Pedotransfer functions to estimate soil bulk density for Northern Africa: Tunisia case |
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| Institution: | 1. Aarhus University, Department of Agroecology, Research Centre Foulum, Blichers Allé 20, P.O. Box 50, DK-8830 Tjele, Denmark;2. University of Guelph, School of Environmental Sciences, Guelph, Ontario N1G 2W1, Canada;3. Agroscope, Department of Natural Resources & Agriculture, Reckenholzstrasse 191, CH-8046 Zürich, Switzerland;4. Swedish University of Agricultural Sciences, Department of Soil & Environment, Box 7014, SE-75007 Uppsala, Sweden;1. LEESU-DR, Ecole des Ponts ParisTech, Université Paris-Est, 6 et 8 avenue Blaise Pascal, Cité Descartes, 77455 Marne-la-Vallée, Cedex 2, France;2. Laboratoire Géomatique et Foncier (GeF), Ecole supérieure des Géomètres et topographes (ESGT), Conservatoire national des arts et métiers (Le Cnam), 1 boulevard Pythagore, F-72000 Le Mans, France;3. CNR-IRPI, Perugia, Italy |
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| Abstract: | Countries should provide regularly national inventories of greenhouse gas emissions and sinks and, and for the agriculture and forestry sectors this comprise national estimates of soil organic carbon (C) stocks. Estimation of soil C stock requires soil bulk density (Db) values. However, direct measurement of Db is often lacking mainly for soils in arid and semi-arid conditions. Much effort has been made in finding alternative solution to predict Db, either improving in situ determinations, either improving estimation procedures based on other soil properties. Regression models or pedotransfer functions (PTFs) based on easily measured soil properties constitute an adequate tool to assess Db, since it needs a minimum data set of indicators. A forward stepwise multiple linear regression routine was used to predict Db from physico-chemical soil properties. In this study, a soil database was organised from published and unpublished data from Tunisia. The database consisted of 238 soil profiles corresponding to 707 soil horizons from Tunisia. A general regression model fitted with all the data showed that OC, Clay, coarse-Sand and pH were the principal contributors to Db prediction (R2 = 0.55, standard error of prediction = 0.14). Additional models based on different set of variables are also provided providing alternative solutions for different levels of soil information. Predictions of the models were often improved when the data were partitioned into groups by soil depth (0–40 and 40–100 cm) and soil orders. This study also showed that CaCO3 might be an important predictor for deeper soil horizon. The proposed PTFs for Tunisia might be useful for a larger range of soil from arid and sub arid regions. |
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