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Data

Which countries overapplied nitrogen without gains in crop yields?

See all data and research on:
Crop Yields

What you should know about this indicator

This metric shows how much nitrogen pollution countries are causing compared to how much they reduce their yield gaps (the difference between attainable and attained yields) relative to directly neighboring countries. Positive values indicate a country is overapplying nitrogen without gains in yield.

Which countries overapplied nitrogen without gains in crop yields?
Countries' estimated effect on their yield gaps versus their nitrogen pollution.
Source
Wuepper et al. (2020) – with minor processing by Our World in Data
Last updated
March 26, 2024
Date range
2020–2020
Unit
%

Related research and writing

Can we reduce fertilizer use without sacrificing food production?

Hannah Ritchie

Sources and processing

This data is based on the following sources

Wuepper et al. – Countries influence the trade-off between crop yields and nitrogen pollution

Retrieved on
March 26, 2024
Retrieved from
https://www.nature.com/articles/s43016-020-00185-6
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.
Wuepper, D., Le Clech, S., Zilberman, D. et al. (2020) - Countries influence the trade-off between crop yields and nitrogen pollution. Nat Food 1, 713-719. https://doi.org/10.1038/s43016-020-00185-6
The data was kindly provided by the authors in xlsx format in a private communication. The data in Stata format can be accessed via the ETH Zürich Research Collection: https://www.research-collection.ethz.ch/handle/20.500.11850/430354
Retrieved on
March 26, 2024
Retrieved from
https://www.nature.com/articles/s43016-020-00185-6
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.
Wuepper, D., Le Clech, S., Zilberman, D. et al. (2020) - Countries influence the trade-off between crop yields and nitrogen pollution. Nat Food 1, 713-719. https://doi.org/10.1038/s43016-020-00185-6
The data was kindly provided by the authors in xlsx format in a private communication. The data in Stata format can be accessed via the ETH Zürich Research Collection: https://www.research-collection.ethz.ch/handle/20.500.11850/430354

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.

Read about our data pipeline

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: Which countries overapplied nitrogen without gains in crop yields?”, part of the following publication: Hannah Ritchie, Pablo Rosado, and Max Roser (2022) - “Crop Yields”. Data adapted from Wuepper et al.. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/yield-gap-vs-nitrogen-pollution.html [online resource] (archived on March 4, 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:

Wuepper et al. (2020) – with minor processing by Our World in Data

Full citation

Wuepper et al. (2020) – with minor processing by Our World in Data. “Which countries overapplied nitrogen without gains in crop yields?” [dataset]. Wuepper et al., “Countries influence the trade-off between crop yields and nitrogen pollution” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/yield-gap-vs-nitrogen-pollution.html (archived on March 4, 2026).

Download

Quick download

Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/yield-gap-vs-nitrogen-pollution.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/yield-gap-vs-nitrogen-pollution.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/yield-gap-vs-nitrogen-pollution.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/yield-gap-vs-nitrogen-pollution.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/yield-gap-vs-nitrogen-pollution.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/yield-gap-vs-nitrogen-pollution.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/yield-gap-vs-nitrogen-pollution.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/yield-gap-vs-nitrogen-pollution.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear