Data Package Metadata View Summary

Typology of agricultural land systems of Germany at a resolution of 100 hectares.

General Information

Data Package:

Local Identifier:

edi.1339.1

Title:

Typology of agricultural land systems of Germany at a resolution of 100 hectares.

Alternate Identifier:

DOI PLACE HOLDER

Abstract:

The decline of farmland biodiversity has widely been recognized in society and politics. Many factors that negatively affect biodiversity are associated with agriculture. European policy instruments and measures, which aimed at mitigating these impacts, have not been successful in counteracting the negative trends of farmland biodiversity. There is a growing recognition that conservation policy instruments need to be spatially targeted, given the heterogeneity of agricultural landscapes and extent of agricultural intensification in Europe.

For Germany, we developed a typology of agricultural land systems (ALS) that captures the regional characteristics of agricultural intensification. For this purpose, we applied a cluster-analysis integrating indicators for land cover, landscape structure, land-use intensity, climate and relief at a resolution of with a spatial resolution of 1 km².

As a result, we present a typology of eight ALS ranging from large-scale, intensive arable farming to extensive grassland/forest mosaics in mountains.

The data included in this package contain the typology ALS and the corresponding values for the indicators for each hexagonal grid cell of 1 km²cell size.

The typology of ALS could be used as a spatial framework for regional targeting of conservation policy instruments and for monitoring regional-specific trends of biodiversity indicators and their drivers.

The data are supplement to the publication: Pingel, M.; Sietz, D.; Röder, N., Klimek, S.; Golla, B. (2022, in prep.): Typology of agricultural land systems to support targeted biodiversity measures: A case study from Germany. (Citation will be added upon publication).

Publication Date:

2023-02-13

For more information:
Visit:	DOI PLACE HOLDER

People and Organizations
Contact:	Pingel, Martin (Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Scientific employee) [ email ]
Creator:	Pingel, Martin (Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants)
Creator:	Röder, Norbert (Johann Heinrich von Thünen Institute - Federal Research Institute for Rural Areas, Forestry and Fisheries)
Creator:	Sinn, Christoph (Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants)
Creator:	Golla, Burkhard (Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants)

Data Entities
Data Table Name:	typology_agric_land_systems_indicators_2022
Description:	The data table contains the typology of agricultural land systems (ALS) for Germany and the indicators used for cluster analysis at a resolution of a hexagonal grid with 100 hectares cell size. Each row represents one hexagon of the hexagonal grid of Germany with a cell size of 100 hectares. Each hexagon as a unique ID (“hxid”, column #1). The columns #2 to #8 are relevant for the identification of ALS for each hexagon. Indicators used for the cluster analysis are stored in columns #9-26 and represent the domains land cover (columns #9-14), landscape structure (columns #15-17), land-use intensity (columns #18-22) and the biophysical environment (i.e. climate and relief; columns #23-26). The data table does not contain the spatially referenced geometries for each hexagon. Hexagon geometries can be found in the geopackage (gpkg) file "typology_agric_land_systems_indicators_2022.gpkg”, where they are stored with the data described above.
Other Name:	geopackage_typology_agric_land_systems_indicators_2022
Description:	The geopackage contains the typology of agricultural land systems (ALS) the indicators used for cluster analysis at a resolution of a hexagonal grid of 100 hectares cell size. Each row represents one hexagon of the hexagonal grid of Germany with a cell size of 100 hectares. Each hexagon as a unique ID (“hxid”, column #1). The columns #2 to #8 are relevant for the identification of ALS for each hexagon. Indicators used for the cluster analysis are stored in columns #9-26 and represent the domains land cover (columns #9-14), landscape structure (columns #15-17), land-use intensity (columns #18-22) and the biophysical environment (i.e. climate and relief; columns #23-26). The geopackage is equvalent to the data table "typology_agric_land_systems_indicators_2022.csv", but contains the spatially referenced geometries of hexagons.

Detailed Metadata

Data Entities

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1339/1/dd60f4daa20d3d1b0985833a3f72f152
Name:	typology_agric_land_systems_indicators_2022
Description:	The data table contains the typology of agricultural land systems (ALS) for Germany and the indicators used for cluster analysis at a resolution of a hexagonal grid with 100 hectares cell size. Each row represents one hexagon of the hexagonal grid of Germany with a cell size of 100 hectares. Each hexagon as a unique ID (“hxid”, column #1). The columns #2 to #8 are relevant for the identification of ALS for each hexagon. Indicators used for the cluster analysis are stored in columns #9-26 and represent the domains land cover (columns #9-14), landscape structure (columns #15-17), land-use intensity (columns #18-22) and the biophysical environment (i.e. climate and relief; columns #23-26). The data table does not contain the spatially referenced geometries for each hexagon. Hexagon geometries can be found in the geopackage (gpkg) file "typology_agric_land_systems_indicators_2022.gpkg”, where they are stored with the data described above.
Number of Records:	361594
Number of Columns:	24

Table Structure

Object Name:

typology_agric_land_systems_indicators_2022.csv

Size:

122805248 byte

Authentication:

27d2baaa1016b924b784b19eb282ffa9 Calculated By MD5

Text Format:

Number of Header Lines:

Record Delimiter:

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

Hexagon ID

TYPE_LETTER

English name

German name

Hex color code

Arable land

Permanent crops/ horticulture

Grassland

Forests

Settlements

Semi-natural habitats

Shannon diversity

Edge density

Patch size

Variable costs for arable cash crops

Variable costs for permanent crops/ horticulture

Variable costs for pig/ poultry farming

Variable costs for dairy farming

Variable costs for extensive livestock farming

Annual temperature

Temperature seasonality

Evapotranspiration March

Relief heterogeneity

Column Name:

hxid

CLUSTER_ANALYSIS

TYPE_LETTER

NAME_ENG

NAME_DT

COLOR_HEX

arable

perm

grass

forest

settle

semi_hab

sh_div

edge_den

patch_size

vc_arable

vc_perm

vc_pig_poultry

vc_dairy

vc_extens

annual_temp

temp_seas

transp_mar

rel_het

Definition:

ID of the geometrical hexagon.

Logical variable indicating, whether the hexagon was included in the cluster analysis, i.e. an ALS was assigned to this hexagon

Upper case letter ID of the ALS (A-H) or masked area (X1, X2, X3).

English name of the ALS or masked area.

German name of the ALS or masked area

Hex color code indicating the color used for the published map of agricultural land systems. Codes start with '#'.

Proportion of arable land features.

Proportion of permanent crop features and horticulture features.

Proportion of grassland features.

Proportion of forest features.

Proportion of settlement features.

Proportion of semi-natural habitat features.

Shannon diversity index of land cover features.

Edge density of agricultural features.

Patch size of agricultural features.

Variable costs for arable cash crops per hectare utilized arable area.

Variable costs for permanent crops and horticulture per hectare utilized arable area.

Variable costs for pig and poultry farming per hectare utilized arable area.

Costs for dairy farming and intensive beef fattening per hectare utilized arable area

Costs for extensive livestock farming per utilized arable area.

Mean annual temperature for the period 2000-2019.

Mean temperature difference between monthly means of July and January for the period 2000-2019.

Mean potential evapotranspiration of March for the period 2000-2019.

Terrain Ruggedness Index indicating topographical relief heterogeneity.

Storage Type:

float

string

float

Measurement Type:

ratio

nominal

ratio

Measurement Values Domain:

Unit	unitless
Type	integer

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	True
Definition	The hexagon was included in the cluster analysis, i.e.an ALS was assigned to this hexagon.
Source

Code Definition

Code	False
Definition	This hexagon was not included in the cluster analysis,i.e. a masked area was assigned to this hexagon.
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	A
Definition	Hexagon was assigned to ALS A.
Source

Code Definition

Code	B
Definition	Hexagon was assigned to ALS B.
Source

Code Definition

Code	C
Definition	Hexagon was assigned to ALS C.
Source

Code Definition

Code	D
Definition	Hexagon was assigned to ALS D.
Source

Code Definition

Code	E
Definition	Hexagon was assigned to ALS E.
Source

Code Definition

Code	F
Definition	Hexagon was assigned to ALS F.
Source

Code Definition

Code	G
Definition	Hexagon was assigned to ALS G.
Source

Code Definition

Code	H
Definition	Hexagon was assigned to ALS H.
Source

Code Definition

Code	X1
Definition	Hexagon was assigned to masked area X1.
Source

Code Definition

Code	X2
Definition	Hexagon was assigned to masked area X2.
Source

Code Definition

Code	X3
Definition	Hexagon was assigned to masked area X3.
Source

Definition

English name of the ALS or masked area.

