A comprehensive dataset gathering 37 cover crop field experiments across France (2004–2022): plant and soil-related agronomic variables on 33 crop species from five botanical families

TL;DR

This paper presents a dataset of 37 cover crop experiments in France, tracking plant and soil variables for 33 species over 19 years.

Contribution

The paper introduces a comprehensive, long-term dataset on cover crops in France, including diverse species and environmental conditions.

Findings

The dataset includes 33 cover crop species from five botanical families, measured over 19 years.

It provides agronomic data on plant and soil variables, along with climate and management details.

The resource supports evaluating cover crop performance and sustainability.

Abstract

Cover crops, sown between cash crops to provide ecosystem services, contribute to sustainability but present challenges related to the use of environmental resources out of the main cropping season. This dataset gathers the results of 37 field experiments related to cover crops, whether grown in sole crops or in mixtures, conducted in France over 19 years. It compiles quantitative data from field measurements and laboratory analyses on 33 species among five botanical families, along with information on technical management and climate conditions, providing a valuable resource for assessing cover crops performance.

Figures

Number of experiments per year in the dataset (ranging from 1 to 4 per year).Fig 1

Average duration of cover crops by species and sowing month. Species are grouped by botanical family: Brassicaceae, Fabaceae, Poaceae, Hydrophyllaceae and Polygonaceae.Fig 2

Biomass, nitrogen and carbon uptake by shoots and roots of black oat and purple vetch grown as sole crop or mixture. SC = measurements for black oat and purple vetch in sole crops; IC = separate measurements for black oat and purple vetch in intercrop; Mix (oat + vetch) = sum of both species in the mixture.Fig 3

Full text

Specifications tableSubjectAgronomy and Crop Science, Soil ScienceSpecific subject areaExperimental dataset of cover crop experiments: management, cover crop emergence, biomass production, plant and soil nutrient contentType of dataRawFilteredTableData collectionData were collected from experimental fields, mainly in southwest France, and include cover crop management details, field and lab measurements, and pedo-climatic data. Measurements were performed using standard agronomic and laboratory protocols. Experimental designs, materials, methods and data collection are detailed in the Experimental design, Materials, and Methods section.Data source location

• •Institution: INRAE
• •Location: Auzeville-Tolosane and Seysses (Haute-Garonne), Orléans (Loiret)
• •Country: France
• •Coordinates: 43.529326, 1.501011 (Auzeville-Tolosane), 43.50946, 1.251009 (Seysses), 47.776, 2.098 (Orléans) Data accessibilityRepository name: Entrepôt Recherche Data GouvData identification number: doi/10.57745/3PRE07Direct URL to data: https://doi.org/10.57745/3PRE07Related research articleA. Couëdel, L. Alletto, H. Tribouillois, É. Justes, Cover crop crucifer-legume mixtures provide effective nitrate catch crop and nitrogen green manure ecosystem services, Agriculture, Ecosystems & Environment 254 (2018) 50–59. https://doi.org/10.1016/j.agee.2017.11.017

Value of the data

1

• •The dataset provides a unique and extensive range of cover crop traits collected over 19 years from field experiments at research stations.
• •The traits measured are categorized into soil nutrients, causes of non-emergence and post-emergence damages, plant size, development, biomass, plant nutrient and glucosinolate content.
• •The cover crops, tested as sole crops or in mixtures, belong to five botanical families (Fabaceae, Brassicaceae, Poaceae, Hydrophyllaceae, Polygonaceae), with 33 species and over 128 cultivars.
• •Data for each variable and modality are provided by block, depending on the experimental design. Detailed descriptions of each trial, including management practices and climate data, are supplied.
• •This dataset can be used to quantify and compare cover crop traits across species and cultivars, assess cover crop performance, and to calibrate crop simulation models.

Background

2

Cover crops play an important role in the agroecological transition, especially in the context of climate change. In this study, we have compiled a unique dataset gathering the results of 37 field experiments in order to study the performance of several species of cover crops across differents environments {3 sites in France: Auzeville-Tolosane, Seysses and Orléans}. This dataset gathers both qualitative and quantitative data, enabling a deeper understanding of the behaviour and contributions of cover crops to sustainable practices. These data can inform methodological approaches to evaluate public policies, and support modelling and analysis of multi-performance outcomes.

Data description

3

This dataset gathers the results of 37 field experiments with cover crops. Cover crops are non-commercial, non-harvested plant species grown alone or in mixtures during the fallow period between successive main crops. Their primary function is to deliver multiple ecosystem services, including soil improvement, erosion control, nutrient cycling, and the management of pests and weeds.

The field experiments were carried in France, in Haute-Garonne (Auzeville-Tolosane, Seysses; n = 35) and in Loiret (Orléans; n = 2), from 2004 to 2022 (Fig. 1).Fig. 1. Number of experiments per year in the dataset (ranging from 1 to 4 per year).Fig 1

The dataset is organized as six tabular data files: three in .csv format—data_trials, data_management, and data_traits— with one observation per row and values within each row separated by a delimiter, and three in .xlsx format—data_climate, metadata, and references. The content of these files is described in Table 1, following the good practices for data tidying [1]. Files, except metadata and data_climate share a common identifier (“experiment_id”) which corresponds to the concatenation of “site name-project name-year of experiment”. Additional information on the data curation and formatting process from a raw to a tidy dataset is presented in Mahmoud et al. [2].Table 1. Name and content of the six files composing the dataset.Table 1. NameContentmetadataDescribes the table header variables (sheet dataset) and experimental measured variables with the unit of measurements (sheet variables).data_trialsIncludes the site of experiment, the project name, the year of experiment, the geographical coordinates of the fields.data_managementThis section provides information on crop management. Each entry represents a single crop cultivar, whether grown as a sole crop or within a mixture, managed in a specific experiment and year.Each cropping modality (sole crop or mixture) is associated with a specific management practice within the same experiment, labeled from M1 to Mn. Management is not necessarily continuous, as some modalities were discontinued in certain experiments (e.g., M3 in auzeville_CIMSON_2021 and auzeville_CIMSON_2022; M29 in *seysses_CRUCIAL_2014).*The table includes, when available:• The genus, cultivar(s), genus mixtures, and cultivar mixtures (if applicable), along with their Latin name.• Sowing density, sowing date and termination date• Relative density (1 = full density, 0.5 = half density, etc.)• Amount of irrigation applied (if applicable)• Use of pesticides on the plot (yes/no)• Previous crop• Subsequent cropdata_climateThis dataset contains daily records of rainfall, mean/minimal/maximal temperature, and Penman-Monteith potential evapotranspiration for Auzeville-Tolosane (2004–2023) and Orléans (2014–2016), all registered by INRAE weather stations.Missing data for Auzeville-Tolosane on the following dates: 2018–04–26; 2020–06–20; 2020–12–19 to 2020–12–29; 2021–01–01; 2021–01–19.data_traitsThis dataset provides measurements conducted in each experiment at the plant, crop, or mixture level, including block-level data when applicable. The file integrates key information from the data_management with the addition of:• Measurement date• Species and species mixtures• Variable name• Block (if applicable)• Measured valuereferencesThis section provides references to publications related to the experiments, linked to the corresponding experiment_id in the dataset.

The dataset includes 33 crop species (Table 2), among five botanical families.Table 2. Summary of crop species included in the dataset.Table 2. Plant familySpeciesLatin nameNo. CultivarsNo. ExperimentsBrassicaceae (10)brown mustardBrassica juncea (L.) Czern716chinese radishRaphanus sativus var. longipinnatus L.H.Bailey23ethiopian mustardBrassica carinata A.Braun78fodder radishRaphanus raphanistrum subsp. sativus (L.) Domin715fodder rapeBrassica napus L.34fodder turnipBrassica rapa var. rapa L.12oilseed radishRaphanus raphanistrum subsp. sativus (L.) Domin11rocketEruca vesicaria var. sativa (Mill.) Thell.22turnip rapeBrassica rapa subsp. oleifera (DC.) Metzg.516white mustardSinapis alba L.1729Fabaceae (14)bitter vetchVicia ervilia (L.) Willd.11black medickMedicago lupulina L.11blue lupinLupinus angustifolius L.12common vetchVicia sativa L.1215crimson cloverTrifolium incarnatum L.311egyptian cloverTrifolium alexandrinum L.39fodder lentilLens nigricans (M.Bieb.) Webb & Berthel.12fodder peaPisum sativum L.36hairy vetchVicia villosa Brot.37narbonne vetchVicia narbonensis L.22purple vetchVicia benghalensis L.626soybeanGlycine max (L.) Merr.14spring fababeanVicia faba L.36winter fababeanVicia faba L.713Hydrophyllaceae (1)phaceliaPhacelia tanacetifolia Benth.310Poaceae (7)black oatAvena strigosa Schreb.911fodder sorghumSorghum bicolor (L.) Moench13Sorghum bicolor (L.) Moench x Sorghum bicolor nothosubsp. drummondii (Nees ex Steud.) de Wet ex Davidse11forest ryeSecale cereale L.35italian ryegrassLolium multiflorum Lam.12mohaSetaria italica subsp. moharia (Alef.) H.Scholz28sorghum sudangrassSorghum bicolor nothosubsp. drummondii (Nees ex Steud) de Wet ex Davidse15spring oatAvena sativa L.37Polygonaceae (1)buckwheatFagopyrum esculentum Moench22

Each species can be present in one or more experiments. Each experiment corresponds to a unique {site * year} combination and includes one or more cultivars.

Some experiments include species mixtures, with 2 to 4 species grown simultaneously. This results in 10 types of intercropping based on botanical family mixtures (Table 3).Table 3. Summary of cover crops mixtures included in the dataset.Table 3. Plant family mixturesSpecies mixturesNo. SpeciesNo. ExperimentsBrassicaceae (1)turnip rape-white mustard21Brassicaceae-Fabaceae (52)brown mustard-common vetch24brown mustard-egyptian clover23brown mustard-fodder radish-purple vetch33brown mustard-purple vetch25brown mustard-spring fababean22chinese radish-spring fababean22ethiopian mustard-common vetch25ethiopian mustard-egyptian clover22ethiopian mustard-hairy vetch22ethiopian mustard-purple vetch23ethiopian mustard-soybean22ethiopian mustard-winter fababean21fodder radish-blue lupin21fodder radish-common vetch22fodder radish-egyptian clover25fodder radish-hairy vetch24fodder radish-purple vetch26fodder radish-turnip rape-purple vetch33fodder radish-white mustard-hairy vetch32fodder rape-blue lupin21fodder rape-common vetch24fodder rape-crimson clover22fodder rape-egyptian clover24fodder rape-hairy vetch22fodder rape-narbonne vetch21fodder rape-purple vetch24fodder rape-soybean22fodder rape-winter fababean22oilseed radish-egyptian clover21oilseed radish-hairy vetch21rocket-common vetch21rocket-crimson clover21rocket-egyptian clover21rocket-purple vetch21turnip rape-black medick21turnip rape-common vetch22turnip rape-crimson clover21turnip rape-egyptian clover23turnip rape-fodder lentil21turnip rape-fodder pea21turnip rape-purple vetch28turnip rape-spring fababean22turnip rape-winter fababean22white mustard-blue lupin21white mustard-common vetch24white mustard-egyptian clover23white mustard-fodder pea21white mustard-hairy vetch22white mustard-narbonne vetch21white mustard-purple vetch29white mustard-soybean22white mustard-winter fababean22Brassicaceae-Fabaceae-Hydrophyllaceae (3)brown mustard-winter fababean-phacelia32chinese radish-common purple vetch-spring fababean-phacelia41turnip rape-purple vetch-spring fababean-phacelia41Brassicaceae-Fabaceae-Poaceae (1)white mustard-crimson clover-winter fababean-moha41Brassicaceae-Poaceae (4)turnip rape-moha-sorghum sudangrass31white mustard-fodder sorghum22white mustard-moha23white mustard-sorghum sudangrass21Fabaceae (6)common vetch-fodder pea21crimson clover-purple vetch21fodder pea-purple vetch22fodder turnip-common vetch21fodder turnip-egyptian clover21fodder turnip-purple vetch21Fabaceae-Hydrophyllaceae (8)black medick-phacelia21common vetch-spring fababean-phacelia31crimson clover-phacelia22fodder lentil-phacelia21fodder pea-phacelia21purple vetch-phacelia24spring fababean-phacelia22winter fababean-phacelia21Fabaceae-Hydrophyllaceae-Poaceae (2)spring fababean-phacelia-forest rye31spring fababean-phacelia-forest rye-sorghum sudangrass41Fabaceae-Poaceae (28)black medick-black oat21black medick-italian ryegrass21black medick-moha21common vetch-moha21common vetch-sorghum sudangrass21common vetch-spring oat26crimson clover-black oat21crimson clover-fodder sorghum21crimson clover-italian ryegrass22crimson clover-moha22crimson clover-winter fababean-black oat33egyptian clover-fodder sorghum21egyptian clover-purple vetch-moha-sorghum sudangrass41egyptian clover-purple vetch-sorghum sudangrass31fodder lentil-black oat21fodder lentil-italian ryegrass21fodder lentil-moha21fodder pea-black oat22fodder pea-italian ryegrass22fodder pea-moha21fodder pea-purple vetch-forest rye32purple vetch-black oat210purple vetch-italian ryegrass21purple vetch-moha22spring fababean-black oat21spring fababean-fodder sorghum21spring fababean-italian ryegrass21spring fababean-moha22Hydrophyllaceae-Poaceae (1)phacelia-black oat23

The dataset includes data on cover crops sown at various times throughout the growing season and for different durations (Table 4). Within the same experiment, sowing and termination dates may vary. For further details, Fig. 2 presents the average duration of the cover crops per species according to the month of sowing. Only species present in at least two experiments are shown.Table 4. Overview of cover crops sowing period and average duration across experiments.Table 4. Sowing monthNo. ExperimentsAverage duration (months) of cover crops from sowing to termination ± sdJuly54.2 ± 2August213.1 ± 1.3September213.3 ± 1.7October73.9 ± 1.2November15.5Fig. 2Average duration of cover crops by species and sowing month. Species are grouped by botanical family: Brassicaceae, Fabaceae, Poaceae, Hydrophyllaceae and Polygonaceae.Fig 2

Pooling the 37 field experiments, a total of 141 variables were measured, though not systematically across all experiments. Table 5 provides an overview of these variables along with the number of experiments in which each was assessed.Table 5. Summary of measured variables categorized by soil nutrients, causes of non-emergence and post-emergence damages, plant size, development and content, biomass, plant nutrient content, and glucosinolate content. Each variable is presented with its unit and the number of experiments (No. Experiments) in which it was measured. Categories are organized chronologically according to the timing of the measurements, with variables listed alphabetically within each category.Table 5. CategoryVariableUnitNo. ExperimentsSoilsoil_nitrogen_0–30kg.ha^-1^35Soilsoil_nitrogen_30–60kg.ha^-1^35Soilsoil_nitrogen_60–90kg.ha^-1^34Soilsoil_nitrogen_90–120kg.ha^-1^25Soilsoil_sulphur_0–30kg.ha^-1^6Soilsoil_sulphur_30–60kg.ha^-1^6Soilsoil_sulphur_60–90kg.ha^-1^6Soilsoil_sulphur_90–120kg.ha^-1^1Soilsoil_swc_0–30mm22Soilsoil_swc_30–60mm22Soilsoil_swc_60–90mm22Soilsoil_swc_90–120mm15Causes of non-emergence and post-emergence damagesCNE_abiotic_stress–2Causes of non-emergence and post-emergence damagesCNE_clods–2Causes of non-emergence and post-emergence damagesCNE_crusting–1Causes of non-emergence and post-emergence damagesCNE_damping_off–1Causes of non-emergence and post-emergence damagesCNE_land_pests–1Causes of non-emergence and post-emergence damagesCNE_seedlessness_birds–2Causes of non-emergence and post-emergence damagesCNE_sowing_depth–2Causes of non-emergence and post-emergence damagesPED_animals–2Causes of non-emergence and post-emergence damagesPED_anoxia–1Causes of non-emergence and post-emergence damagesPED_avifauna–1Causes of non-emergence and post-emergence damagesPED_damping_off–1Causes of non-emergence and post-emergence damagesPED_flea_beetles–2Causes of non-emergence and post-emergence damagesPED_freeze–1Causes of non-emergence and post-emergence damagesPED_land_pests–1Causes of non-emergence and post-emergence damagesPED_slugs–2Causes of non-emergence and post-emergence damagesPED_thermal_stress–1Plant size, development and contentadf_shoot%4Plant size, development and contentadl_shoot%4Plant size, development and contentcellulose_shoot%4Plant size, development and contentcover%5Plant size, development and contentemergence_densityplant.m^-2^15Plant size, development and contentheightcm5Plant size, development and contenthemicellulose_shoot%4Plant size, development and contentleaf_areacm²2Plant size, development and contentleaf_number–1Plant size, development and contentlengthcm2Plant size, development and contentndf_shoot%4Plant size, development and contentstage–5Biomassbiomass_roott.ha^-1^ of dry matter26Biomassbiomass_root_regrowth_previous_cropt.ha^-1^ of dry matter1Biomassbiomass_shoott.ha^-1^ of dry matter36Biomassbiomass_shoot_regrowth_previous_cropt.ha^-1^ of dry matter3Biomassbiomass_shoot_weedt.ha^-1^ of dry matter18Biomasshumidity_root%2Biomasshumidity_shoot%9Biomasshumidity_shoot_weed%3Biomassroot_shoot_ratio–2Plant nutrient contentboron_rootmg.kg^-1^1Plant nutrient contentboron_shootmg.kg^-1^1Plant nutrient contentcalcium_root%1Plant nutrient contentcalcium_shoot%1Plant nutrient contentcarbon_acq_rootkg.ha^-1^16Plant nutrient contentcarbon_acq_root_regrowth_previous_cropkg.ha^-1^1Plant nutrient contentcarbon_acq_shootkg.ha^-1^27Plant nutrient contentcarbon_acq_shoot_regrowth_previous_cropkg.ha^-1^1Plant nutrient contentcarbon_acq_shoot_weedkg.ha^-1^9Plant nutrient contentcarbon_root%17Plant nutrient contentcarbon_root_regrowth_previous_crop%1Plant nutrient contentcarbon_shoot%28Plant nutrient