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Eleven years’ data of grassland management in Germany

Juliane Vogt, Valentin Klaus, Steffen Both, Cornelia Fürstenau, Sonja Gockel, Martin 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 Orcid Logo, Kerstin Wiesner, Manfred Ayasse, Nico Blüthgen, Markus Fischer, Wolfgang Weisser

Biodiversity Data Journal, Volume: 7

Swansea University Author: Konstans Wells Orcid Logo

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DOI (Published version): 10.3897/BDJ.7.e36387

Abstract

The 150 grassland plots were located in three study regions in Germany, 50 in eachregion. The dataset describes the yearly grassland management for each grassland plotusing 116 variables.General information includes plot identifier, study region and survey year. Additionally,grassland plot character...

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Published in: Biodiversity Data Journal
ISSN: 1314-2836 1314-2828
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa52171
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Additionally,grassland plot characteristics describe the presence and starting year of drainage andwhether arable farming had taken place 25 years before our assessment, i.e. between1981 and 2006. In each year, the size of the management unit is given which, in somecases, 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 ofcutting and different technical variables, such as type of machine used or usage ofconditioner.For grazing, the livestock species and age (e.g. cattle, horse, sheep), the number ofanimals, stocking density per hectare and total duration of grazing were recorded. As aderived variable, the mean grazing intensity was then calculated by multiplying thelivestock units with the duration of grazing per hectare [LSU days/ha]. Different grazingperiods during a year, partly involving different herds, were summed up to an annualgrazing intensity for each grassland.For fertilisation, information on the type and amount of different types of fertilisers wasrecorded separately for mineral and organic fertilisers, such as solid farmland manure,slurry and mash from a bioethanol factory. Our fertilisation measures neglect dung droppedby livestock during grazing. For each type of fertiliser, we calculated its total nitrogencontent, derived from chemical analyses by the producer or agricultural guidelinesAll three management types, mowing, fertilisation and grazing, were used to calculate acombined land use intensity index (LUI) which is frequently used to define a measure forthe 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 includinglevelling, to tear-up matted grass covers, rolling, to remove surface irregularities, seedaddition, to close gaps in the sward.New informationInvestigating the relationship between human land use and biodiversity is important tounderstand if and how humans affect it through the way they manage the land and todevelop sustainable land use strategies. Quantifying land use (the &#x2018;X&#x2019; in such graphs) canbe difficult as humans manage land using a multitude of actions, all of which may affectbiodiversity, yet most studies use rather simple measures of land use, for example, bycreating land use categories such as conventional vs. organic agriculture. Here, we providedetailed data on grassland management to allow for detailed analyses and thedevelopment of land use theory. The raw data have already been used for &amp;#62; 100 papers onthe effect of management on biodiversity (e.g. Manning et al. 2015).</abstract><type>Journal Article</type><journal>Biodiversity Data Journal</journal><volume>7</volume><publisher/><issnPrint>1314-2836</issnPrint><issnElectronic>1314-2828</issnElectronic><keywords/><publishedDay>27</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2019</publishedYear><publishedDate>2019-09-27</publishedDate><doi>10.3897/BDJ.7.e36387</doi><url/><notes/><college>COLLEGE NANME</college><department>Biosciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SBI</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2019-10-10T17:01:19.7534114</lastEdited><Created>2019-09-27T15:08:57.2732553</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Biosciences, Geography and Physics - Biosciences</level></path><authors><author><firstname>Juliane</firstname><surname>Vogt</surname><order>1</order></author><author><firstname>Valentin</firstname><surname>Klaus</surname><order>2</order></author><author><firstname>Steffen</firstname><surname>Both</surname><order>3</order></author><author><firstname>Cornelia</firstname><surname>F&#xFC;rstenau</surname><order>4</order></author><author><firstname>Sonja</firstname><surname>Gockel</surname><order>5</order></author><author><firstname>Martin</firstname><surname>Gossner</surname><order>6</order></author><author><firstname>Johannes</firstname><surname>Heinze</surname><order>7</order></author><author><firstname>Andreas</firstname><surname>Hemp</surname><order>8</order></author><author><firstname>Nobert</firstname><surname>H&#xF6;lzel</surname><order>9</order></author><author><firstname>Kirsten</firstname><surname>Jung</surname><order>10</order></author><author><firstname>Till</firstname><surname>Kleinebecker</surname><order>11</order></author><author><firstname>Ralf</firstname><surname>Lauterbach</surname><order>12</order></author><author><firstname>Katrin</firstname><surname>Lorenzen</surname><order>13</order></author><author><firstname>Andreas</firstname><surname>Ostrowski</surname><order>14</order></author><author><firstname>Niclas</firstname><surname>Otto</surname><order>15</order></author><author><firstname>Daniel</firstname><surname>Prati</surname><order>16</order></author><author><firstname>Swen</firstname><surname>Renner</surname><order>17</order></author><author><firstname>Uta</firstname><surname>Schumacher</surname><order>18</order></author><author><firstname>Sebastian</firstname><surname>Seibold</surname><order>19</order></author><author><firstname>Nadja</firstname><surname>Simons</surname><order>20</order></author><author><firstname>Iris</firstname><surname>Steitz</surname><order>21</order></author><author><firstname>Miriam</firstname><surname>Teuscher</surname><order>22</order></author><author><firstname>Jan</firstname><surname>Thiele</surname><order>23</order></author><author><firstname>Sandra</firstname><surname>Weithmann</surname><order>24</order></author><author><firstname>Konstans</firstname><surname>Wells</surname><orcid>0000-0003-0377-2463</orcid><order>25</order></author><author><firstname>Kerstin</firstname><surname>Wiesner</surname><order>26</order></author><author><firstname>Manfred</firstname><surname>Ayasse</surname><order>27</order></author><author><firstname>Nico</firstname><surname>Bl&#xFC;thgen</surname><order>28</order></author><author><firstname>Markus</firstname><surname>Fischer</surname><order>29</order></author><author><firstname>Wolfgang</firstname><surname>Weisser</surname><order>30</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2019-10-10T17:01:19.7534114 v2 52171 2019-09-27 Eleven years’ data of grassland management in Germany d18166c31e89833c55ef0f2cbb551243 0000-0003-0377-2463 Konstans Wells Konstans Wells true false 2019-09-27 SBI The 150 grassland plots were located in three study regions in Germany, 50 in eachregion. The dataset describes the yearly grassland management for each grassland plotusing 116 variables.General information includes plot identifier, study region and survey year. Additionally,grassland plot characteristics describe the presence and starting year of drainage andwhether arable farming had taken place 25 years before our assessment, i.e. between1981 and 2006. In each year, the size of the management unit is given which, in somecases, 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 ofcutting and different technical variables, such as type of machine used or usage ofconditioner.For grazing, the livestock species and age (e.g. cattle, horse, sheep), the number ofanimals, stocking density per hectare and total duration of grazing were recorded. As aderived variable, the mean grazing intensity was then calculated by multiplying thelivestock units with the duration of grazing per hectare [LSU days/ha]. Different grazingperiods during a year, partly involving different herds, were summed up to an annualgrazing intensity for each grassland.For fertilisation, information on the type and amount of different types of fertilisers wasrecorded separately for mineral and organic fertilisers, such as solid farmland manure,slurry and mash from a bioethanol factory. Our fertilisation measures neglect dung droppedby livestock during grazing. For each type of fertiliser, we calculated its total nitrogencontent, derived from chemical analyses by the producer or agricultural guidelinesAll three management types, mowing, fertilisation and grazing, were used to calculate acombined land use intensity index (LUI) which is frequently used to define a measure forthe 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 includinglevelling, to tear-up matted grass covers, rolling, to remove surface irregularities, seedaddition, to close gaps in the sward.New informationInvestigating the relationship between human land use and biodiversity is important tounderstand if and how humans affect it through the way they manage the land and todevelop sustainable land use strategies. Quantifying land use (the ‘X’ in such graphs) canbe difficult as humans manage land using a multitude of actions, all of which may affectbiodiversity, yet most studies use rather simple measures of land use, for example, bycreating land use categories such as conventional vs. organic agriculture. Here, we providedetailed data on grassland management to allow for detailed analyses and thedevelopment of land use theory. The raw data have already been used for &#62; 100 papers onthe effect of management on biodiversity (e.g. Manning et al. 2015). Journal Article Biodiversity Data Journal 7 1314-2836 1314-2828 27 9 2019 2019-09-27 10.3897/BDJ.7.e36387 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University 2019-10-10T17:01:19.7534114 2019-09-27T15:08:57.2732553 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Juliane Vogt 1 Valentin Klaus 2 Steffen Both 3 Cornelia Fürstenau 4 Sonja Gockel 5 Martin Gossner 6 Johannes Heinze 7 Andreas Hemp 8 Nobert Hölzel 9 Kirsten Jung 10 Till Kleinebecker 11 Ralf Lauterbach 12 Katrin Lorenzen 13 Andreas Ostrowski 14 Niclas Otto 15 Daniel Prati 16 Swen Renner 17 Uta Schumacher 18 Sebastian Seibold 19 Nadja Simons 20 Iris Steitz 21 Miriam Teuscher 22 Jan Thiele 23 Sandra Weithmann 24 Konstans Wells 0000-0003-0377-2463 25 Kerstin Wiesner 26 Manfred Ayasse 27 Nico Blüthgen 28 Markus Fischer 29 Wolfgang Weisser 30
title Eleven years’ data of grassland management in Germany
spellingShingle Eleven years’ data of grassland management in Germany
Konstans Wells
title_short Eleven years’ data of grassland management in Germany
title_full Eleven years’ data of grassland management in Germany
title_fullStr Eleven years’ data of grassland management in Germany
title_full_unstemmed Eleven years’ data of grassland management in Germany
title_sort Eleven years’ data of grassland management in Germany
author_id_str_mv d18166c31e89833c55ef0f2cbb551243
author_id_fullname_str_mv d18166c31e89833c55ef0f2cbb551243_***_Konstans Wells
author Konstans Wells
author2 Juliane Vogt
Valentin Klaus
Steffen Both
Cornelia Fürstenau
Sonja Gockel
Martin 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 Weisser
format Journal article
container_title Biodiversity Data Journal
container_volume 7
publishDate 2019
institution Swansea University
issn 1314-2836
1314-2828
doi_str_mv 10.3897/BDJ.7.e36387
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
document_store_str 0
active_str 0
description The 150 grassland plots were located in three study regions in Germany, 50 in eachregion. The dataset describes the yearly grassland management for each grassland plotusing 116 variables.General information includes plot identifier, study region and survey year. Additionally,grassland plot characteristics describe the presence and starting year of drainage andwhether arable farming had taken place 25 years before our assessment, i.e. between1981 and 2006. In each year, the size of the management unit is given which, in somecases, 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 ofcutting and different technical variables, such as type of machine used or usage ofconditioner.For grazing, the livestock species and age (e.g. cattle, horse, sheep), the number ofanimals, stocking density per hectare and total duration of grazing were recorded. As aderived variable, the mean grazing intensity was then calculated by multiplying thelivestock units with the duration of grazing per hectare [LSU days/ha]. Different grazingperiods during a year, partly involving different herds, were summed up to an annualgrazing intensity for each grassland.For fertilisation, information on the type and amount of different types of fertilisers wasrecorded separately for mineral and organic fertilisers, such as solid farmland manure,slurry and mash from a bioethanol factory. Our fertilisation measures neglect dung droppedby livestock during grazing. For each type of fertiliser, we calculated its total nitrogencontent, derived from chemical analyses by the producer or agricultural guidelinesAll three management types, mowing, fertilisation and grazing, were used to calculate acombined land use intensity index (LUI) which is frequently used to define a measure forthe 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 includinglevelling, to tear-up matted grass covers, rolling, to remove surface irregularities, seedaddition, to close gaps in the sward.New informationInvestigating the relationship between human land use and biodiversity is important tounderstand if and how humans affect it through the way they manage the land and todevelop sustainable land use strategies. Quantifying land use (the ‘X’ in such graphs) canbe difficult as humans manage land using a multitude of actions, all of which may affectbiodiversity, yet most studies use rather simple measures of land use, for example, bycreating land use categories such as conventional vs. organic agriculture. Here, we providedetailed data on grassland management to allow for detailed analyses and thedevelopment of land use theory. The raw data have already been used for &#62; 100 papers onthe effect of management on biodiversity (e.g. Manning et al. 2015).
published_date 2019-09-27T04:04:23Z
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score 11.035634

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