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Data Paper (Biosciences)
Eleven years’ data of grassland management in Germany
expand article infoJuliane Vogt, Valentin H. Klaus§,|, Steffen Both‡,, Cornelia Fürstenau#, Sonja Gockel¤,«, Martin M. Gossner‡,», Johannes Heinze˄, Andreas Hemp˅, Nobert Hölzel§, Kirsten Jung¦, Till Kleinebecker§,ˀ, Ralf Lauterbach¦, Katrin Lorenzen, Andreas Ostrowski#, Niclas Otto, Daniel Pratiˁ, Swen Renner, Uta Schumacher, Sebastian Seibold, Nadja K. Simons‡,, Iris Steitz¦, Miriam Teuscher, Jan Thiele, Sandra Weithmann¦, Konstans Wells, Kerstin Wiesner, Manfred Ayasse¦, Nico Blüthgen, Markus Fischerˁ,, Wolfgang W. Weisser
‡ Technische Universität München, School of Life Sciences Weihenstephan, Terrestrial Ecology Research Group, Freising, Germany
§ Westfälische Wilhelms-Universität, Institute of Landscape Ecology, Münster, Germany
| ETH Zürich, Institute of Agricultural Sciences, Zürich, Switzerland
¶ Martin-Luther-Universität Halle-Wittenberg, Institut für Agrar- und Ernährungswissenschaften, Halle, Germany
# Friedrich Schiller Universität Jena, Institute for Computer Science, Heinz Nixdorf Chair for Distributed Information Systems, Jena, Germany
¤ Friedrich Schiller Universität Jena, Institute of Ecology, Jena, Germany
« Georg-August-Universität Göttingen, Silviculture and Forest Ecology of the Temperate Zones, Göttingen, Germany
» Swiss Federal Research Institute WSL, Forest Entomology, Birmensdorf, Switzerland
˄ Universität Potsdam, Biodiversity Research/Systematic Botany, Institute of Biochemistry and Biology, Potsdam, Germany
˅ University of Bayreuth, Department of Plant Systematics, Bayreuth, Germany
¦ University of Ulm, Institute of Evolutionary Ecology and Conservation Genomics, Ulm, Germany
ˀ Justus-Liebig-Universität Gießen, Institute of Landscape Ecology and Resource Management, Gießen, Germany
ˁ University of Bern, Institute of Plant Science, Department of Biology, Bern, Switzerland
₵ University of Natural Resources and Life Sciences BOKU, Institute of Zoology, Vienna, Austria
ℓ Senckenberg Gesellschaft für Naturforschung, Biodiversity and Climate Research Centre BiK-F, Frankfurt, Germany
₰ Technische Universität Darmstadt, Ecological Networks, Darmstadt, Germany
₱ Johann Heinrich von Thünen Institute for Biodiversity, Braunschweig, Germany
₳ Swansea University, Department of Biosciences, Swansea, United Kingdom
Open Access

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).

Keywords

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

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).

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)

ID Text - - Unique identifier composed of the columns PlotID and Year

Study region

Text

-

ALB = Schwäbische Alb

HAI = Hainich

SCH = Schorfheide

Year

Integer

yyyy

-

Year of management

Date

Date

dd.mm.yyyy

-

Date of interview

PlotID

Text

-

-

Experimental 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

Drainage

Text

-

-

Measure of drainage and the description of the method (free text)

StartDrainage

Integer

yyyy

-

Starting year of grassland drainage, if applicable

WaterLogging

Boolean

yes/no

-

Activities on water logging, e.g. for water regulation of fen soils

Agriculture1981

Boolean

yes/no

-

Use of grassland between 1981 to 2006, i.e. (temporal) conversion of grassland into arable land

SizeManagementUnit_ha

Numeric

ha

0.49-187.1

Size of the management unit in the survey year, often larger than the 50 x 50 m study plot itself

Grazing

StartGrazing

Text

Month

-

Starting month of the first grazing period in the survey year

EndGrazing

Text

Month

-

End of the last grazing period in the survey year

Livestock1

Text

-

Type of animal in first grazing period

StartGrazingPeriod1

Text

month

-

Starting month of first grazing period for livestock 1

EndGrazingPeriod1

Text

month

-

Ending month of first grazing period for livestock 1

Livestock, Start/End GrazingPeriod 2-4 …

-

Identical information for grazing periods 2-4, if applicable.

