A global dataset for crop production under conventional tillage and no tillage systems.


Journal

Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192

Informations de publication

Date de publication:
28 01 2021
Historique:
received: 21 04 2020
accepted: 16 12 2020
entrez: 29 1 2021
pubmed: 30 1 2021
medline: 7 2 2021
Statut: epublish

Résumé

No tillage (NT) is often presented as a means to grow crops with positive environmental externalities, such as enhanced carbon sequestration, improved soil quality, reduced soil erosion, and increased biodiversity. However, whether NT systems are as productive as those relying on conventional tillage (CT) is a controversial issue, fraught by a high variability over time and space. Here, we expand existing datasets to include the results of the most recent field experiments, and we produce a global dataset comparing the crop yields obtained under CT and NT systems. In addition to crop yield, our dataset also reports information on crop growing season, management practices, soil characteristics and key climate parameters throughout the experimental year. The final dataset contains 4403 paired yield observations between 1980 and 2017 for eight major staple crops in 50 countries. This dataset can help to gain insight into the main drivers explaining the variability of the productivity of NT and the consequence of its adoption on crop yields.

Identifiants

pubmed: 33510175
doi: 10.1038/s41597-021-00817-x
pii: 10.1038/s41597-021-00817-x
pmc: PMC7844240
doi:

Substances chimiques

Soil 0

Types de publication

Dataset Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

33

Subventions

Organisme : Agence Nationale de la Recherche (French National Research Agency)
ID : ANR-16-CONV-0003
Pays : International
Organisme : Agence Nationale de la Recherche (French National Research Agency)
ID : ANR-16-CONV-0003
Pays : International

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Auteurs

Yang Su (Y)

UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 78850, Thiverval-Grignon, France. yang.su@inrae.fr.

Benoit Gabrielle (B)

UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 78850, Thiverval-Grignon, France.

David Makowski (D)

UMR Agronomie, INRAE AgroParisTech, Université Paris-Saclay, 78850, Thiverval-Grignon, France.
Applied mathematics and computer science (MIA 518), INRAE AgroParisTech, Université Paris-Saclay, 75005, Paris, France.

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