Articles | Volume 2, issue 1
https://doi.org/10.5194/soil-2-25-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/soil-2-25-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Original research article
| 
18 Jan 2016
Original research article |
| 18 Jan 2016
Pedotransfer functions for Irish soils – estimation of bulk density (ρb) per horizon type
B. Reidy
CORRESPONDING AUTHOR
Teagasc, Johnstown Castle, Environment Research Centre, Co. Wexford, Ireland
I. Simo
Teagasc, Johnstown Castle, Environment Research Centre, Co. Wexford, Ireland
P. Sills
Teagasc, Johnstown Castle, Environment Research Centre, Co. Wexford, Ireland
R. E. Creamer
Teagasc, Johnstown Castle, Environment Research Centre, Co. Wexford, Ireland
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Cited
25 citations as recorded by crossref.
- Soil quality – A critical review E. Bünemann et al. 10.1016/j.soilbio.2018.01.030
- Developing pedotransfer functions for predicting soil bulk density in Campania M. Palladino et al. 10.1016/j.geoderma.2022.115726
- Predicting bulk density using pedotransfer functions for soils in the Upper Anthemountas basin, Greece S. Sevastas et al. 10.1016/j.GEODRS.2018.e00169
- Influence of some environmental variables on organic carbon and nitrogen stocks in grassland mineral soils from various temperate-climate ecosystems A. LĂCĂTUŞU et al. 10.1016/j.envexpbot.2023.105554
- Relative and absolute difference in soil organic carbon stocks in grassland soils in Ireland: Impact of rock fragments, bulk density and calculation methods O. Fenton et al. 10.1016/j.geodrs.2024.e00769
- Improving the identification of hydrologically sensitive areas using LiDAR DEMs for the delineation and mitigation of critical source areas of diffuse pollution I. Thomas et al. 10.1016/j.scitotenv.2016.02.183
- Validation of a new gamma ray soil bulk density sensor K. Pepers et al. 10.1111/ejss.13542
- Predicting the soil bulk density using a new spectral PTF based on intact samples X. Wang et al. 10.1016/j.geoderma.2024.117005
- New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems C. Schillaci et al. 10.1016/j.scitotenv.2021.146609
- Modeling soil cation exchange capacity in multiple countries Y. Khaledian et al. 10.1016/j.catena.2017.07.002
- Evaluating pedotransfer functions for predicting soil bulk density using hierarchical mapping information in Campania, Italy P. Nasta et al. 10.1016/j.geodrs.2020.e00267
- Using surface soil layer depth for determining soil quality index for evaluating productivity of potato ( Solanum tuberosum L.) in Hokkaido, Japan M. Yoshimura et al. 10.1080/00380768.2023.2237992
- Influence of Soil Wetting and Drying Cycles on Soil Detachment J. Wang et al. 10.3390/agriengineering4020036
- Field scale estimates of soil carbon stocks on ten heavy textured farms across Ireland P. Tuohy et al. 10.1016/j.jenvman.2020.111903
- A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression A. Arbor et al. 10.1016/j.geoderma.2023.116674
- Digging deeper: Understanding the contribution of subsoil carbon for climate mitigation, a case study of Ireland I. Simo et al. 10.1016/j.envsci.2019.05.004
- Prediction of soil bulk density in agricultural soils using mid-infrared spectroscopy L. Shi et al. 10.1016/j.geoderma.2023.116487
- Bulk-density modelling using optimal power-transformation of measured physical and chemical soil parameters A. Premrov et al. 10.1016/j.geoderma.2017.10.060
- Predicting Cationic Exchange Capacity in Calcareous Soils of East-Azerbaijan Province, Northwest Iran F. Asadzadeh et al. 10.1080/00103624.2019.1604728
- Responses of carbon cycling and soil organic carbon content to nitrogen addition in grasslands globally H. Liu et al. 10.1016/j.soilbio.2023.109164
- Comparison of estimated soil bulk density using proximal soil sensing and pedotransfer functions X. Sun et al. 10.1016/j.jhydrol.2019.124227
- Filling the gaps in soil data: A multi-model framework for addressing data gaps using pedotransfer functions and machine-learning with uncertainty estimates to estimate bulk density A. Arbor et al. 10.1016/j.catena.2024.108310
- Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation G. Bondi et al. 10.1016/j.geoderma.2017.11.035
- Islands of biogeodiversity in arid lands on a polygons map study: Detecting scale invariance patterns from natural resources maps J. Ibáñez et al. 10.1016/j.scitotenv.2016.09.172
- Selected highlights in American soil science history from the 1980s to the mid-2010s E. Brevik et al. 10.1016/j.catena.2016.06.021
25 citations as recorded by crossref.