contentcarbon_shoot_regrowth_previous_crop%1Plant nutrient contentcarbon_shoot_weed%10Plant nutrient contentcopper_rootmg.kg^-1^1Plant nutrient contentcopper_shootmg.kg^-1^1Plant nutrient contentdelta_15N_root–3Plant nutrient contentdelta_15N_shoot–18Plant nutrient contentdelta_15N_shoot_regrowth_previous_crop–3Plant nutrient contentdelta_15N_shoot_weed–6Plant nutrient contentiron_rootmg.kg^-1^1Plant nutrient contentiron_shootmg.kg^-1^1Plant nutrient contentmagnesium_root%1Plant nutrient contentmagnesium_shoot%1Plant nutrient contentmanganese_rootmg.kg^-1^1Plant nutrient contentmanganese_shootmg.kg^-1^1Plant nutrient contentnitrogen_acq_rootkg.ha^-1^25Plant nutrient contentnitrogen_acq_root_regrowth_previous_cropkg.ha^-1^1Plant nutrient contentnitrogen_acq_shootkg.ha^-1^36Plant nutrient contentnitrogen_acq_shoot_regrowth_previous_cropkg.ha^-1^3Plant nutrient contentnitrogen_acq_shoot_weedkg.ha^-1^13Plant nutrient contentnitrogen_root%28Plant nutrient contentnitrogen_root_regrowth_previous_crop%1Plant nutrient contentnitrogen_shoot%37Plant nutrient contentnitrogen_shoot_regrowth_previous_crop%3Plant nutrient contentnitrogen_shoot_weed%14Plant nutrient contentphosphorus_acq_shootkg.ha^-1^1Plant nutrient contentphosphorus_root%1Plant nutrient contentphosphorus_shoot%2Plant nutrient contentpotassium_root%1Plant nutrient contentpotassium_shoot%1Plant nutrient contentsodium_root%1Plant nutrient contentsodium_shoot%1Plant nutrient contentsulphur_acq_rootkg.ha^-1^9Plant nutrient contentsulphur_acq_shootkg.ha^-1^12Plant nutrient contentsulphur_root%11Plant nutrient contentsulphur_shoot%14Plant nutrient contentsulphur_shoot_weed%1Plant nutrient contentzinc_rootmg.kg^-1^1Plant nutrient contentzinc_shootmg.kg^-1^1Glucosinolate content4hydroxyglucobrassicin_rootµmol.g^-1^ of dry matter9Glucosinolate content4hydroxyglucobrassicin_shootµmol.g^-1^ of dry matter9Glucosinolate content4methoxyglucobrassicin_rootµmol.g^-1^ of dry matter9Glucosinolate content4methoxyglucobrassicin_shootµmol.g^-1^ of dry matter9Glucosinolate contentepiprogoitrin_rootµmol.g^-1^ of dry matter2Glucosinolate contentepiprogoitrin_shootµmol.g^-1^ of dry matter2Glucosinolate contentglucoalyssin_rootµmol.g^-1^ of dry matter5Glucosinolate contentglucoalyssin_shootµmol.g^-1^ of dry matter4Glucosinolate contentglucobrassicanapin_rootµmol.g^-1^ of dry matter8Glucosinolate contentglucobrassicanapin_shootµmol.g^-1^ of dry matter7Glucosinolate contentglucobrassicin_rootµmol.g^-1^ of dry matter9Glucosinolate contentglucobrassicin_shootµmol.g^-1^ of dry matter9Glucosinolate contentglucoerucin_rootµmol.g^-1^ of dry matter9Glucosinolate contentglucoerucin_shootµmol.g^-1^ of dry matter9Glucosinolate contentglucoiberin_rootµmol.g^-1^ of dry matter2Glucosinolate contentglucoiberin_shootµmol.g^-1^ of dry matter2Glucosinolate contentgluconapin_rootµmol.g^-1^ of dry matter9Glucosinolate contentgluconapin_shootµmol.g^-1^ of dry matter9Glucosinolate contentgluconapoleiferin_rootµmol.g^-1^ of dry matter7Glucosinolate contentgluconapoleiferin_shootµmol.g^-1^ of dry matter7Glucosinolate contentgluconasturtiin_rootµmol.g^-1^ of dry matter9Glucosinolate contentgluconasturtiin_shootµmol.g^-1^ of dry matter9Glucosinolate contentglucoraphanin_rootµmol.g^-1^ of dry matter9Glucosinolate contentglucoraphanin_shootµmol.g^-1^ of dry matter9Glucosinolate contentglucoraphasatin_E/Z_mixture_II_rootµmol.g^-1^ of dry matter2Glucosinolate contentglucoraphasatin_E/Z_mixture_II_shootµmol.g^-1^ of dry matter2Glucosinolate contentglucoraphasatin_E/Z_mixture_I_rootµmol.g^-1^ of dry matter2Glucosinolate contentglucoraphasatin_E/Z_mixture_I_shootµmol.g^-1^ of dry matter2Glucosinolate contentglucoraphasatin_rootµmol.g^-1^ of dry matter1Glucosinolate contentglucoraphasatin_shootµmol.g^-1^ of dry matter1Glucosinolate contentglucoraphenin_rootµmol.g^-1^ of dry matter2Glucosinolate contentglucoraphenin_shootµmol.g^-1^ of dry matter2Glucosinolate contentglucotropaeolin_rootµmol.g^-1^ of dry matter7Glucosinolate contentglucotropaeolin_shootµmol.g^-1^ of dry matter7Glucosinolate contentneoglucobrassicin_rootµmol.g^-1^ of dry matter9Glucosinolate contentneoglucobrassicin_shootµmol.g^-1^ of dry matter9Glucosinolate contentprogoitrin_rootµmol.g^-1^ of dry matter9Glucosinolate contentprogoitrin_shootµmol.g^-1^ of dry matter9Glucosinolate contentsinalbin_rootµmol.g^-1^ of dry matter7Glucosinolate contentsinalbin_shootµmol.g^-1^ of dry matter7Glucosinolate contentsinigrin_rootµmol.g^-1^ of dry matter9Glucosinolate contentsinigrin_shootµmol.g^-1^ of dry matter9

