Biodiversity Literature Repository
Published September 27, 2019 | Version v1
Journal article Open

Eleven years' data of grassland management in Germany

Creators

  • 1. Technische Universität München, Terrestrial Ecology Research Group, School of Life Sciences Weihenstephan, Freising, Germany
  • 2. ETH Zürich, Institute of Agricultural Sciences, Zürich, Switzerland|Westfälische Wilhelms-Universität, Institute of Landscape Ecology, Münster, Germany
  • 3. Technische Universität München, Terrestrial Ecology Research Group, School of Life Sciences Weihenstephan, Fresing, Germany|Martin-Luther-Universität Halle-Wittenberg, Institut für Agrar- und Ernährungswissenschaften, Halle, Germany
  • 4. Friedrich Schiller Universität Jena, Institute for Computer Science, Heinz Nixdorf Chair for Distributed Information Systems, Jena, Germany
  • 5. Friedrich Schiller Universität Jena, Institute of Ecology, Jena, Germany|ThüringenForst, Forstliches Forschungs- und Kompetenzzentrum Gotha, Gotha, Germany
  • 6. Swiss Federal Research Institute WSL, Forest Entomology, Birmensdorf, Switzerland|Technische Universität München, Terrestrial Ecology Research Group, School of Life Sciences Weihenstephan, Freising, Germany
  • 7. Universität Potsdam, Biodiversity Research/Systematic Botany, Institute of Biochemistry and Biology, Potsdam, Germany
  • 8. University of Bayreuth, Department of Plant Systematics, Bayreuth, Germany
  • 9. Westfälische Wilhelms-Universität, Institute of Landscape Ecology, Münster, Germany
  • 10. University of Ulm, Institute of Evolutionary Ecology, Ulm, Germany
  • 11. Justus-Liebig-Universität Gießen, Institute of Landscape Ecology and Resource Management, Gießen, Germany|Westfälische Wilhelms-Universität, nstitute of Landscape Ecology, Münster, Germany
  • 12. University of Bern, Institute of Plant Science, Department of Biology, Bern, Switzerland
  • 13. University of Natural Resources and Life Sciences BOKU, Institute of Zoology, Vienna, Austria
  • 14. Senckenberg Gesellschaft für Naturforschung, Biodiversity and Climate Research Centre BiK-F, Frankfurt, Germany
  • 15. University Darmstadt, Ecological Networks, Darmstadt, Germany|Technische Universität München, Terrestrial Ecology Research Group, School of Life Sciences Weihenstephan, Freising, Germany
  • 16. Johann Heinrich von Thünen Institute for Biodiversity, Braunschweig, Germany
  • 17. The University of Adelaide, Department of Biosciences, Adelaide, Australia|University of Ulm, Institute of Evolutionary Ecology, Ulm, Georgia
  • 18. University Darmstadt, Ecological Networks, Darmstadt, Germany
  • 19. Senckenberg Gesellschaft für Naturforschung, Biodiversity and Climate Research Centre BiK-F, Frankfurt, Germany|Universität Bern, Institute of Plant Science, Department of Biology, Bern, Germany

Description

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.

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).

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10.3897/BDJ.7.e36387.figure1 (DOI)
10.3897/BDJ.7.e36387.figure3 (DOI)
10.3897/BDJ.7.e36387.figure2 (DOI)
10.3897/BDJ.7.e36387.suppl1 (DOI)
10.3897/BDJ.7.e36387.suppl2 (DOI)