Literature DB >> 31598068

Eleven years' data of grassland management in Germany.

Juliane Vogt1, Valentin H Klaus2,3, Steffen Both4,5, Cornelia Fürstenau6, Sonja Gockel7,8, Martin M Gossner1,9, Johannes Heinze10, Andreas Hemp11, Nobert Hölzel2, Kirsten Jung12, Till Kleinebecker13,14, Ralf Lauterbach12, Katrin Lorenzen1, Andreas Ostrowski6, Niclas Otto1, Daniel Prati15, Swen Renner16, Uta Schumacher17, Sebastian Seibold1, Nadja Simons1,18, Iris Steitz12, Miriam Teuscher17, Jan Thiele19, Sandra Weithmann12, Konstans Wells20,21, Kerstin Wiesner1, Manfred Ayasse12, Nico Blüthgen18, Markus Fischer22,17, Wolfgang W Weisser1.   

Abstract

BACKGROUND: The 150 grassland plots were located in three study regions in Germany, 50 in each region. The dataset describes the yearly grassland management for each grassland plot using 116 variables.General information includes plot identifier, study region and survey year. Additionally, grassland plot characteristics describe the presence and starting year of drainage and whether arable farming had taken place 25 years before our assessment, i.e. between 1981 and 2006. In each year, the size of the management unit is given which, in some cases, changed slightly across years.Mowing, grazing and fertilisation were systematically surveyed: Mowing is characterised by mowing frequency (i.e. number of cuts per year), dates of cutting and different technical variables, such as type of machine used or usage of conditioner.For grazing , the livestock species and age (e.g. cattle, horse, sheep), the number of animals, stocking density per hectare and total duration of grazing were recorded. As a derived variable, the mean grazing intensity was then calculated by multiplying the livestock units with the duration of grazing per hectare [LSU days/ha]. Different grazing periods during a year, partly involving different herds, were summed up to an annual grazing intensity for each grassland.For fertilisation , information on the type and amount of different types of fertilisers was recorded separately for mineral and organic fertilisers, such as solid farmland manure, slurry and mash from a bioethanol factory. Our fertilisation measures neglect dung dropped by livestock during grazing. For each type of fertiliser, we calculated its total nitrogen content, derived from chemical analyses by the producer or agricultural guidelines (Table 3).All three management types, mowing, fertilisation and grazing, were used to calculate a combined land use intensity index (LUI) which is frequently used to define a measure for the land use intensity. Here, fertilisation is expressed as total nitrogen per hectare [kg N/ha], but does not consider potassium and phosphorus.Information on additional management practices in grasslands was also recorded including levelling, to tear-up matted grass covers, rolling, to remove surface irregularities, seed addition, to close gaps in the sward. NEW INFORMATION: Investigating the relationship between human land use and biodiversity is important to understand if and how humans affect it through the way they manage the land and to develop sustainable land use strategies. Quantifying land use (the 'X' in such graphs) can be difficult as humans manage land using a multitude of actions, all of which may affect biodiversity, yet most studies use rather simple measures of land use, for example, by creating land use categories such as conventional vs. organic agriculture. Here, we provide detailed data on grassland management to allow for detailed analyses and the development of land use theory. The raw data have already been used for > 100 papers on the effect of management on biodiversity (e.g. Manning et al. 2015). Juliane Vogt, Valentin H. Klaus, Steffen Both, Cornelia Fürstenau, Sonja Gockel, Martin M. Gossner, Johannes Heinze, Andreas Hemp, Nobert Hölzel, Kirsten Jung, Till Kleinebecker, Ralf Lauterbach, Katrin Lorenzen, Andreas Ostrowski, Niclas Otto, Daniel Prati, Swen Renner, Uta Schumacher, Sebastian Seibold, Nadja Simons, Iris Steitz, Miriam Teuscher, Jan Thiele, Sandra Weithmann, Konstans Wells, Kerstin Wiesner, Manfred Ayasse, Nico Blüthgen, Markus Fischer, Wolfgang W. Weisser.

Entities:  

Keywords:  Biodiversity-Exploratories; Grassland management survey; farming practice; fertilisation; grassland maintenance; grazing; intensification of grassland use; livestock units; mowing; nitrogen; questionnaire; temporal variation

Year:  2019        PMID： 31598068      PMCID： PMC6778154          DOI： 10.3897/BDJ.7.e36387

Source DB:  PubMed          Journal:  Biodivers Data J        ISSN： 1314-2828


Introduction

Grasslands can harbour high biodiversity and fulfil important ecosystem functions and services, such as food and habitat provision for livestock, protection of soil and water resources, carbon sequestration and aesthetic appeal (Carlier et al. 2009, Hönigová et al. 2012, Gossner et al. 2016, Simons et al. 2017). In addition to the conversion of grasslands to other land use forms, grasslands worldwide are also changed by land use intensification. Land use intensification of grasslands includes, for example, increased fertiliser input, application of pesticides, increased number of cuts in meadows or increased stocking densities in pastures (Humbert et al. 2009, Boch et al. 2016, Klaus et al. 2018. As a result of continued land use intensification, high value natural grasslands, i.e. extensively managed grasslands, have seen a decline throughout Europe (Veen et al. 2009). Increasing management intensity in grasslands has been shown to decrease alpha, (i.e. local, diversity) and also beta diversity, i.e. intensification leads to homogenisation of communities across trophic groups including plant, invertebrates and birds (Humbert et al. 2009, Gossner et al. 2016, Manning et al. 2015, Renner et al. 2014, Socher et al. 2013). Intensification affects biodiversity directly and indirectly. For example, mowing itself and the use of conditioners, i.e. a farm implement that uses mechanical force to promote faster and more even drying of biomass, cause direct mortality of insects (Humbert et al. 2010a, Humbert et al. 2010b). Indirect effects include changes in plant community composition, for example, by increased fertilisation, that can then affect insect diversity. Until now, little attention has been paid to long-term in-depth assessments of land use practices in grassland systems. The intensity and timing of mowing, grazing and fertilisation can differ within and between years on particular grasslands (Kleinebecker et al. 2018) and the effect of such variability on biodiversity changes is considerable (but see, e.g. Allan et al. 2014). Grassland management consists of various management components such as mowing, grazing or fertilisation that may jointly or singly affect biodiversity. Moreover, there are interactions between different management activities, for example, fertiliser application results in higher biomass production, which is often associated with more frequent mowing (Blüthgen et al. 2012, Busch et al. 2018, Humbert et al. 2009Busch et al. 2018). To understand more mechanistically how land use intensification in grasslands affects biodiversity, detailed information on grassland management is needed, ideally for a large number of grasslands over several years. Within the framework of the Biodiversity-Exploratories programme (www.biodiversity-exploratories.de), we have thoroughly monitored land use of 150 grassland plots for 11 years to investigate temporal variation in land management within three study regions in Germany (Fig. 1). These plots represented gradients of land use intensity typical for our study regions and were managed by mowing, grazing and fertilisation (Fischer et al. 2010). Detailed information on grassland management of all 150 grassland plots was obtained annually from farmers using a standardised questionnaire (Table 1). Here, we present the data of the corresponding management questionnaire that form the basis of most analyses of effects of land use intensification on biodiversity and ecosystem functioning in grasslands within the Biodiversity-Exploratories. With this dataset, we provide knowledge on how land use intensity in temperate grasslands varies across spatial and temporal scales. The components reported here also form the basis on an integrated land use intensity index used in the programme to study its integral effects on biodiversity in grasslands (Blüthgen et al. 2012).
Figure 1.

The three model regions of the Biodiversity Exploratories project in Germany.

Table 1.

Overview of all variables of the data set: BE_landuse_grassland_2006-2016.csv received from the management questionnaire.

