DOI: 10.5061/dryad.m37pvmdf3
title: Cover crop mixtures enhance multiple ecosystem functions: A global meta-analysis

description: Cover crop mixtures have been increasingly adopted across agroecosystems
 worldwide thanks to their potential to improve soil health and ecosystem
 functioning. However, it is not clear whether and how cover crop
 mixtures vs. single cover crops might influence different
 ecosystem functions. Here, we selected 1,895 paired data from 61 studies
 worldwide to evaluate the role of cover crop mixtures in affecting biomass
 production, soil properties, and subsequent main crop yields in comparison
 with single cover crops. We also assessed whether the number of plant
 species in cover crop mixtures, crop field types, and cover crop
 combinations could affect ecosystem functioning. Multi-species mixtures
 increased cover crop aboveground biomass by 21.7%, carbon and nutrient
 accumulation (C: +27.7%, N: +27.7%, P: +22.8%, K: +24.1%), and subsequent
 crop yields by 4.9% compared to single cover crops. These benefits were
 most pronounced in paddy fields (e.g., a 35.3% increase in biomass), as
 well as in specific species combinations such as milk vetch-rapeseed
 (32.8%) and milk vetch-ryegrass (36.0%). Also, multi-species cover crop
 mixtures increased subsequent soil moisture content (+1.5%), total soil
 nitrogen (+5.8%), soil microbial carbon (+37.8%), soil microbial nitrogen
 (+37.6%), Chao1 index of fungi (+12.6%), and decreased soil bulk density
 (-7.0%), soil electrical conductivity (-9.5%), and polyphenol oxidase
 activity (-7.0%). However, effects on soil pH, organic matter, and most
 nutrients were neutral unless specific combinations. Synthesis and
 applications. Multi-species cover crop mixtures significantly
 enhance agroecosystem productivity and soil health, particularly in paddy
 systems and when combining functionally complementary species (e.g.,
 legumes with Cruciferae, legumes with Gramineae). Farmers and policymakers
 should prioritize tailored mixtures over monocultures to optimize biomass
 production, nutrient cycling, and yield resilience. For maximum impact,
 mixtures should align with local conditions—such as using milk
 vetch-rapeseed or milk vetch-ryegrass in humid regions—to advance
 sustainable agriculture and climate adaptation goals globally.
description: # Cover crop mixtures enhance multiple ecosystem functions: A global
 meta-analysis Dataset DOI:
 [10.5061/dryad.m37pvmdf3](10.5061/dryad.m37pvmdf3) ## Description of the
 data and file structure This global meta-analysis dataset comprises 1,895
 paired observations extracted from 61 peer-reviewed articles across 55
 experimental sites worldwide. Data were systematically organized into
 specialized CSV files corresponding to specific ecosystem response
 variables (e.g., microbial biomass, nutrient cycling, soil physical
 properties). For each pairwise comparison between cover crop mixtures and
 monocultures, we extracted: Mean values (Xmixtures, Xmonocultures)
 Standard deviations (SDmixtures, SDmonocultures) Replicate counts
 (Nmixtures, Nmonocultures) Data classification was implemented through
 four key dimensions: Cover crop complexity: Binary classification by
 species count (mix2 = 2-species mixtures, mix3 = 3+-species mixtures) Crop
 field types: Agricultural systems categorized as paddy fields, wheat
 fields, tobacco fields, corn fields, orchards, or other systems Botanical
 taxonomy: Cover crop family combinations (e.g., Fabaceae-Poaceae pairings)
 Species-specific mixtures: Identified combinations used in pairwise
 analyses (e.g., Vicia faba + Secale cereale) Methodologically, numerical
 data were extracted from figures using GetData Graph Digitizer 2.26, with
 soil organic carbon values converted to organic matter via the van
 Bemmelen factor (1.724). To support advanced statistical modeling, we
 implemented a hierarchical identification system where: Study = Unique