Definition

German name of the ALS or masked area

Definition

Hex color code indicating the color used for the published map of agricultural land systems. Codes start with '#'.

Unit	proportion
Type	real
Min	0
Max	1

Unit	proportion
Type	real
Min	0
Max	1

Unit	proportion
Type	real
Min	0
Max	1

Unit	proportion
Type	real
Min	0
Max	1

Unit	proportion
Type	real
Min	0
Max	1

Unit	proportion
Type	real

Unit	unitless
Type	real
Min	0
Max

Unit	meterperhectare
Type	real
Min	0
Max

Unit	hectare
Type	real
Min	0
Max

Unit	Europerhectare
Type	real
Min	0
Max

Unit	Europerhectare
Type	real
Min	0
Max

Unit	Europerhectare
Type	real
Min	0
Max

Unit	Europerhectare
Type	real
Min	0
Max

Unit	Europerhectare
Type	real
Min	0
Max

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	millimeter
Type	real
Min	0
Max

Unit	unitless
Type	real
Min	0
Max

Missing Value Code:

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Code	NA
Expl	missing data

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Non-Categorized Data Resource

Name:

geopackage_typology_agric_land_systems_indicators_2022

Entity Type:

Geopackage (gpkg)

Description:

The geopackage contains the typology of agricultural land systems (ALS) the indicators used for cluster analysis at a resolution of a hexagonal grid of 100 hectares cell size. Each row represents one hexagon of the hexagonal grid of Germany with a cell size of 100 hectares. Each hexagon as a unique ID (“hxid”, column #1). The columns #2 to #8 are relevant for the identification of ALS for each hexagon. Indicators used for the cluster analysis are stored in columns #9-26 and represent the domains land cover (columns #9-14), landscape structure (columns #15-17), land-use intensity (columns #18-22) and the biophysical environment (i.e. climate and relief; columns #23-26). The geopackage is equvalent to the data table "typology_agric_land_systems_indicators_2022.csv", but contains the spatially referenced geometries of hexagons.

Physical Structure Description:

Object Name:

geopackage_typology_agric_land_systems_indicators_2022.gpkg

Size:

176398336 byte

Authentication:

188553c4e3908c558936953ed502309f Calculated By MD5

Externally Defined Format:

Format Name:

gpkg

Data:

https://pasta-s.lternet.edu/package/data/eml/edi/1339/1/82b7e3ca30736f1c7af86556f6970ca6

Data Package Usage Rights

This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you.

Keywords

By Thesaurus:
(No thesaurus)	spatial data, regionalization, land system classification, agricultural intensification, farmland biodiversity, indicators, Germany

Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

Methods and protocols used in the collection of this data package

Description:

1) All indicators were resampled to a hexagonal grid of 100 ha resolution covering the land area of Germany, Projection: ETRS89 / UTM zone 32N (EPSG: 25832) (Perić et al. (2022): Geografisches hexagonales Gitter mit 1 Quadratkilometer Zellengröße...)

Data Source

Geografisches hexagonales Gitter mit 1 Quadratkilometer Zellengröße für Deutschland - Geographical hexagonal grid with one square kilometer cell size for Germany

Description:

2) Land cover features were obtained from the Basic Digital Landscape Model of the Official Topographic Cartographic Information System of Germany (ATKIS Basic DLM) provided by the German Federal Agency for Cartography and Geodesy, publication year 2017 (BKG, 2017). For each hexagon, the proportion of land cover features of interest were calculated and then attributed to six land cover classes: arable land, permanent crops and horticulture, grassland, forests, settlements (built-up areas for industry and commerce), semi-natural habitats (heaths, bogs, swamps, wetlands, small woody habitats, wasteland).

Data Source

ATKIS Basic Digital Landscape Model (2017)

Description:

3) The Shannon diversity (sh_div) was calculated for each hexagonal grid cell using the proportion of 15 land cover classes to describe the diversity of land cover. The Shannon diversity was calculated based on ATKIS Basic DLM objects using the function ‘diversity’ from R-package ‘vegan’ version 2.5-7 (Oksanen et al., 2019).

