%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Brahim, N.
%A Bernoux, Martial
%A Gallali, T.
%T Pedotransfer functions to estimate soil bulk density for Northern Africa : Tunisia case
%D 2012
%L fdi:010055780
%G ENG
%J Journal of Arid Environments
%@ 0140-1963
%K Bulk density ; Pedotransfer functions ; Multiple linear regressions ; Mediterranean region ; Carbon stock estimates ; Carbonate
%M ISI:000302437600009
%P 77-83
%R 10.1016/j.jaridenv.2012.01.012
%U https://www.documentation.ird.fr/hor/fdi:010055780
%> https://www.documentation.ird.fr/intranet/publi/2012/05/010055780.pdf
%V 81
%W Horizon (IRD)
%X 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 (D-b) values. However, direct measurement of D-b is often lacking mainly for soils in arid and semi-arid conditions. Much effort has been made in finding alternative solution to predict D-b, 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 D-b, since it needs a minimum data set of indicators. A forward stepwise multiple linear regression routine was used to predict D-b 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 In this study, a soil database was organised from published and unpublished data from Tunisia.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 CC, Clay, coarse-Sand and pH were the principal contributors to D-b prediction (R-2 = 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.
%$ 068 ; 020