Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs

Bibliographic Details
Main Author: Xu, Xu
Publication Date: 2021
Other Authors: Li, Huawei, Sun, Chen, Ramos, Tiago B., Darouich, Hanaa, Xiong, Yunwu, Qu, Zhongyi, Huang, Guanhua
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.5/22005
Summary: Many agro-environmental studies focusing on the efficient management of soils and water resources make use of soil water simulation models. Reliable soil hydraulic properties are critical for ensuring the accuracy of model simulations. However, soil hydraulic parameters in northern China are generally derived using external pedotransfer functions (PTFs) that do not take into account the specificities of local edaphoclimatic conditions due to the lack of a better alternative. Therefore, the main objective of this paper was to develop PTFs for estimating the soil water retention curve (SWRC) in northern China agricultural soils (named PTF-ANC). A total of 440 soil horizons were collected from the existing literature. A flexible soil-textural conversion program was first developed to harmonize soil particle-size data into the United States Department of Agriculture (USDA) classification system. The SWRC parameters of the van Genuchten model were also generated by curve fitting. Then, the PTF-ANC were developed using artificial neural networks, with soil texture and bulk density being used as input data and with a basic three-layer back-propagation neural network. The PTF-ANC showed an acceptable accuracy when predicting the SWRC, indicating a strong application potential for northern China soils. Comparison of estimates with two widely used external PTFs also showed that these were not suitable for characterizing the SWRC of northern China agricultural soils. This is due to the fact that the main soil textures (silt and silty loam textures) found in northern China soils were misrepresented in those external soil databases. Overall, this paper presented the absolute necessity of developing specific PTFs for northern China agricultural soils
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spelling Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needsFonctions de pédotransfert pour estimer les propriétés de rétention d'eau du sol des sols agricoles du nord de la Chine: développement et besoinsneural networknon-linear fittingpedotransfer functionssoil hydraulic parameterssoil water modeltextural data conversionMany agro-environmental studies focusing on the efficient management of soils and water resources make use of soil water simulation models. Reliable soil hydraulic properties are critical for ensuring the accuracy of model simulations. However, soil hydraulic parameters in northern China are generally derived using external pedotransfer functions (PTFs) that do not take into account the specificities of local edaphoclimatic conditions due to the lack of a better alternative. Therefore, the main objective of this paper was to develop PTFs for estimating the soil water retention curve (SWRC) in northern China agricultural soils (named PTF-ANC). A total of 440 soil horizons were collected from the existing literature. A flexible soil-textural conversion program was first developed to harmonize soil particle-size data into the United States Department of Agriculture (USDA) classification system. The SWRC parameters of the van Genuchten model were also generated by curve fitting. Then, the PTF-ANC were developed using artificial neural networks, with soil texture and bulk density being used as input data and with a basic three-layer back-propagation neural network. The PTF-ANC showed an acceptable accuracy when predicting the SWRC, indicating a strong application potential for northern China soils. Comparison of estimates with two widely used external PTFs also showed that these were not suitable for characterizing the SWRC of northern China agricultural soils. This is due to the fact that the main soil textures (silt and silty loam textures) found in northern China soils were misrepresented in those external soil databases. Overall, this paper presented the absolute necessity of developing specific PTFs for northern China agricultural soilsWileyRepositório da Universidade de LisboaXu, XuLi, HuaweiSun, ChenRamos, Tiago B.Darouich, HanaaXiong, YunwuQu, ZhongyiHuang, Guanhua2021-09-24T15:19:08Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/22005engIrrig. and Drain. 2021;1–1610.1002/ird.2584info:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T16:12:33Zoai:repositorio.ulisboa.pt:10400.5/22005Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T04:05:58.599290Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs
Fonctions de pédotransfert pour estimer les propriétés de rétention d'eau du sol des sols agricoles du nord de la Chine: développement et besoins
title Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs
spellingShingle Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs
Xu, Xu
neural network
non-linear fitting
pedotransfer functions
soil hydraulic parameters
soil water model
textural data conversion
title_short Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs
title_full Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs
title_fullStr Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs
title_full_unstemmed Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs
title_sort Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs
author Xu, Xu
author_facet Xu, Xu
Li, Huawei
Sun, Chen
Ramos, Tiago B.
Darouich, Hanaa
Xiong, Yunwu
Qu, Zhongyi
Huang, Guanhua
author_role author
author2 Li, Huawei
Sun, Chen
Ramos, Tiago B.
Darouich, Hanaa
Xiong, Yunwu
Qu, Zhongyi
Huang, Guanhua
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Xu, Xu
Li, Huawei
Sun, Chen
Ramos, Tiago B.
Darouich, Hanaa
Xiong, Yunwu
Qu, Zhongyi
Huang, Guanhua
dc.subject.por.fl_str_mv neural network
non-linear fitting
pedotransfer functions
soil hydraulic parameters
soil water model
textural data conversion
topic neural network
non-linear fitting
pedotransfer functions
soil hydraulic parameters
soil water model
textural data conversion
description Many agro-environmental studies focusing on the efficient management of soils and water resources make use of soil water simulation models. Reliable soil hydraulic properties are critical for ensuring the accuracy of model simulations. However, soil hydraulic parameters in northern China are generally derived using external pedotransfer functions (PTFs) that do not take into account the specificities of local edaphoclimatic conditions due to the lack of a better alternative. Therefore, the main objective of this paper was to develop PTFs for estimating the soil water retention curve (SWRC) in northern China agricultural soils (named PTF-ANC). A total of 440 soil horizons were collected from the existing literature. A flexible soil-textural conversion program was first developed to harmonize soil particle-size data into the United States Department of Agriculture (USDA) classification system. The SWRC parameters of the van Genuchten model were also generated by curve fitting. Then, the PTF-ANC were developed using artificial neural networks, with soil texture and bulk density being used as input data and with a basic three-layer back-propagation neural network. The PTF-ANC showed an acceptable accuracy when predicting the SWRC, indicating a strong application potential for northern China soils. Comparison of estimates with two widely used external PTFs also showed that these were not suitable for characterizing the SWRC of northern China agricultural soils. This is due to the fact that the main soil textures (silt and silty loam textures) found in northern China soils were misrepresented in those external soil databases. Overall, this paper presented the absolute necessity of developing specific PTFs for northern China agricultural soils
publishDate 2021
dc.date.none.fl_str_mv 2021-09-24T15:19:08Z
2021
2021-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.5/22005
url http://hdl.handle.net/10400.5/22005
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Irrig. and Drain. 2021;1–16
10.1002/ird.2584
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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