Calculation of edge density (edge_den) and mean patch size (patch_size) was based on land cover features classified as farmland (arable land, grassland, permanent crops and horticulture). Edge density was obtained by summing up the total length (m) of all farmland feature edges within each hexagon. The length was corrected for the size of the hexagon and the coverage of the ATKIS base DLM (BKG, 2017) to obtain values in the unit m/ha.

Mean patch size was calculated by averaging sizes of all farmland features that intersected the corresponding hexagon. To avoid that extreme mean patch sizes distort the cluster analysis, we set the maximum of mean patch size to 148 ha, which corresponds 99th percentile of the distribution.

Hexagons that did not intersect farmland polygons got empty values for edge density and patch size and were excluded from the cluster analysis.

Data Source

ATKIS Basic Digital Landscape Model (2017)

Description:

4) Yield dependent variable input costs (vc) were obtained from the Kuratorium für Technik und Bauwesen in der Landwirtschaft e.V. ("Standarddeckungsbeiträge", KTBL, 2020) and from the Bavarian State Research Center for Agriculture ("Deckungsbeiträge und Kalkulationsdaten", BLfL, 2020) for machinery. Data on natural yields were provided for the main crops by the German statistical office ("Fachserie. 3, Land- und Forstwirtschaft, Fischerei. Reihe 33, Landwirtschaftliche Bodennutzung und pflanzliche Erzeugung", DeStatis, various years) for the main crops and by the Federal Office for Agriculture and Food ("Milcherzeugung und -verwendung", BLE, various years) for milk yield per dairy cow at the spatial resolution of counties. Data on the distribution of the main agricultural production systems at the municipality level were obtained from the "Thünen-Agraratlas".

For the analysis, we used the land-use data for 2016 and for the cost data the 5-year annual average for the period 2014-2018. For this study we did not consider labour costs.

We differentiated variable costs for five major production systems based on the farming typology used in the farm accountancy data network (Commission Regulation (EC) No 1242/2008). The following systems were differentiated:

(vc_arable) Arable cash crops (including cereals, legumes, potatoes, sugar beets, field vegetables, oil rape seed, forage crops).

(vc_perm) Permanent crops and horticulture (including fruits, grapevines, hops, horticultural products, short rotation forestry).

(vc_pig_poultry) Pig and poultry farming.

(vc_dairy) Dairy farming and intensive beef fattening (including: dairy cows, bulls, corn for biogas production). Costs associated to the production of heifers, grass, hey and silage are attributed to this type.

(vc_extens) Extensive livestock farming (including pasture management of suckler cows, sheep, goats and horses).

To avoid distortion of the cluster analysis values for variable costs exceeding the value of the 99th percentile were set to the 99th-percentile value. This step was necessary for all production systems except for arable cash crops (vc_arable).

Data Source

Standarddeckungsbeiträge

Data Source

Deckungsbeiträge und Kalkulationsdaten

Data Source

Fachserie. 3, Land- und Forstwirtschaft, Fischerei. Reihe 33, Landwirtschaftliche Bodennutzung und pflanzliche Erzeugung

Data Source

Milcherzeugung und -verwendung

Data Source

Thünen-Agraratlas: Disaggregierte Darstellung der landwirtschaftlichen Nutzung auf Basis der Daten der Statistischen Ämter der Länder

Description:

5) Climate data were obtained from the Climate Data Center of the German Weather Service (DWD) for the years 2000 to 2019. The following indicators were calculated using grid-based data on monthly mean temperatures and monthly potential evapotranspiration (resolution: 1 square-km):

(annual_temp) Mean annual temperature,

(temp_seas) temperature seasonality (i.e. the difference between monthly means of July and January),

(transp_mar) potential evapotranspiration of March.

Data Source

Grids of monthly averaged daily air temperature (2m) over Germany (v1.0, 2020)

Data Source

Monthly grids of the accumulated potential evapotranspiration over grass, version 0.x, 2020.

Description:

6) For describing topographic relief heterogeneity, the Terrain Ruggedness Index was used. The calculation of the Terrain Ruggedness Index was based on the digital terrain model at a resolution of 200 m (DGM200) provided by the German Federal Agency for Cartography and Geodesy (BKG, 2019) and conducted using the RGDAL function in QGIS version 3.4.15.

Data Source

Digital terrain model 200 m (DGM200)

Description:

7) Masking: Hexagons were excluded from cluster analysis if they matched one of the following criteria:

a) Hexagons at national borders that covered less than 20% of the German territory

b) Hexagons with missing values for any indicator

c) Hexagons that contained less than 5 % of hexagon area covered by arable land, grassland, permanent crops and horticulture, and semi-natural habitats (see step 1))

The masked hexagons were classified by the proportion of land cover features as follows: Urban (X1): > 50 % settlements, Forests (X2): > 75 % Forests, Others(X3): all remaining hexagons.

Description:

8) Cluster analysis for typification: All indicators were z-transformed to zero mean and standard deviation of 1 before clustering. Clustering was done using k-medians, a partitioning algorithm implemented in the R package 'flexclust'. We used the ‘stepFlexclust’ function implemented in 'flexclust' using a cluster number of k = 8.

People and Organizations

Publishers:

Organization:

Environmental Data Initiative

Email Address:

info@edirepository.org

Web Address:

https://edirepository.org

Id:

https://ror.org/0330j0z60

Creators:

Individual:

Martin Pingel

Organization:

Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants

Email Address:

martin.pingel@julius-kuehn.de

Id:

https://orcid.org/0000-0003-2036-3629

Id:

https://ror.org/022d5qt08

Individual:

Norbert Röder

Organization:

Johann Heinrich von Thünen Institute - Federal Research Institute for Rural Areas, Forestry and Fisheries

Email Address:

norbert.roeder@thuenen.de

Id:

https://orcid.org/0000-0002-2491-2624

Id:

https://ror.org/00mr84n67

Individual:

Christoph Sinn

Organization:

Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants

Email Address:

christoph.sinn@julius-kuehn.de

Id:

https://orcid.org/0000-0003-0275-1070

Id:

https://ror.org/022d5qt08

Individual:

Burkhard Golla

Organization:

Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants

Email Address:

burkhard.golla@julius-kuehn.de

Id:

https://orcid.org/0000-0003-3867-6076

Id:

https://ror.org/022d5qt08

Contacts:

Individual:

Martin Pingel

Organization:

Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants

Position:

Scientific employee

Email Address:

martin.pingel@julius-kuehn.de

Id:

https://orcid.org/0000-0003-2036-3629

Temporal, Geographic and Taxonomic Coverage

Temporal, Geographic and/or Taxonomic information that applies to all data in this dataset:

Geographic Region:

Description:

The focus region of the data is Germany.

Bounding Coordinates:

Northern:	55.06787	Southern:	47.2647
Western:	5.85734	Eastern:	15.05431

Project

Parent Project Information:

Title:

"Developing a sound basis for a national monitoring of biodiversity in agricultural landscapes (BM-Landwirtschaft)”

Personnel:

Individual:

Sebastian Klimek

Organization:

Johann Heinrich von Thünen Institute - Federal Research Institute for Rural Areas, Forestry and Fisheries

Email Address:

sebastian.klimek@thuenen.de

Id:

https://orcid.org/0000-0002-2544-640X

Id:

https://ror.org/00mr84n67

Role:

Project coordinator

Individual:

Norbert Röder

Organization:

Johann Heinrich von Thünen Institute - Federal Research Institute for Rural Areas, Forestry and Fisheries

Email Address:

norbert.roeder@thuenen.de

Id:

https://orcid.org/0000-0002-2491-2624

Id:

https://ror.org/00mr84n67

Role:

project partner

Individual:

Burkhard Golla

Organization:

Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants

Email Address:

burkhard.golla@julius-kuehn.de

Id:

https://orcid.org/0000-0003-3867-6076

Id:

https://ror.org/022d5qt08

Role:

project partner

Individual:

Martin Pingel

Organization:

Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants

Email Address:

martin.pingel@julius-kuehn.de

Id:

https://orcid.org/0000-0003-2036-3629

Id:

https://ror.org/022d5qt08

Role:

project partner

Individual:

Diana Sietz

Organization:

Johann Heinrich von Thünen Institute - Federal Research Institute for Rural Areas, Forestry and Fisheries

Email Address:

diana.sietz@thuenen.de

Id:

https://orcid.org/0000-0002-2309-2134

Id:

https://ror.org/00mr84n67

Role:

project partner

Abstract:

The aim of a biodiversity monitoring in agriculture is to continuously provide data with a high spatial-temporal resolution and scientific quality. In order to develop a model for further implementation, important steps are taken in this project: i) the characterization of agricultural areas in Germany; ii) the coordination and definition of agro-area specific goals and (iii) examining and agreeing on relevant sets of indicators. To address the first step, the JKI conducted a systematic literature review of existing landscape characterization approaches at the national and European scale and developed criteria to evaluate these approaches.

