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Field scale SWAT+ modeling of corn and soybean yields for the contiguous United States: National Agroecosystem Model Development
Type of publication
Straipsnis Web of Science ir Scopus duomenų bazėje / Article in Web of Science and Scopus database (S1)
Type of document
text::journal::journal article::research article
Author(s)
LT | Blackland Research and Extension Center | US | ||
White, Michael | USDA-ARS Grassland Soil and Water Research Laboratory | US | ||
Arnold, Jeffrey | USDA-ARS Grassland Soil and Water Research Laboratory | US | ||
Bieger, Katrin | Aarhus University | DK | ||
Allen, Peter | Baylor University | US | ||
Gao, Jungang | Blackland Research and Extension Center | US | ||
Gambone, Marilyn | USDA-ARS Grassland Soil and Water Research Laboratory | US | ||
Meki, Manyowa | Blackland Research and Extension Center | US | ||
Kiniry, James | USDA-ARS Grassland Soil and Water Research Laboratory | US | ||
Gassman, Philip W. | Iowa State University | US |
Title
Field scale SWAT+ modeling of corn and soybean yields for the contiguous United States: National Agroecosystem Model Development
Publisher
Oxford : Elsevier
Date Issued
Date Issued | Volume | Issue | Start Page | End Page |
---|---|---|---|---|
2023-06-01 | vol. 210 | art. no. 103695 | 1 | 16 |
Is part of
Agricultural systems
Field of Science
Abstract
CONTEXT: Despite a steady increase in staple crop yields over the past ten years, current agricultural production
must escalate even more to keep pace with the expected world population growth, which in turn will require
improved agricultural methods that are adapted to many environmental pressures. Comprehensive models that
can simulate crop production systems and the impact of management and conservation practices on natural
resources and the environment, including water quality at large scale present important contributions to this
challenge.
OBJECTIVE: To this end we developed the National Agroecosystem Model (NAM): a comprehensive model that
uses the updated Soil and Water Assessment Tool (SWAT+) to accurately simulate staple crop yields across the
contiguous United States (CONUS), with an initial focus on Corn (Zea mays L.) and Soybean (Glycine max L.
Merr.) yields.
METHODS: Available open-access data was used to setup this high-resolution modeling system, where every 8-
digit hydrologic unit (HUC8) is represented as an individual SWAT+ simulation. A total of 2201 HUC8 simulations across the CONUS were interconnected from upstream to downstream to make the NAM. Field
boundary data was used to setup the NAM in such a way that every identified cultivated field is modeled as a
unique Hydrologic Response Unit (HRU). Simulated corn and soybean yield from over 2.5 million field-type
HRUs were compared to reported average annual corn and soybean yields for the respective area for the
2015–2020 period.
RESULTS AND CONCLUSIONS: Results show a good agreement between simulated and reported yields (R2 = 0.90
for corn and R2 = 0.70 for soybeans), with a very good model performance in the high corn and soybean pro-
duction region of the US Corn Belt (Relative Error < ±5%).
SIGNIFICANCE: Apart from assessing the capability of the updated SWAT+ model, we also demonstrate the new
crop yield calibration module embedded in SWAT+, highlight changes to the plant growth module, and model
parameterization. Results of an analysis of possible crop production differences for corn and soybeans in irri-
gated, tiled, and non-irrigated-non-tiled fields are also discussed. The versatility of the NAM provides the pos-
sibility to analyze information on impacts of changing conservation practices and enables identification of
conservation gains and remaining conservation needs at the national scale.
must escalate even more to keep pace with the expected world population growth, which in turn will require
improved agricultural methods that are adapted to many environmental pressures. Comprehensive models that
can simulate crop production systems and the impact of management and conservation practices on natural
resources and the environment, including water quality at large scale present important contributions to this
challenge.
OBJECTIVE: To this end we developed the National Agroecosystem Model (NAM): a comprehensive model that
uses the updated Soil and Water Assessment Tool (SWAT+) to accurately simulate staple crop yields across the
contiguous United States (CONUS), with an initial focus on Corn (Zea mays L.) and Soybean (Glycine max L.
Merr.) yields.
METHODS: Available open-access data was used to setup this high-resolution modeling system, where every 8-
digit hydrologic unit (HUC8) is represented as an individual SWAT+ simulation. A total of 2201 HUC8 simulations across the CONUS were interconnected from upstream to downstream to make the NAM. Field
boundary data was used to setup the NAM in such a way that every identified cultivated field is modeled as a
unique Hydrologic Response Unit (HRU). Simulated corn and soybean yield from over 2.5 million field-type
HRUs were compared to reported average annual corn and soybean yields for the respective area for the
2015–2020 period.
RESULTS AND CONCLUSIONS: Results show a good agreement between simulated and reported yields (R2 = 0.90
for corn and R2 = 0.70 for soybeans), with a very good model performance in the high corn and soybean pro-
duction region of the US Corn Belt (Relative Error < ±5%).
SIGNIFICANCE: Apart from assessing the capability of the updated SWAT+ model, we also demonstrate the new
crop yield calibration module embedded in SWAT+, highlight changes to the plant growth module, and model
parameterization. Results of an analysis of possible crop production differences for corn and soybeans in irri-
gated, tiled, and non-irrigated-non-tiled fields are also discussed. The versatility of the NAM provides the pos-
sibility to analyze information on impacts of changing conservation practices and enables identification of
conservation gains and remaining conservation needs at the national scale.
ISSN (of the container)
0308-521X
1873-2267
WOS
001023690500001
Scopus
2-s2.0-85162080996
Coverage Spatial
Jungtinė Karalystė / United Kingdom of Great Britain and Northern Ireland (GB)
Language
Anglų / English (en)
Bibliographic Details
110
Access Rights
Atviroji prieiga / Open Access
Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
---|---|---|---|---|---|---|---|---|
AGRICULTURAL SYSTEMS | 6.6 | 3.8 | 3.8 | 3.8 | 1 | 1.737 | 2022 | Q1 |
Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
---|---|---|---|---|---|---|---|---|
AGRICULTURAL SYSTEMS | 6.6 | 3.8 | 3.8 | 3.8 | 1 | 1.737 | 2022 | Q1 |
3.8 | ||||||||
3.594 |
Journal | Cite Score | SNIP | SJR | Year | Quartile |
---|---|---|---|---|---|
Agricultural Systems | 11.9 | 1.965 | 1.574 | 2022 | Q1 |