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Data

Grazing land use over the long-term

See all data and research on:
Population GrowthLand UseUrbanization

What you should know about this indicator

  • 'Grazingland' is equivalent to the FAO category of 'permanent meadows and pastures'.
  • The order of allocation is as follows: first, we allocate cropland, then rice, then irrigation and finally grazing land.
Grazing land use over the long-term
Total grazingland area for each separate country over time.
Source
HYDE (2023) – with minor processing by Our World in Data
Last updated
January 2, 2024
Next expected update
June 2029
Date range
10000 BCE – 2023 CE
Unit
hectares

Related research and writing

A dark blue background with a lighter blue world map superimposed over it. Yellow text that says Land Use by Our World in Data

Land Use

Hannah Ritchie and Max Roser

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Sources and processing

This data is based on the following sources

PBL Netherlands Environmental Assessment Agency – History Database of the Global Environment

This database presents an update and expansion of the History Database of the Global Environment (HYDE, v 3.3) and replaces former HYDE 3.2 version from 2017. HYDE is and internally consistent combination of updated historical population estimates and land use. Categories include cropland, with a new distinction into irrigated and rain fed crops (other than rice) and irrigated and rain fed rice. Also grazing lands are provided, divided into more intensively used pasture, converted rangeland and non-converted natural (less intensively used) rangeland. Population is represented by maps of total, urban, rural population and population density as well as built-up area. The period covered is 10 000 BCE to 2023 CE. Spatial resolution is 5 arc minutes (approx. 85 km2 at the equator), the files are in ESRI ASCII grid format.

Retrieved on
January 2, 2024
Retrieved from
https://doi.org/10.24416/UU01-AEZZIT
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Utrecht University/PBL Netherlands Environmental Assessment Agency - History Database of the Global Environment (HYDE v 3.3, 2023).
Klein Goldewijk, C.G.M., Beusen, A., Doelman, J., Stehfest, E., 2017, Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953

This database presents an update and expansion of the History Database of the Global Environment (HYDE, v 3.3) and replaces former HYDE 3.2 version from 2017. HYDE is and internally consistent combination of updated historical population estimates and land use. Categories include cropland, with a new distinction into irrigated and rain fed crops (other than rice) and irrigated and rain fed rice. Also grazing lands are provided, divided into more intensively used pasture, converted rangeland and non-converted natural (less intensively used) rangeland. Population is represented by maps of total, urban, rural population and population density as well as built-up area. The period covered is 10 000 BCE to 2023 CE. Spatial resolution is 5 arc minutes (approx. 85 km2 at the equator), the files are in ESRI ASCII grid format.

Retrieved on
January 2, 2024
Retrieved from
https://doi.org/10.24416/UU01-AEZZIT
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Utrecht University/PBL Netherlands Environmental Assessment Agency - History Database of the Global Environment (HYDE v 3.3, 2023).
Klein Goldewijk, C.G.M., Beusen, A., Doelman, J., Stehfest, E., 2017, Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

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Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Grazing land use over the long-term”, part of the following publication: Hannah Ritchie, Lucas Rodés-Guirao, Edouard Mathieu, Marcel Gerber, Esteban Ortiz-Ospina, Joe Hasell, and Max Roser (2023) - “Population Growth”. Data adapted from PBL Netherlands Environmental Assessment Agency. Retrieved from https://archive.ourworldindata.org/20260325-171315/grapher/grazing-land-use-over-the-long-term.html [online resource] (archived on March 25, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

HYDE (2023) – with minor processing by Our World in Data

Full citation

HYDE (2023) – with minor processing by Our World in Data. “Grazing land use over the long-term” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260325-171315/grapher/grazing-land-use-over-the-long-term.html (archived on March 25, 2026).

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Data API

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Data URL (CSV format)
https://ourworldindata.org/grapher/grazing-land-use-over-the-long-term.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/grazing-land-use-over-the-long-term.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/grazing-land-use-over-the-long-term.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/grazing-land-use-over-the-long-term.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/grazing-land-use-over-the-long-term.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/grazing-land-use-over-the-long-term.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/grazing-land-use-over-the-long-term.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/grazing-land-use-over-the-long-term.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear