Vogt Juliane, Klaus Valentin H, Both Steffen, Fürstenau Cornelia, Gockel Sonja, Gossner Martin M, Heinze Johannes, Hemp Andreas, Hölzel Nobert, Jung Kirsten, Kleinebecker Till, Lauterbach Ralf, Lorenzen Katrin, Ostrowski Andreas, Otto Niclas, Prati Daniel, Renner Swen, Schumacher Uta, Seibold Sebastian, Simons Nadja, Steitz Iris, Teuscher Miriam, Thiele Jan, Weithmann Sandra, Wells Konstans, Wiesner Kerstin, Ayasse Manfred, Blüthgen Nico, Fischer Markus, Weisser Wolfgang W
Technische Universität München, Terrestrial Ecology Research Group, School of Life Sciences Weihenstephan, Freising, Germany Technische Universität München, Terrestrial Ecology Research Group, School of Life Sciences Weihenstephan Freising Germany.
Westfälische Wilhelms-Universität, Institute of Landscape Ecology, Münster, Germany Westfälische Wilhelms-Universität, Institute of Landscape Ecology Münster Germany.
Biodivers Data J. 2019 Sep 27;7:e36387. doi: 10.3897/BDJ.7.e36387. eCollection 2019.
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: 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 , 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 , 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).
150个草地地块位于德国的三个研究区域,每个区域50个。该数据集使用116个变量描述了每个草地地块的年度草地管理情况。一般信息包括地块标识符、研究区域和调查年份。此外,草地地块特征描述了排水设施的存在情况和起始年份,以及在我们评估前25年(即1981年至2006年之间)是否进行过耕地种植。每年都给出了管理单元的面积,在某些情况下,面积会逐年略有变化。
刈割、放牧和施肥情况均进行了系统调查:刈割以刈割频率(即每年的刈割次数)、刈割日期以及不同的技术变量为特征,如使用的机器类型或调节剂的使用情况。对于放牧,记录了牲畜种类和年龄(如牛、马、羊)、动物数量、每公顷的饲养密度以及总放牧时长。作为派生变量,然后通过将牲畜单位数与每公顷的放牧时长相乘来计算平均放牧强度[牲畜单位天数/公顷]。一年中不同的放牧时期,部分涉及不同的畜群,汇总为每个草地的年度放牧强度。对于施肥,分别记录了矿物肥料和有机肥料(如固体农田粪肥、 slurry和生物乙醇工厂的醪液)等不同类型肥料的类型和数量。我们的施肥措施忽略了放牧期间牲畜掉落的粪便。对于每种肥料类型,我们根据生产商的化学分析或农业指南计算了其总氮含量(表3)。所有三种管理类型,即刈割、施肥和放牧,都用于计算一个综合土地利用强度指数(LUI),该指数经常用于定义土地利用强度的衡量标准。在这里,施肥以每公顷总氮量[千克氮/公顷]表示,但未考虑钾和磷。
还记录了草地额外管理措施的信息,包括平整(用于撕开结块的草皮)、碾压(用于消除表面不平整)、补播(用于填补草皮中的缝隙)。
研究人类土地利用与生物多样性之间的关系对于理解人类是否以及如何通过土地管理方式影响生物多样性以及制定可持续土地利用策略非常重要。量化土地利用(此类图表中的“X”)可能很困难,因为人类通过多种行动管理土地,所有这些行动都可能影响生物多样性,但大多数研究使用相当简单的土地利用衡量方法,例如,通过创建土地利用类别,如传统农业与有机农业。在这里,我们提供了关于草地管理的详细数据,以便进行详细分析并发展土地利用理论。这些原始数据已被用于100多篇关于管理对生物多样性影响的论文(例如,Manning等人,2015年)。