Variable Type of data Units Range of numeric variables (min-max) Description (English)
IDText--Unique identifier composed of the columns PlotID and Year
Study regionText-ALB = Schwäbische AlbHAI = HainichSCH = Schorfheide
YearIntegeryyyy-Year of management
DateDatedd.mm.yyyy-Date of interview
PlotIDText--Experimental Plot IDs formatted as (A|H|S)EG with consecutive numbering. Abbreviations are:A = Schwäbische Alb, H = Hainich, S = SchorfheideE = Experimental PlotG = Grassland, e.g. AEG01
DrainageText--Measure of drainage and the description of the method (free text)
StartDrainageIntegeryyyy-Starting year of grassland drainage, if applicable
WaterLoggingBooleanyes/no-Activities on water logging, e.g. for water regulation of fen soils
Agriculture1981Booleanyes/no-Use of grassland between 1981 to 2006, i.e. (temporal) conversion of grassland into arable land
SizeManagementUnit_haNumericha0.49-187.1Size of the management unit in the survey year, often larger than the 50 x 50 m study plot itself
Grazing
StartGrazingTextMonth-Starting month of the first grazing period in the survey year
EndGrazingTextMonth-End of the last grazing period in the survey year
Livestock1Text-Type of animal in first grazing period
StartGrazingPeriod1Textmonth-Starting month of first grazing period for livestock 1
EndGrazingPeriod1Textmonth-Ending month of first grazing period for livestock 1
Livestock, Start/End GrazingPeriod 2-4 …-Identical information for grazing periods 2-4, if applicable.
Cattle6months1Integer0-95For Grazing period 1: Number of cattle with an age up to 6 months (cattle up to 6 months = 0.3 LS* per day)
Cattle6-24months1Integer0-200For Grazing period 1: Number of cattle with an age between 6 months and 2 years (= 0.6 LS*)
CattlePlus2years1Integer0-300For Grazing period 1: Number of cattle older than 2 years (= 1 LS*)
SheepGoat1year1Integer0-1000For Grazing period 1: Number of sheep or goats with an age up to 1 year (= 0.05 LS*)
SheepGoatPlus1year1Integer0-1500For Grazing period 1: Number of sheep or goats older than 1 year (= 0.1 LS*)
Pony1Integer0-400For Grazing period 1: Number of ponies and small horses (= 0.7 LS*)
Horse3years1Integer0-4For Grazing period 1: Number of horses up to 3 years (= 0.7 LS*)
HorsePlus3years1Integer0-46For Grazing period 1: Number of horses older than 3 years (= 1.1 LS*)
NbLivestock1Integer0-2500For Grazing period 1: Total number of livestock
LivestockUnits1NumericNumber of livestock x conversion factor0-1814For Grazing period 1: Total sum of the livestock units
DayGrazing1Integerdays0-365For Grazing period 1: duration of grazing (in days)
GrazingArea1Numericha0-148.5For Grazing period 1: size of area where livestock grazed
Numeric variables for grazing 2-4…For Grazing periods 2-4 see description of grazing period 1
Cattle6months20-73
Cattle6-24months20-103
CattlePlus2years20-120
SheepGoat1year20-600
SheepGoatPlus1year20-1200
Pony20
Horse3years20
HorsePlus3years20-18
NbLivestock20-1200
LivestockUnits20-144
DayGrazing20-165
GrazingArea20-148.5
Cattle6months30-72
Cattle6-24months30-103
CattlePlus2years30-120
SheepGoat1year30-820
SheepGoatPlus1year30-1300
Pony30
Horse3years30
HorsePlus3years30-25
NbLivestock30-1340
LivestockUnits30-145.5
DayGrazing30-127
GrazingArea30-196.69
Cattle6months40-72
Cattle6-24months40-81
CattlePlus2years40-84
SheepGoat1year40-600
SheepGoatPlus1year40-900
Pony40
Horse3years40
HorsePlus3years40-16
NbLivestock40-900
LivestockUnits40-103.6
DayGrazing40-76
GrazingArea40-148.5
TotalGrazing_LSUdhaNumeric#Livestock*days /ha0-1644.17Total sum of the grazing intensity for all grazing periods
SupplementaryFeedingBooleanyes/no-Additional fodder supply for the livestock
DescFeedingText-Type and amount of supplementary fodder
Mowing
MowingInteger1/year0-4Number of cuts per year
DateMowing1Datedd.mm.yyyy-Date of the first cut
DateMowing2-4…Datedd.mm.yyyy-Dates of the second to fourth cut, if applicable
MowingMachineText-Type of machine which was used for mowing, e.g. rotarymower, doubleknife, mulcher
CutWidth_mNumericm0-12Cutting width of the mowing machine
CutHeight_cmIntegercm0-15Cutting height above soil level of the mowing machine
DriveSpeed_kmhIntegerkm/h, (mean)0-20Speed of the mowing machine, normally mean speed value is given
MowingConditionerBooleanyes/no-Presence of conditioner, i.e. did the mowing machine have a conditioner to improve drying of the clippings
Fertilisation
FertilisationBooleanyes/no-Addition of fertiliser (not including dung depositions by livestock during grazing a parcel)
NbFertilisationInteger0-7Number of fertiliser applications per year
DateFertilisation1Datedd.mm.yyyy-Date of first fertiliser application
DateFertilisation2-7…Datedd.mm.yyyy-Date of 2nd to 7th fertiliser applications
Manure_thaNumerict/ha0-40Total amount of applied solid manure
Slurry_m3haNumericm³/ha0-80Total amount of applied pig or cow slurry and biogas residues, respectively.
DescFertText-Description of applied organic fertiliser
orgNitrogen_kgNhaNumerickg/ha0-371Amount of organic nitrogen applied
minNitrogen_kgNhaNumerickg/ha0-170Amount of nitrogen applied, of mineral origin or the organic fertiliser mash from a bioethanol factory (see in DescFert)
totalNitrogen_kgNhaNumericKg /ha0-433Sum of applied mineral and organic nitrogen [kg N/ha]
minPhosphorus_kgPhaNumerickg/ha0-350Amount of phosphorus applied [kg P2O5/ha], of mineral origin or mash from a bioethanol factory (not given for other organic fertilisers)
minPotassium_kgKhaNumerickg/ha0-100Amount of potassium applied [kg K2O/ha], of mineral origin or mash from a bioethanol factory (not given for other organic fertilisers)
Sulphur_kgShaNumerickg/ha0-25Total amount of applied Sulphur [kg S/ha]
Maintenance
MaintenanceBooleanyes/no-Presence of maintenance measures
LevellingText0-4Maintenance to break up matted grass covers
DateLevellingDatedd.mm.yyyy-Maintenance: date of levelling
RollingText0-2Maintenance: rolling to level unevenness
DateRollingDatedd.mm.yyyy-Maintenance: date of rolling
MulchingText0-4Partial mulching on some spots, e.g. rank patches. The material remains on site after mowing. We consider this not as a mowing event as only a small part of the area is treated.
DateMulchingDatedd.mm.yyyy-Date of partial mulching
ShrubClearanceText-0-1Clearance to avoid shrub encroachment. We consider this not as a mowing event as only individual shrubs are targeted.
DateScrubClDatedd.mm.yyyy-Date of shrub clearance
PlantProtectionAgentBooleanyes/no-Pesticide use: pesticides and herbicides. As pesticides in grasslands are very rare and only used for spot treatment, we do not have further information on this treatment.
SeedsBooleanyes/no-Seed addition
DescSeedsText--Description of usage of the sowing

*LS – Livestock

General description

Purpose

The present dataset summaries management information collected from 2006 to 2016 for 150 grassland plots in three different regions of Germany. Data are based on annual interviews with the respective farmers, land owners or tenants involved in land management activity, using a standardised questionnaire.

Project description

Title

The Biodiversity Exploratories - functional biodiversity research

Personnel

: Markus Fischer, Wolfgang Weisser, Manfred Ayasse, Christian Ammer, Nico Blüthgen, Ellen Kandeler, Birgitta König-Ries, Marion Schrumpf. Within the infrastructure programme of the BE, local management teams in each region ensure the maintenance of survey plots and communication between scientists and local stakeholders. Furthermore, the grassland expert (technician) and the local manager (scientist) of each team are responsible for obtaining the information from the land user by carrying out the annual questionnaire, as well as including additional information by their own observations of the grasslands.

Study area description

The biodiversity studies are carried out in 150 grassland plots managed in different intensities. The grassland sites are distributed in three different regions within Germany including i) the Biosphere Reserve Schorfheide-Chorin ii) the Hainich-Dün Area and iii) the Biosphere-Area Schwäbische Alb. The Schorfheide Chorin Exploratory site is situated in the North-East of Germany with an extent of approx. 1300 km². The geology is characterised by young glacial landscape of altitudes between 3-140 m a.s.l. with different soil types such as brown earth, lessivé, pararendzina, podzols and bog soils, resulting in diverse vegetation. The annual mean temperature is 8-8.5°C and the annual mean precipitation 500-600 mm. The Hainich-Dün Exploratory site (approx. 1300 km²) in Central Germany consists of silty, loamy and clayey soil textures of the calcareous bedrock in altitudes between 285- 550 m a.s.l. The annual mean temperature is 6.5-8°C and the annual mean precipitation 500-800 mm. The Exploratory Schwäbische Alb site (approx. 422 km²) in South West Germany consists of calcareous bedrock with karst phenomena in altitudes between 460-860 m a.s.l. with annual mean temperature of 6-7°C and mean precipitation of 700-1000 mm.

Design description

For an advanced biodiversity research, three large-scale and long-term research sites were established in Germany serving as open research platforms for biodiversity and ecosystem research groups. The BE sustained the scientific infrastructure to develop the intellectual framework needed to address critical questions about changes in biodiversity and to evaluate the impacts of those changes for ecosystem processes. The objectives of the BE are to understand i) the relationship between biodiversity of different taxa and levels, ii) the role of land use and management for biodiversity, iii) the role of biodiversity for ecosystem processes.