The project is supported by funds of the German Government’s Special Purpose Fund held at Landwirtschaftliche Rentenbank.

Maintenance

Maintenance:

Description:	The data table typology_agric_land_systems_indicators_2022 are final. Future versions of the typologies might be produced using updated values for indicators. These data would be added as new data tables.
Frequency:	asNeeded

Other Metadata

Additional Metadata

additionalMetadata
        |___text '\n    '
        |___element 'metadata'
        |     |___text '\n      '
        |     |___element 'unitList'
        |     |     |___text '\n        '
        |     |     |___element 'unit'
        |     |     |     |  \___attribute 'id' = 'proportion'
        |     |     |     |  \___attribute 'name' = 'proportion'
        |     |     |     |___text '\n          '
        |     |     |     |___element 'description'
        |     |     |     |     |___text 'area of land cover / area of hexagon'
        |     |     |     |___text '\n        '
        |     |     |___text '\n        '
        |     |     |___element 'unit'
        |     |     |     |  \___attribute 'id' = 'unitless'
        |     |     |     |  \___attribute 'name' = 'unitless'
        |     |     |     |___text '\n          '
        |     |     |     |___element 'description'
        |     |     |     |___text '\n        '
        |     |     |___text '\n        '
        |     |     |___element 'unit'
        |     |     |     |  \___attribute 'id' = 'meterperhectare'
        |     |     |     |  \___attribute 'name' = 'meterperhectare'
        |     |     |     |___text '\n          '
        |     |     |     |___element 'description'
        |     |     |     |     |___text 'm / ha'
        |     |     |     |___text '\n        '
        |     |     |___text '\n        '
        |     |     |___element 'unit'
        |     |     |     |  \___attribute 'id' = 'Europerhectare'
        |     |     |     |  \___attribute 'name' = 'Europerhectare'
        |     |     |     |___text '\n          '
        |     |     |     |___element 'description'
        |     |     |     |     |___text 'Euro / hectare utilized arable area'
        |     |     |     |___text '\n        '
        |     |     |___text '\n      '
        |     |___text '\n    '
        |___text '\n  '

Additional Metadata

additionalMetadata
        |___text '\n    '
        |___element 'metadata'
        |     |___text '\n      '
        |     |___element 'emlEditor'
        |     |        \___attribute 'app' = 'ezEML'
        |     |        \___attribute 'release' = '2023.01.27'
        |     |___text '\n    '
        |___text '\n  '

Copyright 2024 Environmental Data Initiative. This material is based upon work supported by the National Science Foundation under grants #2223103 and #2223104. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Please contact us with questions, comments, or for technical assistance regarding this web site or the Environmental Data Initiative. Please read our privacy policy to know what information we collect about you and to understand your privacy rights.

EDI is a collaboration between the University of New Mexico and the University of Wisconsin – Madison, Center for Limnology:

Data Package Metadata View Summary

Typology of agricultural land systems of Germany at a resolution of 100 hectares.

Data Entities

Data Table

Non-Categorized Data Resource

Data Package Usage Rights

Keywords

Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

People and Organizations

Temporal, Geographic and Taxonomic Coverage

Project

Parent Project Information:

Maintenance

Additional Metadata

Additional Metadata

Recently Added

Recently Updated

Data Package Metadata View Summary

Typology of agricultural land systems of Germany at a resolution of 100 hectares.

+/- Data Entities

Data Table

Non-Categorized Data Resource

+/- Data Package Usage Rights

+/- Keywords

+/- Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

+/- People and Organizations

+/- Temporal, Geographic and Taxonomic Coverage

+/- Project

Parent Project Information:

+/- Maintenance

+/- Additional Metadata

+/- Additional Metadata

Data Entities

Data Package Usage Rights

Keywords

Methods and Protocols

People and Organizations

Temporal, Geographic and Taxonomic Coverage

Project

Maintenance

Additional Metadata

Additional Metadata