 publication identifier Experiment = Site-year combination code (e.g.,
 Brazil_2019) These identifiers enable proper accounting of random effects
 in meta-analytic models, ensuring robust quantification of cover crop
 mixture impacts on ecosystem functions. The file-per-variable structure
 optimizes reusability while maintaining cross-file consistency in core
 metadata columns. ### Files and variables #### File list: *
 Microbial_biomass_carbon.csv：Measurements of soil microbial biomass carbon
 * Total_phosphorus.csv：Total phosphorus content in soil samples *
 Microbial_biomass_nitrogen.csv：Measurements of soil microbial biomass
 nitrogen * Total_potassium.csv ：Total potassium content in soil samples *
 Phosphatase_activity.csv ：Soil phosphatase enzyme activity levels *
 Fungal_Chao1.csv ：Fungal community richness (Chao1 index) *
 Urease_activity.csv： Soil urease enzyme activity levels *
 Phosphorus_accumulation.csv： Phosphorus accumulation in plant tissues *
 Potassium_accumulation.csv： Potassium accumulation in plant tissues *
 Soil_moisture_content.csv： Volumetric soil water content measurements *
 Sucrase_activity.csv： Soil sucrase enzyme activity levels *
 Polyphenol_Oxidase_activity.csv： Soil polyphenol oxidase enzyme activity
 levels * Total_nitrogen.csv： Total nitrogen content in soil samples *
 Fungal_abundance.csv： Absolute abundance of soil fungal communities *
 Total_porosity.csv： Total pore space measurements in soil *
 Nitrogen_accumulation.csv： Nitrogen accumulation in plant tissues *
 Organic_matter.csv： Soil organic matter content measurements *
 Fungal_Shannon.csv： Fungal community diversity (Shannon index) *
 Electrical_conductivity.csv： Soil electrical conductivity measurements *
 Crop_yield.csv ：Crop yield measurements under different cover crop
 treatments * Bulk_density.csv ：Soil bulk density measurements *
 Bacteria_Shannon.csv ：Bacterial community diversity (Shannon index) *
 Catalase_activity.csv： Soil catalase enzyme activity levels *
 Carbon_accumulation.csv： Carbon accumulation in plant tissues *
 Bacteria_Chao1.csv ：Bacterial community richness (Chao1 index) *
 Bacteria_abundance.csv ：Absolute abundance of soil bacterial communities *
 Available_nitrogen.csv： Plant-available nitrogen content in soil *
 Available_potassium.csv ：Plant-available potassium content in soil *
 Available_phosphorus.csv ：Plant-available phosphorus content in soil *
 Alkaline_PH.csv： Soil pH measurements in alkaline conditions *
 Actinomycetes_abundance.csv： Absolute abundance of soil actinomycetes
 communities * Aboveground_biomass.csv： Aboveground plant biomass
 measurements * Acidic_PH.csv： Soil pH measurements in acidic conditions
 ##### Variables * Study：Publication identity * Experiment：A unique
 combination of site and year * Mixtures：Number of cover crop species (mix2
 = 2 species, mix3 = 3 species) * Crop Field Types：the type that crops
 field classified by their species (paddy fields, wheat fields, tobacco
 fields, corn fields, orchards, and others) * Cover Crop Families：the
 family which plant belongs to * Cover Crop Species：specific cover crop
 species mixtures in the pairwise analysis * Xmixtures：the mean value of
 the response variable under multi-species cover crop mixtures *
 SDmixtures：refer to the standard deviation for the mixtures groups *
 Nmixtures：the number of replicates with multi-species cover crop mixtures
 * Xmonocultures：the mean value under the corresponding monocultures of
 each species included in the mixture * SDmonocultures：refer to the
 standard deviation for the monocultures groups * Nmonocultures：the number
 of replicates with mono-species cover crops * `\`：The symbol `\` indicates
 missing standard deviation (SD) values where: \ in SDmixtures columns =
 Original publications did not report SD for cover crop mixture treatments
 \ in SDmonocultures columns = Original publications did not report SD for
 monoculture control treatments This notation applies uniformly across all
 data files.