Funding

The Biodiversity Exploratories are a German Science Foundation funded research project (DFG Priority Programme 1374).

Sampling methods

Study extent

We monitored 150 grassland plots across three regions in Germany for 11 years since 2006.

Sampling description

Interviews with the land users took place retrospectively for the previous year on all permanently established 150 grassland sites since 2006, based on a standardised questionnaire, which was identical for all three exploratory regions. We did not collect any organisms. During the interviews, the land users provided us with information according to their grassland management. Linear mixed-effect models with logarithmically transformed response variables were calculated to detect temporal trends as well as differences between the exploratory regions (procedure lmer, implemented in R). Land use intensity in the grasslands of our study regions ranged from low-intensive management, for example, meadows with only one cut per year and no fertilisation, to intensive management with four cuts per year and occasionally up to 400 kg N added per year and hectare. Very intensively-used grasslands which, in Central Europe, are characterised by up to seven cuts per year and regular fertilisation of about 400 kg/ha/yr nitrogen did not occur in our study regions. Mean mowing frequency (number of cuts per year) across all 50 plots was between 0.6 and 1.5 and highest in the Alb, lower in Hainich and lowest in Schorfheide. Mowing frequency slightly increased in the Alb and Hainich, but decreased over the years in the Schorfheide. Within plots that were mown, mowing intensity was between 1.3 and 2 cuts per year and was highest in Alb, significantly higher than in Schorfheide (Fig. 2a). Mowing frequency within mown plots decreased over time in Schorfheide (Fig. 2a).
Figure 2.

Annual means and standard error for a) mowing frequency in mown plots, i.e. the number of cuts per year, b) grazing intensity in grazed plots, in livestock unit days per hectare and year, calculated by multiplying the number of livestock by a conversion factor (see Table 3) and the number of grazing days and dividing the product by the size of the management unit and c) nitrogen fertilisation in fertilised plots, calculated as total nitrogen input in kg per hectare and year, in the three study regions of the Biodiversity Exploratories (light grey: Schwäbische Alb (ALB), grey: Hainich-Dün (HAI) and dark grey: Schorfheide-Chorin (SCH)). Only the subset of plots (out of 50 in each region), where the respective management was applied, are included in the figure panels (numbers above the bars).

Grasslands were grazed by different types of livestock, most commonly cattle and sheep, but also horses and goats. Based on this information, the mean grazing intensity was then calculated by multiplying the livestock units ([LSU], (Table 2) with the duration of grazing per hectare [LSU days/ha]. Grazing intensity across all 50 plots in a region was on average between 120 and 200 livestock unit days per hectare in the Schorfheide, significantly higher than in the Alb (z = 3.177, p < 0.01) where yearly means were mostly below 100. Mean grazing intensity in Hainich was intermediate with yearly means below 150 (data not shown). In the Schorfheide, grazing intensity across the 50 plots increased slightly over time (z = 6.091, p < 0.0001, data not shown). In grazed plots, the annual grazing intensity per hectare ranged from 5 to 1644 livestock units x days. Mean grazing intensity in grazed plots was higher in the Schorfheide than in the other two regions, but due to high variability, differences between regions were not significant (p > 0.05, Fig. 2b). Within the grazed plots, grazing intensity in Schorfheide decreased over time (z = -3.270, p < 0.01, Fig. 2b), although the number of plots that were grazed were higher in the second half of the time series (Fig. 2b).
Table 2.

Livestock units derived from the type and age of livestock (Chamber of Agriculture Nordrhein-Westfalen 2018).

Grazing species Age Livestock units (LSU)
Cattle< 6 months0.3
Cattle6 months-2 years0.6
Cattle> 2 years1
Sheep and goats< 1 year0.05
Sheep and goats> 1 year0.1
Ponies and small horses-0.7
Horses< 3 years0.7
Horses> 3 years1.1
Fertilisation intensity across the 50 plots was highest in Hainich, with means mainly higher than 20 kg N*ha-1*yr-1, significantly higher than in the Schorfheide (z = 2.343, p < 0.05), where there was a significant decrease in fertilisation with time (z = -5.017, p < 0.001) and where yearly means dropped from 20 kg N ha-1 yr-1 to close to zero after 2013, which is largely due to a decrease in the number of fertilised plots to just two (Fig. 2c). Fertilisation in the Alb was intermediate (data not shown). Within fertilised plots, fertilisation ranged between 15 and 433 kg N ha-1 yr-1 and there were no differences between regions or changes over time (p > 0.05 in each case, Fig. 2c). To summarise, there were significant differences between the regions in main grassland use, meadows in the Alb and pasture in Schorfheide and also in mean land use intensity of meadows, pastures or mown pastures. Changes over time were largely due to changes in the number of plots that were grazed, mown or fertilised, rather than to changes in mowing, grazing and fertilisation intensity within plots. In the Schorfheide, there was an overall decrease in land use intensity, due to increasing regulations in the biosphere reserve Schorfheide Chorin. In the Hainich, the number of fertilised plots decreased from 25 plots in 2006 to 12 plots in 2012 and then increased again to 22 in 2016 (Fig. 2c). The management of grassland is decisively influenced by subsidies, such as agri-environmental measures (AEM) (Table 4). These AEMs are different within the federal states of Germany, having names such as MEKA or FAKT in Baden-Wuerttemberg and KULAP in Thuringia and Brandenburg, including single measures of different management aspects. The agri-environmental subsidy programmes aim to support environmental friendly and extensive production practices to protect natural resources and to preserve cultural landscapes. These can also be counted as disadvantage compensations and are co-financed by the EU, Germany and the respective federal state. Measures of these progammes determine guidelines regarding organic farming, the timing and type of mowing and grazing or restrictions, according to plant protection agents or fertiliser use (Table 4). Therefore, farmers do not make completely independent decisions by managing their grasslands but follow the regulations of the agri-environmental measures to receive subsidies for their land.Table 5 lists the agri-environmental measures applied for the single study plots for each year. The description of the coding of the agri-environmental measures is found as a legend in Table 6.
Table 4.

The requirements of single agri-environmental measures (MEKA/FAKT for Baden-Wuerttemberg and KULAP for Thuringia and Brandenburg) are characterised by the subprogramme designation listed for every region (ALB- Swabian Alb, HAI- Hainich, SCH- Schorfheide). The abbreviations (R)LSU mean (roughage consuming) livestock units having a livestock-dependent conversion from LSU to RLSU: 1 LSU equals the RLSU for sheep or goat (0.7), horse (0.5), cattle (1).

Agri-environmental measures ALB (MEKA, FAKT) HAI (KULAP) SCH (KULAP)
Requirements
Difficult management due to slope of ≥ 25%N-B3
Adapted, extensive management of biotope (§32 nature conservation)N-G1.1, B4
FFH: lowlands- and mountain-meadowsB5
Conservation of meadow orchards (eligible up to max 100 trees/ha)C1
Low-nutrient and dry habitats (biotopes maintenance by grazing)N21, G21
Low-nutrient and dry habitats (biotopes maintenance by mowing)N31
Wet meadowsN23
Eligible landscape (e.g. Natura 2000)413A, 423B, 613A, 663
Sheep farming and difficult terrainN25
Difficult conditions (regarding terrain, specific management)G31, G33, G53
Location of valuable genetic plantsL4
Compensatory allowance of disadvantaged sites33
Organic farming
Farm is managed according to EU eco-regulationN-D2, D2
Introduction or retention of ecological management of the farmL1, Ö2773, 673,882
Retention of ecological management - compensatory allowance623 A,B,C,D
In general
Main fodder siteB1.2
Min 5% of eligible site managed after 15 JuneN-B1, N-B3
Promoting of endangered livestock breedsC3
No reduction of permanent grassland of the farmN25
Management plan according to nature conservation authorityN231, N31, G21, G31, G33, G53663
Fodder sites are managed at least once per year by grazing or mowing413A, 423B, 613A, 663, 673
Management after 1 July812C
Grazing
Livestock min 0.3 LSU/ha on agriculture areaÖ2
Livestock max 2 LSU/ha on agriculture areaN-B1, N-B3773, 673
Livestock min 0.5 RLSU/ha fodder areaL4
Livestock min 0.3 RLSU/ha fodder areaN-B2, B1.1, B1.2311A, 311C, 773, 673, 661, 411
Livestock max 1.4 RLSU/ ha311A, 311C, 773, 673, 661, 411
Livestock max 1.4 LSU/ haN-B2, B1.1311A, 311C
At least one grazing per yearN25
At least one grazing per year. First grazing period by cattle/horses or sheep/goatsG21
At least one grazing per year. First grazing period by sheep/goatsG33, G53
At least one management per year (grazing or mowing and harvesting of the yield) before 15 October411, 661
Maintenance measures after grazingN-B1, N-B2, N-B3
Grazing by cattle/horses with 0.3-1 LSU/haN211
Grazing by cattle/horses with permanent grazing or at least from 2 May to 15 OctoberG31
Grazing by sheep or goats with a min 0.5 LSU/haN213, N25, G33, G53
Grazing 0.3-1 LSU/haN231
Max 1.5 LSU/ha*d until 1 JulyN231
First management of the year: at least 80% of area by grazing (up to 20% by mowing)N231
First management mowing: grazing possible at least 7 weeks after the first cutN31
No supplementary feedingN233
No supplementary feeding between 1 May and 15 OctoberG21, G31, G33, G53
Mowing
First management mowing and harvesting the yieldN31
Up to 2 cuts with a time lag of at least 7 weeksN31
First mowing not before 15 August on min 5% of the areaN31
No mowing before 16 June313A
No mowing before 1 July313B
Post grazing mowing not before 1 JulyN231, G31, G33, G53
Cut height 10 cm313B, 763
Indicator plant species
Abundance of at least 4 indicator plant species out of 28 specific forbsN-B4L4
Abundance of at least 6 indicator plant species out of 30 specific plantsB3.2G11
Abundance of at least 7 indicator plant species out of specific plantsB5
Fertilisation
No mineral nitrogen fertilisationB1.1
No mineral or organic nitrogen fertilisationB1.2
No slurry fertilisation311C
No fertilisation811A
No chemical-synthetic fertiliser or plant protection agent within the farmD1311A, 311C, 661, 411
No chemical-synthetic fertiliser or plant protection agent on eligible areasN231, N25; N31
No fertiliser or plant protection agentG21, G31, G33
Documentation
Slurry records (amount, date) for eligible areasN-B1, N-B3
Fertilisation and management records for eligible areasN-B4
Fertilisation and mowing records for eligible areasB3.2
Fertilisation and plant protection agent records for all grasslands of the farmB1.2
Records via Thuringian grassland card for eligible areasN231, N25, N31, G21, G31, G33, G53
Restrictions /measures not taken
No ploughing, only seed additionB1.1, B1.2, B3.2
No ploughing on eligible areas
No ploughing on farmN-B1, N-B2, N-B3, N-B4, N-D2413A, 423B, 411, 661
No irrigation or meliorationN-B2, B1.1, B1.2G21, G31, G33, G53
No extensive usage of plant protection agentsN-B1, N-B2, N-B3, N-B4, B1.1, B1.2
No maintenance measures, mowing or seed addition between 1 April and 30 JuneG21, G31, G33, G53
No upturning or limbering tillageG21, G31, G33, G53
Table 5.

Study plots with the geographical coordinates and the coding of the agri-environmental measures. The description of coding is found in the legend of Table 6.