To illustrate key features from the dataset, we focused on i) the most represented species mixture, i.e. oat-vetch (n = 10) and ii) variables that were most frequently measured across experiments i.e. biomass production (biomass_shoot, biomass_root : t.ha^-1^ of dry matter), nitrogen and carbon uptake by plants (carbon_acq_shoot, carbon_acq_root, nitrogen_acq_shoot, nitrogen_acq_root : kg.ha^-1^) (Fig. 3). Shoot and root biomass data were not available for all modalities. Consequently, the shoot and root boxplots are independent and based on different sample sizes. However, whenever biomass shoot is available, biomass root is also present, but the reverse is not always true.Fig. 3. Biomass, nitrogen and carbon uptake by shoots and roots of black oat and purple vetch grown as sole crop or mixture. SC = measurements for black oat and purple vetch in sole crops; IC = separate measurements for black oat and purple vetch in intercrop; Mix (oat + vetch) = sum of both species in the mixture.Fig 3

Experimental design, materials and methods

4

Experimental site and design

4.1

The experiments included in the database were conducted between 2004 and 2022 at three sites: the INRAE research station of Auzeville-Tolosane (43.529326, 1.501011), the Lamothe experimental farm of INP-EI Purpan located at Seysses (43.50946, 1.251009), and a private research station near Orléans (47.776, 2.098).

The experiments were carried out in experimental fields though two types of trials: analytical and cropping system trials. In the analytical trials, cover crops were sown in microplots with a minimum size of 14 m², while in the cropping system trials, cover crops were sown in strips, each covering at least 3000 m². The sites, number of crop campaigns, experimental design and experiment identification are summarized in Table 6, [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14].Table 6. Summary of the database experiments including site, number of campaigns, experimental design and the corresponding experiment_id linked to each experiment.Table 6

The management and features described refer to the main features of the experiments, or specific parameters related to certain experiments. Detailed management-specific data are detailed in the data_management table.

Cover crops management

4.2

Before sowing, shallow tillage was carried out for all cover crops except for two managements. Irrigation was applied at sowing when necessary (19 out of 37 experiments) to ensure uniform emergence and robust plant establishment. Cover crops were not directly fertilized; however, in some cropping system trials, plots received phosphorus (P) fertilization during or just before the sowing of the cover crop to meet the nutrient requirements for subsequent cash crops.

Only a few experiments (8 out of 37) received plant protection treatments during the cover crop growth phase. These treatments were mainly limited to pesticides against slugs, insecticides targeting specific pests such as sawfly, or herbicides for weed control. Some experiments included bare soil as a control treatment, most of them being kept weed-free using mechanical methods.