Cattle6months1

Integer

0-95

For Grazing period 1: Number of cattle with an age up to 6 months (cattle up to 6 months = 0.3 LS* per day)

Cattle6-24months1

Integer

0-200

For Grazing period 1: Number of cattle with an age between 6 months and 2 years (= 0.6 LS*)

CattlePlus2years1

Integer

0-300

For Grazing period 1: Number of cattle older than 2 years (= 1 LS*)

SheepGoat1year1

Integer

0-1000

For Grazing period 1: Number of sheep or goats with an age up to 1 year (= 0.05 LS*)

SheepGoatPlus1year1

Integer

0-1500

For Grazing period 1: Number of sheep or goats older than 1 year (= 0.1 LS*)

Pony1

Integer

0-400

For Grazing period 1: Number of ponies and small horses (= 0.7 LS*)

Horse3years1

Integer

0-4

For Grazing period 1: Number of horses up to 3 years (= 0.7 LS*)

HorsePlus3years1

Integer

0-46

For Grazing period 1: Number of horses older than 3 years (= 1.1 LS*)

NbLivestock1

Integer

0-2500

For Grazing period 1: Total number of livestock

LivestockUnits1

Numeric

Number of livestock x conversion factor

0-1814

For Grazing period 1: Total sum of the livestock units

DayGrazing1

Integer

days

0-365

For Grazing period 1: duration of grazing (in days)

GrazingArea1

Numeric

ha

0-148.5

For 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

Cattle6months2

0-73

Cattle6-24months2

0-103

CattlePlus2years2

0-120

SheepGoat1year2

0-600

SheepGoatPlus1year2

0-1200

Pony2

0

Horse3years2

0

HorsePlus3years2

0-18

NbLivestock2

0-1200

LivestockUnits2

0-144

DayGrazing2

0-165

GrazingArea2

0-148.5

Cattle6months3

0-72

Cattle6-24months3

0-103

CattlePlus2years3

0-120

SheepGoat1year3

0-820

SheepGoatPlus1year3

0-1300

Pony3

0

Horse3years3

0

HorsePlus3years3

0-25

NbLivestock3

0-1340

LivestockUnits3

0-145.5

DayGrazing3

0-127

GrazingArea3

0-196.69

Cattle6months4

0-72

Cattle6-24months4

0-81

CattlePlus2years4

0-84

SheepGoat1year4

0-600

SheepGoatPlus1year4

0-900

Pony4

0

Horse3years4

0

HorsePlus3years4

0-16

NbLivestock4

0-900

LivestockUnits4

0-103.6

DayGrazing4

0-76

GrazingArea4

0-148.5

TotalGrazing_LSUdha

Numeric

#Livestock*days /ha

0-1644.17

Total sum of the grazing intensity for all grazing periods

SupplementaryFeeding

Boolean

yes/no

-

Additional fodder supply for the livestock

DescFeeding

Text

-

Type and amount of supplementary fodder

Mowing

Mowing

Integer

1/year

0-4

Number of cuts per year

DateMowing1

Date

dd.mm.yyyy

-

Date of the first cut

DateMowing2-4…

Date

dd.mm.yyyy

-

Dates of the second to fourth cut, if applicable

MowingMachine

Text

-

Type of machine which was used for mowing, e.g. rotarymower, doubleknife, mulcher

CutWidth_m

Numeric

m

0-12

Cutting width of the mowing machine

CutHeight_cm

Integer

cm

0-15

Cutting height above soil level of the mowing machine

DriveSpeed_kmh

Integer

km/h, (mean)

0-20

Speed of the mowing machine, normally mean speed value is given

MowingConditioner

Boolean

yes/no

-

Presence of conditioner, i.e. did the mowing machine have a conditioner to improve drying of the clippings

Fertilisation

Fertilisation

Boolean

yes/no

-

Addition of fertiliser (not including dung depositions by livestock during grazing a parcel)

NbFertilisation

Integer

0-7

Number of fertiliser applications per year

DateFertilisation1

Date

dd.mm.yyyy

-

Date of first fertiliser application

DateFertilisation2-7…

Date

dd.mm.yyyy

-

Date of 2nd to 7th fertiliser applications

Manure_tha

Numeric

t/ha

0-40

Total amount of applied solid manure

Slurry_m3ha

Numeric

m³/ha

0-80

Total amount of applied pig or cow slurry and biogas residues, respectively.

DescFert

Text

-

Description of applied organic fertiliser

orgNitrogen_kgNha

Numeric

kg/ha

0-371

Amount of organic nitrogen applied

minNitrogen_kgNha

Numeric

kg/ha

0-170

Amount of nitrogen applied, of mineral origin or the organic fertiliser mash from a bioethanol factory (see in DescFert)

totalNitrogen_kgNha

Numeric

Kg /ha

0-433

Sum of applied mineral and organic nitrogen [kg N/ha]

minPhosphorus_kgPha

Numeric

kg/ha

0-350

Amount of phosphorus applied [kg P2O5/ha], of mineral origin or mash from a bioethanol factory (not given for other organic fertilisers)

minPotassium_kgKha

Numeric

kg/ha

0-100

Amount of potassium applied [kg K2O/ha], of mineral origin or mash from a bioethanol factory (not given for other organic fertilisers)

Sulphur_kgSha

Numeric

kg/ha

0-25

Total amount of applied Sulphur [kg S/ha]

Maintenance

Maintenance

Boolean

yes/no

-

Presence of maintenance measures

Levelling

Text

0-4

Maintenance to break up matted grass covers

DateLevelling

Date

dd.mm.yyyy

-

Maintenance: date of levelling

Rolling

Text

0-2

Maintenance: rolling to level unevenness

DateRolling

Date

dd.mm.yyyy

-

Maintenance: date of rolling

Mulching

Text

0-4

Partial 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.

DateMulching

Date

dd.mm.yyyy

-

Date of partial mulching

ShrubClearance

Text

-

0-1

Clearance to avoid shrub encroachment. We consider this not as a mowing event as only individual shrubs are targeted.

DateScrubCl

Date

dd.mm.yyyy

-

Date of shrub clearance

PlantProtectionAgent

Boolean

yes/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.

Seeds

Boolean

yes/no

-

Seed addition

DescSeeds

Text

-

-

Description of usage of the sowing

*LS – Livestock

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

Grazing species

Age

Livestock units (LSU)

Cattle

< 6 months

0.3

Cattle

6 months-2 years

0.6

Cattle

> 2 years

1

Sheep and goats

< 1 year

0.05

Sheep and goats

> 1 year

0.1

Ponies and small horses

-

0.7

Horses

< 3 years

0.7

Horses

> 3 years

1.1

Figure 1.  

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

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: 

Members of the steering committee of the BE: 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).

Nitrogen input conversion factor of manure and slurry.

Type of manure (t/ha)

Conversion Factor for total Nitrogen [kg/t]

Literature and Notes

Cattle

5.6

LWK (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)

Horse

4.9

Sheep

8.13

Type of slurry (m³/ha)

Total Nitrogen [kg/m³]

Cattle

3.85 (3.2-4.5)

Mean values of slurry ranges were used. (LWK Nordrhein-Westfalen(2014))

Pig

5.4 (4.3-6.5)

Mixed

4.45 (4.0-4.9)

Biogas / Digestate

4.4

LWK Baden-Württemberg (2012)

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).

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.

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-meadows

B5

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 meadows

N23

Eligible landscape (e.g. Natura 2000)

413A, 423B, 613A, 663

Sheep farming and difficult terrain

N25

Difficult conditions (regarding terrain, specific management)

G31, G33, G53

Location of valuable genetic plants

L4

Compensatory allowance of disadvantaged sites

33

Organic farming

Farm is managed according to EU eco-regulation

N-D2, D2

Introduction or retention of ecological management of the farm

L1, Ö2

773, 673,882

Retention of ecological management - compensatory allowance

623 A,B,C,D

In general

Main fodder site

B1.2

Min 5% of eligible site managed after 15 June

N-B1, N-B3

Promoting of endangered livestock breeds

C3

No reduction of permanent grassland of the farm

N25

Management plan according to nature conservation authority

N231, N31, G21, G31, G33, G53

663

Fodder sites are managed at least once per year by grazing or mowing

413A, 423B, 613A, 663, 673

Management after 1 July

812C

Grazing

Livestock min 0.3 LSU/ha on agriculture area

Ö2

Livestock max 2 LSU/ha on agriculture area

N-B1, N-B3

773, 673

Livestock min 0.5 RLSU/ha fodder area

L4

Livestock min 0.3 RLSU/ha fodder area

N-B2, B1.1, B1.2

311A, 311C, 773, 673, 661, 411

Livestock max 1.4 RLSU/ ha

311A, 311C, 773, 673, 661, 411

Livestock max 1.4 LSU/ ha

N-B2, B1.1

311A, 311C

At least one grazing per year

N25

At least one grazing per year. First grazing period by cattle/horses or sheep/goats

G21

At least one grazing per year. First grazing period by sheep/goats

G33, G53

At least one management per year (grazing or mowing and harvesting of the yield) before 15 October

411, 661

Maintenance measures after grazing

N-B1, N-B2, N-B3

Grazing by cattle/horses with 0.3-1 LSU/ha

N211

Grazing by cattle/horses with permanent grazing or at least from 2 May to 15 October

