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Article Dans Une Revue Science of the Total Environment Année : 2021

New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems

1 UNIMI - Università degli Studi di Milano = University of Milan
2 LUKE - Natural Resources Institute Finland
3 Dipartimento di Scienze della Terra e dell'Ambiente [Pavia]
4 University of Pisa - Università di Pisa
5 CWR - Centre for Water Research
6 UCL - University College of London [London]
7 ITC - Faculty of Geo-Information Science and Earth Observation
8 Microsoft Research [Redmond]
9 UNIBA - Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro
10 LSHTM - London School of Hygiene and Tropical Medicine
11 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
12 IMAA - Institute of Methodologies for Environmental Analysis of the National Research Council [Italy]
13 LMU - Ludwig Maximilian University [Munich] = Ludwig Maximilians Universität München
14 UNITO - Università degli studi di Torino = University of Turin
15 Unipd - Università degli Studi di Padova = University of Padua
16 CIHEAM-IAMB - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Bari
17 Michigan State University System
18 UniBs - Università degli Studi di Brescia = University of Brescia
19 UNIPV - Università degli Studi di Pavia = University of Pavia
20 UNICAM - Università degli Studi di Camerino = University of Camerino
21 Ankara Üniversitesi
22 Università degli studi della Tuscia [Viterbo]
23 NY-MaSBiC - New York–Marche Structural Biology Center [Ancona, Italia]
24 INIA - Instituto Nacional de Investigación Agropecuaria
25 SCOR SE Zurich Branch

Résumé

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. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R-2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R-2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R-2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R-2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.

Dates et versions

hal-03775123 , version 1 (12-09-2022)

Identifiants

Citer

Calogero Schillaci, Alessia Perego, Elena Valkama, Michael Märker, Sergio Saia, et al.. New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems. Science of the Total Environment, 2021, 780, pp.146609. ⟨10.1016/j.scitotenv.2021.146609⟩. ⟨hal-03775123⟩
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