- Soil quality – A critical review E. Bünemann et al. 10.1016/j.soilbio.2018.01.030
- Developing pedotransfer functions for predicting soil bulk density in Campania M. Palladino et al. 10.1016/j.geoderma.2022.115726
- Predicting bulk density using pedotransfer functions for soils in the Upper Anthemountas basin, Greece S. Sevastas et al. 10.1016/j.GEODRS.2018.e00169
- Influence of some environmental variables on organic carbon and nitrogen stocks in grassland mineral soils from various temperate-climate ecosystems A. LĂCĂTUŞU et al. 10.1016/j.envexpbot.2023.105554
- Relative and absolute difference in soil organic carbon stocks in grassland soils in Ireland: Impact of rock fragments, bulk density and calculation methods O. Fenton et al. 10.1016/j.geodrs.2024.e00769
- Improving the identification of hydrologically sensitive areas using LiDAR DEMs for the delineation and mitigation of critical source areas of diffuse pollution I. Thomas et al. 10.1016/j.scitotenv.2016.02.183
- Validation of a new gamma ray soil bulk density sensor K. Pepers et al. 10.1111/ejss.13542
- Predicting the soil bulk density using a new spectral PTF based on intact samples X. Wang et al. 10.1016/j.geoderma.2024.117005
- New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems C. Schillaci et al. 10.1016/j.scitotenv.2021.146609
- Modeling soil cation exchange capacity in multiple countries Y. Khaledian et al. 10.1016/j.catena.2017.07.002
- Evaluating pedotransfer functions for predicting soil bulk density using hierarchical mapping information in Campania, Italy P. Nasta et al. 10.1016/j.geodrs.2020.e00267
- Using surface soil layer depth for determining soil quality index for evaluating productivity of potato ( Solanum tuberosum L.) in Hokkaido, Japan M. Yoshimura et al. 10.1080/00380768.2023.2237992
- Influence of Soil Wetting and Drying Cycles on Soil Detachment J. Wang et al. 10.3390/agriengineering4020036
- Field scale estimates of soil carbon stocks on ten heavy textured farms across Ireland P. Tuohy et al. 10.1016/j.jenvman.2020.111903
- A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression A. Arbor et al. 10.1016/j.geoderma.2023.116674
- Digging deeper: Understanding the contribution of subsoil carbon for climate mitigation, a case study of Ireland I. Simo et al. 10.1016/j.envsci.2019.05.004
- Prediction of soil bulk density in agricultural soils using mid-infrared spectroscopy L. Shi et al. 10.1016/j.geoderma.2023.116487
- Bulk-density modelling using optimal power-transformation of measured physical and chemical soil parameters A. Premrov et al. 10.1016/j.geoderma.2017.10.060
- Predicting Cationic Exchange Capacity in Calcareous Soils of East-Azerbaijan Province, Northwest Iran F. Asadzadeh et al. 10.1080/00103624.2019.1604728
- Responses of carbon cycling and soil organic carbon content to nitrogen addition in grasslands globally H. Liu et al. 10.1016/j.soilbio.2023.109164
- Comparison of estimated soil bulk density using proximal soil sensing and pedotransfer functions X. Sun et al. 10.1016/j.jhydrol.2019.124227
- Filling the gaps in soil data: A multi-model framework for addressing data gaps using pedotransfer functions and machine-learning with uncertainty estimates to estimate bulk density A. Arbor et al. 10.1016/j.catena.2024.108310
- Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation G. Bondi et al. 10.1016/j.geoderma.2017.11.035
- Islands of biogeodiversity in arid lands on a polygons map study: Detecting scale invariance patterns from natural resources maps J. Ibáñez et al. 10.1016/j.scitotenv.2016.09.172
- Selected highlights in American soil science history from the 1980s to the mid-2010s E. Brevik et al. 10.1016/j.catena.2016.06.021
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Latest update: 23 Nov 2024
Short summary
This study reviews pedotransfer functions from the literature for different soil and horizon types. It uses these formulae to predict bulk density (ρb) per horizon using measured data of other soil properties. These data were compared to known pb per horizon and recalibrated. These calculations were used to fill missing horizon data in the Irish soil database. This allowed the generation of a pb map to 50 cm. These pb data are at horizon level allowing more accurate estimation of C with depth.
This study reviews pedotransfer functions from the literature for different soil and horizon...