Measurement and sampling

4.3

Table 7 provides a description of the methods.Table 7. Summary of the method descriptions by variable category. Categories are organized chronologically according to the timing of the measurements, with variables listed alphabetically within each category and comparable method grouping.Table 7. CategoryVariableMethod descriptionSoilsoil_nitrogen_0–30soil_nitrogen_30–60soil_nitrogen_60–90soil_nitrogen_90–120soil_sulphur_0–30soil_sulphur_30–60soil_sulphur_60–90soil_sulphur_90–120soil_swc_0–30soil_swc_30–60soil_swc_60–90soil_swc_90–120Depending on the experiments, soil samples were collected both before sowing and at the end of the growing season (n = 9), or only at the cover crop termination (n = 26). Soil cores were randomly extracted using a hydraulic core drill at different horizons with depths varying according to the experiment (0–30, 30–60, 60–90, 90–120 cm). Soil mineral nitrogen (nitrate and ammonium) and soil sulfur concentrations were analyzed using a continuous flow auto-analyzer (Skylar 51,000, Skalar Analytic, Breda, Netherlands) following the standard NF ISO 14,256–2. Soil water content was calculated by multiplying the measured gravimetric water content by the estimated bulk density.Causes of non-emergence and post-emergence damagesCNE_abiotic_stressCNE_clodsCNE_crustingCNE_damping_offCNE_land_pestsCNE_seedlessness_birdsCNE_sowing_depthPED_animalsPED_anoxiaPED_avifaunaPED_damping_offPED_flea_beetlesPED_freezePED_land_pestsPED_slugsPED_thermal_stressDuring the emergence phase, the causes of non-emergence were identified at a set number of empty points within subplots, using a visual diagnostic key [15]. At each empty point, once seeds or seedling remnants were found the likely causes of non-emergence were recorded. For each subplot, the percentage of occurrences attributed to each identified cause was calculated.The same approach was applied during the post-emergence phase to assess damage occurring after seedling emergence. Plants with damage were counted and the percentage of each type of damage relative to the total number of plants that had emerged was calculated.Plant size, development and contentadl_shootadf_shootcellulose_shoothemicellulose_shootndf_shootSome plant samples were analyzed in a specialized laboratory to determine cell wall components using the Van Soest method [16], with results expressed as cellulose, hemicellulose, and acid detergent lignin (ADL), acid detergent fiber (ADF), and neutral detergent fiber (NDF) content.coverThe soil cover provided by the cover crops was assessed through visual observation and scored on a scale from 1 to 5, where 1 corresponded to 0–12.5 % cover, 2 to 12.5–25 %, 3 to 25–50 %, 4 to 50–75 % and 5 corresponded to 75–100 % cover. In the database, these results are expressed as a percentage.heightleaf_arealeaf_numberlengthPlant traits were measured or counted on selected plants within plots or subplots, including height, length, and leaf number. Sampled plants were collected for leaf area measurements using a planimeter (LI-COR 3100C) or a manual scanner, these methods are comparable. The reported plant height and length corresponded to the aerial part only (shoot).emergence_densityA few days after sowing, the number of emerged plants for each cover crop species was counted within areas measuring 0.5 to 1 m². The results were then expressed as plants per square meter (plants.m^-2^).stageVisual observation (BBCH scale).Biomassbiomass_rootbiomass_root_regrowth_previous_cropbiomass_shootbiomass_shoot_regrowth_previous_cropbiomass_shoot_weedhumidity_roothumidity_shoothumidity_shoot_weedroot_shoot_ratioTo estimate biomass produced by cover crops, because of the sampling method the estimated biomass include weeds, or regrowth from previous crops, shoot biomass samples were collected for each species at cover crop termination using areas of 0.25 m² to 1 m². In some experiments, root biomass was collected, typically at a depth from 25–30 cm, either for all species or selected ones, and from either all subplots, specific zones, or a subset of plants. This was done to calculate the root:shoot ratio.Fresh shoot and root samples were weighed, sub-sampled, dried at 80 °C for 48 hours, and then reweighed to estimate dry matter biomass per species.Plant nutrient contentboron_rootboron_shootcalcium_rootcalcium_shootcarbon_acq_rootcarbon_acq_root_regrowth_previous_cropcarbon_acq_shootcarbon_acq_shoot_regrowth_previous_cropcarbon_acq_shoot_weedcarbon_rootcarbon_root_regrowth_previous_cropcarbon_shootcarbon_shoot_regrowth_previous_cropcarbon_shoot_weedcopper_rootcopper_shootiron_rootiron_shootmagnesium_rootmagnesium_shootmanganese_rootmanganese_shootnitrogen_acq_rootnitrogen_acq_root_regrowth_previous_cropnitrogen_acq_shootnitrogen_acq_shoot_regrowth_previous_cropnitrogen_acq_shoot_weednitrogen_rootnitrogen_root_regrowth_previous_cropnitrogen_shootnitrogen_shoot_regrowth_previous_cropnitrogen_shoot_weedphosphorus_acq_shootphosphorus_rootphosphorus_shootpotassium_rootpotassium_shootsodium_rootsodium_shootsulphur_acq_rootsulphur_acq_shootsulphur_rootsulphur_shootsulphur_shoot_weedzinc_rootzinc_shootDried biomass samples were ground and analyzed for nitrogen (N), carbon (C), sulphur (S) or other mineral element concentrations using the Dumas method (MicroVario Cube, Elementar, Langenselbold, Germany).delta_15N_rootdelta_15N_shootdelta_15N_shoot_regrowth_previous_cropdelta_15N_shoot_weedIn some experiments, 15 N concentration was measured using a stable isotope ratio mass spectrometer in continuous flow (Isoprime 100, UK) to determine the percentage of plant nitrogen (N) derived from the atmosphere.Glucosinolate content4hydroxyglucobrassicin_root4hydroxyglucobrassicin_shoot4methoxyglucobrassicin_root4methoxyglucobrassicin_shootepiprogoitrin_rootepiprogoitrin_shootglucoalyssin_rootglucoalyssin_shootglucobrassicanapin_rootglucobrassicanapin_shootglucobrassicin_rootglucobrassicin_shootglucoerucin_rootglucoerucin_shootglucoiberin_rootglucoiberin_shootgluconapin_rootgluconapin_shootgluconapoleiferin_rootgluconapoleiferin_shootgluconasturtiin_rootgluconasturtiin_shootglucoraphanin_rootglucoraphanin_shootglucoraphasatin_E/Z_mixture_II_rootglucoraphasatin_E/Z_mixture_II_shootglucoraphasatin_E/Z_mixture_I_rootglucoraphasatin_E/Z_mixture_I_shootglucoraphasatin_rootglucoraphasatin_shootglucoraphenin_rootglucoraphenin_shootglucotropaeolin_rootglucotropaeolin_shootneoglucobrassicin_rootneoglucobrassicin_shootprogoitrin_rootprogoitrin_shootsinalbin_rootsinalbin_shootsinigrin_rootsinigrin_shootFor glucosinolate analysis, a sub-sample of Brassicaceae plants were collected per plot, cut, and frozen at −80 °C before lyophilization. Glucosinolate profiles and concentrations were then analyzed according to their characteristics as described by de Graaf et al. [17].

