoot and root), root-shoot ratios, and species composition. All these parameters are synthesized into a Multifunctionality Index, provided in multiple formats (Z-scores, thresholds, and cluster analysis with logarithmic transformations) to allow for statistical modeling of ecosystem services.