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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
29/03/2021 |
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Actualizado : |
15/06/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
SCHILLACI, C; PEREGO, A.; VALKAMA, E.; MÄRKER, M.; SAIA, S.; VERONESI, F.; LIPANI, A.; LOMBARDO, L.; TADIELLO, T.; GAMPER, H. A.; TEDONE, L.; MOSS, C.; PAREJA-SERRANO, E.; AMATO, G.; KÜHL, K.; DAMATIRCA, C.; COGATO, A.; MZID, N.; EESWARAN, R.; REBELO, M.; SPERANDIO, G.; BOSINO, A.; BUFALINI, M.; TUNÇAY, T.; DING, J.; FIORENTINI, M.; TISCORNIA, G.; CONRADT, S.; BOTTA, M.; ACUTIS, M. |
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Afiliación : |
CALOGERO SCHILLACI, Department of Agricultural and Environmental Science, University of Milan, Milan, Italy; ALESSIA PEREGO, Department of Agricultural and Environmental Science, University of Milan, Milan, Italy; ELENA VALKAMA, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, Jokioinen, Finland; MICHAEL MÄRKER, Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy; SERGIO SAIA, Department of Veterinary Sciences, University of Pisa,Pisa, Italy; FABIO VERONESI, Water Research Centre Limited, Frankland Road, Blagrove, Swindon, England, UK; ALDO LIPANI, Department of Web Intelligence Group, University College London (UCL), London, England, UK; LUIGI LOMBARDO, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, the Netherlands; TOMMASO TADIELLO, Department of Agricultural and Environmental Science, University of Milan, Milan, Italy; HANNES A. GAMPER, Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy; LUIGI TEDONE, Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Bari, Italy; CAMI MOSS, Department of Population Health, London School of Hygiene & Tropical Medicine, London, UK; ELENA PAREJA-SERRANO, NRAE-UMR EMMAH, Domaine Saint Paul - Site Agroparc, Avignon, France; GABRIELE AMATO,, Applied Physics Institute, Nello Carrara - National Research Council of Italy (IFAC-CNR), Sesto Fiorentino (FI), Italy; KERSTEN KÜHL, Department of Geography, Ludwig-Maximilians-Universität München (LMU Munich), Germany; CLAUDIA DAMATIRCA, Department of Agricultural, Forest and Food Sciences, University of Torino, Grugliasco, Italy; ALESSIA COGATO, Department of Land, Environmental, Agriculture and Forestry, University of Padova, Legnaro, Italy; NADA MZID, Department of Agriculture Forestry and Nature (DAFNE), University of Tuscia, Viterbo, Italy; RASU EESWARAN, Department of Plant, Soil and Microbial Sciences, Michigan State University, MI, USA; MARYA REBELO, Department of Agriculture, Food and Environment, University of Pisa, Pisa, Italy; GIORGIO SPERANDIO, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy; ALBERTO BOSINO, Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy; MARGHERITA BUFALINI, University of Camerino, School of Science and Technology-Geology Division, Camerino, Italy; TÜLAY TUNÇAY, Soil Fertilizer and Water Resources Central Research Institute, Ankara, Turkey; JIANQI DING, Department of Biological and Ecological Sciences DEB, Università della Tuscia, Viterbo, Italy; MARCO FIORENTINI, Department of Agricultural, Food and Environmental Sciences (D3A), Marche Polytechnic University, Ancona, Italy; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SARAH CONRADT, SCOR SE, Zurich Branch, Switzerland; MARCO BOTTA, Department of Agricultural and Environmental Science, University of Milan, Milan, Italy; MARCO ACUTIS, Department of Agricultural and Environmental Science, University of Milan,Milan, Italy. |
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Título : |
New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Science of The Total Environment, 2021, Volume 780, Article 146609. Doi: https://doi.org/10.1016/j.scitotenv.2021.146609 |
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DOI : |
10.1016/j.scitotenv.2021.146609 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 26 December 2020; Revised 24 February 2021; Accepted 16 March 2021; Available online 19 March 2021.
Incluye Supplementary data.
Editor: Manuel Esteban Lucas-Borja |
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Contenido : |
ABSTRACT.
For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0?0.4 m) and subsoil (0.4?1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data.
The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution.
The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN).
The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset.
© 2021 Published by Elsevier B.V. MenosABSTRACT.
For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0?0.4 m) and subsoil (0.4?1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data.
The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution.
The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN).
The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) an... Presentar Todo |
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Palabras claves : |
Agriculture; Bulk density (BD); Pedotransfer functions; PTFs; Soil carbon; Soil carbon sequestration; Soil organic carbon stocks; Soil texture. |
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Asunto categoría : |
P36 Erosión conservación y recuperación del suelo |
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Marc : |
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024 7 $a10.1016/j.scitotenv.2021.146609$2DOI
100 1 $aSCHILLACI, C
245 $aNew pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems.$h[electronic resource]
260 $c2021
500 $aArticle history: Received 26 December 2020; Revised 24 February 2021; Accepted 16 March 2021; Available online 19 March 2021. Incluye Supplementary data. Editor: Manuel Esteban Lucas-Borja
520 $aABSTRACT. For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0?0.4 m) and subsoil (0.4?1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. © 2021 Published by Elsevier B.V.
653 $aAgriculture
653 $aBulk density (BD)
653 $aPedotransfer functions
653 $aPTFs
653 $aSoil carbon
653 $aSoil carbon sequestration
653 $aSoil organic carbon stocks
653 $aSoil texture
700 1 $aPEREGO, A.
700 1 $aVALKAMA, E.
700 1 $aMÄRKER, M.
700 1 $aSAIA, S.
700 1 $aVERONESI, F.
700 1 $aLIPANI, A.
700 1 $aLOMBARDO, L.
700 1 $aTADIELLO, T.
700 1 $aGAMPER, H. A.
700 1 $aTEDONE, L.
700 1 $aMOSS, C.
700 1 $aPAREJA-SERRANO, E.
700 1 $aAMATO, G.
700 1 $aKÜHL, K.
700 1 $aDAMATIRCA, C.
700 1 $aCOGATO, A.
700 1 $aMZID, N.
700 1 $aEESWARAN, R.
700 1 $aREBELO, M.
700 1 $aSPERANDIO, G.
700 1 $aBOSINO, A.
700 1 $aBUFALINI, M.
700 1 $aTUNÇAY, T.
700 1 $aDING, J.
700 1 $aFIORENTINI, M.
700 1 $aTISCORNIA, G.
700 1 $aCONRADT, S.
700 1 $aBOTTA, M.
700 1 $aACUTIS, M.
773 $tScience of The Total Environment, 2021, Volume 780, Article 146609. Doi: https://doi.org/10.1016/j.scitotenv.2021.146609
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