G31

Grazing by sheep or goats with a min 0.5 LSU/ha

N213, N25, G33, G53

Grazing 0.3-1 LSU/ha

N231

Max 1.5 LSU/ha*d until 1 July

N231

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 cut

N31

No supplementary feeding

N233

No supplementary feeding between 1 May and 15 October

G21, G31, G33, G53

Mowing

First management mowing and harvesting the yield

N31

Up to 2 cuts with a time lag of at least 7 weeks

N31

First mowing not before 15 August on min 5% of the area

N31

No mowing before 16 June

313A

No mowing before 1 July

313B

Post grazing mowing not before 1 July

N231, G31, G33, G53

Cut height 10 cm

313B, 763

Indicator plant species

Abundance of at least 4 indicator plant species out of 28 specific forbs

N-B4

L4

Abundance of at least 6 indicator plant species out of 30 specific plants

B3.2

G11

Abundance of at least 7 indicator plant species out of specific plants

B5

Fertilisation

No mineral nitrogen fertilisation

B1.1

No mineral or organic nitrogen fertilisation

B1.2

No slurry fertilisation

311C

No fertilisation

811A

No chemical-synthetic fertiliser or plant protection agent within the farm

D1

311A, 311C, 661, 411

No chemical-synthetic fertiliser or plant protection agent on eligible areas

N231, N25; N31

No fertiliser or plant protection agent

G21, G31, G33

Documentation

Slurry records (amount, date) for eligible areas

N-B1, N-B3

Fertilisation and management records for eligible areas

N-B4

Fertilisation and mowing records for eligible areas

B3.2

Fertilisation and plant protection agent records for all grasslands of the farm

B1.2

Records via Thuringian grassland card for eligible areas

N231, N25, N31, G21, G31, G33, G53

Restrictions /measures not taken

No ploughing, only seed addition

B1.1, B1.2, B3.2

No ploughing on eligible areas

No ploughing on farm

N-B1, N-B2, N-B3, N-B4, N-D2

413A, 423B, 411, 661

No irrigation or melioration

N-B2, B1.1, B1.2

G21, G31, G33, G53

No extensive usage of plant protection agents

N-B1, N-B2, N-B3, N-B4, B1.1, B1.2

No maintenance measures, mowing or seed addition between 1 April and 30 June

G21, G31, G33, G53

No upturning or limbering tillage

G21, G31, G33, G53

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_ID Explo Latitude Longitude 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
AEG1 ALB 48.4 9.34 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG10 ALB 48.38 9.21 o 0 n A5 o 0 n A5 o 0 n A5 o 0 n A5 o 0 n A5 o 0 n A5 o 0 n A5 o 0 n A5 o 0 n A5 k 0 n 0 k 0 n 0
AEG11 ALB 48.49 9.35 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG12 ALB 48.39 9.35 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG13 ALB 48.39 9.36 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG14 ALB 48.38 9.52 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG15 ALB 48.49 9.45 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y A12
AEG16 ALB 48.4 9.46 o c y A4/A5 o c y A4/A5 o c y A4/A5 o c y A4/A5 o c y A4/A5 o c y A4/A5 o c y A4/A5 o c y A4/A5 o c y A4/A5 o c y A14/11/A12 o c y A14/A16/A12
AEG17 ALB 48.4 9.52 o a y 0 o a y 0 o a y 0 o a y 0 o a y 0 o a y 0 o a y 0 o a y 0 o a y 0 o c 0 0 o c 0 0
AEG18 ALB 48.38 9.52 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG19 ALB 48.4 9.45 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG2 ALB 48.38 9.47 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG20 ALB 48.49 9.36 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG21 ALB 48.44 9.36 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 0 0 0 0 k 0 n 0 k 0 n 0
AEG22 ALB 48.4 9.51 k 0 y A1 k 0 y A1 k 0 y A1 k 0 y A1 k 0 y A1 k 0 y A1 k 0 y A1 k 0 y A1 k 0 y A1 k 0 y A7/A10 k 0 y A7/A10
AEG23 ALB 48.42 9.51 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 o c y A12 o c y A12
AEG24 ALB 48.4 9.49 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15 k 0 y A15
AEG25 ALB 48.4 9.26 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A19 k 0 y A19
AEG26 ALB 48.4 9.4 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15/A19 k 0 y A15/A19
AEG27 ALB 48.42 9.48 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15/A17/A20/A19 k 0 y A15/A17/A20/A19
AEG28 ALB 48.46 9.49 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15 k 0 y A15
AEG29 ALB 48.42 9.36 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A9/A11 k 0 y A9/A11
AEG3 ALB 48.41 9.53 o a y A5 o a y 0 o a y 0 o a y 0 o a y 0 o a y 0 o a y 0 o a y 0 o c y 0 o c y A12 o c y A12
AEG30 ALB 48.46 9.46 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15 k 0 y A15
AEG31 ALB 48.46 9.46 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15 k 0 y A15
AEG32 ALB 48.47 9.49 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15 k 0 y A15
