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Development of a 10-km resolution global soil profile dataset for crop modeling applications

One major challenge in applying crop simulation models at the regional or global scale is the lack of available global gridded soil profile data. We developed a 10-km resolution global soil profile dataset, at 2 m depth, compatible with DSSAT using SoilGrids1km. Several soil physical and chemical pr...

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Detalles Bibliográficos
Autores principales: Han, Eunjin, Ines, Amor V.M., Koo, Jawoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694752/
https://www.ncbi.nlm.nih.gov/pubmed/31481849
http://dx.doi.org/10.1016/j.envsoft.2019.05.012
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author Han, Eunjin
Ines, Amor V.M.
Koo, Jawoo
author_facet Han, Eunjin
Ines, Amor V.M.
Koo, Jawoo
author_sort Han, Eunjin
collection PubMed
description One major challenge in applying crop simulation models at the regional or global scale is the lack of available global gridded soil profile data. We developed a 10-km resolution global soil profile dataset, at 2 m depth, compatible with DSSAT using SoilGrids1km. Several soil physical and chemical properties required by DSSAT were directly extracted from SoilGrids1km. Pedo-transfer functions were used to derive soil hydraulic properties. Other soil parameters not available from SoilGrids1km were estimated from HarvestChoice HC27 generic soil profiles. The newly developed soil profile dataset was evaluated in different regions of the globe using independent soil databases from other sources. In general, we found that the derived soil properties matched well with data from other soil data sources. An ex-ante assessment for maize intensification in Tanzania is provided to show the potential regional to global uses of the new gridded soil profile dataset.
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spelling pubmed-66947522019-09-01 Development of a 10-km resolution global soil profile dataset for crop modeling applications Han, Eunjin Ines, Amor V.M. Koo, Jawoo Environ Model Softw Article One major challenge in applying crop simulation models at the regional or global scale is the lack of available global gridded soil profile data. We developed a 10-km resolution global soil profile dataset, at 2 m depth, compatible with DSSAT using SoilGrids1km. Several soil physical and chemical properties required by DSSAT were directly extracted from SoilGrids1km. Pedo-transfer functions were used to derive soil hydraulic properties. Other soil parameters not available from SoilGrids1km were estimated from HarvestChoice HC27 generic soil profiles. The newly developed soil profile dataset was evaluated in different regions of the globe using independent soil databases from other sources. In general, we found that the derived soil properties matched well with data from other soil data sources. An ex-ante assessment for maize intensification in Tanzania is provided to show the potential regional to global uses of the new gridded soil profile dataset. Elsevier Science 2019-09 /pmc/articles/PMC6694752/ /pubmed/31481849 http://dx.doi.org/10.1016/j.envsoft.2019.05.012 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Han, Eunjin
Ines, Amor V.M.
Koo, Jawoo
Development of a 10-km resolution global soil profile dataset for crop modeling applications
title Development of a 10-km resolution global soil profile dataset for crop modeling applications
title_full Development of a 10-km resolution global soil profile dataset for crop modeling applications
title_fullStr Development of a 10-km resolution global soil profile dataset for crop modeling applications
title_full_unstemmed Development of a 10-km resolution global soil profile dataset for crop modeling applications
title_short Development of a 10-km resolution global soil profile dataset for crop modeling applications
title_sort development of a 10-km resolution global soil profile dataset for crop modeling applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694752/
https://www.ncbi.nlm.nih.gov/pubmed/31481849
http://dx.doi.org/10.1016/j.envsoft.2019.05.012
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