Limitations

5

The data originate from a variety of experiments conducted on different plots, which introduces a certain degree of pedological variability, despite the relative geographical proximity of the sites. Some level of heterogeneity is also expected due to variations in experimental designs, the types of phenotypic traits recorded, the organizational scale of measurements (organ, plant, crop), and the completeness of available data. In some cases, not all variables were measured across all experiments, which may slightly limit the potential for direct comparisons. Additionally, a few management data are missing. When exact measurement dates were unavailable, a reasonable approximation was made by assigning a date close to the known cover crop termination period.

Ethics statement

6

All participants provided informed consent prior to their inclusion in the study. The study was approved by the appropriate institutional research ethics committees and this dataset does not involve human subjects, animal experiments, or sensitive data.

CRediT authorship contribution statement

Manon Pull: Data curation, Writing – original draft, Writing – review & editing. Lionel Alletto: Conceptualization, Funding acquisition, Investigation, Writing – review & editing. Eric Justes: Conceptualization, Investigation, Funding acquisition, Writing – review & editing. Jay Ram Lamichhane: Conceptualization, Investigation, Writing – review & editing. Elana Dayoub: Data curation. Guillaume Hustet-Caou: Data curation. Emilie Sitnikow: Data curation. Noémie Gaudio: Conceptualization, Data curation, Methodology, Software, Writing – review & editing. Pierre Casadebaig: Conceptualization, Methodology, Software, Writing – review & editing. Rémi Mahmoud: Conceptualization, Methodology, Software. Neïla Ait Kaci Ahmed: Investigation. Julie Constantin: Conceptualization, Investigation. Antoine Couëdel: Investigation. Noémie Deschamps: Investigation. Eric Lecloux: Investigation. Damien Marchand: Investigation. François Perdrieux: Investigation. Didier Raffaillac: Investigation. Lucie Souques: Investigation. Gilles Tison: Investigation. Hélène Tribouillois: Investigation. Alexandre Wojciechowski: Investigation. Célia Seassau: Conceptualization, Funding acquisition, Investigation, Writing – review & editing.