AEG33 ALB 48.45 9.49 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15/A11 k 0 y A15/A11
AEG34 ALB 48.46 9.5 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15 k 0 y A15
AEG35 ALB 48.48 9.29 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG36 ALB 48.48 9.3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG37 ALB 48.4 9.41 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG38 ALB 48.44 9.43 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 n 0 k 0 n 0
AEG39 ALB 48.39 9.43 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG4 ALB 48.38 9.42 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG40 ALB 48.41 9.57 k 0 y A1/A19 k 0 y A1/A19 k 0 y A1/A19 k 0 y A1/A19 k 0 y A1/A19 k 0 y A1/A19 k 0 y A1/A19 k 0 y A1/A19 k 0 y A1/A19 k 0 n 0 k 0 n 0
AEG41 ALB 48.37 9.4 k 0 y A2 k 0 y A2 k 0 y A2 k 0 y A2 k 0 y A2 k 0 y A2 k 0 y A2 k 0 y A2 k 0 y A2 k 0 n 0 k 0 n 0
AEG42 ALB 48.4 9.38 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 n 0 k 0 n 0
AEG43 ALB 48.41 9.54 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 n 0 k 0 y A8 k 0 y A8
AEG44 ALB 48.38 9.43 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 n 0 k 0 n 0
AEG45 ALB 48.4 9.46 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A5 o 0 y A13 o 0 y A13
AEG46 ALB 48.4 9.43 o c y A5 o c y A5 o c y A5 o c y A5 o c y A5 o c y A5 o c y A5 o c y A5 o c y A5 o c y A16/A18 o c y A16/A18
AEG47 ALB 48.42 9.45 k 0 y A5/A1/A6 k 0 y A5/A1/A6 k 0 y A5/A1/A6 k 0 y A5/A1/A6 k 0 y A5/A1/A6 k 0 y A5/A1/A6 k 0 y A5/A1/A6 k 0 y A5/A1/A6 k 0 n 0 k 0 y A19 k 0 y A19
AEG48 ALB 48.42 9.5 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15/A17/A20/A19 k 0 y A15/A17/A20/A19
AEG49 ALB 48.46 9.5 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15 k 0 y A15
AEG5 ALB 48.4 9.44 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG50 ALB 48.41 9.47 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG6 ALB 48.4 9.44 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
AEG7 ALB 48.39 9.38 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A19/A15 k 0 y A19/A15
AEG8 ALB 48.42 9.49 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15/A11 k 0 y A15/A11
AEG9 ALB 48.39 9.5 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y A15/A17/A20/A19 k 0 y A15/A17/A20/A19
HEG1 HAI 50.97 10.41 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
HEG10 HAI 51.28 10.45 k 0 y 0 k 0 y 0 o 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG11 HAI 51.28 10.46 k 0 y 0 k 0 y 0 o 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG12 HAI 51.08 10.58 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H4 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H13 k 0 n 0
HEG13 HAI 51.26 10.38 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 n 0 k 0 y 0
HEG14 HAI 51.29 10.44 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H4 k 0 y 0 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H13 k 0 y H13
HEG15 HAI 51.07 10.49 k 0 y H2 k 0 y H2 k 0 y H2 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0
HEG16 HAI 51.03 10.46 k 0 y H3 k 0 y H3 k 0 y H3 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H16
HEG17 HAI 51.07 10.47 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H17
HEG18 HAI 51.28 10.42 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 7 k 0 y 0 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H16
HEG19 HAI 51.07 10.47 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H8 k 0 y 0 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H17
HEG2 HAI 51 10.43 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
HEG20 HAI 51.22 10.37 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H8 k 0 y H8 k 0 y H9 k 0 y H8 k 0 y H16 k 0 y H16
HEG21 HAI 51.19 10.75 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H16
HEG22 HAI 51.03 10.32 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H9 k 0 y H9 k 0 y H9 k 0 y H9 k 0 y H9 k 0 y H9 k 0 y H13 k 0 y 0
HEG23 HAI 51.13 10.34 o 0 y H1 o 0 y H1 o 0 y H1 o i y H4/H10 o 0 y 0 o i y H10 o i y H10 o i y H10 o i y H10 o i y H12 o a y H12
HEG24 HAI 51.1 10.35 o 0 y H1 o 0 y H1 o 0 y H1 o i y H4/H10 o 0 y 0 o i y H10 o i y H10 o i y H10 o i y H10 o a y H12 o a y H12
HEG25 HAI 51.02 10.32 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H9 k 0 y H9 k 0 y H9 k 0 y H9 k 0 y H9 k 0 y H9 k 0 y H13 k 0 y 0
HEG26 HAI 51.28 10.37 k 0 y 0 o 0 y 0 o 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG27 HAI 51.09 10.6 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H13 k 0 n 0
HEG28 HAI 51.27 10.5 k 0 y H2 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y H2 k 0 y H4 k a y H4 o a y H12 o a y H12
HEG29 HAI 51.26 10.5 k 0 y H2 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y H2 k 0 y H4 k a y H4 o a y H12 o a y H12