EP_Plot_IDExploLatitudeLongitude20062007200820092010201120122013201420152016
AEG1ALB48.49.34k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG10ALB48.389.21o 0 n A5o 0 n A5o 0 n A5o 0 n A5o 0 n A5o 0 n A5o 0 n A5o 0 n A5o 0 n A5k 0 n 0k 0 n 0
AEG11ALB48.499.35k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG12ALB48.399.35k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG13ALB48.399.36k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG14ALB48.389.52k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG15ALB48.499.45k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y A12
AEG16ALB48.49.46o c y A4/A5o c y A4/A5o c y A4/A5o c y A4/A5o c y A4/A5o c y A4/A5o c y A4/A5o c y A4/A5o c y A4/A5o c y A14/11/A12o c y A14/A16/A12
AEG17ALB48.49.52o a y 0o a y 0o a y 0o a y 0o a y 0o a y 0o a y 0o a y 0o a y 0o c 0 0o c 0 0
AEG18ALB48.389.52k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG19ALB48.49.45k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG2ALB48.389.47k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG20ALB48.499.36k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG21ALB48.449.36o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A50 0 0 0k 0 n 0k 0 n 0
AEG22ALB48.49.51k 0 y A1k 0 y A1k 0 y A1k 0 y A1k 0 y A1k 0 y A1k 0 y A1k 0 y A1k 0 y A1k 0 y A7/A10k 0 y A7/A10
AEG23ALB48.429.51k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0o c y A12o c y A12
AEG24ALB48.49.49k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15k 0 y A15
AEG25ALB48.49.26k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A19k 0 y A19
AEG26ALB48.49.4k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15/A19k 0 y A15/A19
AEG27ALB48.429.48k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15/A17/A20/A19k 0 y A15/A17/A20/A19
AEG28ALB48.469.49k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15k 0 y A15
AEG29ALB48.429.36k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A9/A11k 0 y A9/A11
AEG3ALB48.419.53o a y A5o a y 0o a y 0o a y 0o a y 0o a y 0o a y 0o a y 0o c y 0o c y A12o c y A12
AEG30ALB48.469.46k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15k 0 y A15
AEG31ALB48.469.46k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15k 0 y A15
AEG32ALB48.479.49k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15k 0 y A15
AEG33ALB48.459.49k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15/A11k 0 y A15/A11
AEG34ALB48.469.5k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15k 0 y A15
AEG35ALB48.489.29k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG36ALB48.489.3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG37ALB48.49.41k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG38ALB48.449.43k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 n 0k 0 n 0
AEG39ALB48.399.43k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG4ALB48.389.42k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG40ALB48.419.57k 0 y A1/A19k 0 y A1/A19k 0 y A1/A19k 0 y A1/A19k 0 y A1/A19k 0 y A1/A19k 0 y A1/A19k 0 y A1/A19k 0 y A1/A19k 0 n 0k 0 n 0
AEG41ALB48.379.4k 0 y A2k 0 y A2k 0 y A2k 0 y A2k 0 y A2k 0 y A2k 0 y A2k 0 y A2k 0 y A2k 0 n 0k 0 n 0
AEG42ALB48.49.38k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 n 0k 0 n 0
AEG43ALB48.419.54k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 n 0k 0 y A8k 0 y A8
AEG44ALB48.389.43k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 n 0k 0 n 0
AEG45ALB48.49.46o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A5o 0 y A13o 0 y A13
AEG46ALB48.49.43o c y A5o c y A5o c y A5o c y A5o c y A5o c y A5o c y A5o c y A5o c y A5o c y A16/A18o c y A16/A18
AEG47ALB48.429.45k 0 y A5/A1/A6k 0 y A5/A1/A6k 0 y A5/A1/A6k 0 y A5/A1/A6k 0 y A5/A1/A6k 0 y A5/A1/A6k 0 y A5/A1/A6k 0 y A5/A1/A6k 0 n 0k 0 y A19k 0 y A19
AEG48ALB48.429.5k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15/A17/A20/A19k 0 y A15/A17/A20/A19
AEG49ALB48.469.5k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15k 0 y A15
AEG5ALB48.49.44k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG50ALB48.419.47k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG6ALB48.49.44k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
AEG7ALB48.399.38k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A19/A15k 0 y A19/A15
AEG8ALB48.429.49k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15/A11k 0 y A15/A11
AEG9ALB48.399.5k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y A15/A17/A20/A19k 0 y A15/A17/A20/A19
HEG1HAI50.9710.41k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
HEG10HAI51.2810.45k 0 y 0k 0 y 0o 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG11HAI51.2810.46k 0 y 0k 0 y 0o 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG12HAI51.0810.58k 0 y 0k 0 y 0k 0 y 0k 0 y H4k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H13k 0 n 0
HEG13HAI51.2610.38k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 n 0k 0 y 0
HEG14HAI51.2910.44k 0 y 0k 0 y 0k 0 y 0k 0 y H4k 0 y 0k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H13k 0 y H13
HEG15HAI51.0710.49k 0 y H2k 0 y H2k 0 y H2k 0 n 0k 0 n 0k 0 n 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0
HEG16HAI51.0310.46k 0 y H3k 0 y H3k 0 y H3k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H16
HEG17HAI51.0710.47k 0 y 0k 0 y 0k 0 y 0k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H17
HEG18HAI51.2810.42k 0 y 0k 0 y 0k 0 y 0k 0 y 7k 0 y 0k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H16
HEG19HAI51.0710.47k 0 y 0k 0 y 0k 0 y 0k 0 y H8k 0 y 0k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H17
HEG2HAI5110.43k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
HEG20HAI51.2210.37k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y H8k 0 y H8k 0 y H9k 0 y H8k 0 y H16k 0 y H16
HEG21HAI51.1910.75k 0 y 0k 0 y 0k 0 y 0k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H16
HEG22HAI51.0310.32k 0 y 0k 0 y 0k 0 y 0k 0 y H9k 0 y H9k 0 y H9k 0 y H9k 0 y H9k 0 y H9k 0 y H13k 0 y 0
HEG23HAI51.1310.34o 0 y H1o 0 y H1o 0 y H1o i y H4/H10o 0 y 0o i y H10o i y H10o i y H10o i y H10o i y H12o a y H12
HEG24HAI51.110.35o 0 y H1o 0 y H1o 0 y H1o i y H4/H10o 0 y 0o i y H10o i y H10o i y H10o i y H10o a y H12o a y H12
HEG25HAI51.0210.32k 0 y 0k 0 y 0k 0 y 0k 0 y H9k 0 y H9k 0 y H9k 0 y H9k 0 y H9k 0 y H9k 0 y H13k 0 y 0
HEG26HAI51.2810.37k 0 y 0o 0 y 0o 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG27HAI51.0910.6k 0 y 0k 0 y 0k 0 y 0k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H13k 0 n 0
HEG28HAI51.2710.5k 0 y H2k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y H2k 0 y H4k a y H4o a y H12o a y H12
HEG29HAI51.2610.5k 0 y H2k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y H2k 0 y H4k a y H4o a y H12o a y H12
HEG3HAI5110.43k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
HEG30HAI51.210.36k 0 y H2k 0 y H2k 0 y H2k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H13k 0 y H13
HEG31HAI51.1710.22k 0 y 0k 0 y H4k 0 y H4k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H15k 0 y H15
HEG32HAI51.0810.57k 0 y 0k 0 y 0k 0 y 0k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H13k 0 n 0
HEG33HAI51.1110.43k 0 y H2/H18k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y 0k 0 y H2k 0 y H4k a y H4o a y H12o a y H12
HEG34HAI51.2110.39o g y H1/H18o g y H1/H18o g y H1/H18o g y H4o g y 0o g y H1/H18o g y H1/H18o g y H4o g y H4o g y H12o g y H12
HEG35HAI51.2210.41o g y H1/H18o g y H1/H18o g y H1/H18o g y H4o g y 0o g y H1/H18o g y H4o g y H4o g y H4o g y H12o g y H12
HEG36HAI51.0310.51k 0 y H2k 0 y H2k 0 y H2k 0 n 0k 0 y 0k 0 n 0k 0 y 0k 0 y 0k 0 y 0k 0 y H14k 0 y H14
HEG37HAI51.0310.51k 0 y H2k 0 y H2k 0 y H2k 0 n 0k 0 y 0k 0 n 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0
HEG38HAI51.1210.34o 0 y H1o 0 y H1o 0 y H1o 0 y H6o 0 y 0k 0 y H7k 0 y H7o 0 y H7o 0 y H7o a y H12o a y H12
HEG39HAI51.1210.35o 0 y H1o 0 y H1o 0 y H1o i y H4/H6o 0 y 0o i y H6o i y H6o i y H6o i y H6o i y H12o i y H12
HEG4HAI51.1110.44k 0 y H2k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y 0
HEG40HAI50.9710.45k 0 y 0k 0 y 0k 0 y 0k 0 y H7k 0 y H7k 0 y H7k 0 y H7k 0 y H7k 0 y H7k 0 y H13k 0 y H14
HEG41HAI51.2210.37k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y 0k 0 y H9k 0 y H8k 0 y H16k 0 y H16
HEG42HAI51.0710.46k 0 y 0k 0 y 0k 0 y 0k 0 y H8k 0 y 0k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H17k 0 y H17
HEG43HAI51.310.44k 0 y 0k 0 y 0k 0 y 0k 0 y H6k 0 y 0k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H16
HEG44HAI51.0610.48k 0 y H3k 0 y H3k 0 y H3k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H16
HEG45HAI51.0410.51k 0 y H3k 0 y H3k 0 y H3k 0 n 0k 0 y H11k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H16
HEG46HAI51.2110.75k 0 y 0k 0 y 0k 0 y 0k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H8k 0 y H16k 0 y H16
HEG47HAI51.2810.37k 0 y 0k 0 y 0o 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG48HAI51.2910.38k 0 y 0k 0 y 0o 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG49HAI51.2810.39k 0 y 0k 0 y 0o 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG5HAI51.2210.32k 0 y H2k 0 y H2k 0 y H2k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H5k 0 y H13k 0 y H13
HEG50HAI51.2810.42k 0 y 0k 0 y 0k 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG6HAI51.2110.39o g y H1/H18o g y H1/H18o g y H1/H18o g y H4o g y 0o g y H4/H18o g y H4o g y H4o g y H4o g y H12o g y H12
HEG7HAI51.2710.41k 0 y 0o 0 y H4o 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG8HAI51.2710.42k 0 y 0o 0 y H4o 0 y 0o i y H4o i y H4o i y H4o a y H4o a y H4o a y H4o a y H12o a y H12
HEG9HAI51.2210.38o g y H1/H18o g y H1/H18o g y H1/H18o g y H4o g y 0o g y H4/H18o g y H4o a y H4o g y H4k 0 y H16k 0 y H16
SEG1SCH53.0913.97k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG10SCH53.1114k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG11SCH53.1113.99k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG12SCH53.0913.97k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG13SCH52.9713.82o a y S2o a y S6/S2/S7o a y S6/S2/S7o a y S6/S7o a y S6/S7o a y S11/S8/S12/S9o a y S11/S8/S12/S9o a y S11/S8/S12/S9o a y S11/S8/S12/S9o a y S17o a y S17
SEG14SCH53.0913.98k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0o c y S12/S9o c y S15/S17o c y S15/S17
SEG15SCH53.1114.01k 0 y S3k 0 y S3k 0 y S3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y S10k 0 y S15k 0 n 0
SEG16SCH53.1214k 0 y S3k 0 y S3k 0 y S3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y S10k 0 y S15k 0 n 0
SEG17SCH53.113.63o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12/S9o c y S14/S12/S9o c y S14/S12/S9o c y S14/S12/S9o c y S15/S17o c y S15/S17/S1
SEG18SCH53.1413.88o a y S13/S5/S3o a y 0o a y 0o a y S12o a y S12o a y S12/S9o a y S18/S12/S9o a y S18/S12/S9o a y S18/S12/S9o a y S15/S17o a y S15/S17
SEG19SCH53.1214.01k 0 y S3k 0 y S3k 0 y S3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG2SCH53.0913.98k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG20SCH53.113.62o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S12/S9o c y S12/S9o c y S12/S9o c y S14/S12/S9o c y S15/S17o c y S15/S17/S1
SEG21SCH53.1113.61o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S12/S9o c y S12/S9o c y S12/S9o c y S14/S12/S9o c y S15/S17o c y S15/S17/S1
SEG22SCH53.113.97k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG23SCH53.1114.03k 0 y S3k 0 y S3k 0 y S3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y S10k 0 n 0k 0 n 0
SEG24SCH53.0914k 0 y S5/S3k 0 y S3k 0 y S3k 0 y S3k 0 y S3k 0 y S10k 0 y S10k 0 y S10k 0 y S10k 0 n 0k 0 n 0
SEG25SCH53.1113.62o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S12/S9o c y S12/S9o c y S12/S9o c y S14/S12/S9o c y S15/S17o c y S15/S17/S1
SEG26SCH53.1114.02k 0 y S3k 0 y S3k 0 y S3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y S10k 0 n 0k 0 n 0
SEG27SCH53.1213.71o a y S14o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12/S9o a y S15/S17o a y S15/S17
SEG28SCH53.0914.01k 0 y S3k 0 y S3k 0 y S3k 0 y S3k 0 y S3k 0 y S10k 0 y S10k 0 y S10k 0 y S10k 0 n 0k 0 n 0
SEG29SCH53.0914k 0 y S3k 0 y S3k 0 y S3k 0 y S3k 0 y S3k 0 y S10k 0 y S10k 0 y S10k 0 y S10k 0 n 0k 0 n 0
SEG3SCH53.113.99k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG30SCH53.1513.83o a y S4/S3/S14o a y S3/S14o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S15/S17o a y S15/S17
SEG31SCH53.1513.84o a y S4/S3/S14o a y S3/S14o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S15/S17o a y S15/S17
SEG32SCH53.1513.83o a y S4/S3/S14o a y S3/S14o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S15/S17o a y S15/S17
SEG33SCH52.9913.84o a y S2o a y S2/S7o a y S2/S7o a y S7o a y S7o a y S12/S9o a y S12/S9o a y S12/S9o a y S12/S9o a y S15/S17o a y S15/S17
SEG34SCH52.9813.85o a y S2o a y S2/S7o a y S2/S7o a y S7o a y S7o a y S12/S9o a y S12/S9o a y S12/S9o a y S12/S9o a y S15/S17o a y S15/S17
SEG35SCH52.9813.85o a y S2o a y S2/S7o a y S2/S7o a y S7o a y S7o a y S12/S9o a y S12/S9o a y S12/S9o a y S12/S9o a y S15/S17o a y S15/S17
SEG36SCH52.9913.84o a y S2o a y S2/S7o a y S2/S7o a y S7o a y S7o a y S12/S9o a y S12/S9o a y S12/S9o a y S12/S9o a y S15/S17o a y S15/S17
SEG37SCH53.1313.88o a y 0o a y 0o a y 0o a y S13o a y S12o a y S12o a y S12o a y S12o a y S12o a y S15/S17o a y S15/S17
SEG38SCH53.1213.68o a y S14o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12/S9o a y S12o a y S12/S9o a y S15/S17o a y S15/S17
SEG39SCH52.9813.82o a y S2o a y S2/S7o a y S2/S7o a y S7o a y S7o a y S12/S9o a y S12/S9o a y S12/S9o a y S12/S9o a y S15/S17o a y S15/S17
SEG4SCH53.1114k 0 y S3k 0 y S3k 0 y S3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y S10k 0 y S15k 0 n 0
SEG40SCH53.1213.84o a y 0o a y 0o a y 0o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S17o a y S17
SEG41SCH53.1213.85o a y 0o a y 0o a y 0o a y S12o a y S12o a y S12o a y S12o a y S12o a y S12o a y S17o a y S17
SEG42SCH52.8713.97o h y S2o h y S2/S12o h y S2/S12o h y S7o h y S2/S12o h y S2/S12o h y S2/S12o h y S2/S12o h y S2/S12o h y S15/S17o h y S15/S17
SEG43SCH52.8813.97o h y S2o h y S2/S12o h y S2/S12o h y S7o h y S2/S12o h y S2/S12o h y S2/S12o h y S2/S12o h y S2/S12o h y S15/S17o h y S15/S17
SEG44SCH52.8813.97o h y S2o h y S2/S12o h y S2/S12o h y S7o h y S2/S12o h y S2/S12o h y S2/S12o h y S2/S12o h y S2/S12o h y S15/S17o h y S15/S17
SEG45SCH52.8813.96o h y S2o h y S2/S12o h y S2/S12o h y S7o h y S2/S12o h y S2/S12o h y S2/S12o h y S2/S12o h y S2/S12o h y S15/S17o h y S15/S17
SEG46SCH52.9813.83o a y S2o a y S2/S7o a y S2/S7o a y S7o a y S7o a y S12/S9o a y S12/S9o a y S12/S9o a y S12/S9o a y S15/S17o a y S15/S17
SEG47SCH52.9913.83o a y S2o a y S2/S7o a y S2/S7o a y S7o a y S7o a y S12/S9o a y S12/S9o a y S12/S9o a y S12/S9o a y S15/S17o a y S15/S17
SEG48SCH53.113.61o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S12/S9o c y S12/S9o c y S12/S9o c y S14/S12/S9o c y S15/S17o c y S15/S17/S1
SEG49SCH52.9713.86o a y S2o a y S2/S7o a y S2/S7o a y S7o a y S7o a y S12/S9o a y S12/S9o a y S12/S9o a y S12/S9o a y S15/S17o a y S15/S17
SEG5SCH53.1114k 0 y S3k 0 y S3k 0 y S3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y S10k 0 y S15k 0 n 0
SEG50SCH53.1213.75o c y S14o c y S14o c y S14o c y S14o c y S14o c y S14o c y S14o c y S14o c y S14o c y S15/S17o c y S15/S17
SEG6SCH53.113.62o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12/S9o c y S14/S12/S9o c y S14/S12/S9o c y S14/S12/S9o c y S15/S17o c y S15/S17/S1
SEG7SCH53.0913.98k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0
SEG8SCH53.1114.02k 0 y S3k 0 y S3k 0 y S3k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 n 0k 0 y S10k 0 y 0k 0 n 0
SEG9SCH53.113.61o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12o c y S14/S12/S9o c y S14/S12/S9o c y S14/S12/S9o c y S14/S12/S9o c y S15/S17o c y S15/S17/S1
Table 6.

Description of the agri-environmental measures coding of Table 5.

0 = no description or not applicable (0)
Type of farming (1. digit)
k = conventional
o = ecological
Ecological directive (2. digit)
a = EU-Bio
b = DE-Bio
c = Bioland
d = Naturland
e = Biokreis
f = Naturland
g = Demeter
h = Biopark
i = GÄA e.V.
Agri-environmental measure (AEM) (3. digit)
n = no
y = yes
Description AEM (4.digit)
Alb:
MEKA (1992-2013/2014)
A1 = N-B 1: extensive
A2 = N-B 2: extensive and low livestock density
A3 = N-B 3: steep slopes (inclination ≥ 25%) - difficult conditions
A4 = N-B 4: biodiverse
A5 = N-D 2: organic farming
A6 = N-G 1.1: extensive management of protected biotopes
FAKT (2014-2020)
A7 = B 1.1: extensive, max 1.4 RLU/ha, no mineral nitrogen
A8 = B 1.2: extensive, min 0.3 RLU/ha, no nitrogen
A9 = B 3: biodiverse, 4 indicator species
A10 = B 3.2: biodiverse, 6 indicator species
A11 = B 4: extensive management of protected biotopes
A12 = B 5: extensive management of FFH
A13 = C 1: meadow orchards
A14 = C 3: cattle breed
A15 = D 1: no chemical-synthetically plant protection agent or fertiliser
A16 = D 2.2: organic farming
A17 = N 6.1.1: NA (no information)
Other
A18 = SG 1: NA (no information)
A19 = landscape conservation guidelines: (unspecific, measures unknown)
A20 = forest biotope mapping: (unspecific, measures unknown)
Hainich
KULAP (2000-2007)
H1 = Programme A: unspecific (no information of subprogrammes)
H2 = Programme B: unspecific (no information of subprogrammes)
H3 = Programme C: unspecific (no information of subprogrammes)
KULAP (2007-2014)
H4 = L 1: organic farming
H5 = L 4: biodiverse
H6 = N 21: low-nutrient and dry habitats (grazing)
H7 = N 211: low-nutrient and dry habitats (cattle/horse grazing)
H8 = N 213: low-nutrient and dry habitats (sheep/goat grazing)
H9 = N 25: sheep farming and difficult terrain
H10 = N 311: low-nutrient and dry habitats (mowing)
H11 = N 312: low-nutrient and dry habitats (mowing, difficult conditions)
KULAP (2014-2020)
H12 = Ö2: organic farming
H13 = G 11: biodiverse
H14 = G21: biotope management by grazing
H15 = G31: biotope management by grazing and difficult conditions
H16 = G33: biotope management by sheep grazing
H17 = G53: biotope management by sheep grazing and difficult conditions
Other
H18 = FFH: NA (unspecific, measures unknown)
Schorfheide
KULAP
S1 = 33: disadvantaged sites (no further information)
S2 = 311A: extensive with no mineral fertiliser
S3 = 311C: extensive with no fertiliser
S4 = 313A: no mowing before 16 June
S5 = 313B: no mowing before 1 July
S6 = 413A: late and restricted management
S7 = 423B: late and restricted management
S8 = 613A: late and restricted management
S9 = 623B: organic farming
S10 = 661: extensive on farm level
S11 = 663: late and restricted management
S12 = 673: organic farming
S13 = 763: late and restricted management
S14 = 773: organic farming
S15 = 811A: extensive with no fertiliser
S16 = 812C: extensive management starts 1 July
S17 = 882: organic farming
Other
S18 = contractual nature conservation: Mowing after 15 June
In accordance with Blüthgen et al. 2012, mowing and fertilisation were correlated such that grasslands that were mown more frequently also received higher amounts of fertiliser. High grazing intensity was correlated with low mowing frequency such that intensively grazed grasslands were not mown and vice versa (Fig. 3a). Nevertheless, many grasslands were mown pastures, i.e. they are both grazed by varying types of livestock and mown using different practices. Timing of mowing and grazing was also variable between years. Correlations between mowing and grazing and between mowing and fertilisation, were stronger than those between fertilisation and grazing (Fig. 3). Correlations also differed between regions (Fig. 3). For example, in Hainich, there were weaker negative correlations between mowing and grazing compared to the Schwäbische Alb and Schorfheide.
Figure 3.

Boxplots showing the Spearman correlation coefficients (Rho) between the different grassland management components, calculated separately for each 11 years. a) mowing vs. grazing, b) mowing vs. fertilisation and c) fertilisation vs. grazing of the three different regions (ALB- Swabian Alb, HAI- Hainich, SCH- Schorfheide). Boxes with the same letters are not significantly different at p > 0.05 using pairwise Wilcoxon tests with Bonferroni correction.

The value of this dataset lies in the comprehensive and consistent description and characterisation of grassland management of 150 grassland plots over 11 years. Detailed accounting of land-use practices can only be achieved through intensive collaboration between land managers, land users and researchers, as done in our study. The accuracy of the answers given by farmers strongly determined the data quality. While in our study regions, all farmers had to keep records of management, mainly due to regulations of EU agricultural subsidies and cross-compliance obligations, the quality of the records still differed in some detail. To increase the accuracy of the data, members of the BE project additionally recorded data on grassland management over the years, such as cutting and fertilisation dates and maintenance activities. These observations were integrated when questionnaires were filled out together with the farmers. Values for organic fertilisation with slurry or liquid manure were probably less accurate than those for grazing and mowing, due to the fact that often no exact records existed for the amounts of material put on a particular site. Another source of uncertainty was the variation in N content of the material, which depended on many factors, for example, the livestock and the amount of added water etc. Here, we gave raw amounts of slurry and liquid manure, as well as the conversion factors to N per ha (Table 3).
Table 3.

Nitrogen input conversion factor of manure and slurry.

Type of manure (t/ha) Conversion Factor for total Nitrogen [kg/t] Literature and Notes
Cattle5.6LWK (Chamber of Agriculture) Nordrhein-Westfalen (2014), own measurements analysed by LUFA Nord-West (Agricultural Investigation and Research Institute - accredited laboratory of the Chamber of Agriculture in Niedersachen) (2017)
Horse4.9
Sheep8.13
Type of slurry (m³/ha) Total Nitrogen [kg/m³]
Cattle3.85 (3.2-4.5)Mean values of slurry ranges were used. (LWK Nordrhein-Westfalen(2014))
Pig5.4 (4.3-6.5)
Mixed4.45 (4.0-4.9)
Biogas / Digestate4.4LWK Baden-Württemberg (2012)
The specific management data, presented here, have formed the basis for analyses of land use effects on the biodiversity and ecosystem functioning in grasslands (e.g. Allan et al. 2014, Manning et al. 2015, Klaus et al. 2018)Suppl. material 2. The data can be coupled with climate data and soil information to disentangle effects of management from effects of abiotic conditions (Smit et al. 2008). Our data show that ecologists, interested in the effects of land use on biodiversity and ecosystem functions, should pay closer attention to measurements of land use itself, because management can strongly vary between years with significant long-term effects on the target variables measured in any particular year (Klaus et al. 2011, Kleinebecker et al. 2018).

Quality control

Quality assurance took place by checking the plausibility of the values by the grassland experts of the local teams in the following ways i) the answers of the land users were compared with own observations before entering the values in the data table. When uploading the data, the values were again checked ii) by the responsible person for the whole dataset looking at the single values and communicating uncertainties back to the interviewers. Further, unusual values were detected by auxiliary calculations, including minimum and maximum of these values and then double checked with the original hard copy version of the interview.

Step description

The original interviews are stored in hard copies and the information is entered in the joint data table. The data are stored on the Biodiversity-Exploratories Information System (BExIS) (http://doi.org/10.17616/R32P9Q) at https://www.bexis.uni-jena.de. This version is in German due to the annual survey of the land users being carried out in German. The interviews of each grassland site is entered in a default excel data sheet and transformed via a visual basic script before it is uploaded to the joint dataset in BExIS. Daily backups at the BE repository ensures the storage of the actual version of the land use data table. After project end, all datasets are intended to be stored via GFBio in a domain-specific long term archive.

Geographic coverage

Description

The monitored 150 grassland plots are situated within three regions in Germany, with 50 plots in each region, covering a geographic gradient from the North-East (Schorfheide Chorin), Central Germany (Hainich Dün) and South-West (Schwäbische Alb). The grassland plots experience different land use according to the management.

Taxonomic coverage

We did not collect any organisms. During the interviews, the land users provided us with information according to their grassland management.

Temporal coverage

Notes

We obtained land use data of grasslands between 2006 and 2016.

Usage rights

Use license

Other

IP rights notes

The English version of the dataset is added as supplementary material. The original, slightly extended, dataset is stored on the Biodiversity-Exploratories Information System (BExIS) (http://doi.org/10.17616/R32P9Q) at https://www.bexis.uni-jena.de. This version is in German due to the annual survey of the land users being carried out in German. Contact is possible via the Biodiversity Coordination Office (beo@senckenberg.de). Due to sensitive information, such as personal data, the original dataset is not publicly available. Data access can be given by individual request for access. Guidelines can be checked in the data agreement of the BE: https://www.bexis.uni-jena.de/PublicData/Files/PublicData-DataAgreement.txt.

Data resources

Data package title

BE_landuse_grassland_2006-2016.csv

Number of data sets

1

Data set 1.

Data set name

BE_landuse_grassland_2006-2016.csv

Number of columns

56

Description

The data table (628 KB) contains 1651 rows with records of eleven years on 150 grassland sites, including the variable headers and 116 (Suppl. material 1). BE_landuse_grassland_2006-2016.csv Data type: utf 8 - txt Brief description: The present dataset summarises management information collected from 2006 to 2016 for 150 grassland plots in three different regions of Germany. Data are based on annual interviews of the respective farmers, land owners or tenants involved in land management activity, using a standardised questionnaire. Standardisation of missing values: “NA” - if not known, “0” if something was counted but was zero (e.g. no mowing or no cows or no maintenance). Some dates of maintenance or fertilisation are set to "0". For example, in case maintenance measurements were applied on that plot during the year but not the specific one, i.e. mulching was applied and is listed with specific mulching date, but no levelling took place, therefore the levelling date is set to "0" instead of "-1" when generally no maintenance measures were carried out on that plot within the year. “-1” if not possible, for example, if no mowing a “-1” has been given for the question about mowing machine. File: oo_338774.txt Bibliography of the land use index Data type: Text Brief description: This library shows the citations of the LUI developed by Brüthgen et al. 2012. File: oo_330477.txt
Data set 1.
Column labelColumn description
Study regionALB = Schwäbische Alb, HAI = Hainich, SCH = Schorfheide
YearYear of management
DateDate of interview
PlotIDExperimental Plot IDs formatted as (A|H|S)EG with consecutive numbering. Abbreviations are: A = Schwäbische Alb, H = Hainich, S = Schorfheide, E = Experimental Plot, G = Grassland, e.g. AEG01
DrainageMeasure of drainage and the description of the method (free text)
StartDrainageStarting year of grassland drainage if applicable
WaterLoggingActivities on water logging, e.g. for water regulation of fen soils
Agriculture1981Use of grassland between 1981 to 2006, i.e. (temporal) conversion of grassland into arable land
SizeManagementUnit_haSize of the management unit in the survey year, often larger than the 50 x 50 m study plot itself
StartGrazingStarting month of the first grazing period in the survey year
EndGrazingEnd of the last grazing period in the survey year
Livestock1 (2-4)Type of animal in first (second - fourth) grazing period
StartGrazingPeriod1 (2-4)Starting month of first grazing period for livestock 1 (2-4)
EndGrazingPeriod1 (2-4)Ending month of first grazing period for livestock 1 (2-4)
Cattle6months1 (2-4)For Grazing period 1 (2-4): Number of cattle with an age up to 6 months (1 cattle up to 6 month = 0.3 LS* per day)
Cattle6-24months1 (2-4)For Grazing period 1 (2-4): Number of cattle with an age between 6 months and 2 years (=0.6 LS*)
CattlePlus2years1 (2-4)For Grazing period 1 (2-4): Number of cattle older than 2 years (= 1 LS*)
SheepGoat1year1 (2-4)For Grazing period 1 (2-4): Number of sheep or goats with an age up to 1 year (= 0.05 LS*)
Pony1 (2-4)For Grazing period 1 (2-4): Number of ponies and small horses (= 0.7 LS*)
Horse3years1 (2-4)For Grazing period 1 (2-4): Number of horses up to 3 years (= 0.7 LS*)
HorsePlus3years1 (2-4)For Grazing period 1 (2-4): Number of horses older than 3 years (= 1.1 LS*)
NbLivestock1 (2-4)For Grazing period 1 (2-4): Total number of livestock
LivestockUnits1 (2-4)For Grazing period 1 (2-4): Total sum of the livestock units
DayGrazing1 (2-4)For Grazing period 1 (2-4): duration of grazing (in days)
GrazingArea1 (2-4)For Grazing period 1 (2-4): size of area where livestock grazed
MowingNumber of cuts per year
DateMowing1 (2-4)Date of the first (second-fourth) cut
MowingMachineType of machine which was used for mowing e.g. rotarymower, doubleknife, mulcher
CutWidth_mCutting width of the mowing machine
CutHeight_cmCutting height above soil level of the mowing machine
DriveSpeed_kmhSpeed of the mowing machine, normally mean speed value is given
MowingConditionerPresence of conditioner, i.e. did the mowing machine have a conditioner to improve drying of the clippings
FertilisationAddition of fertiliser (not including dung depositions by livestock during grazing a parcel)
NbFertilisationNumber of fertiliser applications per year
DateFertilisation1 (2-7)Date of first (2nd-7th) fertiliser application
Manure_thaTotal amount of applied solid manure
Slurry_m3haTotal amount of applied pig or cow slurry and biogas residues, respectively
DescFertDescription of applied organic fertiliser
orgNitrogen_kgNhaAmount of organic nitrogen applied
minNitrogen_kgNhaAmount of nitrogen applied, of mineral origin or the organic fertiliser mash from a bioethanol factory (see in DescFert)
totalNitrogen_kgNhaSum of applied mineral and organic nitrogen [kg N/ha]
minPhosphorus_kgPhaAmount of phosphorus applied [kg P2O5/ha], of mineral origin or mash from a bioethanol factory (not given for other organic fertilisers)
minPotassium_kgKhaAmount of potassium applied [kg K2O/ha], of mineral origin or mash from a bioethanol factory (not given for other organic fertilisers)
Sulphur_kgShaTotal amount of applied Sulphur [kg S/ha]
MaintenancePresence of maintenance measures
LevellingMaintenance to break up matted grass covers
DateLevellingMaintenance: date of levelling
RollingMaintenance: rolling to level unevenness
DateRollingMaintenance: date of rolling
MulchingPartial mulching on some spots, e.g. rank patches. The material remains on site after mowing. We consider this not as a mowing event, as only a small part of the area is treated.
DateMulchingDate of partial mulching
ShrubClearanceClearance to avoid shrub encroachment. We consider this not as a mowing event, as only individual shrubs are targeted.
DateScrubClDate of shrub clearance
PlantProtectionAgentPesticide use: pesticides and herbicides. As pesticides in grasslands are very rare and only used for spot treatment, we do not have further information on this treatment.
SeedsSeed addition
DescSeedsDescription of usage of the sowing
  5 in total

1.  Interannual variation in land-use intensity enhances grassland multidiversity.

Authors:  Eric Allan; Oliver Bossdorf; Carsten F Dormann; Daniel Prati; Martin M Gossner; Teja Tscharntke; Nico Blüthgen; Michaela Bellach; Klaus Birkhofer; Steffen Boch; Stefan Böhm; Carmen Börschig; Antonis Chatzinotas; Sabina Christ; Rolf Daniel; Tim Diekötter; Christiane Fischer; Thomas Friedl; Karin Glaser; Christine Hallmann; Ladislav Hodac; Norbert Hölzel; Kirsten Jung; Alexandra Maria Klein; Valentin H Klaus; Till Kleinebecker; Jochen Krauss; Markus Lange; E Kathryn Morris; Jörg Müller; Heiko Nacke; Esther Pasalic; Matthias C Rillig; Christoph Rothenwöhrer; Peter Schall; Christoph Scherber; Waltraud Schulze; Stephanie A Socher; Juliane Steckel; Ingolf Steffan-Dewenter; Manfred Türke; Christiane N Weiner; Michael Werner; Catrin Westphal; Volkmar Wolters; Tesfaye Wubet; Sonja Gockel; Martin Gorke; Andreas Hemp; Swen C Renner; Ingo Schöning; Simone Pfeiffer; Birgitta König-Ries; François Buscot; Karl Eduard Linsenmair; Ernst-Detlef Schulze; Wolfgang W Weisser; Markus Fischer
Journal:  Proc Natl Acad Sci U S A       Date:  2013-12-24       Impact factor: 11.205

2.  Land-use intensification causes multitrophic homogenization of grassland communities.

Authors:  Martin M Gossner; Thomas M Lewinsohn; Tiemo Kahl; Fabrice Grassein; Steffen Boch; Daniel Prati; Klaus Birkhofer; Swen C Renner; Johannes Sikorski; Tesfaye Wubet; Hartmut Arndt; Vanessa Baumgartner; Stefan Blaser; Nico Blüthgen; Carmen Börschig; Francois Buscot; Tim Diekötter; Leonardo Ré Jorge; Kirsten Jung; Alexander C Keyel; Alexandra-Maria Klein; Sandra Klemmer; Jochen Krauss; Markus Lange; Jörg Müller; Jörg Overmann; Esther Pašalić; Caterina Penone; David J Perović; Oliver Purschke; Peter Schall; Stephanie A Socher; Ilja Sonnemann; Marco Tschapka; Teja Tscharntke; Manfred Türke; Paul Christiaan Venter; Christiane N Weiner; Michael Werner; Volkmar Wolters; Susanne Wurst; Catrin Westphal; Markus Fischer; Wolfgang W Weisser; Eric Allan
Journal:  Nature       Date:  2016-11-30       Impact factor: 49.962

3.  Land use intensity, rather than plant species richness, affects the leaching risk of multiple nutrients from permanent grasslands.

Authors:  Valentin H Klaus; Till Kleinebecker; Verena Busch; Markus Fischer; Norbert Hölzel; Sascha Nowak; Daniel Prati; Deborah Schäfer; Ingo Schöning; Marion Schrumpf; Ute Hamer
Journal:  Glob Chang Biol       Date:  2018-04-10       Impact factor: 10.863

4.  Temporal changes in randomness of bird communities across Central Europe.

Authors:  Swen C Renner; Martin M Gossner; Tiemo Kahl; Elisabeth K V Kalko; Wolfgang W Weisser; Markus Fischer; Eric Allan
Journal:  PLoS One       Date:  2014-11-11       Impact factor: 3.240

5.  Nutrient stoichiometry and land use rather than species richness determine plant functional diversity.

Authors:  Verena Busch; Valentin H Klaus; Caterina Penone; Deborah Schäfer; Steffen Boch; Daniel Prati; Jörg Müller; Stephanie A Socher; Ülo Niinemets; Josep Peñuelas; Norbert Hölzel; Markus Fischer; Till Kleinebecker
Journal:  Ecol Evol       Date:  2017-12-03       Impact factor: 2.912
  5 in total
  3 in total

1.  Distribution of Medically Relevant Antibiotic Resistance Genes and Mobile Genetic Elements in Soils of Temperate Forests and Grasslands Varying in Land Use.

Authors:  Inka M Willms; Jingyue Yuan; Caterina Penone; Kezia Goldmann; Juliane Vogt; Tesfaye Wubet; Ingo Schöning; Marion Schrumpf; François Buscot; Heiko Nacke
Journal:  Genes (Basel)       Date:  2020-01-30       Impact factor: 4.096

2.  Contrasting responses of above- and belowground diversity to multiple components of land-use intensity.

Authors:  Gaëtane Le Provost; Jan Thiele; Catrin Westphal; Caterina Penone; Eric Allan; Margot Neyret; Fons van der Plas; Manfred Ayasse; Richard D Bardgett; Klaus Birkhofer; Steffen Boch; Michael Bonkowski; Francois Buscot; Heike Feldhaar; Rachel Gaulton; Kezia Goldmann; Martin M Gossner; Valentin H Klaus; Till Kleinebecker; Jochen Krauss; Swen Renner; Pascal Scherreiks; Johannes Sikorski; Dennis Baulechner; Nico Blüthgen; Ralph Bolliger; Carmen Börschig; Verena Busch; Melanie Chisté; Anna Maria Fiore-Donno; Markus Fischer; Hartmut Arndt; Norbert Hoelzel; Katharina John; Kirsten Jung; Markus Lange; Carlo Marzini; Jörg Overmann; Esther Paŝalić; David J Perović; Daniel Prati; Deborah Schäfer; Ingo Schöning; Marion Schrumpf; Ilja Sonnemann; Ingolf Steffan-Dewenter; Marco Tschapka; Manfred Türke; Juliane Vogt; Katja Wehner; Christiane Weiner; Wolfgang Weisser; Konstans Wells; Michael Werner; Volkmar Wolters; Tesfaye Wubet; Susanne Wurst; Andrey S Zaitsev; Peter Manning
Journal:  Nat Commun       Date:  2021-06-24       Impact factor: 14.919

3.  Above- and belowground biodiversity jointly tighten the P cycle in agricultural grasslands.

Authors:  Yvonne Oelmann; Markus Lange; Sophia Leimer; Christiane Roscher; Felipe Aburto; Fabian Alt; Nina Bange; Doreen Berner; Steffen Boch; Runa S Boeddinghaus; François Buscot; Sigrid Dassen; Gerlinde De Deyn; Nico Eisenhauer; Gerd Gleixner; Kezia Goldmann; Norbert Hölzel; Malte Jochum; Ellen Kandeler; Valentin H Klaus; Till Kleinebecker; Gaëtane Le Provost; Peter Manning; Sven Marhan; Daniel Prati; Deborah Schäfer; Ingo Schöning; Marion Schrumpf; Elisabeth Schurig; Cameron Wagg; Tesfaye Wubet; Wolfgang Wilcke
Journal:  Nat Commun       Date:  2021-07-21       Impact factor: 17.694
  3 in total

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