HEG3 HAI 51 10.43 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
HEG30 HAI 51.2 10.36 k 0 y H2 k 0 y H2 k 0 y H2 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H13 k 0 y H13
HEG31 HAI 51.17 10.22 k 0 y 0 k 0 y H4 k 0 y H4 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H15 k 0 y H15
HEG32 HAI 51.08 10.57 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H13 k 0 n 0
HEG33 HAI 51.11 10.43 k 0 y H2/H18 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y 0 k 0 y H2 k 0 y H4 k a y H4 o a y H12 o a y H12
HEG34 HAI 51.21 10.39 o g y H1/H18 o g y H1/H18 o g y H1/H18 o g y H4 o g y 0 o g y H1/H18 o g y H1/H18 o g y H4 o g y H4 o g y H12 o g y H12
HEG35 HAI 51.22 10.41 o g y H1/H18 o g y H1/H18 o g y H1/H18 o g y H4 o g y 0 o g y H1/H18 o g y H4 o g y H4 o g y H4 o g y H12 o g y H12
HEG36 HAI 51.03 10.51 k 0 y H2 k 0 y H2 k 0 y H2 k 0 n 0 k 0 y 0 k 0 n 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H14 k 0 y H14
HEG37 HAI 51.03 10.51 k 0 y H2 k 0 y H2 k 0 y H2 k 0 n 0 k 0 y 0 k 0 n 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0
HEG38 HAI 51.12 10.34 o 0 y H1 o 0 y H1 o 0 y H1 o 0 y H6 o 0 y 0 k 0 y H7 k 0 y H7 o 0 y H7 o 0 y H7 o a y H12 o a y H12
HEG39 HAI 51.12 10.35 o 0 y H1 o 0 y H1 o 0 y H1 o i y H4/H6 o 0 y 0 o i y H6 o i y H6 o i y H6 o i y H6 o i y H12 o i y H12
HEG4 HAI 51.11 10.44 k 0 y H2 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y 0
HEG40 HAI 50.97 10.45 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H7 k 0 y H7 k 0 y H7 k 0 y H7 k 0 y H7 k 0 y H7 k 0 y H13 k 0 y H14
HEG41 HAI 51.22 10.37 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H9 k 0 y H8 k 0 y H16 k 0 y H16
HEG42 HAI 51.07 10.46 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H8 k 0 y 0 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H17 k 0 y H17
HEG43 HAI 51.3 10.44 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H6 k 0 y 0 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H16
HEG44 HAI 51.06 10.48 k 0 y H3 k 0 y H3 k 0 y H3 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H16
HEG45 HAI 51.04 10.51 k 0 y H3 k 0 y H3 k 0 y H3 k 0 n 0 k 0 y H11 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H16
HEG46 HAI 51.21 10.75 k 0 y 0 k 0 y 0 k 0 y 0 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H8 k 0 y H16 k 0 y H16
HEG47 HAI 51.28 10.37 k 0 y 0 k 0 y 0 o 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG48 HAI 51.29 10.38 k 0 y 0 k 0 y 0 o 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG49 HAI 51.28 10.39 k 0 y 0 k 0 y 0 o 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG5 HAI 51.22 10.32 k 0 y H2 k 0 y H2 k 0 y H2 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H5 k 0 y H13 k 0 y H13
HEG50 HAI 51.28 10.42 k 0 y 0 k 0 y 0 k 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG6 HAI 51.21 10.39 o g y H1/H18 o g y H1/H18 o g y H1/H18 o g y H4 o g y 0 o g y H4/H18 o g y H4 o g y H4 o g y H4 o g y H12 o g y H12
HEG7 HAI 51.27 10.41 k 0 y 0 o 0 y H4 o 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG8 HAI 51.27 10.42 k 0 y 0 o 0 y H4 o 0 y 0 o i y H4 o i y H4 o i y H4 o a y H4 o a y H4 o a y H4 o a y H12 o a y H12
HEG9 HAI 51.22 10.38 o g y H1/H18 o g y H1/H18 o g y H1/H18 o g y H4 o g y 0 o g y H4/H18 o g y H4 o a y H4 o g y H4 k 0 y H16 k 0 y H16
SEG1 SCH 53.09 13.97 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG10 SCH 53.11 14 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG11 SCH 53.11 13.99 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG12 SCH 53.09 13.97 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG13 SCH 52.97 13.82 o a y S2 o a y S6/S2/S7 o a y S6/S2/S7 o a y S6/S7 o a y S6/S7 o a y S11/S8/S12/S9 o a y S11/S8/S12/S9 o a y S11/S8/S12/S9 o a y S11/S8/S12/S9 o a y S17 o a y S17
SEG14 SCH 53.09 13.98 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 o c y S12/S9 o c y S15/S17 o c y S15/S17
SEG15 SCH 53.11 14.01 k 0 y S3 k 0 y S3 k 0 y S3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y S10 k 0 y S15 k 0 n 0
SEG16 SCH 53.12 14 k 0 y S3 k 0 y S3 k 0 y S3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y S10 k 0 y S15 k 0 n 0
SEG17 SCH 53.1 13.63 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12/S9 o c y S14/S12/S9 o c y S14/S12/S9 o c y S14/S12/S9 o c y S15/S17 o c y S15/S17/S1
SEG18 SCH 53.14 13.88 o a y S13/S5/S3 o a y 0 o a y 0 o a y S12 o a y S12 o a y S12/S9 o a y S18/S12/S9 o a y S18/S12/S9 o a y S18/S12/S9 o a y S15/S17 o a y S15/S17
SEG19 SCH 53.12 14.01 k 0 y S3 k 0 y S3 k 0 y S3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG2 SCH 53.09 13.98 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG20 SCH 53.1 13.62 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S12/S9 o c y S12/S9 o c y S12/S9 o c y S14/S12/S9 o c y S15/S17 o c y S15/S17/S1
SEG21 SCH 53.11 13.61 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S12/S9 o c y S12/S9 o c y S12/S9 o c y S14/S12/S9 o c y S15/S17 o c y S15/S17/S1
SEG22 SCH 53.1 13.97 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG23 SCH 53.11 14.03 k 0 y S3 k 0 y S3 k 0 y S3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y S10 k 0 n 0 k 0 n 0
SEG24 SCH 53.09 14 k 0 y S5/S3 k 0 y S3 k 0 y S3 k 0 y S3 k 0 y S3 k 0 y S10 k 0 y S10 k 0 y S10 k 0 y S10 k 0 n 0 k 0 n 0
SEG25 SCH 53.11 13.62 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S12/S9 o c y S12/S9 o c y S12/S9 o c y S14/S12/S9 o c y S15/S17 o c y S15/S17/S1
SEG26 SCH 53.11 14.02 k 0 y S3 k 0 y S3 k 0 y S3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y S10 k 0 n 0 k 0 n 0
SEG27 SCH 53.12 13.71 o a y S14 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG28 SCH 53.09 14.01 k 0 y S3 k 0 y S3 k 0 y S3 k 0 y S3 k 0 y S3 k 0 y S10 k 0 y S10 k 0 y S10 k 0 y S10 k 0 n 0 k 0 n 0
SEG29 SCH 53.09 14 k 0 y S3 k 0 y S3 k 0 y S3 k 0 y S3 k 0 y S3 k 0 y S10 k 0 y S10 k 0 y S10 k 0 y S10 k 0 n 0 k 0 n 0
SEG3 SCH 53.1 13.99 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG30 SCH 53.15 13.83 o a y S4/S3/S14 o a y S3/S14 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S15/S17 o a y S15/S17
SEG31 SCH 53.15 13.84 o a y S4/S3/S14 o a y S3/S14 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S15/S17 o a y S15/S17
SEG32 SCH 53.15 13.83 o a y S4/S3/S14 o a y S3/S14 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S15/S17 o a y S15/S17
SEG33 SCH 52.99 13.84 o a y S2 o a y S2/S7 o a y S2/S7 o a y S7 o a y S7 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG34 SCH 52.98 13.85 o a y S2 o a y S2/S7 o a y S2/S7 o a y S7 o a y S7 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG35 SCH 52.98 13.85 o a y S2 o a y S2/S7 o a y S2/S7 o a y S7 o a y S7 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG36 SCH 52.99 13.84 o a y S2 o a y S2/S7 o a y S2/S7 o a y S7 o a y S7 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG37 SCH 53.13 13.88 o a y 0 o a y 0 o a y 0 o a y S13 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S15/S17 o a y S15/S17
SEG38 SCH 53.12 13.68 o a y S14 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12/S9 o a y S12 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG39 SCH 52.98 13.82 o a y S2 o a y S2/S7 o a y S2/S7 o a y S7 o a y S7 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG4 SCH 53.11 14 k 0 y S3 k 0 y S3 k 0 y S3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y S10 k 0 y S15 k 0 n 0
SEG40 SCH 53.12 13.84 o a y 0 o a y 0 o a y 0 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S17 o a y S17
SEG41 SCH 53.12 13.85 o a y 0 o a y 0 o a y 0 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S12 o a y S17 o a y S17
SEG42 SCH 52.87 13.97 o h y S2 o h y S2/S12 o h y S2/S12 o h y S7 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S15/S17 o h y S15/S17
SEG43 SCH 52.88 13.97 o h y S2 o h y S2/S12 o h y S2/S12 o h y S7 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S15/S17 o h y S15/S17
SEG44 SCH 52.88 13.97 o h y S2 o h y S2/S12 o h y S2/S12 o h y S7 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S15/S17 o h y S15/S17
SEG45 SCH 52.88 13.96 o h y S2 o h y S2/S12 o h y S2/S12 o h y S7 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S2/S12 o h y S15/S17 o h y S15/S17
SEG46 SCH 52.98 13.83 o a y S2 o a y S2/S7 o a y S2/S7 o a y S7 o a y S7 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG47 SCH 52.99 13.83 o a y S2 o a y S2/S7 o a y S2/S7 o a y S7 o a y S7 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG48 SCH 53.1 13.61 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S12/S9 o c y S12/S9 o c y S12/S9 o c y S14/S12/S9 o c y S15/S17 o c y S15/S17/S1
SEG49 SCH 52.97 13.86 o a y S2 o a y S2/S7 o a y S2/S7 o a y S7 o a y S7 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S12/S9 o a y S15/S17 o a y S15/S17
SEG5 SCH 53.11 14 k 0 y S3 k 0 y S3 k 0 y S3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y S10 k 0 y S15 k 0 n 0
SEG50 SCH 53.12 13.75 o c y S14 o c y S14 o c y S14 o c y S14 o c y S14 o c y S14 o c y S14 o c y S14 o c y S14 o c y S15/S17 o c y S15/S17
SEG6 SCH 53.1 13.62 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12/S9 o c y S14/S12/S9 o c y S14/S12/S9 o c y S14/S12/S9 o c y S15/S17 o c y S15/S17/S1
SEG7 SCH 53.09 13.98 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0
SEG8 SCH 53.11 14.02 k 0 y S3 k 0 y S3 k 0 y S3 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 n 0 k 0 y S10 k 0 y 0 k 0 n 0
SEG9 SCH 53.1 13.61 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12 o c y S14/S12/S9 o c y S14/S12/S9 o c y S14/S12/S9 o c y S14/S12/S9 o c y S15/S17 o c y S15/S17/S1

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).

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

Description: 

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 name: 
BE_landuse_grassland_2006-2016.csv
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).

Column label Column description
Study region ALB = Schwäbische Alb, HAI = Hainich, SCH = Schorfheide
Year Year of management
Date Date of interview
PlotID Experimental 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
Drainage Measure of drainage and the description of the method (free text)
StartDrainage Starting year of grassland drainage if applicable
WaterLogging Activities on water logging, e.g. for water regulation of fen soils
Agriculture1981 Use of grassland between 1981 to 2006, i.e. (temporal) conversion of grassland into arable land
SizeManagementUnit_ha Size of the management unit in the survey year, often larger than the 50 x 50 m study plot itself
StartGrazing Starting month of the first grazing period in the survey year
EndGrazing End 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
Mowing Number of cuts per year
DateMowing1 (2-4) Date of the first (second-fourth) cut
MowingMachine Type of machine which was used for mowing e.g. rotarymower, doubleknife, mulcher
CutWidth_m Cutting width of the mowing machine
CutHeight_cm Cutting height above soil level of the mowing machine
DriveSpeed_kmh Speed of the mowing machine, normally mean speed value is given
MowingConditioner Presence of conditioner, i.e. did the mowing machine have a conditioner to improve drying of the clippings
Fertilisation Addition of fertiliser (not including dung depositions by livestock during grazing a parcel)
NbFertilisation Number of fertiliser applications per year
DateFertilisation1 (2-7) Date of first (2nd-7th) fertiliser application
Manure_tha Total amount of applied solid manure
Slurry_m3ha Total amount of applied pig or cow slurry and biogas residues, respectively
DescFert Description of applied organic fertiliser
orgNitrogen_kgNha Amount of organic nitrogen applied
minNitrogen_kgNha Amount of nitrogen applied, of mineral origin or the organic fertiliser mash from a bioethanol factory (see in DescFert)
totalNitrogen_kgNha Sum of applied mineral and organic nitrogen [kg N/ha]
minPhosphorus_kgPha Amount of phosphorus applied [kg P2O5/ha], of mineral origin or mash from a bioethanol factory (not given for other organic fertilisers)
minPotassium_kgKha Amount of potassium applied [kg K2O/ha], of mineral origin or mash from a bioethanol factory (not given for other organic fertilisers)
Sulphur_kgSha Total amount of applied Sulphur [kg S/ha]
Maintenance Presence of maintenance measures
Levelling Maintenance to break up matted grass covers
DateLevelling Maintenance: date of levelling
Rolling Maintenance: rolling to level unevenness
DateRolling Maintenance: date of rolling
Mulching Partial 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.
DateMulching Date of partial mulching
ShrubClearance Clearance to avoid shrub encroachment. We consider this not as a mowing event, as only individual shrubs are targeted.
DateScrubCl Date of shrub clearance
PlantProtectionAgent 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.
Seeds Seed addition
DescSeeds Description of usage of the sowing

Acknowledgements

Our special thanks go to all farmers and other stakeholders for providing information on grassland management and their strong support for our project. We thank the manager Martin Gorke and Metke Lilienthal for their work in maintaining the plot and project infrastructure; Simone Pfeiffer, Maren Gleisberg, Christiane Fischer and Jule Mangels for giving support through the central office, Jens Nieschulze, Michael Owonibi and Andreas Ostrowski for managing the central data base and Eduard Linsenmair, Dominik Hessenmöller, Ingo Schöning, François Buscot, Ernst-Detlef Schulze and the late Elisabeth Kalko, for their role in setting up the Biodiversity-Exploratories project.The work has been funded by the DFG Priority Programme 1374 "Infrastructure-Biodiversity-Exploratories". Fieldwork permits were issued by the responsible state environmental offices of Baden-Württemberg, Thüringen and Brandenburg).

References

Supplementary materials

Suppl. material 1: BE_landuse_grassland_2006-2016.csv 
Authors:  Juliane Vogt, Valentin H. Klaus, Ralf Lauterbach, Niclas Otto, Uta Schumacher, Cornelia Fürstenau, Katrin Lorenzen, Andreas Ostrowski, Wolfgang W. Weisser
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.

Suppl. material 2: Bibliography of the land use index 
Authors:  Juliane Vogt
Data type:  Text
Brief description: 

This library shows the citations of the LUI developed by Brüthgen et al. 2012.